update changelog

Merge branch 'release_candidate'
Merge pull request #9969 from AUTOMATIC1111/restore_progress_fix
2023-05-01 14:33:44 +03:00 · 2023-05-01 14:27:53 +03:00 · 2023-05-01 14:09:32 +03:00 · 2023-05-01 19:59:52 +09:00 · 2023-05-01 13:58:10 +03:00 · 2023-05-01 13:47:46 +03:00
127 changed files with 9365 additions and 3005 deletions
@@ -37,20 +37,20 @@ body:
    id: what-should
    attributes:
      label: What should have happened?
-      description: tell what you think the normal behavior should be
+      description: Tell what you think the normal behavior should be
    validations:
      required: true
  - type: input
    id: commit
    attributes:
      label: Commit where the problem happens
-      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit hash** shown in the cmd/terminal when you launch the UI)
+      description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
    validations:
      required: true
  - type: dropdown
    id: platforms
    attributes:
-      label: What platforms do you use to access UI ?
+      label: What platforms do you use to access the UI ?
      multiple: true
      options:
        - Windows
@@ -74,10 +74,27 @@ body:
    id: cmdargs
    attributes:
      label: Command Line Arguments
-      description: Are you using any launching parameters/command line arguments (modified webui-user.py) ? If yes, please write them below
+      description: Are you using any launching parameters/command line arguments (modified webui-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
      render: Shell
+    validations:
+      required: true
+  - type: textarea
+    id: extensions
+    attributes:
+      label: List of extensions
+      description: Are you using any extensions other than built-ins? If yes, provide a list, you can copy it at "Extensions" tab. Write "No" otherwise.
+    validations:
+      required: true
+  - type: textarea
+    id: logs
+    attributes:
+      label: Console logs
+      description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
+      render: Shell
+    validations:
+      required: true
  - type: textarea
    id: misc
    attributes:
-      label: Additional information, context and logs
-      description: Please provide us with any relevant additional info, context or log output.
+      label: Additional information
+      description: Please provide us with any relevant additional info or context.
@@ -18,7 +18,7 @@ jobs:
          cache-dependency-path: |
            **/requirements*txt
      - name: Run tests
-        run: python launch.py --tests --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
+        run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
      - name: Upload main app stdout-stderr
        uses: actions/upload-artifact@v3
        if: always()
@@ -32,4 +32,5 @@ notification.mp3
 /extensions
 /test/stdout.txt
 /test/stderr.txt
-/cache.json
+/cache.json*
+/config_states/
@@ -0,0 +1,58 @@
+## 1.1.0
+### Features:
+ * switch to torch 2.0.0 (except for AMD GPUs)
+ * visual improvements to custom code scripts
+ * add filename patterns: [clip_skip], [hasprompt<>], [batch_number], [generation_number]
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducability
+ * automatically select current word when adjusting weight with ctrl+up/down
+ * add dropdowns for X/Y/Z plot
+ * setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
+ * support Gradio's theme API
+ * use TCMalloc on Linux by default; possible fix for memory leaks
+ * (optimization) option to remove negative conditioning at low sigma values #9177
+ * embed model merge metadata in .safetensors file
+ * extension settings backup/restore feature #9169
+ * add "resize by" and "resize to" tabs to img2img
+ * add option "keep original size" to textual inversion images preprocess
+ * image viewer scrolling via analog stick
+ * button to restore the progress from session lost / tab reload
+
+### Minor:
+ * gradio bumped to 3.28.1
+ * in extra tab, change extras "scale to" to sliders
+ * add labels to tool buttons to make it possible to hide them
+ * add tiled inference support for ScuNET
+ * add branch support for extension installation
+ * change linux installation script to insall into current directory rather than /home/username
+ * sort textual inversion embeddings by name (case insensitive)
+ * allow styles.csv to be symlinked or mounted in docker
+ * remove the "do not add watermark to images" option
+ * make selected tab configurable with UI config
+ * extra networks UI in now fixed height and scrollable
+ * add disable_tls_verify arg for use with self-signed certs
+
+### Extensions:
+ * Add reload callback
+ * add is_hr_pass field for processing
+
+### Bug Fixes:
+ * fix broken batch image processing on 'Extras/Batch Process' tab
+ * add "None" option to extra networks dropdowns
+ * fix FileExistsError for CLIP Interrogator
+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319
+ * fix disappearing live previews and progressbar during slow tasks
+ * fix fullscreen image view not working properly in some cases
+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
+ * fix prompt schedule for second order samplers
+ * fix image mask/composite for weird resolutions #9628
+ * use correct images for previews when using AND (see #9491)
+ * one broken image in img2img batch won't stop all processing
+ * fix image orientation bug in train/preprocess
+ * fix Ngrok recreating tunnels every reload
+ * fix --realesrgan-models-path and --ldsr-models-path not working
+ * fix --skip-install not working
+ * outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format
+ * do not fail all Loras if some have failed to load when making a picture
+
+## 1.0.0
+  * everything
@@ -13,11 +13,11 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Prompt Matrix
 - Stable Diffusion Upscale
 - Attention, specify parts of text that the model should pay more attention to
-    - a man in a ((tuxedo)) - will pay more attention to tuxedo
-    - a man in a (tuxedo:1.21) - alternative syntax
-    - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user)
+    - a man in a `((tuxedo))` - will pay more attention to tuxedo
+    - a man in a `(tuxedo:1.21)` - alternative syntax
+    - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
 - Loopback, run img2img processing multiple times
- X/Y plot, a way to draw a 2 dimensional plot of images with different parameters
+- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
 - Textual Inversion
    - have as many embeddings as you want and use any names you like for them
    - use multiple embeddings with different numbers of vectors per token
@@ -28,7 +28,7 @@ A browser interface based on Gradio library for Stable Diffusion.
    - CodeFormer, face restoration tool as an alternative to GFPGAN
    - RealESRGAN, neural network upscaler
    - ESRGAN, neural network upscaler with a lot of third party models
-    - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+    - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
    - LDSR, Latent diffusion super resolution upscaling
 - Resizing aspect ratio options
 - Sampling method selection
@@ -46,7 +46,7 @@ A browser interface based on Gradio library for Stable Diffusion.
     - drag and drop an image/text-parameters to promptbox
 - Read Generation Parameters Button, loads parameters in promptbox to UI
 - Settings page
- Running arbitrary python code from UI (must run with --allow-code to enable)
+- Running arbitrary python code from UI (must run with `--allow-code` to enable)
 - Mouseover hints for most UI elements
 - Possible to change defaults/mix/max/step values for UI elements via text config
 - Tiling support, a checkbox to create images that can be tiled like textures
@@ -69,7 +69,7 @@ A browser interface based on Gradio library for Stable Diffusion.
     - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts
- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
 - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
 - Generate forever option
 - Training tab
@@ -78,11 +78,11 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Clip skip
 - Hypernetworks
 - Loras (same as Hypernetworks but more pretty)
- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt. 
+- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt 
 - Can select to load a different VAE from settings screen
 - Estimated completion time in progress bar
 - API
- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML.
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
 - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
 - [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
 - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
@@ -91,7 +91,6 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen
- 

 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -101,11 +100,10 @@ Alternatively, use online services (like Google Colab):
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)

 ### Automatic Installation on Windows
-1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH"
+1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
 2. Install [git](https://git-scm.com/download/win).
 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
-4. Place stable diffusion checkpoint (`model.ckpt`) in the `models/Stable-diffusion` directory (see [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) for where to get it).
-5. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
+4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.

 ### Automatic Installation on Linux
 1. Install the dependencies:
@@ -117,11 +115,12 @@ sudo dnf install wget git python3
 # Arch-based:
 sudo pacman -S wget git python3
 ```
-2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run:
+2. Navigate to the directory you would like the webui to be installed and execute the following command:
 ```bash
 bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
 ```
-
+3. Run `webui.sh`.
+4. Check `webui-user.sh` for options.
 ### Installation on Apple Silicon

 Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
@@ -155,6 +154,9 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
 - xformers - https://github.com/facebookresearch/xformers
 - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
+- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
+- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
 - Security advice - RyotaK
+- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)
@@ -0,0 +1,98 @@
+# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
+# See more details in LICENSE.
+
+model:
+  base_learning_rate: 1.0e-04
+  target: modules.models.diffusion.ddpm_edit.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: edited
+    cond_stage_key: edit
+    # image_size: 64
+    # image_size: 32
+    image_size: 16
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: false
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 0 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 128
+    num_workers: 1
+    wrap: false
+    validation:
+      target: edit_dataset.EditDataset
+      params:
+        path: data/clip-filtered-dataset
+        cache_dir:  data/
+        cache_name: data_10k
+        split: val
+        min_text_sim: 0.2
+        min_image_sim: 0.75
+        min_direction_sim: 0.2
+        max_samples_per_prompt: 1
+        min_resize_res: 512
+        max_resize_res: 512
+        crop_res: 512
+        output_as_edit: False
+        real_input: True
@@ -1,8 +1,7 @@
 model:
-  base_learning_rate: 1.0e-4
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  base_learning_rate: 7.5e-05
+  target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
  params:
-    parameterization: "v"
    linear_start: 0.00085
    linear_end: 0.0120
    num_timesteps_cond: 1
@@ -12,29 +11,36 @@ model:
    cond_stage_key: "txt"
    image_size: 64
    channels: 4
-    cond_stage_trainable: false
-    conditioning_key: crossattn
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid   # important
    monitor: val/loss_simple_ema
    scale_factor: 0.18215
-    use_ema: False # we set this to false because this is an inference only config
+    finetune_keys: null
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]

    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
-        use_checkpoint: True
-        use_fp16: True
        image_size: 32 # unused
-        in_channels: 4
+        in_channels: 9  # 4 data + 4 downscaled image + 1 mask
        out_channels: 4
        model_channels: 320
        attention_resolutions: [ 4, 2, 1 ]
        num_res_blocks: 2
        channel_mult: [ 1, 2, 4, 4 ]
-        num_head_channels: 64 # need to fix for flash-attn
+        num_heads: 8
        use_spatial_transformer: True
-        use_linear_in_transformer: True
        transformer_depth: 1
-        context_dim: 1024
+        context_dim: 768
+        use_checkpoint: True
        legacy: False

    first_stage_config:
@@ -43,7 +49,6 @@ model:
        embed_dim: 4
        monitor: val/rec_loss
        ddconfig:
-          #attn_type: "vanilla-xformers"
          double_z: true
          z_channels: 4
          resolution: 256
@@ -62,7 +67,4 @@ model:
          target: torch.nn.Identity

    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
-      params:
-        freeze: True
-        layer: "penultimate"
+      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
@@ -4,8 +4,8 @@ channels:
  - defaults
 dependencies:
  - python=3.10
-  - pip=22.2.2
-  - cudatoolkit=11.3
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-  - numpy=1.23.1
+  - pip=23.0
+  - cudatoolkit=11.8
+  - pytorch=2.0
+  - torchvision=0.15
+  - numpy=1.23
@@ -25,22 +25,28 @@ class UpscalerLDSR(Upscaler):
        yaml_path = os.path.join(self.model_path, "project.yaml")
        old_model_path = os.path.join(self.model_path, "model.pth")
        new_model_path = os.path.join(self.model_path, "model.ckpt")
-        safetensors_model_path = os.path.join(self.model_path, "model.safetensors")
+
+        local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
+        local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
+        local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
+        local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
+
        if os.path.exists(yaml_path):
            statinfo = os.stat(yaml_path)
            if statinfo.st_size >= 10485760:
                print("Removing invalid LDSR YAML file.")
                os.remove(yaml_path)
+
        if os.path.exists(old_model_path):
            print("Renaming model from model.pth to model.ckpt")
            os.rename(old_model_path, new_model_path)
-        if os.path.exists(safetensors_model_path):
-            model = safetensors_model_path
+
+        if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
+            model = local_safetensors_path
        else:
-            model = load_file_from_url(url=self.model_url, model_dir=self.model_path,
-                                       file_name="model.ckpt", progress=True)
-        yaml = load_file_from_url(url=self.yaml_url, model_dir=self.model_path,
-                                  file_name="project.yaml", progress=True)
+            model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True)
+
+        yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True)

        try:
            return LDSR(model, yaml)
@@ -1,4 +1,4 @@
-from modules import extra_networks
+from modules import extra_networks, shared
 import lora

 class ExtraNetworkLora(extra_networks.ExtraNetwork):
@@ -6,6 +6,12 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
        super().__init__('lora')

    def activate(self, p, params_list):
+        additional = shared.opts.sd_lora
+
+        if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
+            p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
        names = []
        multipliers = []
        for params in params_list:
@@ -2,18 +2,34 @@ import glob
 import os
 import re
 import torch
+from typing import Union

-from modules import shared, devices, sd_models
+from modules import shared, devices, sd_models, errors
+
+metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}

 re_digits = re.compile(r"\d+")
-re_unet_down_blocks = re.compile(r"lora_unet_down_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_unet_mid_blocks = re.compile(r"lora_unet_mid_block_attentions_(\d+)_(.+)")
-re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)")
-re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+    "attentions": {},
+    "resnets": {
+        "conv1": "in_layers_2",
+        "conv2": "out_layers_3",
+        "time_emb_proj": "emb_layers_1",
+        "conv_shortcut": "skip_connection",
+    }
+}


-def convert_diffusers_name_to_compvis(key):
-    def match(match_list, regex):
+def convert_diffusers_name_to_compvis(key, is_sd2):
+    def match(match_list, regex_text):
+        regex = re_compiled.get(regex_text)
+        if regex is None:
+            regex = re.compile(regex_text)
+            re_compiled[regex_text] = regex
+
        r = re.match(regex, key)
        if not r:
            return False
@@ -24,16 +40,33 @@ def convert_diffusers_name_to_compvis(key):

    m = []

-    if match(m, re_unet_down_blocks):
-        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[1]}_1_{m[2]}"
+    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"

-    if match(m, re_unet_mid_blocks):
-        return f"diffusion_model_middle_block_1_{m[1]}"
+    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+        return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"

-    if match(m, re_unet_up_blocks):
-        return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"
+    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+        return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+        return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+        if is_sd2:
+            if 'mlp_fc1' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+            elif 'mlp_fc2' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+            else:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"

-    if match(m, re_text_block):
        return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"

    return key
@@ -43,6 +76,23 @@ class LoraOnDisk:
    def __init__(self, name, filename):
        self.name = name
        self.filename = filename
+        self.metadata = {}
+
+        _, ext = os.path.splitext(filename)
+        if ext.lower() == ".safetensors":
+            try:
+                self.metadata = sd_models.read_metadata_from_safetensors(filename)
+            except Exception as e:
+                errors.display(e, f"reading lora {filename}")
+
+        if self.metadata:
+            m = {}
+            for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
+                m[k] = v
+
+            self.metadata = m
+
+        self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None)  # those are cover images and they are too big to display in UI as text


 class LoraModule:
@@ -82,15 +132,22 @@ def load_lora(name, filename):

    sd = sd_models.read_state_dict(filename)

-    keys_failed_to_match = []
+    keys_failed_to_match = {}
+    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping

    for key_diffusers, weight in sd.items():
-        fullkey = convert_diffusers_name_to_compvis(key_diffusers)
-        key, lora_key = fullkey.split(".", 1)
+        key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
+        key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)

        sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
+
        if sd_module is None:
-            keys_failed_to_match.append(key_diffusers)
+            m = re_x_proj.match(key)
+            if m:
+                sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
+
+        if sd_module is None:
+            keys_failed_to_match[key_diffusers] = key
            continue

        lora_module = lora.modules.get(key, None)
@@ -104,15 +161,21 @@ def load_lora(name, filename):

        if type(sd_module) == torch.nn.Linear:
            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
+            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif type(sd_module) == torch.nn.MultiheadAttention:
+            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
        elif type(sd_module) == torch.nn.Conv2d:
            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
        else:
+            print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
+            continue
            assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'

        with torch.no_grad():
            module.weight.copy_(weight)

-        module.to(device=devices.device, dtype=devices.dtype)
+        module.to(device=devices.cpu, dtype=devices.dtype)

        if lora_key == "lora_up.weight":
            lora_module.up = module
@@ -148,7 +211,11 @@ def load_loras(names, multipliers=None):
        lora_on_disk = loras_on_disk[i]
        if lora_on_disk is not None:
            if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
-                lora = load_lora(name, lora_on_disk.filename)
+                try:
+                    lora = load_lora(name, lora_on_disk.filename)
+                except Exception as e:
+                    errors.display(e, f"loading Lora {lora_on_disk.filename}")
+                    continue

        if lora is None:
            print(f"Couldn't find Lora with name {name}")
@@ -158,25 +225,120 @@ def load_loras(names, multipliers=None):
        loaded_loras.append(lora)


-def lora_forward(module, input, res):
-    if len(loaded_loras) == 0:
-        return res
+def lora_calc_updown(lora, module, target):
+    with torch.no_grad():
+        up = module.up.weight.to(target.device, dtype=target.dtype)
+        down = module.down.weight.to(target.device, dtype=target.dtype)

-    lora_layer_name = getattr(module, 'lora_layer_name', None)
-    for lora in loaded_loras:
-        module = lora.modules.get(lora_layer_name, None)
-        if module is not None:
-            res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+        if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
+            updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+        else:
+            updown = up @ down

-    return res
+        updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+        return updown
+
+
+def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
+    """
+    Applies the currently selected set of Loras to the weights of torch layer self.
+    If weights already have this particular set of loras applied, does nothing.
+    If not, restores orginal weights from backup and alters weights according to loras.
+    """
+
+    lora_layer_name = getattr(self, 'lora_layer_name', None)
+    if lora_layer_name is None:
+        return
+
+    current_names = getattr(self, "lora_current_names", ())
+    wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
+
+    weights_backup = getattr(self, "lora_weights_backup", None)
+    if weights_backup is None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
+        else:
+            weights_backup = self.weight.to(devices.cpu, copy=True)
+
+        self.lora_weights_backup = weights_backup
+
+    if current_names != wanted_names:
+        if weights_backup is not None:
+            if isinstance(self, torch.nn.MultiheadAttention):
+                self.in_proj_weight.copy_(weights_backup[0])
+                self.out_proj.weight.copy_(weights_backup[1])
+            else:
+                self.weight.copy_(weights_backup)
+
+        for lora in loaded_loras:
+            module = lora.modules.get(lora_layer_name, None)
+            if module is not None and hasattr(self, 'weight'):
+                self.weight += lora_calc_updown(lora, module, self.weight)
+                continue
+
+            module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
+            module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
+            module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
+            module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
+
+            if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
+                updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
+                updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
+                updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
+                updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+
+                self.in_proj_weight += updown_qkv
+                self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
+                continue
+
+            if module is None:
+                continue
+
+            print(f'failed to calculate lora weights for layer {lora_layer_name}')
+
+        setattr(self, "lora_current_names", wanted_names)
+
+
+def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+    setattr(self, "lora_current_names", ())
+    setattr(self, "lora_weights_backup", None)


 def lora_Linear_forward(self, input):
-    return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
+    lora_apply_weights(self)
+
+    return torch.nn.Linear_forward_before_lora(self, input)
+
+
+def lora_Linear_load_state_dict(self, *args, **kwargs):
+    lora_reset_cached_weight(self)
+
+    return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)


 def lora_Conv2d_forward(self, input):
-    return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
+    lora_apply_weights(self)
+
+    return torch.nn.Conv2d_forward_before_lora(self, input)
+
+
+def lora_Conv2d_load_state_dict(self, *args, **kwargs):
+    lora_reset_cached_weight(self)
+
+    return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_forward(self, *args, **kwargs):
+    lora_apply_weights(self)
+
+    return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
+
+
+def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+    lora_reset_cached_weight(self)
+
+    return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)


 def list_available_loras():
@@ -189,7 +351,7 @@ def list_available_loras():
        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.safetensors'), recursive=True) + \
        glob.glob(os.path.join(shared.cmd_opts.lora_dir, '**/*.ckpt'), recursive=True)

-    for filename in sorted(candidates):
+    for filename in sorted(candidates, key=str.lower):
        if os.path.isdir(filename):
            continue

@@ -1,14 +1,19 @@
 import torch
+import gradio as gr

 import lora
 import extra_networks_lora
 import ui_extra_networks_lora
-from modules import script_callbacks, ui_extra_networks, extra_networks
+from modules import script_callbacks, ui_extra_networks, extra_networks, shared


 def unload():
    torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
+    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
+    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
+    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora


 def before_ui():
@@ -19,12 +24,33 @@ def before_ui():
 if not hasattr(torch.nn, 'Linear_forward_before_lora'):
    torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward

+if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
+    torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+
 if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
    torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward

+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
+    torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
+    torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
+
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
+    torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
+
 torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
 torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
+torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict

 script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
 script_callbacks.on_before_ui(before_ui)
+
+
+shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
+}))
@@ -15,20 +15,15 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
    def list_items(self):
        for name, lora_on_disk in lora.available_loras.items():
            path, ext = os.path.splitext(lora_on_disk.filename)
-            previews = [path + ".png", path + ".preview.png"]
-
-            preview = None
-            for file in previews:
-                if os.path.isfile(file):
-                    preview = "./file=" + file.replace('\\', '/') + "?mtime=" + str(os.path.getmtime(file))
-                    break
-
            yield {
                "name": name,
                "filename": path,
-                "preview": preview,
+                "preview": self.find_preview(path),
+                "description": self.find_description(path),
+                "search_term": self.search_terms_from_path(lora_on_disk.filename),
                "prompt": json.dumps(f"<lora:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
-                "local_preview": path + ".png",
+                "local_preview": f"{path}.{shared.opts.samples_format}",
+                "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
            }

    def allowed_directories_for_previews(self):
@@ -5,11 +5,15 @@ import traceback
 import PIL.Image
 import numpy as np
 import torch
+from tqdm import tqdm
+
 from basicsr.utils.download_util import load_file_from_url

 import modules.upscaler
 from modules import devices, modelloader
 from scunet_model_arch import SCUNet as net
+from modules.shared import opts
+from modules import images


 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -42,28 +46,78 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
            scalers.append(scaler_data2)
        self.scalers = scalers

-    def do_upscale(self, img: PIL.Image, selected_file):
+    @staticmethod
+    @torch.no_grad()
+    def tiled_inference(img, model):
+        # test the image tile by tile
+        h, w = img.shape[2:]
+        tile = opts.SCUNET_tile
+        tile_overlap = opts.SCUNET_tile_overlap
+        if tile == 0:
+            return model(img)
+
+        device = devices.get_device_for('scunet')
+        assert tile % 8 == 0, "tile size should be a multiple of window_size"
+        sf = 1
+
+        stride = tile - tile_overlap
+        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
+        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
+        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
+        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
+
+        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
+            for h_idx in h_idx_list:
+
+                for w_idx in w_idx_list:
+
+                    in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+
+                    out_patch = model(in_patch)
+                    out_patch_mask = torch.ones_like(out_patch)
+
+                    E[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch)
+                    W[
+                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+                    ].add_(out_patch_mask)
+                    pbar.update(1)
+        output = E.div_(W)
+
+        return output
+
+    def do_upscale(self, img: PIL.Image.Image, selected_file):
+
        torch.cuda.empty_cache()

        model = self.load_model(selected_file)
        if model is None:
+            print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
            return img

        device = devices.get_device_for('scunet')
-        img = np.array(img)
-        img = img[:, :, ::-1]
-        img = np.moveaxis(img, 2, 0) / 255
-        img = torch.from_numpy(img).float()
-        img = img.unsqueeze(0).to(device)
+        tile = opts.SCUNET_tile
+        h, w = img.height, img.width
+        np_img = np.array(img)
+        np_img = np_img[:, :, ::-1]  # RGB to BGR
+        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
+        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore

-        with torch.no_grad():
-            output = model(img)
-        output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-        output = 255. * np.moveaxis(output, 0, 2)
-        output = output.astype(np.uint8)
-        output = output[:, :, ::-1]
+        if tile > h or tile > w:
+            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
+            _img[:, :, :h, :w] = torch_img # pad image
+            torch_img = _img
+
+        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
+        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
+        del torch_img, torch_output
        torch.cuda.empty_cache()
-        return PIL.Image.fromarray(output, 'RGB')
+
+        output = np_output.transpose((1, 2, 0))  # CHW to HWC
+        output = output[:, :, ::-1]  # BGR to RGB
+        return PIL.Image.fromarray((output * 255).astype(np.uint8))

    def load_model(self, path: str):
        device = devices.get_device_for('scunet')
@@ -84,4 +138,3 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
        model = model.to(device)

        return model
-
@@ -1,110 +1,42 @@
 // Stable Diffusion WebUI - Bracket checker
-// Version 1.0
-// By Hingashi no Florin/Bwin4L
+// By Hingashi no Florin/Bwin4L & @akx
 // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
 // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.

-function checkBrackets(evt, textArea, counterElt) {
-  errorStringParen  = '(...) - Different number of opening and closing parentheses detected.\n';
-  errorStringSquare = '[...] - Different number of opening and closing square brackets detected.\n';
-  errorStringCurly  = '{...} - Different number of opening and closing curly brackets detected.\n';
+function checkBrackets(textArea, counterElt) {
+  var counts = {};
+  (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
+    counts[bracket] = (counts[bracket] || 0) + 1;
+  });
+  var errors = [];

-  openBracketRegExp = /\(/g;
-  closeBracketRegExp = /\)/g;
-
-  openSquareBracketRegExp = /\[/g;
-  closeSquareBracketRegExp = /\]/g;
-
-  openCurlyBracketRegExp = /\{/g;
-  closeCurlyBracketRegExp = /\}/g;
-
-  totalOpenBracketMatches = 0;
-  totalCloseBracketMatches = 0;
-  totalOpenSquareBracketMatches = 0;
-  totalCloseSquareBracketMatches = 0;
-  totalOpenCurlyBracketMatches = 0;
-  totalCloseCurlyBracketMatches = 0;
-
-  openBracketMatches = textArea.value.match(openBracketRegExp);
-  if(openBracketMatches) {
-    totalOpenBracketMatches = openBracketMatches.length;
-  }
-
-  closeBracketMatches = textArea.value.match(closeBracketRegExp);
-  if(closeBracketMatches) {
-    totalCloseBracketMatches = closeBracketMatches.length;
-  }
-
-  openSquareBracketMatches = textArea.value.match(openSquareBracketRegExp);
-  if(openSquareBracketMatches) {
-    totalOpenSquareBracketMatches = openSquareBracketMatches.length;
-  }
-
-  closeSquareBracketMatches = textArea.value.match(closeSquareBracketRegExp);
-  if(closeSquareBracketMatches) {
-    totalCloseSquareBracketMatches = closeSquareBracketMatches.length;
-  }
-
-  openCurlyBracketMatches = textArea.value.match(openCurlyBracketRegExp);
-  if(openCurlyBracketMatches) {
-    totalOpenCurlyBracketMatches = openCurlyBracketMatches.length;
-  }
-
-  closeCurlyBracketMatches = textArea.value.match(closeCurlyBracketRegExp);
-  if(closeCurlyBracketMatches) {
-    totalCloseCurlyBracketMatches = closeCurlyBracketMatches.length;
-  }
-
-  if(totalOpenBracketMatches != totalCloseBracketMatches) {
-    if(!counterElt.title.includes(errorStringParen)) {
-      counterElt.title += errorStringParen;
+  function checkPair(open, close, kind) {
+    if (counts[open] !== counts[close]) {
+      errors.push(
+        `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
+      );
    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringParen, '');
  }

-  if(totalOpenSquareBracketMatches != totalCloseSquareBracketMatches) {
-    if(!counterElt.title.includes(errorStringSquare)) {
-      counterElt.title += errorStringSquare;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringSquare, '');
-  }
+  checkPair('(', ')', 'round brackets');
+  checkPair('[', ']', 'square brackets');
+  checkPair('{', '}', 'curly brackets');
+  counterElt.title = errors.join('\n');
+  counterElt.classList.toggle('error', errors.length !== 0);
+}

-  if(totalOpenCurlyBracketMatches != totalCloseCurlyBracketMatches) {
-    if(!counterElt.title.includes(errorStringCurly)) {
-      counterElt.title += errorStringCurly;
-    }
-  } else {
-    counterElt.title = counterElt.title.replace(errorStringCurly, '');
-  }
+function setupBracketChecking(id_prompt, id_counter) {
+  var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
+  var counter = gradioApp().getElementById(id_counter)

-  if(counterElt.title != '') {
-    counterElt.classList.add('error');
-  } else {
-    counterElt.classList.remove('error');
+  if (textarea && counter) {
+    textarea.addEventListener("input", () => checkBrackets(textarea, counter));
  }
 }

-function setupBracketChecking(id_prompt, id_counter){
-    var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
-    var counter = gradioApp().getElementById(id_counter)
-    textarea.addEventListener("input", function(evt){
-        checkBrackets(evt, textarea, counter)
-    });
-}
-
-var shadowRootLoaded = setInterval(function() {
-    var shadowRoot = document.querySelector('gradio-app').shadowRoot;
-    if(! shadowRoot)  return false;
-
-    var shadowTextArea = shadowRoot.querySelectorAll('#txt2img_prompt > label > textarea');
-    if(shadowTextArea.length < 1)  return false;
-
-    clearInterval(shadowRootLoaded);
-
-    setupBracketChecking('txt2img_prompt', 'txt2img_token_counter')
-    setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter')
-    setupBracketChecking('img2img_prompt', 'imgimg_token_counter')
-    setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter')
-}, 1000);
+onUiLoaded(function () {
+  setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
+  setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
+  setupBracketChecking('img2img_prompt', 'img2img_token_counter');
+  setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
+});
@@ -1,11 +1,15 @@
-<div class='card' {preview_html} onclick={card_clicked}>
+<div class='card' style={style} onclick={card_clicked}>
+	{metadata_button}
+
 	<div class='actions'>
 		<div class='additional'>
 			<ul>
 				<a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
 			</ul>
+			<span style="display:none" class='search_term'>{search_term}</span>
 		</div>
 		<span class='name'>{name}</span>
+		<span class='description'>{description}</span>
 	</div>
 </div>

@@ -417,3 +417,248 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 </pre>

+<h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
+<small>Some small amounts of code borrowed and reworked.</small>
+<pre>
+   Copyright 2023 The HuggingFace Team. All rights reserved.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+   1. Definitions.
+
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+
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+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
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+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
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+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
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+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+</pre>
+
+<h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
+<small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
+<pre>
+The MIT License (MIT)
+
+Copyright (C) 2021 ExplosionAI GmbH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
+</pre>
@@ -12,7 +12,7 @@ function dimensionChange(e, is_width, is_height){
 		currentHeight = e.target.value*1.0
 	}

-	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
+	var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";

 	if(!inImg2img){
 		return;
@@ -22,7 +22,7 @@ function dimensionChange(e, is_width, is_height){

    var tabIndex = get_tab_index('mode_img2img')
 	if(tabIndex == 0){ // img2img
-		targetElement = gradioApp().querySelector('div[data-testid=image] img');
+		targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
 	} else if(tabIndex == 1){ //Sketch
 		targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
 	} else if(tabIndex == 2){ // Inpaint
@@ -30,7 +30,7 @@ function dimensionChange(e, is_width, is_height){
 	} else if(tabIndex == 3){ // Inpaint sketch
 		targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
 	}
-	
+

 	if(targetElement){

@@ -38,7 +38,7 @@ function dimensionChange(e, is_width, is_height){
 		if(!arPreviewRect){
 		    arPreviewRect = document.createElement('div')
 		    arPreviewRect.id = "imageARPreview";
-		    gradioApp().getRootNode().appendChild(arPreviewRect)
+		    gradioApp().appendChild(arPreviewRect)
 		}


@@ -91,23 +91,26 @@ onUiUpdate(function(){
 	if(arPreviewRect){
 		arPreviewRect.style.display = 'none';
 	}
-	var inImg2img   = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200"))
-	if(inImg2img){
-		let inputs = gradioApp().querySelectorAll('input');
-		inputs.forEach(function(e){
-		    var is_width = e.parentElement.id == "img2img_width"
-		    var is_height = e.parentElement.id == "img2img_height"
+    var tabImg2img = gradioApp().querySelector("#tab_img2img");
+    if (tabImg2img) {
+        var inImg2img = tabImg2img.style.display == "block";
+        if(inImg2img){
+            let inputs = gradioApp().querySelectorAll('input');
+            inputs.forEach(function(e){
+                var is_width = e.parentElement.id == "img2img_width"
+                var is_height = e.parentElement.id == "img2img_height"

-			if((is_width || is_height) && !e.classList.contains('scrollwatch')){
-				e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
-				e.classList.add('scrollwatch')
-			}
-			if(is_width){
-				currentWidth = e.value*1.0
-			}
-			if(is_height){
-				currentHeight = e.value*1.0
-			}
-		})
-	}
+                if((is_width || is_height) && !e.classList.contains('scrollwatch')){
+                    e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
+                    e.classList.add('scrollwatch')
+                }
+                if(is_width){
+                    currentWidth = e.value*1.0
+                }
+                if(is_height){
+                    currentHeight = e.value*1.0
+                }
+            })
+        }
+    }
 });
@@ -43,7 +43,7 @@ contextMenuInit = function(){

    })

-    gradioApp().getRootNode().appendChild(contextMenu)
+    gradioApp().appendChild(contextMenu)

    let menuWidth = contextMenu.offsetWidth + 4;
    let menuHeight = contextMenu.offsetHeight + 4;
@@ -161,14 +161,6 @@ addContextMenuEventListener = initResponse[2];
  appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
  appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)

-  appendContextMenuOption('#roll','Roll three',
-    function(){
-      let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
-      setTimeout(function(){rollbutton.click()},100)
-      setTimeout(function(){rollbutton.click()},200)
-      setTimeout(function(){rollbutton.click()},300)
-    }
-  )
 })();
 //End example Context Menu Items

@@ -1,6 +1,6 @@
 function keyupEditAttention(event){
 	let target = event.originalTarget || event.composedPath()[0];
-	if (!target.matches("[id*='_toprow'] textarea.gr-text-input[placeholder]")) return;
+	if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
 	if (! (event.metaKey || event.ctrlKey)) return;

 	let isPlus = event.key == "ArrowUp"
@@ -17,7 +17,7 @@ function keyupEditAttention(event){
 		// Find opening parenthesis around current cursor
 		const before = text.substring(0, selectionStart);
 		let beforeParen = before.lastIndexOf(OPEN);
-		if (beforeParen == -1) return  false;
+		if (beforeParen == -1) return false;
 		let beforeParenClose = before.lastIndexOf(CLOSE);
 		while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
 			beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
@@ -27,7 +27,7 @@ function keyupEditAttention(event){
 		// Find closing parenthesis around current cursor
 		const after = text.substring(selectionStart);
 		let afterParen = after.indexOf(CLOSE);
-		if (afterParen == -1) return  false;
+		if (afterParen == -1) return false;
 		let afterParenOpen = after.indexOf(OPEN);
 		while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
 			afterParen = after.indexOf(CLOSE, afterParen + 1);
@@ -43,10 +43,28 @@ function keyupEditAttention(event){
 		target.setSelectionRange(selectionStart, selectionEnd);
 		return true;
    }
+    
+    function selectCurrentWord(){
+        if (selectionStart !== selectionEnd) return false;
+        const delimiters = opts.keyedit_delimiters + " \r\n\t";
+        
+        // seek backward until to find beggining
+        while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
+            selectionStart--;
+        }
+        
+        // seek forward to find end
+        while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
+            selectionEnd++;
+        }

-	// If the user hasn't selected anything, let's select their current parenthesis block
-    if(! selectCurrentParenthesisBlock('<', '>')){
-        selectCurrentParenthesisBlock('(', ')')
+        target.setSelectionRange(selectionStart, selectionEnd);
+        return true;
+    }
+
+	// If the user hasn't selected anything, let's select their current parenthesis block or word
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
+        selectCurrentWord();
    }

 	event.preventDefault();
@@ -81,7 +99,13 @@ function keyupEditAttention(event){
 	weight = parseFloat(weight.toPrecision(12));
 	if(String(weight).length == 1) weight += ".0"

-	text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+    if (closeCharacter == ')' && weight == 1) {
+        text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
+        selectionStart--;
+        selectionEnd--;
+    } else {
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
+    }

 	target.focus();
 	target.value = text;
@@ -93,4 +117,4 @@ function keyupEditAttention(event){

 addEventListener('keydown', (event) => {
    keyupEditAttention(event);
-});
+});
@@ -1,7 +1,8 @@

-function extensions_apply(_, _){
-    disable = []
-    update = []
+function extensions_apply(_, _, disable_all){
+    var disable = []
+    var update = []
+
    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
        if(x.name.startsWith("enable_") && ! x.checked)
            disable.push(x.name.substr(7))
@@ -12,15 +13,28 @@ function extensions_apply(_, _){

    restart_reload()

-    return [JSON.stringify(disable), JSON.stringify(update)]
+    return [JSON.stringify(disable), JSON.stringify(update), disable_all]
 }

-function extensions_check(){
+function extensions_check(_, _){
+    var disable = []
+
+    gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
+        if(x.name.startsWith("enable_") && ! x.checked)
+            disable.push(x.name.substr(7))
+    })
+
    gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
        x.innerHTML = "Loading..."
    })

-    return []
+
+    var id = randomId()
+    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
+
+    })
+
+    return [id, JSON.stringify(disable)]
 }

 function install_extension_from_index(button, url){
@@ -33,3 +47,25 @@ function install_extension_from_index(button, url){

    gradioApp().querySelector('#install_extension_button').click()
 }
+
+function config_state_confirm_restore(_, config_state_name, config_restore_type) {
+    if (config_state_name == "Current") {
+        return [false, config_state_name, config_restore_type];
+    }
+    let restored = "";
+    if (config_restore_type == "extensions") {
+        restored = "all saved extension versions";
+    } else if (config_restore_type == "webui") {
+        restored = "the webui version";
+    } else {
+        restored = "the webui version and all saved extension versions";
+    }
+    let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
+    if (confirmed) {
+        restart_reload();
+        gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
+            x.innerHTML = "Loading..."
+        })
+    }
+    return [confirmed, config_state_name, config_restore_type];
+}
@@ -5,18 +5,16 @@ function setupExtraNetworksForTab(tabname){
    var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div')
    var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea')
    var refresh = gradioApp().getElementById(tabname+'_extra_refresh')
-    var close = gradioApp().getElementById(tabname+'_extra_close')

    search.classList.add('search')
    tabs.appendChild(search)
    tabs.appendChild(refresh)
-    tabs.appendChild(close)

    search.addEventListener("input", function(evt){
        searchTerm = search.value.toLowerCase()

        gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
-            text = elem.querySelector('.name').textContent.toLowerCase()
+            text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
            elem.style.display = text.indexOf(searchTerm) == -1 ? "none" : ""
        })
    });
@@ -48,10 +46,39 @@ function setupExtraNetworks(){

 onUiLoaded(setupExtraNetworks)

+var re_extranet   =    /<([^:]+:[^:]+):[\d\.]+>/;
+var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
+
+function tryToRemoveExtraNetworkFromPrompt(textarea, text){
+    var m = text.match(re_extranet)
+    if(! m) return false
+
+    var partToSearch = m[1]
+    var replaced = false
+    var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, index){
+        m = found.match(re_extranet);
+        if(m[1] == partToSearch){
+            replaced = true;
+            return ""
+        }
+        return found;
+    })
+
+    if(replaced){
+        textarea.value = newTextareaText
+        return true;
+    }
+
+    return false
+}
+
 function cardClicked(tabname, textToAdd, allowNegativePrompt){
    var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")

-    textarea.value = textarea.value + " " + textToAdd
+    if(! tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)){
+        textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd
+    }
+
    updateInput(textarea)
 }

@@ -67,3 +94,86 @@ function saveCardPreview(event, tabname, filename){
    event.stopPropagation()
    event.preventDefault()
 }
+
+function extraNetworksSearchButton(tabs_id, event){
+    searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
+    button = event.target
+    text = button.classList.contains("search-all") ? "" : button.textContent.trim()
+
+    searchTextarea.value = text
+    updateInput(searchTextarea)
+}
+
+var globalPopup = null;
+var globalPopupInner = null;
+function popup(contents){
+    if(! globalPopup){
+        globalPopup = document.createElement('div')
+        globalPopup.onclick = function(){ globalPopup.style.display = "none"; };
+        globalPopup.classList.add('global-popup');
+
+        var close = document.createElement('div')
+        close.classList.add('global-popup-close');
+        close.onclick = function(){ globalPopup.style.display = "none"; };
+        close.title = "Close";
+        globalPopup.appendChild(close)
+
+        globalPopupInner = document.createElement('div')
+        globalPopupInner.onclick = function(event){ event.stopPropagation(); return false; };
+        globalPopupInner.classList.add('global-popup-inner');
+        globalPopup.appendChild(globalPopupInner)
+
+        gradioApp().appendChild(globalPopup);
+    }
+
+    globalPopupInner.innerHTML = '';
+    globalPopupInner.appendChild(contents);
+
+    globalPopup.style.display = "flex";
+}
+
+function extraNetworksShowMetadata(text){
+    elem = document.createElement('pre')
+    elem.classList.add('popup-metadata');
+    elem.textContent = text;
+
+    popup(elem);
+}
+
+function requestGet(url, data, handler, errorHandler){
+    var xhr = new XMLHttpRequest();
+    var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
+    xhr.open("GET", url + "?" + args, true);
+
+    xhr.onreadystatechange = function () {
+        if (xhr.readyState === 4) {
+            if (xhr.status === 200) {
+                try {
+                    var js = JSON.parse(xhr.responseText);
+                    handler(js)
+                } catch (error) {
+                    console.error(error);
+                    errorHandler()
+                }
+            } else{
+                errorHandler()
+            }
+        }
+    };
+    var js = JSON.stringify(data);
+    xhr.send(js);
+}
+
+function extraNetworksRequestMetadata(event, extraPage, cardName){
+    showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
+
+    requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
+        if(data && data.metadata){
+            extraNetworksShowMetadata(data.metadata)
+        } else{
+            showError()
+        }
+    }, showError)
+
+    event.stopPropagation()
+}
@@ -16,9 +16,9 @@ onUiUpdate(function(){

 let modalObserver = new MutationObserver(function(mutations) {
 	mutations.forEach(function(mutationRecord) {
-		let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
-		if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
-			gradioApp().getElementById(selectedTab+"_generation_info_button").click()
+		let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
+		if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
+			gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
 	});
 });

@@ -6,10 +6,11 @@ titles = {
 	"GFPGAN": "Restore low quality faces using GFPGAN neural network",
 	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
 	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
+	"UniPC": "Unified Predictor-Corrector Framework for Fast Sampling of Diffusion Models",
 	"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", 

-	"Batch count": "How many batches of images to create",
-	"Batch size": "How many image to create in a single batch",
+	"Batch count": "How many batches of images to create (has no impact on generation performance or VRAM usage)",
+	"Batch size": "How many image to create in a single batch (increases generation performance at cost of higher VRAM usage)",
    "CFG Scale": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results",
    "Seed": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result",
    "\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
@@ -17,11 +18,11 @@ titles = {
    "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
    "\u{1f4c2}": "Open images output directory",
    "\u{1f4be}": "Save style",
-    "\U0001F5D1": "Clear prompt",
+    "\u{1f5d1}\ufe0f": "Clear prompt",
    "\u{1f4cb}": "Apply selected styles to current prompt",
    "\u{1f4d2}": "Paste available values into the field",
-    "\u{1f3b4}": "Show extra networks",
-
+    "\u{1f3b4}": "Show/hide extra networks",
+    "\u{1f300}": "Restore progress",

    "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
    "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",
@@ -39,8 +40,7 @@ titles = {
    "Inpaint at full resolution": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image",

    "Denoising strength": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.",
-    "Denoising strength change factor": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.",
-
+    
    "Skip": "Stop processing current image and continue processing.",
    "Interrupt": "Stop processing images and return any results accumulated so far.",
    "Save": "Write image to a directory (default - log/images) and generation parameters into csv file.",
@@ -50,7 +50,7 @@ titles = {

    "None": "Do not do anything special",
    "Prompt matrix": "Separate prompts into parts using vertical pipe character (|) and the script will create a picture for every combination of them (except for the first part, which will be present in all combinations)",
-    "X/Y plot": "Create a grid where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows",
+    "X/Y/Z plot": "Create grid(s) where images will have different parameters. Use inputs below to specify which parameters will be shared by columns and rows",
    "Custom code": "Run Python code. Advanced user only. Must run program with --allow-code for this to work",

    "Prompt S/R": "Separate a list of words with commas, and the first word will be used as a keyword: script will search for this word in the prompt, and replace it with others",
@@ -66,12 +66,14 @@ titles = {

    "Interrogate": "Reconstruct prompt from existing image and put it into the prompt field.",

-    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
-    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp]; leave empty for default.",
+    "Images filename pattern": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
+    "Directory name pattern": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [clip_skip], [batch_number], [generation_number], [prompt_hash], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [model_name], [prompt_words], [date], [datetime], [datetime<Format>], [datetime<Format><Time Zone>], [job_timestamp], [hasprompt<prompt1|default><prompt2>..]; leave empty for default.",
    "Max prompt words": "Set the maximum number of words to be used in the [prompt_words] option; ATTENTION: If the words are too long, they may exceed the maximum length of the file path that the system can handle",

-    "Loopback": "Process an image, use it as an input, repeat.",
-    "Loops": "How many times to repeat processing an image and using it as input for the next iteration",
+    "Loopback": "Performs img2img processing multiple times. Output images are used as input for the next loop.",
+    "Loops": "How many times to process an image. Each output is used as the input of the next loop. If set to 1, behavior will be as if this script were not used.",
+    "Final denoising strength": "The denoising strength for the final loop of each image in the batch.",
+    "Denoising strength curve": "The denoising curve controls the rate of denoising strength change each loop. Aggressive: Most of the change will happen towards the start of the loops. Linear: Change will be constant through all loops. Lazy: Most of the change will happen towards the end of the loops.",

    "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",
    "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",
@@ -84,7 +86,6 @@ titles = {
    "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",

    "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",

    "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
    "Filename join string": "This string will be used to join split words into a single line if the option above is enabled.",
@@ -110,7 +111,8 @@ titles = {
    "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders.",
    "Multiplier for extra networks": "When adding extra network such as Hypernetwork or Lora to prompt, use this multiplier for it.",
    "Discard weights with matching name": "Regular expression; if weights's name matches it, the weights is not written to the resulting checkpoint. Use ^model_ema to discard EMA weights.",
-    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited."
+    "Extra networks tab order": "Comma-separated list of tab names; tabs listed here will appear in the extra networks UI first and in order lsited.",
+    "Negative Guidance minimum sigma": "Skip negative prompt for steps where image is already mostly denoised; the higher this value, the more skips there will be; provides increased performance in exchange for minor quality reduction."
 }


@@ -11,7 +11,7 @@ function showModal(event) {
    if (modalImage.style.display === 'none') {
        lb.style.setProperty('background-image', 'url(' + source.src + ')');
    }
-    lb.style.display = "block";
+    lb.style.display = "flex";
    lb.focus()

    const tabTxt2Img = gradioApp().getElementById("tab_txt2img")
@@ -32,13 +32,7 @@ function negmod(n, m) {
 function updateOnBackgroundChange() {
    const modalImage = gradioApp().getElementById("modalImage")
    if (modalImage && modalImage.offsetParent) {
-        let allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
-        let currentButton = null
-        allcurrentButtons.forEach(function(elem) {
-            if (elem.parentElement.offsetParent) {
-                currentButton = elem;
-            }
-        })
+        let currentButton = selected_gallery_button();

        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
            modalImage.src = currentButton.children[0].src;
@@ -50,22 +44,10 @@ function updateOnBackgroundChange() {
 }

 function modalImageSwitch(offset) {
-    var allgalleryButtons = gradioApp().querySelectorAll(".gallery-item.transition-all")
-    var galleryButtons = []
-    allgalleryButtons.forEach(function(elem) {
-        if (elem.parentElement.offsetParent) {
-            galleryButtons.push(elem);
-        }
-    })
+    var galleryButtons = all_gallery_buttons();

    if (galleryButtons.length > 1) {
-        var allcurrentButtons = gradioApp().querySelectorAll(".gallery-item.transition-all.\\!ring-2")
-        var currentButton = null
-        allcurrentButtons.forEach(function(elem) {
-            if (elem.parentElement.offsetParent) {
-                currentButton = elem;
-            }
-        })
+        var currentButton = selected_gallery_button();

        var result = -1
        galleryButtons.forEach(function(v, i) {
@@ -136,37 +118,29 @@ function modalKeyHandler(event) {
    }
 }

-function showGalleryImage() {
-    setTimeout(function() {
-        fullImg_preview = gradioApp().querySelectorAll('img.w-full.object-contain')
+function setupImageForLightbox(e) {
+	if (e.dataset.modded)
+		return;

-        if (fullImg_preview != null) {
-            fullImg_preview.forEach(function function_name(e) {
-                if (e.dataset.modded)
-                    return;
-                e.dataset.modded = true;
-                if(e && e.parentElement.tagName == 'DIV'){
-                    e.style.cursor='pointer'
-                    e.style.userSelect='none'
+	e.dataset.modded = true;
+	e.style.cursor='pointer'
+	e.style.userSelect='none'

-                    var isFirefox = isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1
+	var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1

-                    // For Firefox, listening on click first switched to next image then shows the lightbox.
-                    // If you know how to fix this without switching to mousedown event, please.
-                    // For other browsers the event is click to make it possiblr to drag picture.
-                    var event = isFirefox ? 'mousedown' : 'click'
+	// For Firefox, listening on click first switched to next image then shows the lightbox.
+	// If you know how to fix this without switching to mousedown event, please.
+	// For other browsers the event is click to make it possiblr to drag picture.
+	var event = isFirefox ? 'mousedown' : 'click'

-                    e.addEventListener(event, function (evt) {
-                        if(!opts.js_modal_lightbox || evt.button != 0) return;
-                        modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
-                        evt.preventDefault()
-                        showModal(evt)
-                    }, true);
-                }
-            });
-        }
+	e.addEventListener(event, function (evt) {
+		if(!opts.js_modal_lightbox || evt.button != 0) return;
+
+		modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
+		evt.preventDefault()
+		showModal(evt)
+	}, true);

-    }, 100);
 }

 function modalZoomSet(modalImage, enable) {
@@ -199,21 +173,21 @@ function modalTileImageToggle(event) {
 }

 function galleryImageHandler(e) {
-    if (e && e.parentElement.tagName == 'BUTTON') {
+    //if (e && e.parentElement.tagName == 'BUTTON') {
        e.onclick = showGalleryImage;
-    }
+    //}
 }

 onUiUpdate(function() {
-    fullImg_preview = gradioApp().querySelectorAll('img.w-full')
+    fullImg_preview = gradioApp().querySelectorAll('.gradio-gallery > div > img')
    if (fullImg_preview != null) {
-        fullImg_preview.forEach(galleryImageHandler);
+        fullImg_preview.forEach(setupImageForLightbox);
    }
    updateOnBackgroundChange();
 })

 document.addEventListener("DOMContentLoaded", function() {
-    const modalFragment = document.createDocumentFragment();
+    //const modalFragment = document.createDocumentFragment();
    const modal = document.createElement('div')
    modal.onclick = closeModal;
    modal.id = "lightboxModal";
@@ -277,9 +251,12 @@ document.addEventListener("DOMContentLoaded", function() {

    modal.appendChild(modalNext)

+    try {
+		gradioApp().appendChild(modal);
+	} catch (e) {
+		gradioApp().body.appendChild(modal);
+	}

-    gradioApp().getRootNode().appendChild(modal)
-
-    document.body.appendChild(modalFragment);
+    document.body.appendChild(modal);

 });
@@ -0,0 +1,36 @@
+    let delay = 350//ms
+    window.addEventListener('gamepadconnected', (e) => {
+        console.log("Gamepad connected!")
+        const gamepad = e.gamepad;
+        setInterval(() => {
+            const xValue = gamepad.axes[0].toFixed(2);
+            if (xValue < -0.3) {
+                modalPrevImage(e);
+            } else if (xValue > 0.3) {
+                modalNextImage(e);
+            }
+
+        }, delay);
+    });
+
+
+    /*
+    Primarily for vr controller type pointer devices.
+    I use the wheel event because there's currently no way to do it properly with web xr.
+     */
+
+    let isScrolling = false;
+    window.addEventListener('wheel', (e) => {
+        if (isScrolling) return;
+        isScrolling = true;
+
+        if (e.deltaX <= -0.6) {
+            modalPrevImage(e);
+        } else if (e.deltaX >= 0.6) {
+            modalNextImage(e);
+        }
+
+        setTimeout(() => {
+            isScrolling = false;
+        }, delay);
+    });
@@ -15,7 +15,7 @@ onUiUpdate(function(){
        }
    }

-    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] img.h-full.w-full.overflow-hidden');
+    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] .thumbnail-item > img');

    if (galleryPreviews == null) return;

@@ -1,78 +1,13 @@
 // code related to showing and updating progressbar shown as the image is being made

-
-galleries = {}
-storedGallerySelections = {}
-galleryObservers = {}
-
 function rememberGallerySelection(id_gallery){
-    storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery)
+
 }

 function getGallerySelectedIndex(id_gallery){
-    let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
-    let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')

-     let currentlySelectedIndex = -1
-     galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } })
-
-     return currentlySelectedIndex
 }

-// this is a workaround for https://github.com/gradio-app/gradio/issues/2984
-function check_gallery(id_gallery){
-    let gallery = gradioApp().getElementById(id_gallery)
-    // if gallery has no change, no need to setting up observer again.
-    if (gallery && galleries[id_gallery] !== gallery){
-        galleries[id_gallery] = gallery;
-        if(galleryObservers[id_gallery]){
-            galleryObservers[id_gallery].disconnect();
-        }
-
-        storedGallerySelections[id_gallery] = -1
-
-        galleryObservers[id_gallery] = new MutationObserver(function (){
-            let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
-            let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
-            let currentlySelectedIndex = getGallerySelectedIndex(id_gallery)
-            prevSelectedIndex = storedGallerySelections[id_gallery]
-            storedGallerySelections[id_gallery] = -1
-
-            if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
-                // automatically re-open previously selected index (if exists)
-                activeElement = gradioApp().activeElement;
-                let scrollX = window.scrollX;
-                let scrollY = window.scrollY;
-
-                galleryButtons[prevSelectedIndex].click();
-                showGalleryImage();
-
-                // When the gallery button is clicked, it gains focus and scrolls itself into view
-                // We need to scroll back to the previous position
-                setTimeout(function (){
-                    window.scrollTo(scrollX, scrollY);
-                }, 50);
-
-                if(activeElement){
-                    // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it
-                    // if someone has a better solution please by all means
-                    setTimeout(function (){
-                        activeElement.focus({
-                            preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it
-                        })
-                    }, 1);
-                }
-            }
-        })
-        galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
-    }
-}
-
-onUiUpdate(function(){
-    check_gallery('txt2img_gallery')
-    check_gallery('img2img_gallery')
-})
-
 function request(url, data, handler, errorHandler){
    var xhr = new XMLHttpRequest();
    var url = url;
@@ -131,7 +66,7 @@ function randomId(){
 // starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and
 // preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd.
 // calls onProgress every time there is a progress update
-function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress){
+function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress, inactivityTimeout=40){
    var dateStart = new Date()
    var wasEverActive = false
    var parentProgressbar = progressbarContainer.parentNode
@@ -139,7 +74,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre

    var divProgress = document.createElement('div')
    divProgress.className='progressDiv'
-    divProgress.style.display = opts.show_progressbar ? "" : "none"
+    divProgress.style.display = opts.show_progressbar ? "block" : "none"
    var divInner = document.createElement('div')
    divInner.className='progress'

@@ -203,7 +138,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                return
            }

-            if(elapsedFromStart > 5 && !res.queued && !res.active){
+            if(elapsedFromStart > inactivityTimeout && !res.queued && !res.active){
                removeProgressBar()
                return
            }
@@ -7,9 +7,31 @@ function set_theme(theme){
    }
 }

+function all_gallery_buttons() {
+    var allGalleryButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnails > .thumbnail-item.thumbnail-small');
+    var visibleGalleryButtons = [];
+    allGalleryButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            visibleGalleryButtons.push(elem);
+        }
+    })
+    return visibleGalleryButtons;
+}
+
+function selected_gallery_button() {
+    var allCurrentButtons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery].gradio-gallery .thumbnail-item.thumbnail-small.selected');
+    var visibleCurrentButton = null;
+    allCurrentButtons.forEach(function(elem) {
+        if (elem.parentElement.offsetParent) {
+            visibleCurrentButton = elem;
+        }
+    })
+    return visibleCurrentButton;
+}
+
 function selected_gallery_index(){
-    var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item')
-    var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2')
+    var buttons = all_gallery_buttons();
+    var button = selected_gallery_button();

    var result = -1
    buttons.forEach(function(v, i){ if(v==button) { result = i } })
@@ -18,14 +40,18 @@ function selected_gallery_index(){
 }

 function extract_image_from_gallery(gallery){
-    if(gallery.length == 1){
-        return [gallery[0]]
+    if (gallery.length == 0){
+        return [null];
+    }
+    if (gallery.length == 1){
+        return [gallery[0]];
    }

    index = selected_gallery_index()

    if (index < 0 || index >= gallery.length){
-        return [null]
+        // Use the first image in the gallery as the default
+        index = 0;
    }

    return [gallery[index]];
@@ -86,7 +112,7 @@ function get_tab_index(tabId){
    var res = 0

    gradioApp().getElementById(tabId).querySelector('div').querySelectorAll('button').forEach(function(button, i){
-        if(button.className.indexOf('bg-white') != -1)
+        if(button.className.indexOf('selected') != -1)
            res = i
    })

@@ -133,14 +159,24 @@ function showSubmitButtons(tabname, show){
    gradioApp().getElementById(tabname+'_skip').style.display = show ? "none" : "block"
 }

+function showRestoreProgressButton(tabname, show){
+    button = gradioApp().getElementById(tabname + "_restore_progress")
+    if(! button) return
+
+    button.style.display = show ? "flex" : "none"
+}
+
 function submit(){
    rememberGallerySelection('txt2img_gallery')
    showSubmitButtons('txt2img', false)

    var id = randomId()
+    localStorage.setItem("txt2img_task_id", id);
+
    requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){
        showSubmitButtons('txt2img', true)
-
+        localStorage.removeItem("txt2img_task_id")
+        showRestoreProgressButton('txt2img', false)
    })

    var res = create_submit_args(arguments)
@@ -155,8 +191,12 @@ function submit_img2img(){
    showSubmitButtons('img2img', false)

    var id = randomId()
+    localStorage.setItem("img2img_task_id", id);
+
    requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
        showSubmitButtons('img2img', true)
+        localStorage.removeItem("img2img_task_id")
+        showRestoreProgressButton('img2img', false)
    })

    var res = create_submit_args(arguments)
@@ -167,6 +207,40 @@ function submit_img2img(){
    return res
 }

+function restoreProgressTxt2img(x){
+    showRestoreProgressButton("txt2img", false)
+
+    id = localStorage.getItem("txt2img_task_id")
+
+    if(id) {
+        requestProgress(id, gradioApp().getElementById('txt2img_gallery_container'), gradioApp().getElementById('txt2img_gallery'), function(){
+            showSubmitButtons('txt2img', true)
+        }, null, 0)
+    }
+
+    return id
+}
+function restoreProgressImg2img(x){
+    showRestoreProgressButton("img2img", false)
+
+    id = localStorage.getItem("img2img_task_id")
+
+    if(id) {
+        requestProgress(id, gradioApp().getElementById('img2img_gallery_container'), gradioApp().getElementById('img2img_gallery'), function(){
+            showSubmitButtons('img2img', true)
+        }, null, 0)
+    }
+
+    return id
+}
+
+
+onUiLoaded(function () {
+    showRestoreProgressButton('txt2img', localStorage.getItem("txt2img_task_id"))
+    showRestoreProgressButton('img2img', localStorage.getItem("img2img_task_id"))
+});
+
+
 function modelmerger(){
    var id = randomId()
    requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function(){})
@@ -191,6 +265,28 @@ function confirm_clear_prompt(prompt, negative_prompt) {
    return [prompt, negative_prompt]
 }

+
+promptTokecountUpdateFuncs = {}
+
+function recalculatePromptTokens(name){
+    if(promptTokecountUpdateFuncs[name]){
+        promptTokecountUpdateFuncs[name]()
+    }
+}
+
+function recalculate_prompts_txt2img(){
+    recalculatePromptTokens('txt2img_prompt')
+    recalculatePromptTokens('txt2img_neg_prompt')
+    return args_to_array(arguments);
+}
+
+function recalculate_prompts_img2img(){
+    recalculatePromptTokens('img2img_prompt')
+    recalculatePromptTokens('img2img_neg_prompt')
+    return args_to_array(arguments);
+}
+
+
 opts = {}
 onUiUpdate(function(){
 	if(Object.keys(opts).length != 0) return;
@@ -232,14 +328,11 @@ onUiUpdate(function(){
            return
        }

-
        prompt.parentElement.insertBefore(counter, prompt)
-        counter.classList.add("token-counter")
        prompt.parentElement.style.position = "relative"

-		textarea.addEventListener("input", function(){
-		    update_token_counter(id_button);
-		});
+		promptTokecountUpdateFuncs[id] = function(){ update_token_counter(id_button); }
+		textarea.addEventListener("input", promptTokecountUpdateFuncs[id]);
    }

    registerTextarea('txt2img_prompt', 'txt2img_token_counter', 'txt2img_token_button')
@@ -273,7 +366,7 @@ onOptionsChanged(function(){

 let txt2img_textarea, img2img_textarea = undefined;
 let wait_time = 800
-let token_timeout;
+let token_timeouts = {};

 function update_txt2img_tokens(...args) {
 	update_token_counter("txt2img_token_button")
@@ -290,9 +383,9 @@ function update_img2img_tokens(...args) {
 }

 function update_token_counter(button_id) {
-	if (token_timeout)
-		clearTimeout(token_timeout);
-	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
+	if (token_timeouts[button_id])
+		clearTimeout(token_timeouts[button_id]);
+	token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }

 function restart_reload(){
@@ -309,3 +402,26 @@ function updateInput(target){
 	Object.defineProperty(e, "target", {value: target})
 	target.dispatchEvent(e);
 }
+
+
+var desiredCheckpointName = null;
+function selectCheckpoint(name){
+    desiredCheckpointName = name;
+    gradioApp().getElementById('change_checkpoint').click()
+}
+
+function currentImg2imgSourceResolution(_, _, scaleBy){
+    var img = gradioApp().querySelector('#mode_img2img > div[style="display: block;"] img')
+    return img ? [img.naturalWidth, img.naturalHeight, scaleBy] : [0, 0, scaleBy]
+}
+
+function updateImg2imgResizeToTextAfterChangingImage(){
+    // At the time this is called from gradio, the image has no yet been replaced.
+    // There may be a better solution, but this is simple and straightforward so I'm going with it.
+
+    setTimeout(function() {
+        gradioApp().getElementById('img2img_update_resize_to').click()
+    }, 500);
+
+    return []
+}
@@ -5,16 +5,55 @@ import sys
 import importlib.util
 import shlex
 import platform
-import argparse
 import json

-dir_repos = "repositories"
-dir_extensions = "extensions"
+from modules import cmd_args
+from modules.paths_internal import script_path, extensions_dir
+
+commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
+sys.argv += shlex.split(commandline_args)
+
+args, _ = cmd_args.parser.parse_known_args()
+
 python = sys.executable
 git = os.environ.get('GIT', "git")
 index_url = os.environ.get('INDEX_URL', "")
 stored_commit_hash = None
-skip_install = False
+dir_repos = "repositories"
+
+if 'GRADIO_ANALYTICS_ENABLED' not in os.environ:
+    os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
+
+
+def check_python_version():
+    is_windows = platform.system() == "Windows"
+    major = sys.version_info.major
+    minor = sys.version_info.minor
+    micro = sys.version_info.micro
+
+    if is_windows:
+        supported_minors = [10]
+    else:
+        supported_minors = [7, 8, 9, 10, 11]
+
+    if not (major == 3 and minor in supported_minors):
+        import modules.errors
+
+        modules.errors.print_error_explanation(f"""
+INCOMPATIBLE PYTHON VERSION
+
+This program is tested with 3.10.6 Python, but you have {major}.{minor}.{micro}.
+If you encounter an error with "RuntimeError: Couldn't install torch." message,
+or any other error regarding unsuccessful package (library) installation,
+please downgrade (or upgrade) to the latest version of 3.10 Python
+and delete current Python and "venv" folder in WebUI's directory.
+
+You can download 3.10 Python from here: https://www.python.org/downloads/release/python-3106/
+
+{"Alternatively, use a binary release of WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases" if is_windows else ""}
+
+Use --skip-python-version-check to suppress this warning.
+""")


 def commit_hash():
@@ -31,23 +70,6 @@ def commit_hash():
    return stored_commit_hash


-def extract_arg(args, name):
-    return [x for x in args if x != name], name in args
-
-
-def extract_opt(args, name):
-    opt = None
-    is_present = False
-    if name in args:
-        is_present = True
-        idx = args.index(name)
-        del args[idx]
-        if idx < len(args) and args[idx][0] != "-":
-            opt = args[idx]
-            del args[idx]
-    return args, is_present, opt
-
-
 def run(command, desc=None, errdesc=None, custom_env=None, live=False):
    if desc is not None:
        print(desc)
@@ -91,19 +113,19 @@ def is_installed(package):


 def repo_dir(name):
-    return os.path.join(dir_repos, name)
+    return os.path.join(script_path, dir_repos, name)


 def run_python(code, desc=None, errdesc=None):
    return run(f'"{python}" -c "{code}"', desc, errdesc)


-def run_pip(args, desc=None):
-    if skip_install:
+def run_pip(command, desc=None, live=False):
+    if args.skip_install:
        return

    index_url_line = f' --index-url {index_url}' if index_url != '' else ''
-    return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+    return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}", live=live)


 def check_run_python(code):
@@ -130,7 +152,17 @@ def git_clone(url, dir, name, commithash=None):
    if commithash is not None:
        run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")

-        
+
+def git_pull_recursive(dir):
+    for subdir, _, _ in os.walk(dir):
+        if os.path.exists(os.path.join(subdir, '.git')):
+            try:
+                output = subprocess.check_output([git, '-C', subdir, 'pull', '--autostash'])
+                print(f"Pulled changes for repository in '{subdir}':\n{output.decode('utf-8').strip()}\n")
+            except subprocess.CalledProcessError as e:
+                print(f"Couldn't perform 'git pull' on repository in '{subdir}':\n{e.output.decode('utf-8').strip()}\n")
+
+
 def version_check(commit):
    try:
        import requests
@@ -173,27 +205,27 @@ def list_extensions(settings_file):
        print(e, file=sys.stderr)

    disabled_extensions = set(settings.get('disabled_extensions', []))
+    disable_all_extensions = settings.get('disable_all_extensions', 'none')

-    return [x for x in os.listdir(dir_extensions) if x not in disabled_extensions]
+    if disable_all_extensions != 'none':
+        return []
+
+    return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]


 def run_extensions_installers(settings_file):
-    if not os.path.isdir(dir_extensions):
+    if not os.path.isdir(extensions_dir):
        return

    for dirname_extension in list_extensions(settings_file):
-        run_extension_installer(os.path.join(dir_extensions, dirname_extension))
+        run_extension_installer(os.path.join(extensions_dir, dirname_extension))


 def prepare_environment():
-    global skip_install
-
-    pip_installer_location = os.environ.get('PIP_INSTALLER_LOCATION', None)
-
-    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117")
+    torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==2.0.0 torchvision==0.15.1 --extra-index-url https://download.pytorch.org/whl/cu118")
    requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
-    commandline_args = os.environ.get('COMMANDLINE_ARGS', "")

+    xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.17')
    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
    clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
    openclip_package = os.environ.get('OPENCLIP_PACKAGE', "git+https://github.com/mlfoundations/open_clip.git@bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b")
@@ -204,40 +236,24 @@ def prepare_environment():
    codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
    blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')

-    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e")
+    stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
    taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "5b3af030dd83e0297272d861c19477735d0317ec")
    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
    blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")

-    sys.argv += shlex.split(commandline_args)
-
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default='config.json')
-    args, _ = parser.parse_known_args(sys.argv)
-
-    sys.argv, _ = extract_arg(sys.argv, '-f')
-    sys.argv, skip_torch_cuda_test = extract_arg(sys.argv, '--skip-torch-cuda-test')
-    sys.argv, reinstall_xformers = extract_arg(sys.argv, '--reinstall-xformers')
-    sys.argv, reinstall_torch = extract_arg(sys.argv, '--reinstall-torch')
-    sys.argv, update_check = extract_arg(sys.argv, '--update-check')
-    sys.argv, run_tests, test_dir = extract_opt(sys.argv, '--tests')
-    sys.argv, skip_install = extract_arg(sys.argv, '--skip-install')
-    xformers = '--xformers' in sys.argv
-    ngrok = '--ngrok' in sys.argv
+    if not args.skip_python_version_check:
+        check_python_version()

    commit = commit_hash()

    print(f"Python {sys.version}")
    print(f"Commit hash: {commit}")

-    if pip_installer_location is not None and not is_installed("pip"):
-        run(f'"{python}" "{pip_installer_location}"', "Installing pip", "Couldn't install pip")
-
-    if reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
+    if args.reinstall_torch or not is_installed("torch") or not is_installed("torchvision"):
        run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True)

-    if not skip_torch_cuda_test:
+    if not args.skip_torch_cuda_test:
        run_python("import torch; assert torch.cuda.is_available(), 'Torch is not able to use GPU; add --skip-torch-cuda-test to COMMANDLINE_ARGS variable to disable this check'")

    if not is_installed("gfpgan"):
@@ -249,22 +265,22 @@ def prepare_environment():
    if not is_installed("open_clip"):
        run_pip(f"install {openclip_package}", "open_clip")

-    if (not is_installed("xformers") or reinstall_xformers) and xformers:
+    if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
        if platform.system() == "Windows":
            if platform.python_version().startswith("3.10"):
-                run_pip(f"install -U -I --no-deps xformers==0.0.16rc425", "xformers")
+                run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True)
            else:
                print("Installation of xformers is not supported in this version of Python.")
                print("You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness")
                if not is_installed("xformers"):
                    exit(0)
        elif platform.system() == "Linux":
-            run_pip("install xformers==0.0.16rc425", "xformers")
+            run_pip(f"install {xformers_package}", "xformers")

-    if not is_installed("pyngrok") and ngrok:
+    if not is_installed("pyngrok") and args.ngrok:
        run_pip("install pyngrok", "ngrok")

-    os.makedirs(dir_repos, exist_ok=True)
+    os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)

    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
@@ -273,21 +289,26 @@ def prepare_environment():
    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)

    if not is_installed("lpips"):
-        run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")
+        run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")

-    run_pip(f"install -r {requirements_file}", "requirements for Web UI")
+    if not os.path.isfile(requirements_file):
+        requirements_file = os.path.join(script_path, requirements_file)
+    run_pip(f"install -r \"{requirements_file}\"", "requirements")

    run_extensions_installers(settings_file=args.ui_settings_file)

-    if update_check:
+    if args.update_check:
        version_check(commit)
+
+    if args.update_all_extensions:
+        git_pull_recursive(extensions_dir)
    
    if "--exit" in sys.argv:
        print("Exiting because of --exit argument")
        exit(0)

-    if run_tests:
-        exitcode = tests(test_dir)
+    if args.tests and not args.no_tests:
+        exitcode = tests(args.tests)
        exit(exitcode)


@@ -296,16 +317,18 @@ def tests(test_dir):
        sys.argv.append("--api")
    if "--ckpt" not in sys.argv:
        sys.argv.append("--ckpt")
-        sys.argv.append("./test/test_files/empty.pt")
+        sys.argv.append(os.path.join(script_path, "test/test_files/empty.pt"))
    if "--skip-torch-cuda-test" not in sys.argv:
        sys.argv.append("--skip-torch-cuda-test")
    if "--disable-nan-check" not in sys.argv:
        sys.argv.append("--disable-nan-check")
+    if "--no-tests" not in sys.argv:
+        sys.argv.append("--no-tests")

    print(f"Launching Web UI in another process for testing with arguments: {' '.join(sys.argv[1:])}")

    os.environ['COMMANDLINE_ARGS'] = ""
-    with open('test/stdout.txt', "w", encoding="utf8") as stdout, open('test/stderr.txt', "w", encoding="utf8") as stderr:
+    with open(os.path.join(script_path, 'test/stdout.txt'), "w", encoding="utf8") as stdout, open(os.path.join(script_path, 'test/stderr.txt'), "w", encoding="utf8") as stderr:
        proc = subprocess.Popen([sys.executable, *sys.argv], stdout=stdout, stderr=stderr)

    import test.server_poll
@@ -3,11 +3,14 @@ import io
 import time
 import datetime
 import uvicorn
+import gradio as gr
 from threading import Lock
 from io import BytesIO
-from gradio.processing_utils import decode_base64_to_file
-from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
+from fastapi import APIRouter, Depends, FastAPI, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from fastapi.exceptions import HTTPException
+from fastapi.responses import JSONResponse
+from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest

 import modules.shared as shared
@@ -18,7 +21,8 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list, find_checkpoint_config
+from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
+from modules.sd_models_config import find_checkpoint_config_near_filename
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
 from typing import List
@@ -89,6 +93,16 @@ def encode_pil_to_base64(image):
    return base64.b64encode(bytes_data)

 def api_middleware(app: FastAPI):
+    rich_available = True
+    try:
+        import anyio # importing just so it can be placed on silent list
+        import starlette # importing just so it can be placed on silent list
+        from rich.console import Console
+        console = Console()
+    except:
+        import traceback
+        rich_available = False
+
    @app.middleware("http")
    async def log_and_time(req: Request, call_next):
        ts = time.time()
@@ -109,6 +123,36 @@ def api_middleware(app: FastAPI):
            ))
        return res

+    def handle_exception(request: Request, e: Exception):
+        err = {
+            "error": type(e).__name__,
+            "detail": vars(e).get('detail', ''),
+            "body": vars(e).get('body', ''),
+            "errors": str(e),
+        }
+        if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
+            print(f"API error: {request.method}: {request.url} {err}")
+            if rich_available:
+                console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
+            else:
+                traceback.print_exc()
+        return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
+
+    @app.middleware("http")
+    async def exception_handling(request: Request, call_next):
+        try:
+            return await call_next(request)
+        except Exception as e:
+            return handle_exception(request, e)
+
+    @app.exception_handler(Exception)
+    async def fastapi_exception_handler(request: Request, e: Exception):
+        return handle_exception(request, e)
+
+    @app.exception_handler(HTTPException)
+    async def http_exception_handler(request: Request, e: HTTPException):
+        return handle_exception(request, e)
+

 class Api:
    def __init__(self, app: FastAPI, queue_lock: Lock):
@@ -149,6 +193,12 @@ class Api:
        self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse)
        self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse)
        self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse)
+        self.add_api_route("/sdapi/v1/unload-checkpoint", self.unloadapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/reload-checkpoint", self.reloadapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/scripts", self.get_scripts_list, methods=["GET"], response_model=ScriptsList)
+
+        self.default_script_arg_txt2img = []
+        self.default_script_arg_img2img = []

    def add_api_route(self, path: str, endpoint, **kwargs):
        if shared.cmd_opts.api_auth:
@@ -162,47 +212,113 @@ class Api:

        raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})

-    def get_script(self, script_name, script_runner):
-        if script_name is None:
+    def get_selectable_script(self, script_name, script_runner):
+        if script_name is None or script_name == "":
            return None, None

-        if not script_runner.scripts:
-            script_runner.initialize_scripts(False)
-            ui.create_ui()
-
        script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
        script = script_runner.selectable_scripts[script_idx]
        return script, script_idx
+    
+    def get_scripts_list(self):
+        t2ilist = [str(title.lower()) for title in scripts.scripts_txt2img.titles]
+        i2ilist = [str(title.lower()) for title in scripts.scripts_img2img.titles]
+
+        return ScriptsList(txt2img = t2ilist, img2img = i2ilist)  
+
+    def get_script(self, script_name, script_runner):
+        if script_name is None or script_name == "":
+            return None, None
+        
+        script_idx = script_name_to_index(script_name, script_runner.scripts)
+        return script_runner.scripts[script_idx]
+
+    def init_default_script_args(self, script_runner):
+        #find max idx from the scripts in runner and generate a none array to init script_args
+        last_arg_index = 1
+        for script in script_runner.scripts:
+            if last_arg_index < script.args_to:
+                last_arg_index = script.args_to
+        # None everywhere except position 0 to initialize script args
+        script_args = [None]*last_arg_index
+        script_args[0] = 0
+
+        # get default values
+        with gr.Blocks(): # will throw errors calling ui function without this
+            for script in script_runner.scripts:
+                if script.ui(script.is_img2img):
+                    ui_default_values = []
+                    for elem in script.ui(script.is_img2img):
+                        ui_default_values.append(elem.value)
+                    script_args[script.args_from:script.args_to] = ui_default_values
+        return script_args
+
+    def init_script_args(self, request, default_script_args, selectable_scripts, selectable_idx, script_runner):
+        script_args = default_script_args.copy()
+        # position 0 in script_arg is the idx+1 of the selectable script that is going to be run when using scripts.scripts_*2img.run()
+        if selectable_scripts:
+            script_args[selectable_scripts.args_from:selectable_scripts.args_to] = request.script_args
+            script_args[0] = selectable_idx + 1
+
+        # Now check for always on scripts
+        if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
+            for alwayson_script_name in request.alwayson_scripts.keys():
+                alwayson_script = self.get_script(alwayson_script_name, script_runner)
+                if alwayson_script == None:
+                    raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
+                # Selectable script in always on script param check
+                if alwayson_script.alwayson == False:
+                    raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
+                # always on script with no arg should always run so you don't really need to add them to the requests
+                if "args" in request.alwayson_scripts[alwayson_script_name]:
+                    # min between arg length in scriptrunner and arg length in the request
+                    for idx in range(0, min((alwayson_script.args_to - alwayson_script.args_from), len(request.alwayson_scripts[alwayson_script_name]["args"]))):
+                        script_args[alwayson_script.args_from + idx] = request.alwayson_scripts[alwayson_script_name]["args"][idx]
+        return script_args

    def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
-        script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img)
+        script_runner = scripts.scripts_txt2img
+        if not script_runner.scripts:
+            script_runner.initialize_scripts(False)
+            ui.create_ui()
+        if not self.default_script_arg_txt2img:
+            self.default_script_arg_txt2img = self.init_default_script_args(script_runner)
+        selectable_scripts, selectable_script_idx = self.get_selectable_script(txt2imgreq.script_name, script_runner)

-        populate = txt2imgreq.copy(update={ # Override __init__ params
+        populate = txt2imgreq.copy(update={  # Override __init__ params
            "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
-            "do_not_save_samples": True,
-            "do_not_save_grid": True
-            }
-        )
+            "do_not_save_samples": not txt2imgreq.save_images,
+            "do_not_save_grid": not txt2imgreq.save_images,
+        })
        if populate.sampler_name:
            populate.sampler_index = None  # prevent a warning later on

        args = vars(populate)
        args.pop('script_name', None)
+        args.pop('script_args', None) # will refeed them to the pipeline directly after initializing them
+        args.pop('alwayson_scripts', None)
+
+        script_args = self.init_script_args(txt2imgreq, self.default_script_arg_txt2img, selectable_scripts, selectable_script_idx, script_runner)
+
+        send_images = args.pop('send_images', True)
+        args.pop('save_images', None)

        with self.queue_lock:
            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
+            p.scripts = script_runner
+            p.outpath_grids = opts.outdir_txt2img_grids
+            p.outpath_samples = opts.outdir_txt2img_samples

            shared.state.begin()
-            if script is not None:
-                p.outpath_grids = opts.outdir_txt2img_grids
-                p.outpath_samples = opts.outdir_txt2img_samples
-                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
-                processed = scripts.scripts_txt2img.run(p, *p.script_args)
+            if selectable_scripts != None:
+                p.script_args = script_args
+                processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
            else:
+                p.script_args = tuple(script_args) # Need to pass args as tuple here
                processed = process_images(p)
            shared.state.end()

-        b64images = list(map(encode_pil_to_base64, processed.images))
+        b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []

        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())

@@ -211,41 +327,55 @@ class Api:
        if init_images is None:
            raise HTTPException(status_code=404, detail="Init image not found")

-        script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img)
-
        mask = img2imgreq.mask
        if mask:
            mask = decode_base64_to_image(mask)

-        populate = img2imgreq.copy(update={ # Override __init__ params
+        script_runner = scripts.scripts_img2img
+        if not script_runner.scripts:
+            script_runner.initialize_scripts(True)
+            ui.create_ui()
+        if not self.default_script_arg_img2img:
+            self.default_script_arg_img2img = self.init_default_script_args(script_runner)
+        selectable_scripts, selectable_script_idx = self.get_selectable_script(img2imgreq.script_name, script_runner)
+
+        populate = img2imgreq.copy(update={  # Override __init__ params
            "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index),
-            "do_not_save_samples": True,
-            "do_not_save_grid": True,
-            "mask": mask
-            }
-        )
+            "do_not_save_samples": not img2imgreq.save_images,
+            "do_not_save_grid": not img2imgreq.save_images,
+            "mask": mask,
+        })
        if populate.sampler_name:
            populate.sampler_index = None  # prevent a warning later on

        args = vars(populate)
        args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
        args.pop('script_name', None)
+        args.pop('script_args', None)  # will refeed them to the pipeline directly after initializing them
+        args.pop('alwayson_scripts', None)
+
+        script_args = self.init_script_args(img2imgreq, self.default_script_arg_img2img, selectable_scripts, selectable_script_idx, script_runner)
+
+        send_images = args.pop('send_images', True)
+        args.pop('save_images', None)

        with self.queue_lock:
            p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
            p.init_images = [decode_base64_to_image(x) for x in init_images]
+            p.scripts = script_runner
+            p.outpath_grids = opts.outdir_img2img_grids
+            p.outpath_samples = opts.outdir_img2img_samples

            shared.state.begin()
-            if script is not None:
-                p.outpath_grids = opts.outdir_img2img_grids
-                p.outpath_samples = opts.outdir_img2img_samples
-                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
-                processed = scripts.scripts_img2img.run(p, *p.script_args)
+            if selectable_scripts != None:
+                p.script_args = script_args
+                processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
            else:
+                p.script_args = tuple(script_args) # Need to pass args as tuple here
                processed = process_images(p)
            shared.state.end()

-        b64images = list(map(encode_pil_to_base64, processed.images))
+        b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []

        if not img2imgreq.include_init_images:
            img2imgreq.init_images = None
@@ -266,16 +396,11 @@ class Api:
    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
        reqDict = setUpscalers(req)

-        def prepareFiles(file):
-            file = decode_base64_to_file(file.data, file_path=file.name)
-            file.orig_name = file.name
-            return file
-
-        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
-        reqDict.pop('imageList')
+        image_list = reqDict.pop('imageList', [])
+        image_folder = [decode_base64_to_image(x.data) for x in image_list]

        with self.queue_lock:
-            result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
+            result = postprocessing.run_extras(extras_mode=1, image_folder=image_folder, image="", input_dir="", output_dir="", save_output=False, **reqDict)

        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])

@@ -347,6 +472,16 @@ class Api:

        return {}

+    def unloadapi(self):
+        unload_model_weights()
+
+        return {}
+
+    def reloadapi(self):
+        reload_model_weights()
+
+        return {}
+
    def skip(self):
        shared.state.skip()

@@ -387,7 +522,7 @@ class Api:
        ]

    def get_sd_models(self):
-        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
+        return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]

    def get_hypernetworks(self):
        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
@@ -497,7 +632,7 @@ class Api:
            if not apply_optimizations:
                sd_hijack.undo_optimizations()
            try:
-                hypernetwork, filename = train_hypernetwork(*args)
+                hypernetwork, filename = train_hypernetwork(**args)
            except Exception as e:
                error = e
            finally:
@@ -14,8 +14,8 @@ API_NOT_ALLOWED = [
    "outpath_samples",
    "outpath_grids",
    "sampler_index",
-    "do_not_save_samples",
-    "do_not_save_grid",
+    # "do_not_save_samples",
+    # "do_not_save_grid",
    "extra_generation_params",
    "overlay_images",
    "do_not_reload_embeddings",
@@ -100,13 +100,31 @@ class PydanticModelGenerator:
 StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
    "StableDiffusionProcessingTxt2Img",
    StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
+    [
+        {"key": "sampler_index", "type": str, "default": "Euler"},
+        {"key": "script_name", "type": str, "default": None},
+        {"key": "script_args", "type": list, "default": []},
+        {"key": "send_images", "type": bool, "default": True},
+        {"key": "save_images", "type": bool, "default": False},
+        {"key": "alwayson_scripts", "type": dict, "default": {}},
+    ]
 ).generate_model()

 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
    "StableDiffusionProcessingImg2Img",
    StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
+    [
+        {"key": "sampler_index", "type": str, "default": "Euler"},
+        {"key": "init_images", "type": list, "default": None},
+        {"key": "denoising_strength", "type": float, "default": 0.75},
+        {"key": "mask", "type": str, "default": None},
+        {"key": "include_init_images", "type": bool, "default": False, "exclude" : True},
+        {"key": "script_name", "type": str, "default": None},
+        {"key": "script_args", "type": list, "default": []},
+        {"key": "send_images", "type": bool, "default": True},
+        {"key": "save_images", "type": bool, "default": False},
+        {"key": "alwayson_scripts", "type": dict, "default": {}},
+    ]
 ).generate_model()

 class TextToImageResponse(BaseModel):
@@ -228,7 +246,7 @@ class SDModelItem(BaseModel):
    hash: Optional[str] = Field(title="Short hash")
    sha256: Optional[str] = Field(title="sha256 hash")
    filename: str = Field(title="Filename")
-    config: str = Field(title="Config file")
+    config: Optional[str] = Field(title="Config file")

 class HypernetworkItem(BaseModel):
    name: str = Field(title="Name")
@@ -267,3 +285,7 @@ class EmbeddingsResponse(BaseModel):
 class MemoryResponse(BaseModel):
    ram: dict = Field(title="RAM", description="System memory stats")
    cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats")
+
+class ScriptsList(BaseModel):
+    txt2img: list = Field(default=None,title="Txt2img", description="Titles of scripts (txt2img)")
+    img2img: list = Field(default=None,title="Img2img", description="Titles of scripts (img2img)")
@@ -35,6 +35,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):

            try:
                res = func(*args, **kwargs)
+                progress.record_results(id_task, res)
            finally:
                progress.finish_task(id_task)

@@ -0,0 +1,104 @@
+import argparse
+import os
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, sd_default_config, sd_model_file
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("-f", action='store_true', help=argparse.SUPPRESS)  # allows running as root; implemented outside of webui
+parser.add_argument("--update-all-extensions", action='store_true', help="launch.py argument: download updates for all extensions when starting the program")
+parser.add_argument("--skip-python-version-check", action='store_true', help="launch.py argument: do not check python version")
+parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
+parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
+parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
+parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
+parser.add_argument("--tests", type=str, default=None, help="launch.py argument: run tests in the specified directory")
+parser.add_argument("--no-tests", action='store_true', help="launch.py argument: do not run tests even if --tests option is specified")
+parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
+parser.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored")
+parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
+parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
+parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
+parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
+parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
+parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
+parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
+parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
+parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
+parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
+parser.add_argument("--embeddings-dir", type=str, default=os.path.join(data_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
+parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
+parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
+parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
+parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
+parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
+parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
+parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
+parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
+parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
+parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
+parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
+parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
+parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
+parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
+parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
+parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
+parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
+parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
+parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
+parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
+parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
+parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
+parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
+parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
+parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--opt-sdp-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization; requires PyTorch 2.*")
+parser.add_argument("--opt-sdp-no-mem-attention", action='store_true', help="enable scaled dot product cross-attention layer optimization without memory efficient attention, makes image generation deterministic; requires PyTorch 2.*")
+parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
+parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
+parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
+parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
+parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
+parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(data_path, 'ui-config.json'))
+parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
+parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
+parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(data_path, 'config.json'))
+parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
+parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--gradio-auth-path", type=str, help='set gradio authentication file path ex. "/path/to/auth/file" same auth format as --gradio-auth', default=None)
+parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
+parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
+parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
+parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(data_path, 'styles.csv'))
+parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
+parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
+parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
+parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
+parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
+parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
+parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
+parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
+parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
+parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
+parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
+parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
+parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
+parser.add_argument("--disable-tls-verify", action="store_false", help="When passed, enables the use of self-signed certificates.", default=None)
+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
+parser.add_argument("--gradio-queue", action='store_true', help="does not do anything", default=True)
+parser.add_argument("--no-gradio-queue", action='store_true', help="Disables gradio queue; causes the webpage to use http requests instead of websockets; was the defaul in earlier versions")
+parser.add_argument("--skip-version-check", action='store_true', help="Do not check versions of torch and xformers")
+parser.add_argument("--no-hashing", action='store_true', help="disable sha256 hashing of checkpoints to help loading performance", default=False)
+parser.add_argument("--no-download-sd-model", action='store_true', help="don't download SD1.5 model even if no model is found in --ckpt-dir", default=False)
@@ -8,7 +8,7 @@ import torch
 import modules.face_restoration
 import modules.shared
 from modules import shared, devices, modelloader
-from modules.paths import script_path, models_path
+from modules.paths import models_path

 # codeformer people made a choice to include modified basicsr library to their project which makes
 # it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
@@ -55,7 +55,7 @@ def setup_model(dirname):
                if self.net is not None and self.face_helper is not None:
                    self.net.to(devices.device_codeformer)
                    return self.net, self.face_helper
-                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth')
+                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
                if len(model_paths) != 0:
                    ckpt_path = model_paths[0]
                else:
@@ -0,0 +1,200 @@
+"""
+Supports saving and restoring webui and extensions from a known working set of commits
+"""
+
+import os
+import sys
+import traceback
+import json
+import time
+import tqdm
+
+from datetime import datetime
+from collections import OrderedDict
+import git
+
+from modules import shared, extensions
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path, config_states_dir
+
+
+all_config_states = OrderedDict()
+
+
+def list_config_states():
+    global all_config_states
+
+    all_config_states.clear()
+    os.makedirs(config_states_dir, exist_ok=True)
+
+    config_states = []
+    for filename in os.listdir(config_states_dir):
+        if filename.endswith(".json"):
+            path = os.path.join(config_states_dir, filename)
+            with open(path, "r", encoding="utf-8") as f:
+                j = json.load(f)
+                j["filepath"] = path
+                config_states.append(j)
+
+    config_states = list(sorted(config_states, key=lambda cs: cs["created_at"], reverse=True))
+
+    for cs in config_states:
+        timestamp = time.asctime(time.gmtime(cs["created_at"]))
+        name = cs.get("name", "Config")
+        full_name = f"{name}: {timestamp}"
+        all_config_states[full_name] = cs
+
+    return all_config_states
+
+
+def get_webui_config():
+    webui_repo = None
+
+    try:
+        if os.path.exists(os.path.join(script_path, ".git")):
+            webui_repo = git.Repo(script_path)
+    except Exception:
+        print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+    webui_remote = None
+    webui_commit_hash = None
+    webui_commit_date = None
+    webui_branch = None
+    if webui_repo and not webui_repo.bare:
+        try:
+            webui_remote = next(webui_repo.remote().urls, None)
+            head = webui_repo.head.commit
+            webui_commit_date = webui_repo.head.commit.committed_date
+            webui_commit_hash = head.hexsha
+            webui_branch = webui_repo.active_branch.name
+
+        except Exception:
+            webui_remote = None
+
+    return {
+        "remote": webui_remote,
+        "commit_hash": webui_commit_hash,
+        "commit_date": webui_commit_date,
+        "branch": webui_branch,
+    }
+
+
+def get_extension_config():
+    ext_config = {}
+
+    for ext in extensions.extensions:
+        entry = {
+            "name": ext.name,
+            "path": ext.path,
+            "enabled": ext.enabled,
+            "is_builtin": ext.is_builtin,
+            "remote": ext.remote,
+            "commit_hash": ext.commit_hash,
+            "commit_date": ext.commit_date,
+            "branch": ext.branch,
+            "have_info_from_repo": ext.have_info_from_repo
+        }
+
+        ext_config[ext.name] = entry
+
+    return ext_config
+
+
+def get_config():
+    creation_time = datetime.now().timestamp()
+    webui_config = get_webui_config()
+    ext_config = get_extension_config()
+
+    return {
+        "created_at": creation_time,
+        "webui": webui_config,
+        "extensions": ext_config
+    }
+
+
+def restore_webui_config(config):
+    print("* Restoring webui state...")
+
+    if "webui" not in config:
+        print("Error: No webui data saved to config")
+        return
+
+    webui_config = config["webui"]
+
+    if "commit_hash" not in webui_config:
+        print("Error: No commit saved to webui config")
+        return
+
+    webui_commit_hash = webui_config.get("commit_hash", None)
+    webui_repo = None
+
+    try:
+        if os.path.exists(os.path.join(script_path, ".git")):
+            webui_repo = git.Repo(script_path)
+    except Exception:
+        print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+        return
+
+    try:
+        webui_repo.git.fetch(all=True)
+        webui_repo.git.reset(webui_commit_hash, hard=True)
+        print(f"* Restored webui to commit {webui_commit_hash}.")
+    except Exception:
+        print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr)
+        print(traceback.format_exc(), file=sys.stderr)
+
+
+def restore_extension_config(config):
+    print("* Restoring extension state...")
+
+    if "extensions" not in config:
+        print("Error: No extension data saved to config")
+        return
+
+    ext_config = config["extensions"]
+
+    results = []
+    disabled = []
+
+    for ext in tqdm.tqdm(extensions.extensions):
+        if ext.is_builtin:
+            continue
+
+        ext.read_info_from_repo()
+        current_commit = ext.commit_hash
+
+        if ext.name not in ext_config:
+            ext.disabled = True
+            disabled.append(ext.name)
+            results.append((ext, current_commit[:8], False, "Saved extension state not found in config, marking as disabled"))
+            continue
+
+        entry = ext_config[ext.name]
+
+        if "commit_hash" in entry and entry["commit_hash"]:
+            try:
+                ext.fetch_and_reset_hard(entry["commit_hash"])
+                ext.read_info_from_repo()
+                if current_commit != entry["commit_hash"]:
+                    results.append((ext, current_commit[:8], True, entry["commit_hash"][:8]))
+            except Exception as ex:
+                results.append((ext, current_commit[:8], False, ex))
+        else:
+            results.append((ext, current_commit[:8], False, "No commit hash found in config"))
+
+        if not entry.get("enabled", False):
+            ext.disabled = True
+            disabled.append(ext.name)
+        else:
+            ext.disabled = False
+
+    shared.opts.disabled_extensions = disabled
+    shared.opts.save(shared.config_filename)
+
+    print("* Finished restoring extensions. Results:")
+    for ext, prev_commit, success, result in results:
+        if success:
+            print(f"  + {ext.name}: {prev_commit} -> {result}")
+        else:
+            print(f"  ! {ext.name}: FAILURE ({result})")
@@ -2,6 +2,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F

+from modules import devices
+
 # see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more


@@ -196,7 +198,7 @@ class DeepDanbooruModel(nn.Module):
        t_358, = inputs
        t_359 = t_358.permute(*[0, 3, 1, 2])
        t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)
-        t_360 = self.n_Conv_0(t_359_padded)
+        t_360 = self.n_Conv_0(t_359_padded.to(self.n_Conv_0.bias.dtype) if devices.unet_needs_upcast else t_359_padded)
        t_361 = F.relu(t_360)
        t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))
        t_362 = self.n_MaxPool_0(t_361)
@@ -1,21 +1,17 @@
-import sys, os, shlex
+import sys
 import contextlib
 import torch
 from modules import errors
-from packaging import version
+
+if sys.platform == "darwin":
+    from modules import mac_specific


-# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
-# check `getattr` and try it for compatibility
 def has_mps() -> bool:
-    if not getattr(torch, 'has_mps', False):
+    if sys.platform != "darwin":
        return False
-    try:
-        torch.zeros(1).to(torch.device("mps"))
-        return True
-    except Exception:
-        return False
-
+    else:
+        return mac_specific.has_mps

 def extract_device_id(args, name):
    for x in range(len(args)):
@@ -34,14 +30,18 @@ def get_cuda_device_string():
    return "cuda"


-def get_optimal_device():
+def get_optimal_device_name():
    if torch.cuda.is_available():
-        return torch.device(get_cuda_device_string())
+        return get_cuda_device_string()

    if has_mps():
-        return torch.device("mps")
+        return "mps"

-    return cpu
+    return "cpu"
+
+
+def get_optimal_device():
+    return torch.device(get_optimal_device_name())


 def get_device_for(task):
@@ -79,17 +79,31 @@ cpu = torch.device("cpu")
 device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
 dtype = torch.float16
 dtype_vae = torch.float16
+dtype_unet = torch.float16
+unet_needs_upcast = False
+
+
+def cond_cast_unet(input):
+    return input.to(dtype_unet) if unet_needs_upcast else input
+
+
+def cond_cast_float(input):
+    return input.float() if unet_needs_upcast else input


 def randn(seed, shape):
+    from modules.shared import opts
+
    torch.manual_seed(seed)
-    if device.type == 'mps':
+    if opts.randn_source == "CPU" or device.type == 'mps':
        return torch.randn(shape, device=cpu).to(device)
    return torch.randn(shape, device=device)


 def randn_without_seed(shape):
-    if device.type == 'mps':
+    from modules.shared import opts
+
+    if opts.randn_source == "CPU" or device.type == 'mps':
        return torch.randn(shape, device=cpu).to(device)
    return torch.randn(shape, device=device)

@@ -106,6 +120,10 @@ def autocast(disable=False):
    return torch.autocast("cuda")


+def without_autocast(disable=False):
+    return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()
+
+
 class NansException(Exception):
    pass

@@ -123,7 +141,7 @@ def test_for_nans(x, where):
        message = "A tensor with all NaNs was produced in Unet."

        if not shared.cmd_opts.no_half:
-            message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try using --no-half commandline argument to fix this."
+            message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this."

    elif where == "vae":
        message = "A tensor with all NaNs was produced in VAE."
@@ -133,60 +151,6 @@ def test_for_nans(x, where):
    else:
        message = "A tensor with all NaNs was produced."

+    message += " Use --disable-nan-check commandline argument to disable this check."
+
    raise NansException(message)
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
-orig_tensor_to = torch.Tensor.to
-def tensor_to_fix(self, *args, **kwargs):
-    if self.device.type != 'mps' and \
-       ((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
-       (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
-        self = self.contiguous()
-    return orig_tensor_to(self, *args, **kwargs)
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
-orig_layer_norm = torch.nn.functional.layer_norm
-def layer_norm_fix(*args, **kwargs):
-    if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
-        args = list(args)
-        args[0] = args[0].contiguous()
-    return orig_layer_norm(*args, **kwargs)
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
-orig_tensor_numpy = torch.Tensor.numpy
-def numpy_fix(self, *args, **kwargs):
-    if self.requires_grad:
-        self = self.detach()
-    return orig_tensor_numpy(self, *args, **kwargs)
-
-
-# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
-orig_cumsum = torch.cumsum
-orig_Tensor_cumsum = torch.Tensor.cumsum
-def cumsum_fix(input, cumsum_func, *args, **kwargs):
-    if input.device.type == 'mps':
-        output_dtype = kwargs.get('dtype', input.dtype)
-        if output_dtype == torch.int64:
-            return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
-        elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
-            return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
-    return cumsum_func(input, *args, **kwargs)
-
-
-if has_mps():
-    if version.parse(torch.__version__) < version.parse("1.13"):
-        # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
-        torch.Tensor.to = tensor_to_fix
-        torch.nn.functional.layer_norm = layer_norm_fix
-        torch.Tensor.numpy = numpy_fix
-    elif version.parse(torch.__version__) > version.parse("1.13.1"):
-        cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
-        cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
-        torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
-        torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
-        orig_narrow = torch.narrow
-        torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )
-
@@ -1,5 +1,6 @@
 # this file is adapted from https://github.com/victorca25/iNNfer

+from collections import OrderedDict
 import math
 import functools
 import torch
@@ -2,17 +2,26 @@ import os
 import sys
 import traceback

+import time
+from datetime import datetime
 import git

-from modules import paths, shared
+from modules import shared
+from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path

 extensions = []
-extensions_dir = os.path.join(paths.script_path, "extensions")
-extensions_builtin_dir = os.path.join(paths.script_path, "extensions-builtin")
+
+if not os.path.exists(extensions_dir):
+    os.makedirs(extensions_dir)


 def active():
-    return [x for x in extensions if x.enabled]
+    if shared.opts.disable_all_extensions == "all":
+        return []
+    elif shared.opts.disable_all_extensions == "extra":
+        return [x for x in extensions if x.enabled and x.is_builtin]
+    else:
+        return [x for x in extensions if x.enabled]


 class Extension:
@@ -23,22 +32,43 @@ class Extension:
        self.status = ''
        self.can_update = False
        self.is_builtin = is_builtin
+        self.commit_hash = ''
+        self.commit_date = None
+        self.version = ''
+        self.branch = None
+        self.remote = None
+        self.have_info_from_repo = False
+
+    def read_info_from_repo(self):
+        if self.is_builtin or self.have_info_from_repo:
+            return
+
+        self.have_info_from_repo = True

        repo = None
        try:
-            if os.path.exists(os.path.join(path, ".git")):
-                repo = git.Repo(path)
+            if os.path.exists(os.path.join(self.path, ".git")):
+                repo = git.Repo(self.path)
        except Exception:
-            print(f"Error reading github repository info from {path}:", file=sys.stderr)
+            print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)

        if repo is None or repo.bare:
            self.remote = None
        else:
            try:
-                self.remote = next(repo.remote().urls, None)
                self.status = 'unknown'
-            except Exception:
+                self.remote = next(repo.remote().urls, None)
+                head = repo.head.commit
+                self.commit_date = repo.head.commit.committed_date
+                ts = time.asctime(time.gmtime(self.commit_date))
+                if repo.active_branch:
+                    self.branch = repo.active_branch.name
+                self.commit_hash = head.hexsha
+                self.version = f'{self.commit_hash[:8]} ({ts})'
+
+            except Exception as ex:
+                print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
                self.remote = None

    def list_files(self, subdir, extension):
@@ -58,21 +88,33 @@ class Extension:

    def check_updates(self):
        repo = git.Repo(self.path)
-        for fetch in repo.remote().fetch("--dry-run"):
+        for fetch in repo.remote().fetch(dry_run=True):
            if fetch.flags != fetch.HEAD_UPTODATE:
                self.can_update = True
-                self.status = "behind"
+                self.status = "new commits"
                return

+        try:
+            origin = repo.rev_parse('origin')
+            if repo.head.commit != origin:
+                self.can_update = True
+                self.status = "behind HEAD"
+                return
+        except Exception:
+            self.can_update = False
+            self.status = "unknown (remote error)"
+            return
+
        self.can_update = False
        self.status = "latest"

-    def fetch_and_reset_hard(self):
+    def fetch_and_reset_hard(self, commit='origin'):
        repo = git.Repo(self.path)
        # Fix: `error: Your local changes to the following files would be overwritten by merge`,
        # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
-        repo.git.fetch('--all')
-        repo.git.reset('--hard', 'origin')
+        repo.git.fetch(all=True)
+        repo.git.reset(commit, hard=True)
+        self.have_info_from_repo = False


 def list_extensions():
@@ -81,7 +123,12 @@ def list_extensions():
    if not os.path.isdir(extensions_dir):
        return

-    paths = []
+    if shared.opts.disable_all_extensions == "all":
+        print("*** \"Disable all extensions\" option was set, will not load any extensions ***")
+    elif shared.opts.disable_all_extensions == "extra":
+        print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
+
+    extension_paths = []
    for dirname in [extensions_dir, extensions_builtin_dir]:
        if not os.path.isdir(dirname):
            return
@@ -91,9 +138,8 @@ def list_extensions():
            if not os.path.isdir(path):
                continue

-            paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
+            extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))

-    for dirname, path, is_builtin in paths:
+    for dirname, path, is_builtin in extension_paths:
        extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
        extensions.append(extension)
-
@@ -1,4 +1,4 @@
-from modules import extra_networks
+from modules import extra_networks, shared, extra_networks
 from modules.hypernetworks import hypernetwork


@@ -7,6 +7,12 @@ class ExtraNetworkHypernet(extra_networks.ExtraNetwork):
        super().__init__('hypernet')

    def activate(self, p, params_list):
+        additional = shared.opts.sd_hypernetwork
+
+        if additional != "None" and additional in shared.hypernetworks and len([x for x in params_list if x.items[0] == additional]) == 0:
+            p.all_prompts = [x + f"<hypernet:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
        names = []
        multipliers = []
        for params in params_list:
@@ -1,12 +1,13 @@
 import os
 import re
 import shutil
+import json


 import torch
 import tqdm

-from modules import shared, images, sd_models, sd_vae
+from modules import shared, images, sd_models, sd_vae, sd_models_config
 from modules.ui_common import plaintext_to_html
 import gradio as gr
 import safetensors.torch
@@ -37,7 +38,7 @@ def run_pnginfo(image):

 def create_config(ckpt_result, config_source, a, b, c):
    def config(x):
-        res = sd_models.find_checkpoint_config(x) if x else None
+        res = sd_models_config.find_checkpoint_config_near_filename(x) if x else None
        return res if res != shared.sd_default_config else None

    if config_source == 0:
@@ -71,7 +72,7 @@ def to_half(tensor, enable):
    return tensor


-def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
+def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata):
    shared.state.begin()
    shared.state.job = 'model-merge'

@@ -132,6 +133,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
    tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None

    result_is_inpainting_model = False
+    result_is_instruct_pix2pix_model = False

    if theta_func2:
        shared.state.textinfo = f"Loading B"
@@ -185,14 +187,19 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
            if a.shape != b.shape and a.shape[0:1] + a.shape[2:] == b.shape[0:1] + b.shape[2:]:
                if a.shape[1] == 4 and b.shape[1] == 9:
                    raise RuntimeError("When merging inpainting model with a normal one, A must be the inpainting model.")
+                if a.shape[1] == 4 and b.shape[1] == 8:
+                    raise RuntimeError("When merging instruct-pix2pix model with a normal one, A must be the instruct-pix2pix model.")

-                assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
-
-                theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
-                result_is_inpainting_model = True
+                if a.shape[1] == 8 and b.shape[1] == 4:#If we have an Instruct-Pix2Pix model...
+                    theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)#Merge only the vectors the models have in common.  Otherwise we get an error due to dimension mismatch.
+                    result_is_instruct_pix2pix_model = True
+                else:
+                    assert a.shape[1] == 9 and b.shape[1] == 4, f"Bad dimensions for merged layer {key}: A={a.shape}, B={b.shape}"
+                    theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier)
+                    result_is_inpainting_model = True
            else:
                theta_0[key] = theta_func2(a, b, multiplier)
-
+            
            theta_0[key] = to_half(theta_0[key], save_as_half)

        shared.state.sampling_step += 1
@@ -226,6 +233,7 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_

    filename = filename_generator() if custom_name == '' else custom_name
    filename += ".inpainting" if result_is_inpainting_model else ""
+    filename += ".instruct-pix2pix" if result_is_instruct_pix2pix_model else ""
    filename += "." + checkpoint_format

    output_modelname = os.path.join(ckpt_dir, filename)
@@ -234,13 +242,54 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
    shared.state.textinfo = "Saving"
    print(f"Saving to {output_modelname}...")

+    metadata = {"format": "pt", "sd_merge_models": {}, "sd_merge_recipe": None}
+
+    if save_metadata:
+        merge_recipe = {
+            "type": "webui", # indicate this model was merged with webui's built-in merger
+            "primary_model_hash": primary_model_info.sha256,
+            "secondary_model_hash": secondary_model_info.sha256 if secondary_model_info else None,
+            "tertiary_model_hash": tertiary_model_info.sha256 if tertiary_model_info else None,
+            "interp_method": interp_method,
+            "multiplier": multiplier,
+            "save_as_half": save_as_half,
+            "custom_name": custom_name,
+            "config_source": config_source,
+            "bake_in_vae": bake_in_vae,
+            "discard_weights": discard_weights,
+            "is_inpainting": result_is_inpainting_model,
+            "is_instruct_pix2pix": result_is_instruct_pix2pix_model
+        }
+        metadata["sd_merge_recipe"] = json.dumps(merge_recipe)
+
+        def add_model_metadata(checkpoint_info):
+            checkpoint_info.calculate_shorthash()
+            metadata["sd_merge_models"][checkpoint_info.sha256] = {
+                "name": checkpoint_info.name,
+                "legacy_hash": checkpoint_info.hash,
+                "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None)
+            }
+
+            metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {}))
+
+        add_model_metadata(primary_model_info)
+        if secondary_model_info:
+            add_model_metadata(secondary_model_info)
+        if tertiary_model_info:
+            add_model_metadata(tertiary_model_info)
+
+        metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"])
+
    _, extension = os.path.splitext(output_modelname)
    if extension.lower() == ".safetensors":
-        safetensors.torch.save_file(theta_0, output_modelname, metadata={"format": "pt"})
+        safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata)
    else:
        torch.save(theta_0, output_modelname)

    sd_models.list_models()
+    created_model = next((ckpt for ckpt in sd_models.checkpoints_list.values() if ckpt.name == filename), None)
+    if created_model:
+        created_model.calculate_shorthash()

    create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)

@@ -1,4 +1,5 @@
 import base64
+import html
 import io
 import math
 import os
@@ -6,24 +7,34 @@ import re
 from pathlib import Path

 import gradio as gr
-from modules.shared import script_path
+from modules.paths import data_path
 from modules import shared, ui_tempdir, script_callbacks
 import tempfile
 from PIL import Image

-re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
+re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
-re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
 type_of_gr_update = type(gr.update())
+
 paste_fields = {}
-bind_list = []
+registered_param_bindings = []
+
+
+class ParamBinding:
+    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=[]):
+        self.paste_button = paste_button
+        self.tabname = tabname
+        self.source_text_component = source_text_component
+        self.source_image_component = source_image_component
+        self.source_tabname = source_tabname
+        self.override_settings_component = override_settings_component
+        self.paste_field_names = paste_field_names


 def reset():
    paste_fields.clear()
-    bind_list.clear()


 def quote(text):
@@ -64,8 +75,8 @@ def image_from_url_text(filedata):
    return image


-def add_paste_fields(tabname, init_img, fields):
-    paste_fields[tabname] = {"init_img": init_img, "fields": fields}
+def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}

    # backwards compatibility for existing extensions
    import modules.ui
@@ -75,26 +86,6 @@ def add_paste_fields(tabname, init_img, fields):
        modules.ui.img2img_paste_fields = fields


-def integrate_settings_paste_fields(component_dict):
-    from modules import ui
-
-    settings_map = {
-        'CLIP_stop_at_last_layers': 'Clip skip',
-        'inpainting_mask_weight': 'Conditional mask weight',
-        'sd_model_checkpoint': 'Model hash',
-        'eta_noise_seed_delta': 'ENSD',
-        'initial_noise_multiplier': 'Noise multiplier',
-    }
-    settings_paste_fields = [
-        (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))
-        for k, v in settings_map.items()
-    ]
-
-    for tabname, info in paste_fields.items():
-        if info["fields"] is not None:
-            info["fields"] += settings_paste_fields
-
-
 def create_buttons(tabs_list):
    buttons = {}
    for tab in tabs_list:
@@ -102,9 +93,61 @@ def create_buttons(tabs_list):
    return buttons


-#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
 def bind_buttons(buttons, send_image, send_generate_info):
-    bind_list.append([buttons, send_image, send_generate_info])
+    """old function for backwards compatibility; do not use this, use register_paste_params_button"""
+    for tabname, button in buttons.items():
+        source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
+        source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
+
+        register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname))
+
+
+def register_paste_params_button(binding: ParamBinding):
+    registered_param_bindings.append(binding)
+
+
+def connect_paste_params_buttons():
+    binding: ParamBinding
+    for binding in registered_param_bindings:
+        destination_image_component = paste_fields[binding.tabname]["init_img"]
+        fields = paste_fields[binding.tabname]["fields"]
+        override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
+
+        destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+        destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+        if binding.source_image_component and destination_image_component:
+            if isinstance(binding.source_image_component, gr.Gallery):
+                func = send_image_and_dimensions if destination_width_component else image_from_url_text
+                jsfunc = "extract_image_from_gallery"
+            else:
+                func = send_image_and_dimensions if destination_width_component else lambda x: x
+                jsfunc = None
+
+            binding.paste_button.click(
+                fn=func,
+                _js=jsfunc,
+                inputs=[binding.source_image_component],
+                outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+            )
+
+        if binding.source_text_component is not None and fields is not None:
+            connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
+
+        if binding.source_tabname is not None and fields is not None:
+            paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) + binding.paste_field_names
+            binding.paste_button.click(
+                fn=lambda *x: x,
+                inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
+                outputs=[field for field, name in fields if name in paste_field_names],
+            )
+
+        binding.paste_button.click(
+            fn=None,
+            _js=f"switch_to_{binding.tabname}",
+            inputs=None,
+            outputs=None,
+        )


 def send_image_and_dimensions(x):
@@ -123,49 +166,6 @@ def send_image_and_dimensions(x):
    return img, w, h


-def run_bind():
-    for buttons, source_image_component, send_generate_info in bind_list:
-        for tab in buttons:
-            button = buttons[tab]
-            destination_image_component = paste_fields[tab]["init_img"]
-            fields = paste_fields[tab]["fields"]
-
-            destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
-            destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
-
-            if source_image_component and destination_image_component:
-                if isinstance(source_image_component, gr.Gallery):
-                    func = send_image_and_dimensions if destination_width_component else image_from_url_text
-                    jsfunc = "extract_image_from_gallery"
-                else:
-                    func = send_image_and_dimensions if destination_width_component else lambda x: x
-                    jsfunc = None
-
-                button.click(
-                    fn=func,
-                    _js=jsfunc,
-                    inputs=[source_image_component],
-                    outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
-                )
-
-            if send_generate_info and fields is not None:
-                if send_generate_info in paste_fields:
-                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
-                    button.click(
-                        fn=lambda *x: x,
-                        inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
-                        outputs=[field for field, name in fields if name in paste_field_names],
-                    )
-                else:
-                    connect_paste(button, fields, send_generate_info)
-
-            button.click(
-                fn=None,
-                _js=f"switch_to_{tab}",
-                inputs=None,
-                outputs=None,
-            )
-

 def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
    """Determines the config parameter name to use for the hypernet based on the parameters in the infotext.
@@ -243,7 +243,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
    done_with_prompt = False

    *lines, lastline = x.strip().split("\n")
-    if not re_params.match(lastline):
+    if len(re_param.findall(lastline)) < 3:
        lines.append(lastline)
        lastline = ''

@@ -262,6 +262,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
    res["Negative prompt"] = negative_prompt

    for k, v in re_param.findall(lastline):
+        v = v[1:-1] if v[0] == '"' and v[-1] == '"' else v
        m = re_imagesize.match(v)
        if m is not None:
            res[k+"-1"] = m.group(1)
@@ -283,13 +284,68 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model

    restore_old_hires_fix_params(res)

+    # Missing RNG means the default was set, which is GPU RNG
+    if "RNG" not in res:
+        res["RNG"] = "GPU"
+
    return res


-def connect_paste(button, paste_fields, input_comp, jsfunc=None):
+settings_map = {}
+
+
+
+infotext_to_setting_name_mapping = [
+    ('Clip skip', 'CLIP_stop_at_last_layers', ),
+    ('Conditional mask weight', 'inpainting_mask_weight'),
+    ('Model hash', 'sd_model_checkpoint'),
+    ('ENSD', 'eta_noise_seed_delta'),
+    ('Noise multiplier', 'initial_noise_multiplier'),
+    ('Eta', 'eta_ancestral'),
+    ('Eta DDIM', 'eta_ddim'),
+    ('Discard penultimate sigma', 'always_discard_next_to_last_sigma'),
+    ('UniPC variant', 'uni_pc_variant'),
+    ('UniPC skip type', 'uni_pc_skip_type'),
+    ('UniPC order', 'uni_pc_order'),
+    ('UniPC lower order final', 'uni_pc_lower_order_final'),
+    ('RNG', 'randn_source'),
+    ('NGMS', 's_min_uncond'),
+]
+
+
+def create_override_settings_dict(text_pairs):
+    """creates processing's override_settings parameters from gradio's multiselect
+
+    Example input:
+        ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
+
+    Example output:
+        {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
+    """
+
+    res = {}
+
+    params = {}
+    for pair in text_pairs:
+        k, v = pair.split(":", maxsplit=1)
+
+        params[k] = v.strip()
+
+    for param_name, setting_name in infotext_to_setting_name_mapping:
+        value = params.get(param_name, None)
+
+        if value is None:
+            continue
+
+        res[setting_name] = shared.opts.cast_value(setting_name, value)
+
+    return res
+
+
+def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
    def paste_func(prompt):
        if not prompt and not shared.cmd_opts.hide_ui_dir_config:
-            filename = os.path.join(script_path, "params.txt")
+            filename = os.path.join(data_path, "params.txt")
            if os.path.exists(filename):
                with open(filename, "r", encoding="utf8") as file:
                    prompt = file.read()
@@ -323,11 +379,42 @@ def connect_paste(button, paste_fields, input_comp, jsfunc=None):

        return res

+    if override_settings_component is not None:
+        def paste_settings(params):
+            vals = {}
+
+            for param_name, setting_name in infotext_to_setting_name_mapping:
+                v = params.get(param_name, None)
+                if v is None:
+                    continue
+
+                if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
+                    continue
+
+                v = shared.opts.cast_value(setting_name, v)
+                current_value = getattr(shared.opts, setting_name, None)
+
+                if v == current_value:
+                    continue
+
+                vals[param_name] = v
+
+            vals_pairs = [f"{k}: {v}" for k, v in vals.items()]
+
+            return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=len(vals_pairs) > 0)
+
+        paste_fields = paste_fields + [(override_settings_component, paste_settings)]
+
    button.click(
        fn=paste_func,
-        _js=jsfunc,
        inputs=[input_comp],
        outputs=[x[0] for x in paste_fields],
    )
+    button.click(
+        fn=None,
+        _js=f"recalculate_prompts_{tabname}",
+        inputs=[],
+        outputs=[],
+    )


@@ -6,12 +6,11 @@ import facexlib
 import gfpgan

 import modules.face_restoration
-from modules import shared, devices, modelloader
-from modules.paths import models_path
+from modules import paths, shared, devices, modelloader

 model_dir = "GFPGAN"
 user_path = None
-model_path = os.path.join(models_path, model_dir)
+model_path = os.path.join(paths.models_path, model_dir)
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
 have_gfpgan = False
 loaded_gfpgan_model = None
@@ -4,8 +4,11 @@ import os.path

 import filelock

+from modules import shared
+from modules.paths import data_path

-cache_filename = "cache.json"
+
+cache_filename = os.path.join(data_path, "cache.json")
 cache_data = None


@@ -66,6 +69,9 @@ def sha256(filename, title):
    if sha256_value is not None:
        return sha256_value

+    if shared.cmd_opts.no_hashing:
+        return None
+
    print(f"Calculating sha256 for {filename}: ", end='')
    sha256_value = calculate_sha256(filename)
    print(f"{sha256_value}")
@@ -307,12 +307,12 @@ class Hypernetwork:
    def shorthash(self):
        sha256 = hashes.sha256(self.filename, f'hypernet/{self.name}')

-        return sha256[0:10]
+        return sha256[0:10] if sha256 else None


 def list_hypernetworks(path):
    res = {}
-    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):
+    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True), key=str.lower):
        name = os.path.splitext(os.path.basename(filename))[0]
        # Prevent a hypothetical "None.pt" from being listed.
        if name != "None":
@@ -380,8 +380,8 @@ def apply_single_hypernetwork(hypernetwork, context_k, context_v, layer=None):
        layer.hyper_k = hypernetwork_layers[0]
        layer.hyper_v = hypernetwork_layers[1]

-    context_k = hypernetwork_layers[0](context_k)
-    context_v = hypernetwork_layers[1](context_v)
+    context_k = devices.cond_cast_unet(hypernetwork_layers[0](devices.cond_cast_float(context_k)))
+    context_v = devices.cond_cast_unet(hypernetwork_layers[1](devices.cond_cast_float(context_v)))
    return context_k, context_v


@@ -496,7 +496,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
    shared.reload_hypernetworks()


-def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_hypernetwork_every, template_filename, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
    from modules import images

@@ -554,7 +554,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi

    pin_memory = shared.opts.pin_memory

-    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)

    if shared.opts.save_training_settings_to_txt:
        saved_params = dict(
@@ -640,13 +640,19 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                
                with devices.autocast():
                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    if use_weight:
+                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                    if tag_drop_out != 0 or shuffle_tags:
                        shared.sd_model.cond_stage_model.to(devices.device)
                        c = shared.sd_model.cond_stage_model(batch.cond_text).to(devices.device, non_blocking=pin_memory)
                        shared.sd_model.cond_stage_model.to(devices.cpu)
                    else:
                        c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
-                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    if use_weight:
+                        loss = shared.sd_model.weighted_forward(x, c, w)[0] / gradient_step
+                        del w
+                    else:
+                        loss = shared.sd_model.forward(x, c)[0] / gradient_step
                    del x
                    del c

@@ -16,8 +16,9 @@ from PIL import Image, ImageFont, ImageDraw, PngImagePlugin
 from fonts.ttf import Roboto
 import string
 import json
+import hashlib

-from modules import sd_samplers, shared, script_callbacks
+from modules import sd_samplers, shared, script_callbacks, errors
 from modules.shared import opts, cmd_opts

 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@@ -36,6 +37,8 @@ def image_grid(imgs, batch_size=1, rows=None):
        else:
            rows = math.sqrt(len(imgs))
            rows = round(rows)
+    if rows > len(imgs):
+        rows = len(imgs)

    cols = math.ceil(len(imgs) / rows)

@@ -128,7 +131,7 @@ class GridAnnotation:
        self.size = None


-def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
+def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
    def wrap(drawing, text, font, line_length):
        lines = ['']
        for word in text.split():
@@ -192,32 +195,35 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts):
            line.allowed_width = allowed_width

    hor_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing for lines in hor_texts]
-    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in
-                        ver_texts]
+    ver_text_heights = [sum([line.size[1] + line_spacing for line in lines]) - line_spacing * len(lines) for lines in ver_texts]

-    pad_top = max(hor_text_heights) + line_spacing * 2
+    pad_top = 0 if sum(hor_text_heights) == 0 else max(hor_text_heights) + line_spacing * 2

-    result = Image.new("RGB", (im.width + pad_left, im.height + pad_top), "white")
-    result.paste(im, (pad_left, pad_top))
+    result = Image.new("RGB", (im.width + pad_left + margin * (cols-1), im.height + pad_top + margin * (rows-1)), "white")
+
+    for row in range(rows):
+        for col in range(cols):
+            cell = im.crop((width * col, height * row, width * (col+1), height * (row+1)))
+            result.paste(cell, (pad_left + (width + margin) * col, pad_top + (height + margin) * row))

    d = ImageDraw.Draw(result)

    for col in range(cols):
-        x = pad_left + width * col + width / 2
+        x = pad_left + (width + margin) * col + width / 2
        y = pad_top / 2 - hor_text_heights[col] / 2

        draw_texts(d, x, y, hor_texts[col], fnt, fontsize)

    for row in range(rows):
        x = pad_left / 2
-        y = pad_top + height * row + height / 2 - ver_text_heights[row] / 2
+        y = pad_top + (height + margin) * row + height / 2 - ver_text_heights[row] / 2

        draw_texts(d, x, y, ver_texts[row], fnt, fontsize)

    return result


-def draw_prompt_matrix(im, width, height, all_prompts):
+def draw_prompt_matrix(im, width, height, all_prompts, margin=0):
    prompts = all_prompts[1:]
    boundary = math.ceil(len(prompts) / 2)

@@ -227,7 +233,7 @@ def draw_prompt_matrix(im, width, height, all_prompts):
    hor_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_horiz)] for pos in range(1 << len(prompts_horiz))]
    ver_texts = [[GridAnnotation(x, is_active=pos & (1 << i) != 0) for i, x in enumerate(prompts_vert)] for pos in range(1 << len(prompts_vert))]

-    return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
+    return draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin)


 def resize_image(resize_mode, im, width, height, upscaler_name=None):
@@ -255,9 +261,12 @@ def resize_image(resize_mode, im, width, height, upscaler_name=None):

        if scale > 1.0:
            upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
-            assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
+            if len(upscalers) == 0:
+                upscaler = shared.sd_upscalers[0]
+                print(f"could not find upscaler named {upscaler_name or '<empty string>'}, using {upscaler.name} as a fallback")
+            else:
+                upscaler = upscalers[0]

-            upscaler = upscalers[0]
            im = upscaler.scaler.upscale(im, scale, upscaler.data_path)

        if im.width != w or im.height != h:
@@ -309,6 +318,7 @@ re_nonletters = re.compile(r'[\s' + string.punctuation + ']+')
 re_pattern = re.compile(r"(.*?)(?:\[([^\[\]]+)\]|$)")
 re_pattern_arg = re.compile(r"(.*)<([^>]*)>$")
 max_filename_part_length = 128
+NOTHING_AND_SKIP_PREVIOUS_TEXT = object()


 def sanitize_filename_part(text, replace_spaces=True):
@@ -338,10 +348,15 @@ class FilenameGenerator:
        'date': lambda self: datetime.datetime.now().strftime('%Y-%m-%d'),
        'datetime': lambda self, *args: self.datetime(*args),  # accepts formats: [datetime], [datetime<Format>], [datetime<Format><Time Zone>]
        'job_timestamp': lambda self: getattr(self.p, "job_timestamp", shared.state.job_timestamp),
+        'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8],
        'prompt': lambda self: sanitize_filename_part(self.prompt),
        'prompt_no_styles': lambda self: self.prompt_no_style(),
        'prompt_spaces': lambda self: sanitize_filename_part(self.prompt, replace_spaces=False),
        'prompt_words': lambda self: self.prompt_words(),
+        'batch_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.batch_size == 1 else self.p.batch_index + 1,
+        'generation_number': lambda self: NOTHING_AND_SKIP_PREVIOUS_TEXT if self.p.n_iter == 1 and self.p.batch_size == 1 else self.p.iteration * self.p.batch_size + self.p.batch_index + 1,
+        'hasprompt': lambda self, *args: self.hasprompt(*args),  # accepts formats:[hasprompt<prompt1|default><prompt2>..]
+        'clip_skip': lambda self: opts.data["CLIP_stop_at_last_layers"],
    }
    default_time_format = '%Y%m%d%H%M%S'

@@ -350,6 +365,22 @@ class FilenameGenerator:
        self.seed = seed
        self.prompt = prompt
        self.image = image
+        
+    def hasprompt(self, *args):
+        lower = self.prompt.lower()
+        if self.p is None or self.prompt is None:
+            return None
+        outres = ""
+        for arg in args:
+            if arg != "":
+                division = arg.split("|")
+                expected = division[0].lower()
+                default = division[1] if len(division) > 1 else ""
+                if lower.find(expected) >= 0:
+                    outres = f'{outres}{expected}'
+                else:
+                    outres = outres if default == "" else f'{outres}{default}'
+        return sanitize_filename_part(outres)

    def prompt_no_style(self):
        if self.p is None or self.prompt is None:
@@ -393,9 +424,9 @@ class FilenameGenerator:

        for m in re_pattern.finditer(x):
            text, pattern = m.groups()
-            res += text

            if pattern is None:
+                res += text
                continue

            pattern_args = []
@@ -416,11 +447,13 @@ class FilenameGenerator:
                    print(f"Error adding [{pattern}] to filename", file=sys.stderr)
                    print(traceback.format_exc(), file=sys.stderr)

-                if replacement is not None:
-                    res += str(replacement)
+                if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
+                    continue
+                elif replacement is not None:
+                    res += text + str(replacement)
                    continue

-            res += f'[{pattern}]'
+            res += f'{text}[{pattern}]'

        return res

@@ -546,8 +579,10 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
        elif extension.lower() in (".jpg", ".jpeg", ".webp"):
            if image_to_save.mode == 'RGBA':
                image_to_save = image_to_save.convert("RGB")
+            elif image_to_save.mode == 'I;16':
+                image_to_save = image_to_save.point(lambda p: p * 0.0038910505836576).convert("RGB" if extension.lower() == ".webp" else "L")

-            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
+            image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, lossless=opts.webp_lossless)

            if opts.enable_pnginfo and info is not None:
                exif_bytes = piexif.dump({
@@ -564,21 +599,28 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
        os.replace(temp_file_path, filename_without_extension + extension)

    fullfn_without_extension, extension = os.path.splitext(params.filename)
+    if hasattr(os, 'statvfs'):
+        max_name_len = os.statvfs(path).f_namemax
+        fullfn_without_extension = fullfn_without_extension[:max_name_len - max(4, len(extension))]
+        params.filename = fullfn_without_extension + extension
+        fullfn = params.filename
    _atomically_save_image(image, fullfn_without_extension, extension)

    image.already_saved_as = fullfn

-    target_side_length = 4000
-    oversize = image.width > target_side_length or image.height > target_side_length
-    if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > 4 * 1024 * 1024):
+    oversize = image.width > opts.target_side_length or image.height > opts.target_side_length
+    if opts.export_for_4chan and (oversize or os.stat(fullfn).st_size > opts.img_downscale_threshold * 1024 * 1024):
        ratio = image.width / image.height

        if oversize and ratio > 1:
-            image = image.resize((target_side_length, image.height * target_side_length // image.width), LANCZOS)
+            image = image.resize((round(opts.target_side_length), round(image.height * opts.target_side_length / image.width)), LANCZOS)
        elif oversize:
-            image = image.resize((image.width * target_side_length // image.height, target_side_length), LANCZOS)
+            image = image.resize((round(image.width * opts.target_side_length / image.height), round(opts.target_side_length)), LANCZOS)

-        _atomically_save_image(image, fullfn_without_extension, ".jpg")
+        try:
+            _atomically_save_image(image, fullfn_without_extension, ".jpg")
+        except Exception as e:
+            errors.display(e, "saving image as downscaled JPG")

    if opts.save_txt and info is not None:
        txt_fullfn = f"{fullfn_without_extension}.txt"
@@ -629,6 +671,8 @@ Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}


 def image_data(data):
+    import gradio as gr
+
    try:
        image = Image.open(io.BytesIO(data))
        textinfo, _ = read_info_from_image(image)
@@ -644,7 +688,7 @@ def image_data(data):
    except Exception:
        pass

-    return '', None
+    return gr.update(), None


 def flatten(img, bgcolor):
@@ -4,9 +4,10 @@ import sys
 import traceback

 import numpy as np
-from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops
+from PIL import Image, ImageOps, ImageFilter, ImageEnhance, ImageChops, UnidentifiedImageError

 from modules import devices, sd_samplers
+from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
@@ -16,11 +17,18 @@ import modules.images as images
 import modules.scripts


-def process_batch(p, input_dir, output_dir, args):
+def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
    processing.fix_seed(p)

    images = shared.listfiles(input_dir)

+    is_inpaint_batch = False
+    if inpaint_mask_dir:
+        inpaint_masks = shared.listfiles(inpaint_mask_dir)
+        is_inpaint_batch = len(inpaint_masks) > 0
+    if is_inpaint_batch:
+        print(f"\nInpaint batch is enabled. {len(inpaint_masks)} masks found.")
+
    print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")

    save_normally = output_dir == ''
@@ -38,11 +46,23 @@ def process_batch(p, input_dir, output_dir, args):
        if state.interrupted:
            break

-        img = Image.open(image)
+        try:
+            img = Image.open(image)
+        except UnidentifiedImageError:
+            continue
        # Use the EXIF orientation of photos taken by smartphones.
        img = ImageOps.exif_transpose(img)
        p.init_images = [img] * p.batch_size

+        if is_inpaint_batch:
+            # try to find corresponding mask for an image using simple filename matching
+            mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
+            # if not found use first one ("same mask for all images" use-case)
+            if not mask_image_path in inpaint_masks:
+                mask_image_path = inpaint_masks[0]
+            mask_image = Image.open(mask_image_path)
+            p.image_mask = mask_image
+
        proc = modules.scripts.scripts_img2img.run(p, *args)
        if proc is None:
            proc = process_images(p)
@@ -56,10 +76,14 @@ def process_batch(p, input_dir, output_dir, args):

            if not save_normally:
                os.makedirs(output_dir, exist_ok=True)
+                if processed_image.mode == 'RGBA':
+                    processed_image = processed_image.convert("RGB")
                processed_image.save(os.path.join(output_dir, filename))


-def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, selected_scale_tab: int, height: int, width: int, scale_by: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args):
+    override_settings = create_override_settings_dict(override_settings_texts)
+
    is_batch = mode == 5

    if mode == 0:  # img2img
@@ -93,6 +117,12 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
    if image is not None:
        image = ImageOps.exif_transpose(image)

+    if selected_scale_tab == 1:
+        assert image, "Can't scale by because no image is selected"
+
+        width = int(image.width * scale_by)
+        height = int(image.height * scale_by)
+
    assert 0. <= denoising_strength <= 1., 'can only work with strength in [0.0, 1.0]'

    p = StableDiffusionProcessingImg2Img(
@@ -123,23 +153,26 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
        inpainting_fill=inpainting_fill,
        resize_mode=resize_mode,
        denoising_strength=denoising_strength,
+        image_cfg_scale=image_cfg_scale,
        inpaint_full_res=inpaint_full_res,
        inpaint_full_res_padding=inpaint_full_res_padding,
        inpainting_mask_invert=inpainting_mask_invert,
+        override_settings=override_settings,
    )

-    p.scripts = modules.scripts.scripts_txt2img
+    p.scripts = modules.scripts.scripts_img2img
    p.script_args = args

    if shared.cmd_opts.enable_console_prompts:
        print(f"\nimg2img: {prompt}", file=shared.progress_print_out)

-    p.extra_generation_params["Mask blur"] = mask_blur
+    if mask:
+        p.extra_generation_params["Mask blur"] = mask_blur

    if is_batch:
        assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"

-        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, args)
+        process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args)

        processed = Processed(p, [], p.seed, "")
    else:
@@ -12,7 +12,7 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode

 import modules.shared as shared
-from modules import devices, paths, lowvram, modelloader, errors
+from modules import devices, paths, shared, lowvram, modelloader, errors

 blip_image_eval_size = 384
 clip_model_name = 'ViT-L/14'
@@ -32,7 +32,7 @@ def download_default_clip_interrogate_categories(content_dir):
    category_types = ["artists", "flavors", "mediums", "movements"]

    try:
-        os.makedirs(tmpdir)
+        os.makedirs(tmpdir, exist_ok=True)
        for category_type in category_types:
            torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
        os.rename(tmpdir, content_dir)
@@ -41,7 +41,7 @@ def download_default_clip_interrogate_categories(content_dir):
        errors.display(e, "downloading default CLIP interrogate categories")
    finally:
        if os.path.exists(tmpdir):
-            os.remove(tmpdir)
+            os.removedirs(tmpdir)


 class InterrogateModels:
@@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram):
    if hasattr(sd_model.cond_stage_model, 'model'):
        sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model

-    # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then
+    # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then
    # send the model to GPU. Then put modules back. the modules will be in CPU.
-    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None
+    stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None
    sd_model.to(devices.device)
-    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored
+    sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored

    # register hooks for those the first three models
    sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
@@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram):
    sd_model.first_stage_model.decode = first_stage_model_decode_wrap
    if sd_model.depth_model:
        sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu)
+    if sd_model.embedder:
+        sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
    parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model

    if hasattr(sd_model.cond_stage_model, 'model'):
@@ -0,0 +1,59 @@
+import torch
+import platform
+from modules import paths
+from modules.sd_hijack_utils import CondFunc
+from packaging import version
+
+
+# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
+# check `getattr` and try it for compatibility
+def check_for_mps() -> bool:
+    if not getattr(torch, 'has_mps', False):
+        return False
+    try:
+        torch.zeros(1).to(torch.device("mps"))
+        return True
+    except Exception:
+        return False
+has_mps = check_for_mps()
+
+
+# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
+def cumsum_fix(input, cumsum_func, *args, **kwargs):
+    if input.device.type == 'mps':
+        output_dtype = kwargs.get('dtype', input.dtype)
+        if output_dtype == torch.int64:
+            return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
+        elif output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
+            return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
+    return cumsum_func(input, *args, **kwargs)
+
+
+if has_mps:
+    # MPS fix for randn in torchsde
+    CondFunc('torchsde._brownian.brownian_interval._randn', lambda _, size, dtype, device, seed: torch.randn(size, dtype=dtype, device=torch.device("cpu"), generator=torch.Generator(torch.device("cpu")).manual_seed(int(seed))).to(device), lambda _, size, dtype, device, seed: device.type == 'mps')
+
+    if platform.mac_ver()[0].startswith("13.2."):
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/95188, thanks to danieldk (https://github.com/explosion/curated-transformers/pull/124)
+        CondFunc('torch.nn.functional.linear', lambda _, input, weight, bias: (torch.matmul(input, weight.t()) + bias) if bias is not None else torch.matmul(input, weight.t()), lambda _, input, weight, bias: input.numel() > 10485760)
+
+    if version.parse(torch.__version__) < version.parse("1.13"):
+        # PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
+
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
+        CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
+                                                          lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/80800 
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
+                                                                                        lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/90532
+        CondFunc('torch.Tensor.numpy', lambda orig_func, self, *args, **kwargs: orig_func(self.detach(), *args, **kwargs), lambda _, self, *args, **kwargs: self.requires_grad)
+    elif version.parse(torch.__version__) > version.parse("1.13.1"):
+        cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
+        cumsum_fix_func = lambda orig_func, input, *args, **kwargs: cumsum_fix(input, orig_func, *args, **kwargs)
+        CondFunc('torch.cumsum', cumsum_fix_func, None)
+        CondFunc('torch.Tensor.cumsum', cumsum_fix_func, None)
+        CondFunc('torch.narrow', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).clone(), None)
+    if version.parse(torch.__version__) == version.parse("2.0"):
+        # MPS workaround for https://github.com/pytorch/pytorch/issues/96113
+        CondFunc('torch.nn.functional.layer_norm', lambda orig_func, x, normalized_shape, weight, bias, eps, **kwargs: orig_func(x.float(), normalized_shape, weight.float() if weight is not None else None, bias.float() if bias is not None else bias, eps).to(x.dtype), lambda *args, **kwargs: len(args) == 6)
@@ -23,12 +23,16 @@ class MemUsageMonitor(threading.Thread):
        self.data = defaultdict(int)

        try:
-            torch.cuda.mem_get_info()
+            self.cuda_mem_get_info()
            torch.cuda.memory_stats(self.device)
        except Exception as e:  # AMD or whatever
            print(f"Warning: caught exception '{e}', memory monitor disabled")
            self.disabled = True

+    def cuda_mem_get_info(self):
+        index = self.device.index if self.device.index is not None else torch.cuda.current_device()
+        return torch.cuda.mem_get_info(index)
+
    def run(self):
        if self.disabled:
            return
@@ -43,10 +47,10 @@ class MemUsageMonitor(threading.Thread):
                self.run_flag.clear()
                continue

-            self.data["min_free"] = torch.cuda.mem_get_info()[0]
+            self.data["min_free"] = self.cuda_mem_get_info()[0]

            while self.run_flag.is_set():
-                free, total = torch.cuda.mem_get_info()  # calling with self.device errors, torch bug?
+                free, total = self.cuda_mem_get_info()
                self.data["min_free"] = min(self.data["min_free"], free)

                time.sleep(1 / self.opts.memmon_poll_rate)
@@ -70,7 +74,7 @@ class MemUsageMonitor(threading.Thread):

    def read(self):
        if not self.disabled:
-            free, total = torch.cuda.mem_get_info()
+            free, total = self.cuda_mem_get_info()
            self.data["free"] = free
            self.data["total"] = total

@@ -4,9 +4,8 @@ import shutil
 import importlib
 from urllib.parse import urlparse

-from basicsr.utils.download_util import load_file_from_url
 from modules import shared
-from modules.upscaler import Upscaler
+from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
 from modules.paths import script_path, models_path


@@ -45,6 +44,9 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                    full_path = file
                    if os.path.isdir(full_path):
                        continue
+                    if os.path.islink(full_path) and not os.path.exists(full_path):
+                        print(f"Skipping broken symlink: {full_path}")
+                        continue
                    if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
                        continue
                    if len(ext_filter) != 0:
@@ -56,6 +58,7 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None

        if model_url is not None and len(output) == 0:
            if download_name is not None:
+                from basicsr.utils.download_util import load_file_from_url
                dl = load_file_from_url(model_url, model_path, True, download_name)
                output.append(dl)
            else:
@@ -166,4 +169,8 @@ def load_upscalers():
        scaler = cls(commandline_options.get(cmd_name, None))
        datas += scaler.scalers

-    shared.sd_upscalers = datas
+    shared.sd_upscalers = sorted(
+        datas,
+        # Special case for UpscalerNone keeps it at the beginning of the list.
+        key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
+    )
@@ -0,0 +1 @@
+from .sampler import UniPCSampler
@@ -0,0 +1,100 @@
+"""SAMPLING ONLY."""
+
+import torch
+
+from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
+from modules import shared, devices
+
+
+class UniPCSampler(object):
+    def __init__(self, model, **kwargs):
+        super().__init__()
+        self.model = model
+        to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+        self.before_sample = None
+        self.after_sample = None
+        self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+    def register_buffer(self, name, attr):
+        if type(attr) == torch.Tensor:
+            if attr.device != devices.device:
+                attr = attr.to(devices.device)
+        setattr(self, name, attr)
+
+    def set_hooks(self, before_sample, after_sample, after_update):
+        self.before_sample = before_sample
+        self.after_sample = after_sample
+        self.after_update = after_update
+
+    @torch.no_grad()
+    def sample(self,
+               S,
+               batch_size,
+               shape,
+               conditioning=None,
+               callback=None,
+               normals_sequence=None,
+               img_callback=None,
+               quantize_x0=False,
+               eta=0.,
+               mask=None,
+               x0=None,
+               temperature=1.,
+               noise_dropout=0.,
+               score_corrector=None,
+               corrector_kwargs=None,
+               verbose=True,
+               x_T=None,
+               log_every_t=100,
+               unconditional_guidance_scale=1.,
+               unconditional_conditioning=None,
+               # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+               **kwargs
+               ):
+        if conditioning is not None:
+            if isinstance(conditioning, dict):
+                ctmp = conditioning[list(conditioning.keys())[0]]
+                while isinstance(ctmp, list): ctmp = ctmp[0]
+                cbs = ctmp.shape[0]
+                if cbs != batch_size:
+                    print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            elif isinstance(conditioning, list):
+                for ctmp in conditioning:
+                    if ctmp.shape[0] != batch_size:
+                        print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+            else:
+                if conditioning.shape[0] != batch_size:
+                    print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+        # sampling
+        C, H, W = shape
+        size = (batch_size, C, H, W)
+        # print(f'Data shape for UniPC sampling is {size}')
+
+        device = self.model.betas.device
+        if x_T is None:
+            img = torch.randn(size, device=device)
+        else:
+            img = x_T
+
+        ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+        # SD 1.X is "noise", SD 2.X is "v"
+        model_type = "v" if self.model.parameterization == "v" else "noise"
+
+        model_fn = model_wrapper(
+            lambda x, t, c: self.model.apply_model(x, t, c),
+            ns,
+            model_type=model_type,
+            guidance_type="classifier-free",
+            #condition=conditioning,
+            #unconditional_condition=unconditional_conditioning,
+            guidance_scale=unconditional_guidance_scale,
+        )
+
+        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant, condition=conditioning, unconditional_condition=unconditional_conditioning, before_sample=self.before_sample, after_sample=self.after_sample, after_update=self.after_update)
+        x = uni_pc.sample(img, steps=S, skip_type=shared.opts.uni_pc_skip_type, method="multistep", order=shared.opts.uni_pc_order, lower_order_final=shared.opts.uni_pc_lower_order_final)
+
+        return x.to(device), None
@@ -0,0 +1,857 @@
+import torch
+import torch.nn.functional as F
+import math
+from tqdm.auto import trange
+
+
+class NoiseScheduleVP:
+    def __init__(
+            self,
+            schedule='discrete',
+            betas=None,
+            alphas_cumprod=None,
+            continuous_beta_0=0.1,
+            continuous_beta_1=20.,
+        ):
+        """Create a wrapper class for the forward SDE (VP type).
+
+        ***
+        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+        ***
+
+        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+
+            log_alpha_t = self.marginal_log_mean_coeff(t)
+            sigma_t = self.marginal_std(t)
+            lambda_t = self.marginal_lambda(t)
+
+        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+
+            t = self.inverse_lambda(lambda_t)
+
+        ===============================================================
+
+        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+
+        1. For discrete-time DPMs:
+
+            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+                t_i = (i + 1) / N
+            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+
+            Args:
+                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+
+            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+
+            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
+                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
+                and
+                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+
+
+        2. For continuous-time DPMs:
+
+            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+            schedule are the default settings in DDPM and improved-DDPM:
+
+            Args:
+                beta_min: A `float` number. The smallest beta for the linear schedule.
+                beta_max: A `float` number. The largest beta for the linear schedule.
+                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+                T: A `float` number. The ending time of the forward process.
+
+        ===============================================================
+
+        Args:
+            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+                    'linear' or 'cosine' for continuous-time DPMs.
+        Returns:
+            A wrapper object of the forward SDE (VP type).
+
+        ===============================================================
+
+        Example:
+
+        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', betas=betas)
+
+        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+
+        # For continuous-time DPMs (VPSDE), linear schedule:
+        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+
+        """
+
+        if schedule not in ['discrete', 'linear', 'cosine']:
+            raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule))
+
+        self.schedule = schedule
+        if schedule == 'discrete':
+            if betas is not None:
+                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+            else:
+                assert alphas_cumprod is not None
+                log_alphas = 0.5 * torch.log(alphas_cumprod)
+            self.total_N = len(log_alphas)
+            self.T = 1.
+            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+            self.log_alpha_array = log_alphas.reshape((1, -1,))
+        else:
+            self.total_N = 1000
+            self.beta_0 = continuous_beta_0
+            self.beta_1 = continuous_beta_1
+            self.cosine_s = 0.008
+            self.cosine_beta_max = 999.
+            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+            self.schedule = schedule
+            if schedule == 'cosine':
+                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+                self.T = 0.9946
+            else:
+                self.T = 1.
+
+    def marginal_log_mean_coeff(self, t):
+        """
+        Compute log(alpha_t) of a given continuous-time label t in [0, T].
+        """
+        if self.schedule == 'discrete':
+            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1))
+        elif self.schedule == 'linear':
+            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+        elif self.schedule == 'cosine':
+            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+            log_alpha_t =  log_alpha_fn(t) - self.cosine_log_alpha_0
+            return log_alpha_t
+
+    def marginal_alpha(self, t):
+        """
+        Compute alpha_t of a given continuous-time label t in [0, T].
+        """
+        return torch.exp(self.marginal_log_mean_coeff(t))
+
+    def marginal_std(self, t):
+        """
+        Compute sigma_t of a given continuous-time label t in [0, T].
+        """
+        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+    def marginal_lambda(self, t):
+        """
+        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+        """
+        log_mean_coeff = self.marginal_log_mean_coeff(t)
+        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+        return log_mean_coeff - log_std
+
+    def inverse_lambda(self, lamb):
+        """
+        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+        """
+        if self.schedule == 'linear':
+            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            Delta = self.beta_0**2 + tmp
+            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+        elif self.schedule == 'discrete':
+            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1]))
+            return t.reshape((-1,))
+        else:
+            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s
+            t = t_fn(log_alpha)
+            return t
+
+
+def model_wrapper(
+    model,
+    noise_schedule,
+    model_type="noise",
+    model_kwargs={},
+    guidance_type="uncond",
+    #condition=None,
+    #unconditional_condition=None,
+    guidance_scale=1.,
+    classifier_fn=None,
+    classifier_kwargs={},
+):
+    """Create a wrapper function for the noise prediction model.
+
+    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+
+    We support four types of the diffusion model by setting `model_type`:
+
+        1. "noise": noise prediction model. (Trained by predicting noise).
+
+        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+
+        3. "v": velocity prediction model. (Trained by predicting the velocity).
+            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+
+            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+                arXiv preprint arXiv:2202.00512 (2022).
+            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+                arXiv preprint arXiv:2210.02303 (2022).
+
+        4. "score": marginal score function. (Trained by denoising score matching).
+            Note that the score function and the noise prediction model follows a simple relationship:
+            ```
+                noise(x_t, t) = -sigma_t * score(x_t, t)
+            ```
+
+    We support three types of guided sampling by DPMs by setting `guidance_type`:
+        1. "uncond": unconditional sampling by DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+
+        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+            ``
+
+            The input `classifier_fn` has the following format:
+            ``
+                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+            ``
+
+            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+
+        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+            The input `model` has the following format:
+            ``
+                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+            ``
+            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+
+            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+                arXiv preprint arXiv:2207.12598 (2022).
+
+
+    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+    or continuous-time labels (i.e. epsilon to T).
+
+    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+    ``
+        def model_fn(x, t_continuous) -> noise:
+            t_input = get_model_input_time(t_continuous)
+            return noise_pred(model, x, t_input, **model_kwargs)
+    ``
+    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+
+    ===============================================================
+
+    Args:
+        model: A diffusion model with the corresponding format described above.
+        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+        model_type: A `str`. The parameterization type of the diffusion model.
+                    "noise" or "x_start" or "v" or "score".
+        model_kwargs: A `dict`. A dict for the other inputs of the model function.
+        guidance_type: A `str`. The type of the guidance for sampling.
+                    "uncond" or "classifier" or "classifier-free".
+        condition: A pytorch tensor. The condition for the guided sampling.
+                    Only used for "classifier" or "classifier-free" guidance type.
+        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+                    Only used for "classifier-free" guidance type.
+        guidance_scale: A `float`. The scale for the guided sampling.
+        classifier_fn: A classifier function. Only used for the classifier guidance.
+        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+    Returns:
+        A noise prediction model that accepts the noised data and the continuous time as the inputs.
+    """
+
+    def get_model_input_time(t_continuous):
+        """
+        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+        For continuous-time DPMs, we just use `t_continuous`.
+        """
+        if noise_schedule.schedule == 'discrete':
+            return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+        else:
+            return t_continuous
+
+    def noise_pred_fn(x, t_continuous, cond=None):
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        t_input = get_model_input_time(t_continuous)
+        if cond is None:
+            output = model(x, t_input, None, **model_kwargs)
+        else:
+            output = model(x, t_input, cond, **model_kwargs)
+        if model_type == "noise":
+            return output
+        elif model_type == "x_start":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+        elif model_type == "v":
+            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+        elif model_type == "score":
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            dims = x.dim()
+            return -expand_dims(sigma_t, dims) * output
+
+    def cond_grad_fn(x, t_input, condition):
+        """
+        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+        """
+        with torch.enable_grad():
+            x_in = x.detach().requires_grad_(True)
+            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+            return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+    def model_fn(x, t_continuous, condition, unconditional_condition):
+        """
+        The noise predicition model function that is used for DPM-Solver.
+        """
+        if t_continuous.reshape((-1,)).shape[0] == 1:
+            t_continuous = t_continuous.expand((x.shape[0]))
+        if guidance_type == "uncond":
+            return noise_pred_fn(x, t_continuous)
+        elif guidance_type == "classifier":
+            assert classifier_fn is not None
+            t_input = get_model_input_time(t_continuous)
+            cond_grad = cond_grad_fn(x, t_input, condition)
+            sigma_t = noise_schedule.marginal_std(t_continuous)
+            noise = noise_pred_fn(x, t_continuous)
+            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+        elif guidance_type == "classifier-free":
+            if guidance_scale == 1. or unconditional_condition is None:
+                return noise_pred_fn(x, t_continuous, cond=condition)
+            else:
+                x_in = torch.cat([x] * 2)
+                t_in = torch.cat([t_continuous] * 2)
+                if isinstance(condition, dict):
+                    assert isinstance(unconditional_condition, dict)
+                    c_in = dict()
+                    for k in condition:
+                        if isinstance(condition[k], list):
+                            c_in[k] = [torch.cat([
+                                unconditional_condition[k][i],
+                                condition[k][i]]) for i in range(len(condition[k]))]
+                        else:
+                            c_in[k] = torch.cat([
+                                unconditional_condition[k],
+                                condition[k]])
+                elif isinstance(condition, list):
+                    c_in = list()
+                    assert isinstance(unconditional_condition, list)
+                    for i in range(len(condition)):
+                        c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
+                else:
+                    c_in = torch.cat([unconditional_condition, condition])
+                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+                return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+    assert model_type in ["noise", "x_start", "v"]
+    assert guidance_type in ["uncond", "classifier", "classifier-free"]
+    return model_fn
+
+
+class UniPC:
+    def __init__(
+        self,
+        model_fn,
+        noise_schedule,
+        predict_x0=True,
+        thresholding=False,
+        max_val=1.,
+        variant='bh1',
+        condition=None,
+        unconditional_condition=None,
+        before_sample=None,
+        after_sample=None,
+        after_update=None
+    ):
+        """Construct a UniPC.
+
+        We support both data_prediction and noise_prediction.
+        """
+        self.model_fn_ = model_fn
+        self.noise_schedule = noise_schedule
+        self.variant = variant
+        self.predict_x0 = predict_x0
+        self.thresholding = thresholding
+        self.max_val = max_val
+        self.condition = condition
+        self.unconditional_condition = unconditional_condition
+        self.before_sample = before_sample
+        self.after_sample = after_sample
+        self.after_update = after_update
+
+    def dynamic_thresholding_fn(self, x0, t=None):
+        """
+        The dynamic thresholding method.
+        """
+        dims = x0.dim()
+        p = self.dynamic_thresholding_ratio
+        s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+        s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims)
+        x0 = torch.clamp(x0, -s, s) / s
+        return x0
+
+    def model(self, x, t):
+        cond = self.condition
+        uncond = self.unconditional_condition
+        if self.before_sample is not None:
+            x, t, cond, uncond = self.before_sample(x, t, cond, uncond)
+        res = self.model_fn_(x, t, cond, uncond)
+        if self.after_sample is not None:
+            x, t, cond, uncond, res = self.after_sample(x, t, cond, uncond, res)
+
+        if isinstance(res, tuple):
+            # (None, pred_x0)
+            res = res[1]
+
+        return res
+
+    def noise_prediction_fn(self, x, t):
+        """
+        Return the noise prediction model.
+        """
+        return self.model(x, t)
+
+    def data_prediction_fn(self, x, t):
+        """
+        Return the data prediction model (with thresholding).
+        """
+        noise = self.noise_prediction_fn(x, t)
+        dims = x.dim()
+        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+        if self.thresholding:
+            p = 0.995   # A hyperparameter in the paper of "Imagen" [1].
+            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+            x0 = torch.clamp(x0, -s, s) / s
+        return x0
+
+    def model_fn(self, x, t):
+        """
+        Convert the model to the noise prediction model or the data prediction model.
+        """
+        if self.predict_x0:
+            return self.data_prediction_fn(x, t)
+        else:
+            return self.noise_prediction_fn(x, t)
+
+    def get_time_steps(self, skip_type, t_T, t_0, N, device):
+        """Compute the intermediate time steps for sampling.
+        """
+        if skip_type == 'logSNR':
+            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+            return self.noise_schedule.inverse_lambda(logSNR_steps)
+        elif skip_type == 'time_uniform':
+            return torch.linspace(t_T, t_0, N + 1).to(device)
+        elif skip_type == 'time_quadratic':
+            t_order = 2
+            t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device)
+            return t
+        else:
+            raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+        """
+        Get the order of each step for sampling by the singlestep DPM-Solver.
+        """
+        if order == 3:
+            K = steps // 3 + 1
+            if steps % 3 == 0:
+                orders = [3,] * (K - 2) + [2, 1]
+            elif steps % 3 == 1:
+                orders = [3,] * (K - 1) + [1]
+            else:
+                orders = [3,] * (K - 1) + [2]
+        elif order == 2:
+            if steps % 2 == 0:
+                K = steps // 2
+                orders = [2,] * K
+            else:
+                K = steps // 2 + 1
+                orders = [2,] * (K - 1) + [1]
+        elif order == 1:
+            K = steps
+            orders = [1,] * steps
+        else:
+            raise ValueError("'order' must be '1' or '2' or '3'.")
+        if skip_type == 'logSNR':
+            # To reproduce the results in DPM-Solver paper
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+        else:
+            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)]
+        return timesteps_outer, orders
+
+    def denoise_to_zero_fn(self, x, s):
+        """
+        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
+        """
+        return self.data_prediction_fn(x, s)
+
+    def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs):
+        if len(t.shape) == 0:
+            t = t.view(-1)
+        if 'bh' in self.variant:
+            return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+        else:
+            assert self.variant == 'vary_coeff'
+            return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
+
+    def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
+        #print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
+        ns = self.noise_schedule
+        assert order <= len(model_prev_list)
+
+        # first compute rks
+        t_prev_0 = t_prev_list[-1]
+        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+        lambda_t = ns.marginal_lambda(t)
+        model_prev_0 = model_prev_list[-1]
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        log_alpha_t = ns.marginal_log_mean_coeff(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h = lambda_t - lambda_prev_0
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            t_prev_i = t_prev_list[-(i + 1)]
+            model_prev_i = model_prev_list[-(i + 1)]
+            lambda_prev_i = ns.marginal_lambda(t_prev_i)
+            rk = (lambda_prev_i - lambda_prev_0) / h
+            rks.append(rk)
+            D1s.append((model_prev_i - model_prev_0) / rk)
+
+        rks.append(1.)
+        rks = torch.tensor(rks, device=x.device)
+
+        K = len(rks)
+        # build C matrix
+        C = []
+
+        col = torch.ones_like(rks)
+        for k in range(1, K + 1):
+            C.append(col)
+            col = col * rks / (k + 1)
+        C = torch.stack(C, dim=1)
+
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1) # (B, K)
+            C_inv_p = torch.linalg.inv(C[:-1, :-1])
+            A_p = C_inv_p
+
+        if use_corrector:
+            #print('using corrector')
+            C_inv = torch.linalg.inv(C)
+            A_c = C_inv
+
+        hh = -h if self.predict_x0 else h
+        h_phi_1 = torch.expm1(hh)
+        h_phi_ks = []
+        factorial_k = 1
+        h_phi_k = h_phi_1
+        for k in range(1, K + 2):
+            h_phi_ks.append(h_phi_k)
+            h_phi_k = h_phi_k / hh - 1 / factorial_k
+            factorial_k *= (k + 1)
+
+        model_t = None
+        if self.predict_x0:
+            x_t_ = (
+                sigma_t / sigma_prev_0 * x
+                - alpha_t * h_phi_1 * model_prev_0
+            )
+            # now predictor
+            x_t = x_t_
+            if len(D1s) > 0:
+                # compute the residuals for predictor
+                for k in range(K - 1):
+                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+            # now corrector
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_
+                k = 0
+                for k in range(K - 1):
+                    x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+                x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+        else:
+            log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+            x_t_ = (
+                (torch.exp(log_alpha_t - log_alpha_prev_0)) * x
+                - (sigma_t * h_phi_1) * model_prev_0
+            )
+            # now predictor
+            x_t = x_t_
+            if len(D1s) > 0:
+                # compute the residuals for predictor
+                for k in range(K - 1):
+                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k])
+            # now corrector
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_
+                k = 0
+                for k in range(K - 1):
+                    x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1])
+                x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1])
+        return x_t, model_t
+
+    def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True):
+        #print(f'using unified predictor-corrector with order {order} (solver type: B(h))')
+        ns = self.noise_schedule
+        assert order <= len(model_prev_list)
+        dims = x.dim()
+
+        # first compute rks
+        t_prev_0 = t_prev_list[-1]
+        lambda_prev_0 = ns.marginal_lambda(t_prev_0)
+        lambda_t = ns.marginal_lambda(t)
+        model_prev_0 = model_prev_list[-1]
+        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+        alpha_t = torch.exp(log_alpha_t)
+
+        h = lambda_t - lambda_prev_0
+
+        rks = []
+        D1s = []
+        for i in range(1, order):
+            t_prev_i = t_prev_list[-(i + 1)]
+            model_prev_i = model_prev_list[-(i + 1)]
+            lambda_prev_i = ns.marginal_lambda(t_prev_i)
+            rk = ((lambda_prev_i - lambda_prev_0) / h)[0]
+            rks.append(rk)
+            D1s.append((model_prev_i - model_prev_0) / rk)
+
+        rks.append(1.)
+        rks = torch.tensor(rks, device=x.device)
+
+        R = []
+        b = []
+
+        hh = -h[0] if self.predict_x0 else h[0]
+        h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1
+        h_phi_k = h_phi_1 / hh - 1
+
+        factorial_i = 1
+
+        if self.variant == 'bh1':
+            B_h = hh
+        elif self.variant == 'bh2':
+            B_h = torch.expm1(hh)
+        else:
+            raise NotImplementedError()
+
+        for i in range(1, order + 1):
+            R.append(torch.pow(rks, i - 1))
+            b.append(h_phi_k * factorial_i / B_h)
+            factorial_i *= (i + 1)
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
+
+        R = torch.stack(R)
+        b = torch.tensor(b, device=x.device)
+
+        # now predictor
+        use_predictor = len(D1s) > 0 and x_t is None
+        if len(D1s) > 0:
+            D1s = torch.stack(D1s, dim=1) # (B, K)
+            if x_t is None:
+                # for order 2, we use a simplified version
+                if order == 2:
+                    rhos_p = torch.tensor([0.5], device=b.device)
+                else:
+                    rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1])
+        else:
+            D1s = None
+
+        if use_corrector:
+            #print('using corrector')
+            # for order 1, we use a simplified version
+            if order == 1:
+                rhos_c = torch.tensor([0.5], device=b.device)
+            else:
+                rhos_c = torch.linalg.solve(R, b)
+
+        model_t = None
+        if self.predict_x0:
+            x_t_ = (
+                expand_dims(sigma_t / sigma_prev_0, dims) * x
+                - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0
+            )
+
+            if x_t is None:
+                if use_predictor:
+                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+                else:
+                    pred_res = 0
+                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
+
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                if D1s is not None:
+                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+                else:
+                    corr_res = 0
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+        else:
+            x_t_ = (
+                expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+                - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0
+            )
+            if x_t is None:
+                if use_predictor:
+                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+                else:
+                    pred_res = 0
+                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res
+
+            if use_corrector:
+                model_t = self.model_fn(x_t, t)
+                if D1s is not None:
+                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+                else:
+                    corr_res = 0
+                D1_t = (model_t - model_prev_0)
+                x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t)
+        return x_t, model_t
+
+
+    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+        method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+        atol=0.0078, rtol=0.05, corrector=False,
+    ):
+        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+        t_T = self.noise_schedule.T if t_start is None else t_start
+        device = x.device
+        if method == 'multistep':
+            assert steps >= order, "UniPC order must be < sampling steps"
+            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+            #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
+            assert timesteps.shape[0] - 1 == steps
+            with torch.no_grad():
+                vec_t = timesteps[0].expand((x.shape[0]))
+                model_prev_list = [self.model_fn(x, vec_t)]
+                t_prev_list = [vec_t]
+                # Init the first `order` values by lower order multistep DPM-Solver.
+                for init_order in range(1, order):
+                    vec_t = timesteps[init_order].expand(x.shape[0])
+                    x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True)
+                    if model_x is None:
+                        model_x = self.model_fn(x, vec_t)
+                    if self.after_update is not None:
+                        self.after_update(x, model_x)
+                    model_prev_list.append(model_x)
+                    t_prev_list.append(vec_t)
+                for step in trange(order, steps + 1):
+                    vec_t = timesteps[step].expand(x.shape[0])
+                    if lower_order_final:
+                        step_order = min(order, steps + 1 - step)
+                    else:
+                        step_order = order
+                    #print('this step order:', step_order)
+                    if step == steps:
+                        #print('do not run corrector at the last step')
+                        use_corrector = False
+                    else:
+                        use_corrector = True
+                    x, model_x =  self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector)
+                    if self.after_update is not None:
+                        self.after_update(x, model_x)
+                    for i in range(order - 1):
+                        t_prev_list[i] = t_prev_list[i + 1]
+                        model_prev_list[i] = model_prev_list[i + 1]
+                    t_prev_list[-1] = vec_t
+                    # We do not need to evaluate the final model value.
+                    if step < steps:
+                        if model_x is None:
+                            model_x = self.model_fn(x, vec_t)
+                        model_prev_list[-1] = model_x
+        else:
+            raise NotImplementedError()
+        if denoise_to_zero:
+            x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+        return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+    """
+    A piecewise linear function y = f(x), using xp and yp as keypoints.
+    We implement f(x) in a differentiable way (i.e. applicable for autograd).
+    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+
+    Args:
+        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+        yp: PyTorch tensor with shape [C, K].
+    Returns:
+        The function values f(x), with shape [N, C].
+    """
+    N, K = x.shape[0], xp.shape[1]
+    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+    x_idx = torch.argmin(x_indices, dim=2)
+    cand_start_idx = x_idx - 1
+    start_idx = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(1, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+    start_idx2 = torch.where(
+        torch.eq(x_idx, 0),
+        torch.tensor(0, device=x.device),
+        torch.where(
+            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+        ),
+    )
+    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+    return cand
+
+
+def expand_dims(v, dims):
+    """
+    Expand the tensor `v` to the dim `dims`.
+
+    Args:
+        `v`: a PyTorch tensor with shape [N].
+        `dim`: a `int`.
+    Returns:
+        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+    """
+    return v[(...,) + (None,)*(dims - 1)]
@@ -13,6 +13,18 @@ def connect(token, port, region):
    config = conf.PyngrokConfig(
        auth_token=token, region=region
    )
+    
+    # Guard for existing tunnels
+    existing = ngrok.get_tunnels(pyngrok_config=config)
+    if existing:
+        for established in existing:
+            # Extra configuration in the case that the user is also using ngrok for other tunnels
+            if established.config['addr'][-4:] == str(port):
+                public_url = existing[0].public_url
+                print(f'ngrok has already been connected to localhost:{port}! URL: {public_url}\n'
+                    'You can use this link after the launch is complete.')
+                return
+    
    try:
        if account is None:
            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
@@ -1,10 +1,11 @@
-import argparse
 import os
 import sys
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir
+
 import modules.safe

-script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-models_path = os.path.join(script_path, "models")
+
+# data_path = cmd_opts_pre.data
 sys.path.insert(0, script_path)

 # search for directory of stable diffusion in following places
@@ -0,0 +1,23 @@
+"""this module defines internal paths used by program and is safe to import before dependencies are installed in launch.py"""
+
+import argparse
+import os
+
+script_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
+
+sd_configs_path = os.path.join(script_path, "configs")
+sd_default_config = os.path.join(sd_configs_path, "v1-inference.yaml")
+sd_model_file = os.path.join(script_path, 'model.ckpt')
+default_sd_model_file = sd_model_file
+
+# Parse the --data-dir flag first so we can use it as a base for our other argument default values
+parser_pre = argparse.ArgumentParser(add_help=False)
+parser_pre.add_argument("--data-dir", type=str, default=os.path.dirname(os.path.dirname(os.path.realpath(__file__))), help="base path where all user data is stored",)
+cmd_opts_pre = parser_pre.parse_known_args()[0]
+
+data_path = cmd_opts_pre.data_dir
+
+models_path = os.path.join(data_path, "models")
+extensions_dir = os.path.join(data_path, "extensions")
+extensions_builtin_dir = os.path.join(script_path, "extensions-builtin")
+config_states_dir = os.path.join(script_path, "config_states")
@@ -18,9 +18,14 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,

    if extras_mode == 1:
        for img in image_folder:
-            image = Image.open(img)
+            if isinstance(img, Image.Image):
+                image = img
+                fn = ''
+            else:
+                image = Image.open(os.path.abspath(img.name))
+                fn = os.path.splitext(img.orig_name)[0]
            image_data.append(image)
-            image_names.append(os.path.splitext(img.orig_name)[0])
+            image_names.append(fn)
    elif extras_mode == 2:
        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
        assert input_dir, 'input directory not selected'
@@ -3,6 +3,7 @@ import math
 import os
 import sys
 import warnings
+import hashlib

 import torch
 import numpy as np
@@ -13,10 +14,11 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional

 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
+import modules.paths as paths
 import modules.face_restoration
 import modules.images as images
 import modules.styles
@@ -77,28 +79,34 @@ def apply_overlay(image, paste_loc, index, overlays):


 def txt2img_image_conditioning(sd_model, x, width, height):
-    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
-        # Dummy zero conditioning if we're not using inpainting model.
+    if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models
+
+        # The "masked-image" in this case will just be all zeros since the entire image is masked.
+        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+        image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)
+
+        return image_conditioning
+
+    elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models
+
+        return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device)
+
+    else:
+        # Dummy zero conditioning if we're not using inpainting or unclip models.
        # Still takes up a bit of memory, but no encoder call.
        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)

-    # The "masked-image" in this case will just be all zeros since the entire image is masked.
-    image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-    image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
-
-    # Add the fake full 1s mask to the first dimension.
-    image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-    image_conditioning = image_conditioning.to(x.dtype)
-
-    return image_conditioning
-

 class StableDiffusionProcessing:
    """
    The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
    """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_min_uncond: float = 0.0, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
        if sampler_index is not None:
            print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)

@@ -133,6 +141,7 @@ class StableDiffusionProcessing:
        self.denoising_strength: float = denoising_strength
        self.sampler_noise_scheduler_override = None
        self.ddim_discretize = ddim_discretize or opts.ddim_discretize
+        self.s_min_uncond = s_min_uncond or opts.s_min_uncond
        self.s_churn = s_churn or opts.s_churn
        self.s_tmin = s_tmin or opts.s_tmin
        self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option
@@ -155,6 +164,8 @@ class StableDiffusionProcessing:
        self.all_seeds = None
        self.all_subseeds = None
        self.iteration = 0
+        self.is_hr_pass = False
+        

    @property
    def sd_model(self):
@@ -184,7 +195,20 @@ class StableDiffusionProcessing:
        conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
        return conditioning

-    def inpainting_image_conditioning(self, source_image, latent_image, image_mask = None):
+    def edit_image_conditioning(self, source_image):
+        conditioning_image = self.sd_model.encode_first_stage(source_image).mode()
+
+        return conditioning_image
+
+    def unclip_image_conditioning(self, source_image):
+        c_adm = self.sd_model.embedder(source_image)
+        if self.sd_model.noise_augmentor is not None:
+            noise_level = 0 # TODO: Allow other noise levels?
+            c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0]))
+            c_adm = torch.cat((c_adm, noise_level_emb), 1)
+        return c_adm
+
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
        self.is_using_inpainting_conditioning = True

        # Handle the different mask inputs
@@ -203,7 +227,7 @@ class StableDiffusionProcessing:

        # Create another latent image, this time with a masked version of the original input.
        # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
-        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)
+        conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
        conditioning_image = torch.lerp(
            source_image,
            source_image * (1.0 - conditioning_mask),
@@ -222,14 +246,22 @@ class StableDiffusionProcessing:
        return image_conditioning

    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+        source_image = devices.cond_cast_float(source_image)
+
        # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
        # identify itself with a field common to all models. The conditioning_key is also hybrid.
        if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
            return self.depth2img_image_conditioning(source_image)

+        if self.sd_model.cond_stage_key == "edit":
+            return self.edit_image_conditioning(source_image)
+
        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)

+        if self.sampler.conditioning_key == "crossattn-adm":
+            return self.unclip_image_conditioning(source_image)
+
        # Dummy zero conditioning if we're not using inpainting or depth model.
        return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)

@@ -257,6 +289,7 @@ class Processed:
        self.height = p.height
        self.sampler_name = p.sampler_name
        self.cfg_scale = p.cfg_scale
+        self.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
        self.steps = p.steps
        self.batch_size = p.batch_size
        self.restore_faces = p.restore_faces
@@ -434,21 +467,22 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
        "Steps": p.steps,
        "Sampler": p.sampler_name,
        "CFG scale": p.cfg_scale,
+        "Image CFG scale": getattr(p, 'image_cfg_scale', None),
        "Seed": all_seeds[index],
        "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
        "Size": f"{p.width}x{p.height}",
        "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
-        "Batch size": (None if p.batch_size < 2 else p.batch_size),
-        "Batch pos": (None if p.batch_size < 2 else position_in_batch),
        "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
        "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
        "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
        "Denoising strength": getattr(p, 'denoising_strength', None),
        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
-        "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
        "Clip skip": None if clip_skip <= 1 else clip_skip,
        "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
+        "Init image hash": getattr(p, 'init_img_hash', None),
+        "RNG": opts.randn_source if opts.randn_source != "GPU" else None,
+        "NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
    }

    generation_params.update(p.extra_generation_params)
@@ -464,6 +498,11 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
    stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}

    try:
+        # if no checkpoint override or the override checkpoint can't be found, remove override entry and load opts checkpoint
+        if sd_models.checkpoint_alisases.get(p.override_settings.get('sd_model_checkpoint')) is None:
+            p.override_settings.pop('sd_model_checkpoint', None)
+            sd_models.reload_model_weights()
+
        for k, v in p.override_settings.items():
            setattr(opts, k, v)

@@ -480,8 +519,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
        if p.override_settings_restore_afterwards:
            for k, v in stored_opts.items():
                setattr(opts, k, v)
-                if k == 'sd_model_checkpoint':
-                    sd_models.reload_model_weights()

                if k == 'sd_vae':
                    sd_vae.reload_vae_weights()
@@ -533,8 +570,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
        model_hijack.embedding_db.load_textual_inversion_embeddings()

-    _, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
-
    if p.scripts is not None:
        p.scripts.process(p)

@@ -568,16 +603,14 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
        with devices.autocast():
            p.init(p.all_prompts, p.all_seeds, p.all_subseeds)

-            if not p.disable_extra_networks:
-                extra_networks.activate(p, extra_network_data)
-
-        with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
-            processed = Processed(p, [], p.seed, "")
-            file.write(processed.infotext(p, 0))
+            # for OSX, loading the model during sampling changes the generated picture, so it is loaded here
+            if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
+                sd_vae_approx.model()

        if state.job_count == -1:
            state.job_count = p.n_iter

+        extra_network_data = None
        for n in range(p.n_iter):
            p.iteration = n

@@ -592,16 +625,38 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
            subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

+            if p.scripts is not None:
+                p.scripts.before_process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
+
            if len(prompts) == 0:
                break

-            prompts, _ = extra_networks.parse_prompts(prompts)
+            prompts, extra_network_data = extra_networks.parse_prompts(prompts)
+
+            if not p.disable_extra_networks:
+                with devices.autocast():
+                    extra_networks.activate(p, extra_network_data)

            if p.scripts is not None:
                p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)

-            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
-            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
+            # params.txt should be saved after scripts.process_batch, since the
+            # infotext could be modified by that callback
+            # Example: a wildcard processed by process_batch sets an extra model
+            # strength, which is saved as "Model Strength: 1.0" in the infotext
+            if n == 0:
+                with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
+                    processed = Processed(p, [], p.seed, "")
+                    file.write(processed.infotext(p, 0))
+
+            step_multiplier = 1
+            if not shared.opts.dont_fix_second_order_samplers_schedule:
+                try:
+                    step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
+                except:
+                    pass
+            uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
+            c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)

            if len(model_hijack.comments) > 0:
                for comment in model_hijack.comments:
@@ -610,7 +665,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            if p.n_iter > 1:
                shared.state.job = f"Batch {n+1} out of {p.n_iter}"

-            with devices.autocast():
+            with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)

            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
@@ -631,6 +686,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)

            for i, x_sample in enumerate(x_samples_ddim):
+                p.batch_index = i
+
                x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
                x_sample = x_sample.astype(np.uint8)

@@ -645,6 +702,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:

                image = Image.fromarray(x_sample)

+                if p.scripts is not None:
+                    pp = scripts.PostprocessImageArgs(image)
+                    p.scripts.postprocess_image(p, pp)
+                    image = pp.image
+
                if p.color_corrections is not None and i < len(p.color_corrections):
                    if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
@@ -662,6 +724,22 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                    image.info["parameters"] = text
                output_images.append(image)

+                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay and any([opts.save_mask, opts.save_mask_composite, opts.return_mask, opts.return_mask_composite]):
+                    image_mask = p.mask_for_overlay.convert('RGB')
+                    image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
+
+                    if opts.save_mask:
+                        images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask")
+
+                    if opts.save_mask_composite:
+                        images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite")
+
+                    if opts.return_mask:
+                        output_images.append(image_mask)
+
+                    if opts.return_mask_composite:
+                        output_images.append(image_mask_composite)
+
            del x_samples_ddim

            devices.torch_gc()
@@ -686,7 +764,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
            if opts.grid_save:
                images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)

-    if not p.disable_extra_networks:
+    if not p.disable_extra_networks and extra_network_data:
        extra_networks.deactivate(p, extra_network_data)

    devices.torch_gc()
@@ -811,6 +889,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
        if not self.enable_hr:
            return samples

+        self.is_hr_pass = True
+
        target_width = self.hr_upscale_to_x
        target_height = self.hr_upscale_to_y

@@ -865,7 +945,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

        shared.state.nextjob()

-        img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM'  # PLMS does not support img2img so we just silently switch ot DDIM
+        img2img_sampler_name = self.sampler_name
+        if self.sampler_name in ['PLMS', 'UniPC']:  # PLMS/UniPC do not support img2img so we just silently switch to DDIM
+            img2img_sampler_name = 'DDIM'
        self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)

        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
@@ -878,18 +960,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)

+        self.is_hr_pass = False
+
        return samples


 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
    sampler = None

-    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
+    def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
        super().__init__(**kwargs)

        self.init_images = init_images
        self.resize_mode: int = resize_mode
        self.denoising_strength: float = denoising_strength
+        self.image_cfg_scale: float = image_cfg_scale if shared.sd_model.cond_stage_key == "edit" else None
        self.init_latent = None
        self.image_mask = mask
        self.latent_mask = None
@@ -944,6 +1029,12 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
            self.color_corrections = []
        imgs = []
        for img in self.init_images:
+
+            # Save init image
+            if opts.save_init_img:
+                self.init_img_hash = hashlib.md5(img.tobytes()).hexdigest()
+                images.save_image(img, path=opts.outdir_init_images, basename=None, forced_filename=self.init_img_hash, save_to_dirs=False)
+
            image = images.flatten(img, opts.img2img_background_color)

            if crop_region is None and self.resize_mode != 3:
@@ -13,6 +13,8 @@ import modules.shared as shared
 current_task = None
 pending_tasks = {}
 finished_tasks = []
+recorded_results = []
+recorded_results_limit = 2


 def start_task(id_task):
@@ -33,6 +35,12 @@ def finish_task(id_task):
        finished_tasks.pop(0)


+def record_results(id_task, res):
+    recorded_results.append((id_task, res))
+    if len(recorded_results) > recorded_results_limit:
+        recorded_results.pop(0)
+
+
 def add_task_to_queue(id_job):
    pending_tasks[id_job] = time.time()

@@ -97,3 +105,13 @@ def progressapi(req: ProgressRequest):

    return ProgressResponse(active=active, queued=queued, completed=completed, progress=progress, eta=eta, live_preview=live_preview, id_live_preview=id_live_preview, textinfo=shared.state.textinfo)

+
+def restore_progress(id_task):
+    while id_task == current_task or id_task in pending_tasks:
+        time.sleep(0.1)
+
+    res = next(iter([x[1] for x in recorded_results if id_task == x[0]]), None)
+    if res is not None:
+        return res
+
+    return gr.update(), gr.update(), gr.update(), f"Couldn't restore progress for {id_task}: results either have been discarded or never were obtained"
@@ -9,7 +9,7 @@ from realesrgan import RealESRGANer

 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import cmd_opts, opts
-
+from modules import modelloader

 class UpscalerRealESRGAN(Upscaler):
    def __init__(self, path):
@@ -23,7 +23,15 @@ class UpscalerRealESRGAN(Upscaler):
            self.enable = True
            self.scalers = []
            scalers = self.load_models(path)
+
+            local_model_paths = self.find_models(ext_filter=[".pth"])
            for scaler in scalers:
+                if scaler.local_data_path.startswith("http"):
+                    filename = modelloader.friendly_name(scaler.local_data_path)
+                    local = next(iter([local_model for local_model in local_model_paths if local_model.endswith(filename + '.pth')]), None)
+                    if local:
+                        scaler.local_data_path = local
+
                if scaler.name in opts.realesrgan_enabled_models:
                    self.scalers.append(scaler)

@@ -46,7 +54,7 @@ class UpscalerRealESRGAN(Upscaler):
            scale=info.scale,
            model_path=info.local_data_path,
            model=info.model(),
-            half=not cmd_opts.no_half,
+            half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
            tile=opts.ESRGAN_tile,
            tile_pad=opts.ESRGAN_tile_overlap,
        )
@@ -64,7 +72,9 @@ class UpscalerRealESRGAN(Upscaler):
                print(f"Unable to find model info: {path}")
                return None

-            info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+            if info.local_data_path.startswith("http"):
+                info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_path, progress=True)
+
            return info
        except Exception as e:
            print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
@@ -1,6 +1,5 @@
 # this code is adapted from the script contributed by anon from /h/

-import io
 import pickle
 import collections
 import sys
@@ -12,11 +11,9 @@ import _codecs
 import zipfile
 import re

-
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
 TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage

-
 def encode(*args):
    out = _codecs.encode(*args)
    return out
@@ -27,7 +24,7 @@ class RestrictedUnpickler(pickle.Unpickler):

    def persistent_load(self, saved_id):
        assert saved_id[0] == 'storage'
-        return TypedStorage()
+        return TypedStorage(_internal=True)

    def find_class(self, module, name):
        if self.extra_handler is not None:
@@ -29,7 +29,7 @@ class ImageSaveParams:


 class CFGDenoiserParams:
-    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps):
+    def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
        self.x = x
        """Latent image representation in the process of being denoised"""
        
@@ -44,6 +44,24 @@ class CFGDenoiserParams:
        
        self.total_sampling_steps = total_sampling_steps
        """Total number of sampling steps planned"""
+        
+        self.text_cond = text_cond
+        """ Encoder hidden states of text conditioning from prompt"""
+        
+        self.text_uncond = text_uncond
+        """ Encoder hidden states of text conditioning from negative prompt"""
+
+
+class CFGDenoisedParams:
+    def __init__(self, x, sampling_step, total_sampling_steps):
+        self.x = x
+        """Latent image representation in the process of being denoised"""
+
+        self.sampling_step = sampling_step
+        """Current Sampling step number"""
+
+        self.total_sampling_steps = total_sampling_steps
+        """Total number of sampling steps planned"""


 class UiTrainTabParams:
@@ -68,12 +86,14 @@ callback_map = dict(
    callbacks_before_image_saved=[],
    callbacks_image_saved=[],
    callbacks_cfg_denoiser=[],
+    callbacks_cfg_denoised=[],
    callbacks_before_component=[],
    callbacks_after_component=[],
    callbacks_image_grid=[],
    callbacks_infotext_pasted=[],
    callbacks_script_unloaded=[],
    callbacks_before_ui=[],
+    callbacks_on_reload=[],
 )


@@ -90,6 +110,14 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
            report_exception(c, 'app_started_callback')


+def app_reload_callback():
+    for c in callback_map['callbacks_on_reload']:
+        try:
+            c.callback()
+        except Exception:
+            report_exception(c, 'callbacks_on_reload')
+
+
 def model_loaded_callback(sd_model):
    for c in callback_map['callbacks_model_loaded']:
        try:
@@ -150,6 +178,14 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
            report_exception(c, 'cfg_denoiser_callback')


+def cfg_denoised_callback(params: CFGDenoisedParams):
+    for c in callback_map['callbacks_cfg_denoised']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'cfg_denoised_callback')
+
+
 def before_component_callback(component, **kwargs):
    for c in callback_map['callbacks_before_component']:
        try:
@@ -227,6 +263,11 @@ def on_app_started(callback):
    add_callback(callback_map['callbacks_app_started'], callback)


+def on_before_reload(callback):
+    """register a function to be called just before the server reloads."""
+    add_callback(callback_map['callbacks_on_reload'], callback)
+
+
 def on_model_loaded(callback):
    """register a function to be called when the stable diffusion model is created; the model is
    passed as an argument; this function is also called when the script is reloaded. """
@@ -283,6 +324,14 @@ def on_cfg_denoiser(callback):
    add_callback(callback_map['callbacks_cfg_denoiser'], callback)


+def on_cfg_denoised(callback):
+    """register a function to be called in the kdiffussion cfg_denoiser method after building the inner model inputs.
+    The callback is called with one argument:
+        - params: CFGDenoisedParams - parameters to be passed to the inner model and sampling state details.
+    """
+    add_callback(callback_map['callbacks_cfg_denoised'], callback)
+
+
 def on_before_component(callback):
    """register a function to be called before a component is created.
    The callback is called with arguments:
@@ -1,16 +1,14 @@
 import os
 import sys
 import traceback
+import importlib.util
 from types import ModuleType


 def load_module(path):
-    with open(path, "r", encoding="utf8") as file:
-        text = file.read()
-
-    compiled = compile(text, path, 'exec')
-    module = ModuleType(os.path.basename(path))
-    exec(compiled, module.__dict__)
+    module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
+    module = importlib.util.module_from_spec(module_spec)
+    module_spec.loader.exec_module(module)

    return module

@@ -6,12 +6,16 @@ from collections import namedtuple

 import gradio as gr

-from modules.processing import StableDiffusionProcessing
 from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing

 AlwaysVisible = object()


+class PostprocessImageArgs:
+    def __init__(self, image):
+        self.image = image
+
+
 class Script:
    filename = None
    args_from = None
@@ -29,6 +33,11 @@ class Script:
    parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example
    """

+    paste_field_names = None
+    """if set in ui(), this is a list of names of infotext fields; the fields will be sent through the
+    various "Send to <X>" buttons when clicked
+    """
+
    def title(self):
        """this function should return the title of the script. This is what will be displayed in the dropdown menu."""

@@ -65,7 +74,7 @@ class Script:
        args contains all values returned by components from ui()
        """

-        raise NotImplementedError()
+        pass

    def process(self, p, *args):
        """
@@ -76,6 +85,20 @@ class Script:

        pass

+    def before_process_batch(self, p, *args, **kwargs):
+        """
+        Called before extra networks are parsed from the prompt, so you can add
+        new extra network keywords to the prompt with this callback.
+
+        **kwargs will have those items:
+          - batch_number - index of current batch, from 0 to number of batches-1
+          - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things
+          - seeds - list of seeds for current batch
+          - subseeds - list of subseeds for current batch
+        """
+
+        pass
+
    def process_batch(self, p, *args, **kwargs):
        """
        Same as process(), but called for every batch.
@@ -100,6 +123,13 @@ class Script:

        pass

+    def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
+        """
+        Called for every image after it has been generated.
+        """
+
+        pass
+
    def postprocess(self, p, processed, *args):
        """
        This function is called after processing ends for AlwaysVisible scripts.
@@ -209,7 +239,15 @@ def load_scripts():
            elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
                postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))

-    for scriptfile in sorted(scripts_list):
+    def orderby(basedir):
+        # 1st webui, 2nd extensions-builtin, 3rd extensions
+        priority = {os.path.join(paths.script_path, "extensions-builtin"):1, paths.script_path:0}
+        for key in priority:
+            if basedir.startswith(key):
+                return priority[key]
+        return 9999
+
+    for scriptfile in sorted(scripts_list, key=lambda x: [orderby(x.basedir), x]):
        try:
            if scriptfile.basedir != paths.script_path:
                sys.path = [scriptfile.basedir] + sys.path
@@ -245,13 +283,18 @@ class ScriptRunner:
        self.alwayson_scripts = []
        self.titles = []
        self.infotext_fields = []
+        self.paste_field_names = []

    def initialize_scripts(self, is_img2img):
+        from modules import scripts_auto_postprocessing
+
        self.scripts.clear()
        self.alwayson_scripts.clear()
        self.selectable_scripts.clear()

-        for script_class, path, basedir, script_module in scripts_data:
+        auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
+
+        for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
            script = script_class()
            script.filename = path
            script.is_txt2img = not is_img2img
@@ -289,6 +332,9 @@ class ScriptRunner:
            if script.infotext_fields is not None:
                self.infotext_fields += script.infotext_fields

+            if script.paste_field_names is not None:
+                self.paste_field_names += script.paste_field_names
+
            inputs += controls
            inputs_alwayson += [script.alwayson for _ in controls]
            script.args_to = len(inputs)
@@ -330,9 +376,23 @@ class ScriptRunner:
            outputs=[script.group for script in self.selectable_scripts]
        )

+        self.script_load_ctr = 0
+        def onload_script_visibility(params):
+            title = params.get('Script', None)
+            if title:
+                title_index = self.titles.index(title)
+                visibility = title_index == self.script_load_ctr
+                self.script_load_ctr = (self.script_load_ctr + 1) % len(self.titles)
+                return gr.update(visible=visibility)
+            else:
+                return gr.update(visible=False)
+
+        self.infotext_fields.append( (dropdown, lambda x: gr.update(value=x.get('Script', 'None'))) )
+        self.infotext_fields.extend( [(script.group, onload_script_visibility) for script in self.selectable_scripts] )
+
        return inputs

-    def run(self, p: StableDiffusionProcessing, *args):
+    def run(self, p, *args):
        script_index = args[0]

        if script_index == 0:
@@ -359,6 +419,15 @@ class ScriptRunner:
                print(f"Error running process: {script.filename}", file=sys.stderr)
                print(traceback.format_exc(), file=sys.stderr)

+    def before_process_batch(self, p, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.before_process_batch(p, *script_args, **kwargs)
+            except Exception:
+                print(f"Error running before_process_batch: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
    def process_batch(self, p, **kwargs):
        for script in self.alwayson_scripts:
            try:
@@ -386,6 +455,15 @@ class ScriptRunner:
                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
                print(traceback.format_exc(), file=sys.stderr)

+    def postprocess_image(self, p, pp: PostprocessImageArgs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_image(p, pp, *script_args)
+            except Exception:
+                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
    def before_component(self, component, **kwargs):
        for script in self.scripts:
            try:
@@ -443,6 +521,18 @@ def reload_scripts():
    scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()


+def add_classes_to_gradio_component(comp):
+    """
+    this adds gradio-* to the component for css styling (ie gradio-button to gr.Button), as well as some others
+    """
+
+    comp.elem_classes = ["gradio-" + comp.get_block_name(), *(comp.elem_classes or [])]
+
+    if getattr(comp, 'multiselect', False):
+        comp.elem_classes.append('multiselect')
+
+
+
 def IOComponent_init(self, *args, **kwargs):
    if scripts_current is not None:
        scripts_current.before_component(self, **kwargs)
@@ -451,6 +541,8 @@ def IOComponent_init(self, *args, **kwargs):

    res = original_IOComponent_init(self, *args, **kwargs)

+    add_classes_to_gradio_component(self)
+
    script_callbacks.after_component_callback(self, **kwargs)

    if scripts_current is not None:
@@ -461,3 +553,15 @@ def IOComponent_init(self, *args, **kwargs):

 original_IOComponent_init = gr.components.IOComponent.__init__
 gr.components.IOComponent.__init__ = IOComponent_init
+
+
+def BlockContext_init(self, *args, **kwargs):
+    res = original_BlockContext_init(self, *args, **kwargs)
+
+    add_classes_to_gradio_component(self)
+
+    return res
+
+
+original_BlockContext_init = gr.blocks.BlockContext.__init__
+gr.blocks.BlockContext.__init__ = BlockContext_init
@@ -0,0 +1,42 @@
+from modules import scripts, scripts_postprocessing, shared
+
+
+class ScriptPostprocessingForMainUI(scripts.Script):
+    def __init__(self, script_postproc):
+        self.script: scripts_postprocessing.ScriptPostprocessing = script_postproc
+        self.postprocessing_controls = None
+
+    def title(self):
+        return self.script.name
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible
+
+    def ui(self, is_img2img):
+        self.postprocessing_controls = self.script.ui()
+        return self.postprocessing_controls.values()
+
+    def postprocess_image(self, p, script_pp, *args):
+        args_dict = {k: v for k, v in zip(self.postprocessing_controls, args)}
+
+        pp = scripts_postprocessing.PostprocessedImage(script_pp.image)
+        pp.info = {}
+        self.script.process(pp, **args_dict)
+        p.extra_generation_params.update(pp.info)
+        script_pp.image = pp.image
+
+
+def create_auto_preprocessing_script_data():
+    from modules import scripts
+
+    res = []
+
+    for name in shared.opts.postprocessing_enable_in_main_ui:
+        script = next(iter([x for x in scripts.postprocessing_scripts_data if x.script_class.name == name]), None)
+        if script is None:
+            continue
+
+        constructor = lambda s=script: ScriptPostprocessingForMainUI(s.script_class())
+        res.append(scripts.ScriptClassData(script_class=constructor, path=script.path, basedir=script.basedir, module=script.module))
+
+    return res
@@ -46,6 +46,8 @@ class ScriptPostprocessing:
        pass


+
+
 def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
    try:
        res = func(*args, **kwargs)
@@ -68,6 +70,9 @@ class ScriptPostprocessingRunner:
            script: ScriptPostprocessing = script_class()
            script.filename = path

+            if script.name == "Simple Upscale":
+                continue
+
            self.scripts.append(script)

    def create_script_ui(self, script, inputs):
@@ -87,12 +92,11 @@ class ScriptPostprocessingRunner:
            import modules.scripts
            self.initialize_scripts(modules.scripts.postprocessing_scripts_data)

-        scripts_order = [x.lower().strip() for x in shared.opts.postprocessing_scipts_order.split(",")]
+        scripts_order = shared.opts.postprocessing_operation_order

        def script_score(name):
-            name = name.lower()
            for i, possible_match in enumerate(scripts_order):
-                if possible_match in name:
+                if possible_match == name:
                    return i

            return len(self.scripts)
@@ -105,7 +109,7 @@ class ScriptPostprocessingRunner:
        inputs = []

        for script in self.scripts_in_preferred_order():
-            with gr.Box() as group:
+            with gr.Row() as group:
                self.create_script_ui(script, inputs)

            script.group = group
@@ -145,3 +149,4 @@ class ScriptPostprocessingRunner:
    def image_changed(self):
        for script in self.scripts_in_preferred_order():
            script.image_changed()
+
@@ -20,8 +20,9 @@ class DisableInitialization:
    ```
    """

-    def __init__(self):
+    def __init__(self, disable_clip=True):
        self.replaced = []
+        self.disable_clip = disable_clip

    def replace(self, obj, field, func):
        original = getattr(obj, field, None)
@@ -75,12 +76,14 @@ class DisableInitialization:
        self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
        self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
        self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
-        self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
-        self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
-        self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
-        self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
-        self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
-        self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
+
+        if self.disable_clip:
+            self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
+            self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
+            self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
+            self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
+            self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
+            self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)

    def __exit__(self, exc_type, exc_val, exc_tb):
        for obj, field, original in self.replaced:
@@ -1,5 +1,6 @@
 import torch
 from torch.nn.functional import silu
+from types import MethodType

 import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
@@ -36,11 +37,23 @@ def apply_optimizations():
    
    optimization_method = None

+    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention")) # not everyone has torch 2.x to use sdp
+
    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
        print("Applying xformers cross attention optimization.")
        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
        optimization_method = 'xformers'
+    elif cmd_opts.opt_sdp_no_mem_attention and can_use_sdp:
+        print("Applying scaled dot product cross attention optimization (without memory efficient attention).")
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_no_mem_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_no_mem_attnblock_forward
+        optimization_method = 'sdp-no-mem'
+    elif cmd_opts.opt_sdp_attention and can_use_sdp:
+        print("Applying scaled dot product cross attention optimization.")
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.scaled_dot_product_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sdp_attnblock_forward
+        optimization_method = 'sdp'
    elif cmd_opts.opt_sub_quad_attention:
        print("Applying sub-quadratic cross attention optimization.")
        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
@@ -76,6 +89,54 @@ def fix_checkpoint():
    pass


+def weighted_loss(sd_model, pred, target, mean=True):
+    #Calculate the weight normally, but ignore the mean
+    loss = sd_model._old_get_loss(pred, target, mean=False)
+    
+    #Check if we have weights available
+    weight = getattr(sd_model, '_custom_loss_weight', None)
+    if weight is not None:
+        loss *= weight
+    
+    #Return the loss, as mean if specified
+    return loss.mean() if mean else loss
+
+def weighted_forward(sd_model, x, c, w, *args, **kwargs):
+    try:
+        #Temporarily append weights to a place accessible during loss calc
+        sd_model._custom_loss_weight = w
+        
+        #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
+        #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
+        if not hasattr(sd_model, '_old_get_loss'):
+            sd_model._old_get_loss = sd_model.get_loss
+        sd_model.get_loss = MethodType(weighted_loss, sd_model)
+
+        #Run the standard forward function, but with the patched 'get_loss'
+        return sd_model.forward(x, c, *args, **kwargs)
+    finally:
+        try:
+            #Delete temporary weights if appended
+            del sd_model._custom_loss_weight
+        except AttributeError as e:
+            pass
+            
+        #If we have an old loss function, reset the loss function to the original one
+        if hasattr(sd_model, '_old_get_loss'):
+            sd_model.get_loss = sd_model._old_get_loss
+            del sd_model._old_get_loss
+
+def apply_weighted_forward(sd_model):
+    #Add new function 'weighted_forward' that can be called to calc weighted loss
+    sd_model.weighted_forward = MethodType(weighted_forward, sd_model)
+
+def undo_weighted_forward(sd_model):
+    try:
+        del sd_model.weighted_forward
+    except AttributeError as e:
+        pass
+
+
 class StableDiffusionModelHijack:
    fixes = None
    comments = []
@@ -104,6 +165,10 @@ class StableDiffusionModelHijack:
            m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
            m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)

+        apply_weighted_forward(m)
+        if m.cond_stage_key == "edit":
+            sd_hijack_unet.hijack_ddpm_edit()
+
        self.optimization_method = apply_optimizations()

        self.clip = m.cond_stage_model
@@ -131,6 +196,9 @@ class StableDiffusionModelHijack:
            m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped
            m.cond_stage_model = m.cond_stage_model.wrapped

+        undo_optimizations()
+        undo_weighted_forward(m)
+
        self.apply_circular(False)
        self.layers = None
        self.clip = None
@@ -171,7 +239,7 @@ class EmbeddingsWithFixes(torch.nn.Module):
        vecs = []
        for fixes, tensor in zip(batch_fixes, inputs_embeds):
            for offset, embedding in fixes:
-                emb = embedding.vec
+                emb = devices.cond_cast_unet(embedding.vec)
                emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
                tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])

@@ -11,6 +11,7 @@ import ldm.models.diffusion.plms
 from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding


@torch.no_grad()
@@ -96,15 +97,6 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
    return x_prev, pred_x0, e_t


-def should_hijack_inpainting(checkpoint_info):
-    from modules import sd_models
-
-    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
-    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
-
-    return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
-
-
 def do_inpainting_hijack():
    # p_sample_plms is needed because PLMS can't work with dicts as conditionings

@@ -0,0 +1,13 @@
+import collections
+import os.path
+import sys
+import gc
+import time
+
+def should_hijack_ip2p(checkpoint_info):
+    from modules import sd_models_config
+
+    ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
+    cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
+
+    return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename
@@ -9,7 +9,7 @@ from torch import einsum
 from ldm.util import default
 from einops import rearrange

-from modules import shared, errors
+from modules import shared, errors, devices
 from modules.hypernetworks import hypernetwork

 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -52,18 +52,25 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
    del q_in, k_in, v_in

-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
-    for i in range(0, q.shape[0], 2):
-        end = i + 2
-        s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
-        s1 *= self.scale
+    dtype = q.dtype
+    if shared.opts.upcast_attn:
+        q, k, v = q.float(), k.float(), v.float()

-        s2 = s1.softmax(dim=-1)
-        del s1
+    with devices.without_autocast(disable=not shared.opts.upcast_attn):
+        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+        for i in range(0, q.shape[0], 2):
+            end = i + 2
+            s1 = einsum('b i d, b j d -> b i j', q[i:end], k[i:end])
+            s1 *= self.scale
+    
+            s2 = s1.softmax(dim=-1)
+            del s1
+    
+            r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
+            del s2
+        del q, k, v

-        r1[i:end] = einsum('b i j, b j d -> b i d', s2, v[i:end])
-        del s2
-    del q, k, v
+    r1 = r1.to(dtype)

    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
    del r1
@@ -82,45 +89,52 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
    k_in = self.to_k(context_k)
    v_in = self.to_v(context_v)

-    k_in *= self.scale
+    dtype = q_in.dtype
+    if shared.opts.upcast_attn:
+        q_in, k_in, v_in = q_in.float(), k_in.float(), v_in if v_in.device.type == 'mps' else v_in.float()

-    del context, x
+    with devices.without_autocast(disable=not shared.opts.upcast_attn):
+        k_in = k_in * self.scale
+    
+        del context, x
+    
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+        del q_in, k_in, v_in
+    
+        r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+    
+        mem_free_total = get_available_vram()
+    
+        gb = 1024 ** 3
+        tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
+        modifier = 3 if q.element_size() == 2 else 2.5
+        mem_required = tensor_size * modifier
+        steps = 1
+    
+        if mem_required > mem_free_total:
+            steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+            # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
+            #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
+    
+        if steps > 64:
+            max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+            raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
+                               f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
+    
+        slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+        for i in range(0, q.shape[1], slice_size):
+            end = i + slice_size
+            s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
+    
+            s2 = s1.softmax(dim=-1, dtype=q.dtype)
+            del s1
+    
+            r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
+            del s2
+    
+        del q, k, v

-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
-    del q_in, k_in, v_in
-
-    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-
-    mem_free_total = get_available_vram()
-
-    gb = 1024 ** 3
-    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
-    modifier = 3 if q.element_size() == 2 else 2.5
-    mem_required = tensor_size * modifier
-    steps = 1
-
-    if mem_required > mem_free_total:
-        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
-        # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
-        #       f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
-
-    if steps > 64:
-        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
-        raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
-                           f'Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free')
-
-    slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
-    for i in range(0, q.shape[1], slice_size):
-        end = i + slice_size
-        s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k)
-
-        s2 = s1.softmax(dim=-1, dtype=q.dtype)
-        del s1
-
-        r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
-        del s2
-
-    del q, k, v
+    r1 = r1.to(dtype)

    r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
    del r1
@@ -204,12 +218,20 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
    context = default(context, x)

    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
-    k = self.to_k(context_k) * self.scale
+    k = self.to_k(context_k)
    v = self.to_v(context_v)
    del context, context_k, context_v, x

-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-    r = einsum_op(q, k, v)
+    dtype = q.dtype
+    if shared.opts.upcast_attn:
+        q, k, v = q.float(), k.float(), v if v.device.type == 'mps' else v.float()
+
+    with devices.without_autocast(disable=not shared.opts.upcast_attn):
+        k = k * self.scale
+    
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+        r = einsum_op(q, k, v)
+    r = r.to(dtype)
    return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))

 # -- End of code from https://github.com/invoke-ai/InvokeAI --
@@ -234,8 +256,14 @@ def sub_quad_attention_forward(self, x, context=None, mask=None):
    k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
    v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)

+    dtype = q.dtype
+    if shared.opts.upcast_attn:
+        q, k = q.float(), k.float()
+
    x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)

+    x = x.to(dtype)
+
    x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)

    out_proj, dropout = self.to_out
@@ -268,15 +296,16 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
        query_chunk_size = q_tokens
        kv_chunk_size = k_tokens

-    return efficient_dot_product_attention(
-        q,
-        k,
-        v,
-        query_chunk_size=q_chunk_size,
-        kv_chunk_size=kv_chunk_size,
-        kv_chunk_size_min = kv_chunk_size_min,
-        use_checkpoint=use_checkpoint,
-    )
+    with devices.without_autocast(disable=q.dtype == v.dtype):
+        return efficient_dot_product_attention(
+            q,
+            k,
+            v,
+            query_chunk_size=q_chunk_size,
+            kv_chunk_size=kv_chunk_size,
+            kv_chunk_size_min = kv_chunk_size_min,
+            use_checkpoint=use_checkpoint,
+        )


 def get_xformers_flash_attention_op(q, k, v):
@@ -306,11 +335,63 @@ def xformers_attention_forward(self, x, context=None, mask=None):
    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
    del q_in, k_in, v_in

+    dtype = q.dtype
+    if shared.opts.upcast_attn:
+        q, k, v = q.float(), k.float(), v.float()
+
    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))

+    out = out.to(dtype)
+
    out = rearrange(out, 'b n h d -> b n (h d)', h=h)
    return self.to_out(out)

+# Based on Diffusers usage of scaled dot product attention from https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/src/diffusers/models/cross_attention.py
+# The scaled_dot_product_attention_forward function contains parts of code under Apache-2.0 license listed under Scaled Dot Product Attention in the Licenses section of the web UI interface
+def scaled_dot_product_attention_forward(self, x, context=None, mask=None):
+    batch_size, sequence_length, inner_dim = x.shape
+
+    if mask is not None:
+        mask = self.prepare_attention_mask(mask, sequence_length, batch_size)
+        mask = mask.view(batch_size, self.heads, -1, mask.shape[-1])
+
+    h = self.heads
+    q_in = self.to_q(x)
+    context = default(context, x)
+
+    context_k, context_v = hypernetwork.apply_hypernetworks(shared.loaded_hypernetworks, context)
+    k_in = self.to_k(context_k)
+    v_in = self.to_v(context_v)
+
+    head_dim = inner_dim // h
+    q = q_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+    k = k_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+    v = v_in.view(batch_size, -1, h, head_dim).transpose(1, 2)
+    
+    del q_in, k_in, v_in
+
+    dtype = q.dtype
+    if shared.opts.upcast_attn:
+        q, k, v = q.float(), k.float(), v.float()
+
+    # the output of sdp = (batch, num_heads, seq_len, head_dim)
+    hidden_states = torch.nn.functional.scaled_dot_product_attention(
+        q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False
+    )
+
+    hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, h * head_dim)
+    hidden_states = hidden_states.to(dtype)
+
+    # linear proj
+    hidden_states = self.to_out[0](hidden_states)
+    # dropout
+    hidden_states = self.to_out[1](hidden_states)
+    return hidden_states
+
+def scaled_dot_product_no_mem_attention_forward(self, x, context=None, mask=None):
+    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+        return scaled_dot_product_attention_forward(self, x, context, mask)
+
 def cross_attention_attnblock_forward(self, x):
        h_ = x
        h_ = self.norm(h_)
@@ -378,16 +459,44 @@ def xformers_attnblock_forward(self, x):
        v = self.v(h_)
        b, c, h, w = q.shape
        q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        dtype = q.dtype
+        if shared.opts.upcast_attn:
+            q, k = q.float(), k.float()
        q = q.contiguous()
        k = k.contiguous()
        v = v.contiguous()
        out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
+        out = out.to(dtype)
        out = rearrange(out, 'b (h w) c -> b c h w', h=h)
        out = self.proj_out(out)
        return x + out
    except NotImplementedError:
        return cross_attention_attnblock_forward(self, x)

+def sdp_attnblock_forward(self, x):
+    h_ = x
+    h_ = self.norm(h_)
+    q = self.q(h_)
+    k = self.k(h_)
+    v = self.v(h_)
+    b, c, h, w = q.shape
+    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    dtype = q.dtype
+    if shared.opts.upcast_attn:
+        q, k = q.float(), k.float()
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=0.0, is_causal=False)
+    out = out.to(dtype)
+    out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+    out = self.proj_out(out)
+    return x + out
+
+def sdp_no_mem_attnblock_forward(self, x):
+    with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=False):
+        return sdp_attnblock_forward(self, x)
+
 def sub_quad_attnblock_forward(self, x):
    h_ = x
    h_ = self.norm(h_)
@@ -1,4 +1,8 @@
 import torch
+from packaging import version
+
+from modules import devices
+from modules.sd_hijack_utils import CondFunc


 class TorchHijackForUnet:
@@ -28,3 +32,48 @@ class TorchHijackForUnet:


 th = TorchHijackForUnet()
+
+
+# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
+def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
+
+    if isinstance(cond, dict):
+        for y in cond.keys():
+            cond[y] = [x.to(devices.dtype_unet) if isinstance(x, torch.Tensor) else x for x in cond[y]]
+
+    with devices.autocast():
+        return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
+
+
+class GELUHijack(torch.nn.GELU, torch.nn.Module):
+    def __init__(self, *args, **kwargs):
+        torch.nn.GELU.__init__(self, *args, **kwargs)
+    def forward(self, x):
+        if devices.unet_needs_upcast:
+            return torch.nn.GELU.forward(self.float(), x.float()).to(devices.dtype_unet)
+        else:
+            return torch.nn.GELU.forward(self, x)
+
+
+ddpm_edit_hijack = None
+def hijack_ddpm_edit():
+    global ddpm_edit_hijack
+    if not ddpm_edit_hijack:
+        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+        CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
+        ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+
+
+unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
+CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
+if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
+    CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
+    CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
+    CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
+
+first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16
+first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
+CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
@@ -0,0 +1,28 @@
+import importlib
+
+class CondFunc:
+    def __new__(cls, orig_func, sub_func, cond_func):
+        self = super(CondFunc, cls).__new__(cls)
+        if isinstance(orig_func, str):
+            func_path = orig_func.split('.')
+            for i in range(len(func_path)-1, -1, -1):
+                try:
+                    resolved_obj = importlib.import_module('.'.join(func_path[:i]))
+                    break
+                except ImportError:
+                    pass
+            for attr_name in func_path[i:-1]:
+                resolved_obj = getattr(resolved_obj, attr_name)
+            orig_func = getattr(resolved_obj, func_path[-1])
+            setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
+        self.__init__(orig_func, sub_func, cond_func)
+        return lambda *args, **kwargs: self(*args, **kwargs)
+    def __init__(self, orig_func, sub_func, cond_func):
+        self.__orig_func = orig_func
+        self.__sub_func = sub_func
+        self.__cond_func = cond_func
+    def __call__(self, *args, **kwargs):
+        if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
+            return self.__sub_func(self.__orig_func, *args, **kwargs)
+        else:
+            return self.__orig_func(*args, **kwargs)
@@ -2,8 +2,6 @@ import collections
 import os.path
 import sys
 import gc
-import time
-from collections import namedtuple
 import torch
 import re
 import safetensors.torch
@@ -14,12 +12,13 @@ import ldm.modules.midas as midas

 from ldm.util import instantiate_from_config

-from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config
 from modules.paths import models_path
-from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
+from modules.sd_hijack_inpainting import do_inpainting_hijack
+from modules.timer import Timer

 model_dir = "Stable-diffusion"
-model_path = os.path.abspath(os.path.join(models_path, model_dir))
+model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))

 checkpoints_list = {}
 checkpoint_alisases = {}
@@ -42,6 +41,7 @@ class CheckpointInfo:
            name = name[1:]

        self.name = name
+        self.name_for_extra = os.path.splitext(os.path.basename(filename))[0]
        self.model_name = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
        self.hash = model_hash(filename)

@@ -52,6 +52,15 @@ class CheckpointInfo:

        self.ids = [self.hash, self.model_name, self.title, name, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])

+        self.metadata = {}
+
+        _, ext = os.path.splitext(self.filename)
+        if ext.lower() == ".safetensors":
+            try:
+                self.metadata = read_metadata_from_safetensors(filename)
+            except Exception as e:
+                errors.display(e, f"reading checkpoint metadata: {filename}")
+
    def register(self):
        checkpoints_list[self.title] = self
        for id in self.ids:
@@ -59,13 +68,17 @@ class CheckpointInfo:

    def calculate_shorthash(self):
        self.sha256 = hashes.sha256(self.filename, "checkpoint/" + self.name)
+        if self.sha256 is None:
+            return
+
        self.shorthash = self.sha256[0:10]

        if self.shorthash not in self.ids:
-            self.ids += [self.shorthash, self.sha256]
-            self.register()
+            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']

+        checkpoints_list.pop(self.title)
        self.title = f'{self.name} [{self.shorthash}]'
+        self.register()

        return self.shorthash

@@ -98,23 +111,18 @@ def checkpoint_tiles():
    return sorted([x.title for x in checkpoints_list.values()], key=alphanumeric_key)


-def find_checkpoint_config(info):
-    if info is None:
-        return shared.cmd_opts.config
-
-    config = os.path.splitext(info.filename)[0] + ".yaml"
-    if os.path.exists(config):
-        return config
-
-    return shared.cmd_opts.config
-
-
 def list_models():
    checkpoints_list.clear()
    checkpoint_alisases.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])

    cmd_ckpt = shared.cmd_opts.ckpt
+    if shared.cmd_opts.no_download_sd_model or cmd_ckpt != shared.sd_model_file or os.path.exists(cmd_ckpt):
+        model_url = None
+    else:
+        model_url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.safetensors"
+
+    model_list = modelloader.load_models(model_path=model_path, model_url=model_url, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], download_name="v1-5-pruned-emaonly.safetensors", ext_blacklist=[".vae.ckpt", ".vae.safetensors"])
+
    if os.path.exists(cmd_ckpt):
        checkpoint_info = CheckpointInfo(cmd_ckpt)
        checkpoint_info.register()
@@ -123,7 +131,7 @@ def list_models():
    elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
        print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)

-    for filename in model_list:
+    for filename in sorted(model_list, key=str.lower):
        checkpoint_info = CheckpointInfo(filename)
        checkpoint_info.register()

@@ -169,7 +177,7 @@ def select_checkpoint():
        print(f" - directory {model_path}", file=sys.stderr)
        if shared.cmd_opts.ckpt_dir is not None:
            print(f" - directory {os.path.abspath(shared.cmd_opts.ckpt_dir)}", file=sys.stderr)
-        print("Can't run without a checkpoint. Find and place a .ckpt file into any of those locations. The program will exit.", file=sys.stderr)
+        print("Can't run without a checkpoint. Find and place a .ckpt or .safetensors file into any of those locations. The program will exit.", file=sys.stderr)
        exit(1)

    checkpoint_info = next(iter(checkpoints_list.values()))
@@ -179,7 +187,7 @@ def select_checkpoint():
    return checkpoint_info


-chckpoint_dict_replacements = {
+checkpoint_dict_replacements = {
    'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
    'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
    'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
@@ -187,7 +195,7 @@ chckpoint_dict_replacements = {


 def transform_checkpoint_dict_key(k):
-    for text, replacement in chckpoint_dict_replacements.items():
+    for text, replacement in checkpoint_dict_replacements.items():
        if k.startswith(text):
            k = replacement + k[len(text):]

@@ -211,12 +219,34 @@ def get_state_dict_from_checkpoint(pl_sd):
    return pl_sd


+def read_metadata_from_safetensors(filename):
+    import json
+
+    with open(filename, mode="rb") as file:
+        metadata_len = file.read(8)
+        metadata_len = int.from_bytes(metadata_len, "little")
+        json_start = file.read(2)
+
+        assert metadata_len > 2 and json_start in (b'{"', b"{'"), f"{filename} is not a safetensors file"
+        json_data = json_start + file.read(metadata_len-2)
+        json_obj = json.loads(json_data)
+
+        res = {}
+        for k, v in json_obj.get("__metadata__", {}).items():
+            res[k] = v
+            if isinstance(v, str) and v[0:1] == '{':
+                try:
+                    res[k] = json.loads(v)
+                except Exception as e:
+                    pass
+
+        return res
+
+
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
    _, extension = os.path.splitext(checkpoint_file)
    if extension.lower() == ".safetensors":
-        device = map_location or shared.weight_load_location
-        if device is None:
-            device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
+        device = map_location or shared.weight_load_location or devices.get_optimal_device_name()
        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
    else:
        pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
@@ -228,52 +258,72 @@ def read_state_dict(checkpoint_file, print_global_state=False, map_location=None
    return sd


-def load_model_weights(model, checkpoint_info: CheckpointInfo):
-    title = checkpoint_info.title
+def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
    sd_model_hash = checkpoint_info.calculate_shorthash()
-    if checkpoint_info.title != title:
-        shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
+    timer.record("calculate hash")

-    cache_enabled = shared.opts.sd_checkpoint_cache > 0
-
-    if cache_enabled and checkpoint_info in checkpoints_loaded:
+    if checkpoint_info in checkpoints_loaded:
        # use checkpoint cache
        print(f"Loading weights [{sd_model_hash}] from cache")
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
-    else:
-        # load from file
-        print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
+        return checkpoints_loaded[checkpoint_info]

-        sd = read_state_dict(checkpoint_info.filename)
-        model.load_state_dict(sd, strict=False)
-        del sd
-        
-        if cache_enabled:
-            # cache newly loaded model
-            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+    print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
+    res = read_state_dict(checkpoint_info.filename)
+    timer.record("load weights from disk")

-        if shared.cmd_opts.opt_channelslast:
-            model.to(memory_format=torch.channels_last)
+    return res

-        if not shared.cmd_opts.no_half:
-            vae = model.first_stage_model

-            # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
-            if shared.cmd_opts.no_half_vae:
-                model.first_stage_model = None
+def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
+    sd_model_hash = checkpoint_info.calculate_shorthash()
+    timer.record("calculate hash")

-            model.half()
-            model.first_stage_model = vae
+    shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title

-        devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-        devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
+    if state_dict is None:
+        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)

-        model.first_stage_model.to(devices.dtype_vae)
+    model.load_state_dict(state_dict, strict=False)
+    del state_dict
+    timer.record("apply weights to model")
+
+    if shared.opts.sd_checkpoint_cache > 0:
+        # cache newly loaded model
+        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+
+    if shared.cmd_opts.opt_channelslast:
+        model.to(memory_format=torch.channels_last)
+        timer.record("apply channels_last")
+
+    if not shared.cmd_opts.no_half:
+        vae = model.first_stage_model
+        depth_model = getattr(model, 'depth_model', None)
+
+        # with --no-half-vae, remove VAE from model when doing half() to prevent its weights from being converted to float16
+        if shared.cmd_opts.no_half_vae:
+            model.first_stage_model = None
+        # with --upcast-sampling, don't convert the depth model weights to float16
+        if shared.cmd_opts.upcast_sampling and depth_model:
+            model.depth_model = None
+
+        model.half()
+        model.first_stage_model = vae
+        if depth_model:
+            model.depth_model = depth_model
+
+        timer.record("apply half()")
+
+    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
+    devices.dtype_unet = model.model.diffusion_model.dtype
+    devices.unet_needs_upcast = shared.cmd_opts.upcast_sampling and devices.dtype == torch.float16 and devices.dtype_unet == torch.float16
+
+    model.first_stage_model.to(devices.dtype_vae)
+    timer.record("apply dtype to VAE")

    # clean up cache if limit is reached
-    if cache_enabled:
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
-            checkpoints_loaded.popitem(last=False)  # LRU
+    while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+        checkpoints_loaded.popitem(last=False)

    model.sd_model_hash = sd_model_hash
    model.sd_model_checkpoint = checkpoint_info.filename
@@ -286,6 +336,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo):
    sd_vae.clear_loaded_vae()
    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
    sd_vae.load_vae(model, vae_file, vae_source)
+    timer.record("load VAE")


 def enable_midas_autodownload():
@@ -298,7 +349,7 @@ def enable_midas_autodownload():
    location automatically.
    """

-    midas_path = os.path.join(models_path, 'midas')
+    midas_path = os.path.join(paths.models_path, 'midas')

    # stable-diffusion-stability-ai hard-codes the midas model path to
    # a location that differs from where other scripts using this model look.
@@ -331,24 +382,31 @@ def enable_midas_autodownload():
    midas.api.load_model = load_model_wrapper


-class Timer:
-    def __init__(self):
-        self.start = time.time()
+def repair_config(sd_config):

-    def elapsed(self):
-        end = time.time()
-        res = end - self.start
-        self.start = end
-        return res
+    if not hasattr(sd_config.model.params, "use_ema"):
+        sd_config.model.params.use_ema = False
+
+    if shared.cmd_opts.no_half:
+        sd_config.model.params.unet_config.params.use_fp16 = False
+    elif shared.cmd_opts.upcast_sampling:
+        sd_config.model.params.unet_config.params.use_fp16 = True
+
+    if getattr(sd_config.model.params.first_stage_config.params.ddconfig, "attn_type", None) == "vanilla-xformers" and not shared.xformers_available:
+        sd_config.model.params.first_stage_config.params.ddconfig.attn_type = "vanilla"
+
+    # For UnCLIP-L, override the hardcoded karlo directory
+    if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"):
+        karlo_path = os.path.join(paths.models_path, 'karlo')
+        sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path)


-def load_model(checkpoint_info=None):
+sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight'
+sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight'
+
+def load_model(checkpoint_info=None, already_loaded_state_dict=None, time_taken_to_load_state_dict=None):
    from modules import lowvram, sd_hijack
    checkpoint_info = checkpoint_info or select_checkpoint()
-    checkpoint_config = find_checkpoint_config(checkpoint_info)
-
-    if checkpoint_config != shared.cmd_opts.config:
-        print(f"Loading config from: {checkpoint_config}")

    if shared.sd_model:
        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
@@ -356,29 +414,30 @@ def load_model(checkpoint_info=None):
        gc.collect()
        devices.torch_gc()

-    sd_config = OmegaConf.load(checkpoint_config)
-    
-    if should_hijack_inpainting(checkpoint_info):
-        # Hardcoded config for now...
-        sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion"
-        sd_config.model.params.conditioning_key = "hybrid"
-        sd_config.model.params.unet_config.params.in_channels = 9
-        sd_config.model.params.finetune_keys = None
-
-    if not hasattr(sd_config.model.params, "use_ema"):
-        sd_config.model.params.use_ema = False
-
    do_inpainting_hijack()

-    if shared.cmd_opts.no_half:
-        sd_config.model.params.unet_config.params.use_fp16 = False
-
    timer = Timer()

-    sd_model = None
+    if already_loaded_state_dict is not None:
+        state_dict = already_loaded_state_dict
+    else:
+        state_dict = get_checkpoint_state_dict(checkpoint_info, timer)

+    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+    clip_is_included_into_sd = sd1_clip_weight in state_dict or sd2_clip_weight in state_dict
+
+    timer.record("find config")
+
+    sd_config = OmegaConf.load(checkpoint_config)
+    repair_config(sd_config)
+
+    timer.record("load config")
+
+    print(f"Creating model from config: {checkpoint_config}")
+
+    sd_model = None
    try:
-        with sd_disable_initialization.DisableInitialization():
+        with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
            sd_model = instantiate_from_config(sd_config.model)
    except Exception as e:
        pass
@@ -387,29 +446,35 @@ def load_model(checkpoint_info=None):
        print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
        sd_model = instantiate_from_config(sd_config.model)

-    elapsed_create = timer.elapsed()
+    sd_model.used_config = checkpoint_config

-    load_model_weights(sd_model, checkpoint_info)
+    timer.record("create model")

-    elapsed_load_weights = timer.elapsed()
+    load_model_weights(sd_model, checkpoint_info, state_dict, timer)

    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
    else:
        sd_model.to(shared.device)

+    timer.record("move model to device")
+
    sd_hijack.model_hijack.hijack(sd_model)

+    timer.record("hijack")
+
    sd_model.eval()
    shared.sd_model = sd_model

    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model

+    timer.record("load textual inversion embeddings")
+
    script_callbacks.model_loaded_callback(sd_model)

-    elapsed_the_rest = timer.elapsed()
+    timer.record("scripts callbacks")

-    print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")
+    print(f"Model loaded in {timer.summary()}.")

    return sd_model

@@ -420,6 +485,7 @@ def reload_model_weights(sd_model=None, info=None):

    if not sd_model:
        sd_model = shared.sd_model
+
    if sd_model is None:  # previous model load failed
        current_checkpoint_info = None
    else:
@@ -427,38 +493,64 @@ def reload_model_weights(sd_model=None, info=None):
        if sd_model.sd_model_checkpoint == checkpoint_info.filename:
            return

-    checkpoint_config = find_checkpoint_config(current_checkpoint_info)
+        if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+            lowvram.send_everything_to_cpu()
+        else:
+            sd_model.to(devices.cpu)

-    if current_checkpoint_info is None or checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
-        del sd_model
-        checkpoints_loaded.clear()
-        load_model(checkpoint_info)
-        return shared.sd_model
-
-    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-        lowvram.send_everything_to_cpu()
-    else:
-        sd_model.to(devices.cpu)
-
-    sd_hijack.model_hijack.undo_hijack(sd_model)
+        sd_hijack.model_hijack.undo_hijack(sd_model)

    timer = Timer()

+    state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
+
+    checkpoint_config = sd_models_config.find_checkpoint_config(state_dict, checkpoint_info)
+
+    timer.record("find config")
+
+    if sd_model is None or checkpoint_config != sd_model.used_config:
+        del sd_model
+        checkpoints_loaded.clear()
+        load_model(checkpoint_info, already_loaded_state_dict=state_dict)
+        return shared.sd_model
+
    try:
-        load_model_weights(sd_model, checkpoint_info)
+        load_model_weights(sd_model, checkpoint_info, state_dict, timer)
    except Exception as e:
        print("Failed to load checkpoint, restoring previous")
-        load_model_weights(sd_model, current_checkpoint_info)
+        load_model_weights(sd_model, current_checkpoint_info, None, timer)
        raise
    finally:
        sd_hijack.model_hijack.hijack(sd_model)
+        timer.record("hijack")
+
        script_callbacks.model_loaded_callback(sd_model)
+        timer.record("script callbacks")

        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
            sd_model.to(devices.device)
+            timer.record("move model to device")

-    elapsed = timer.elapsed()
-
-    print(f"Weights loaded in {elapsed:.1f}s.")
+    print(f"Weights loaded in {timer.summary()}.")
+
+    return sd_model
+
+def unload_model_weights(sd_model=None, info=None):
+    from modules import lowvram, devices, sd_hijack
+    timer = Timer()
+
+    if shared.sd_model:
+
+        # shared.sd_model.cond_stage_model.to(devices.cpu)
+        # shared.sd_model.first_stage_model.to(devices.cpu)
+        shared.sd_model.to(devices.cpu)
+        sd_hijack.model_hijack.undo_hijack(shared.sd_model)
+        shared.sd_model = None
+        sd_model = None
+        gc.collect()
+        devices.torch_gc()
+        torch.cuda.empty_cache()
+
+    print(f"Unloaded weights {timer.summary()}.")

    return sd_model
@@ -0,0 +1,119 @@
+import re
+import os
+
+import torch
+
+from modules import shared, paths, sd_disable_initialization
+
+sd_configs_path = shared.sd_configs_path
+sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
+
+
+config_default = shared.sd_default_config
+config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml")
+config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml")
+config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml")
+config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml")
+config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml")
+config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml")
+config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml")
+config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml")
+config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml")
+
+
+def is_using_v_parameterization_for_sd2(state_dict):
+    """
+    Detects whether unet in state_dict is using v-parameterization. Returns True if it is. You're welcome.
+    """
+
+    import ldm.modules.diffusionmodules.openaimodel
+    from modules import devices
+
+    device = devices.cpu
+
+    with sd_disable_initialization.DisableInitialization():
+        unet = ldm.modules.diffusionmodules.openaimodel.UNetModel(
+            use_checkpoint=True,
+            use_fp16=False,
+            image_size=32,
+            in_channels=4,
+            out_channels=4,
+            model_channels=320,
+            attention_resolutions=[4, 2, 1],
+            num_res_blocks=2,
+            channel_mult=[1, 2, 4, 4],
+            num_head_channels=64,
+            use_spatial_transformer=True,
+            use_linear_in_transformer=True,
+            transformer_depth=1,
+            context_dim=1024,
+            legacy=False
+        )
+        unet.eval()
+
+    with torch.no_grad():
+        unet_sd = {k.replace("model.diffusion_model.", ""): v for k, v in state_dict.items() if "model.diffusion_model." in k}
+        unet.load_state_dict(unet_sd, strict=True)
+        unet.to(device=device, dtype=torch.float)
+
+        test_cond = torch.ones((1, 2, 1024), device=device) * 0.5
+        x_test = torch.ones((1, 4, 8, 8), device=device) * 0.5
+
+        out = (unet(x_test, torch.asarray([999], device=device), context=test_cond) - x_test).mean().item()
+
+    return out < -1
+
+
+def guess_model_config_from_state_dict(sd, filename):
+    sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None)
+    diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None)
+    sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None)
+
+    if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None:
+        return config_depth_model
+    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768:
+        return config_unclip
+    elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024:
+        return config_unopenclip
+
+    if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024:
+        if diffusion_model_input.shape[1] == 9:
+            return config_sd2_inpainting
+        elif is_using_v_parameterization_for_sd2(sd):
+            return config_sd2v
+        else:
+            return config_sd2
+
+    if diffusion_model_input is not None:
+        if diffusion_model_input.shape[1] == 9:
+            return config_inpainting
+        if diffusion_model_input.shape[1] == 8:
+            return config_instruct_pix2pix
+
+    if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
+        return config_alt_diffusion
+
+    return config_default
+
+
+def find_checkpoint_config(state_dict, info):
+    if info is None:
+        return guess_model_config_from_state_dict(state_dict, "")
+
+    config = find_checkpoint_config_near_filename(info)
+    if config is not None:
+        return config
+
+    return guess_model_config_from_state_dict(state_dict, info.filename)
+
+
+def find_checkpoint_config_near_filename(info):
+    if info is None:
+        return None
+
+    config = os.path.splitext(info.filename)[0] + ".yaml"
+    if os.path.exists(config):
+        return config
+
+    return None
+
@@ -1,53 +1,11 @@
-from collections import namedtuple, deque
-import numpy as np
-from math import floor
-import torch
-import tqdm
-from PIL import Image
-import inspect
-import k_diffusion.sampling
-import torchsde._brownian.brownian_interval
-import ldm.models.diffusion.ddim
-import ldm.models.diffusion.plms
-from modules import prompt_parser, devices, processing, images, sd_vae_approx
+from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared

-from modules.shared import opts, cmd_opts, state
-import modules.shared as shared
-from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
-
-
-SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
-
-samplers_k_diffusion = [
-    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
-    ('Euler', 'sample_euler', ['k_euler'], {}),
-    ('LMS', 'sample_lms', ['k_lms'], {}),
-    ('Heun', 'sample_heun', ['k_heun'], {}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
-    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
-    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
-    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
-    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
-    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
-    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
-    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
-    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
-    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
-]
-
-samplers_data_k_diffusion = [
-    SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
-    for label, funcname, aliases, options in samplers_k_diffusion
-    if hasattr(k_diffusion.sampling, funcname)
-]
+# imports for functions that previously were here and are used by other modules
+from modules.sd_samplers_common import samples_to_image_grid, sample_to_image

 all_samplers = [
-    *samplers_data_k_diffusion,
-    SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
-    SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+    *sd_samplers_kdiffusion.samplers_data_k_diffusion,
+    *sd_samplers_compvis.samplers_data_compvis,
 ]
 all_samplers_map = {x.name: x for x in all_samplers}

@@ -73,8 +31,8 @@ def create_sampler(name, model):
 def set_samplers():
    global samplers, samplers_for_img2img

-    hidden = set(opts.hide_samplers)
-    hidden_img2img = set(opts.hide_samplers + ['PLMS'])
+    hidden = set(shared.opts.hide_samplers)
+    hidden_img2img = set(shared.opts.hide_samplers + ['PLMS', 'UniPC'])

    samplers = [x for x in all_samplers if x.name not in hidden]
    samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
@@ -87,466 +45,3 @@ def set_samplers():


 set_samplers()
-
-sampler_extra_params = {
-    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-
-def setup_img2img_steps(p, steps=None):
-    if opts.img2img_fix_steps or steps is not None:
-        requested_steps = (steps or p.steps)
-        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
-        t_enc = requested_steps - 1
-    else:
-        steps = p.steps
-        t_enc = int(min(p.denoising_strength, 0.999) * steps)
-
-    return steps, t_enc
-
-
-approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
-
-
-def single_sample_to_image(sample, approximation=None):
-    if approximation is None:
-        approximation = approximation_indexes.get(opts.show_progress_type, 0)
-
-    if approximation == 2:
-        x_sample = sd_vae_approx.cheap_approximation(sample)
-    elif approximation == 1:
-        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
-    else:
-        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
-
-    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-    x_sample = x_sample.astype(np.uint8)
-    return Image.fromarray(x_sample)
-
-
-def sample_to_image(samples, index=0, approximation=None):
-    return single_sample_to_image(samples[index], approximation)
-
-
-def samples_to_image_grid(samples, approximation=None):
-    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
-
-
-def store_latent(decoded):
-    state.current_latent = decoded
-
-    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
-        if not shared.parallel_processing_allowed:
-            shared.state.assign_current_image(sample_to_image(decoded))
-
-
-class InterruptedException(BaseException):
-    pass
-
-
-class VanillaStableDiffusionSampler:
-    def __init__(self, constructor, sd_model):
-        self.sampler = constructor(sd_model)
-        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
-        self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.sampler_noises = None
-        self.step = 0
-        self.stop_at = None
-        self.eta = None
-        self.default_eta = 0.0
-        self.config = None
-        self.last_latent = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def number_of_needed_noises(self, p):
-        return 0
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except InterruptedException:
-            return self.last_latent
-
-    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
-        if state.interrupted or state.skipped:
-            raise InterruptedException
-
-        if self.stop_at is not None and self.step > self.stop_at:
-            raise InterruptedException
-
-        # Have to unwrap the inpainting conditioning here to perform pre-processing
-        image_conditioning = None
-        if isinstance(cond, dict):
-            image_conditioning = cond["c_concat"][0]
-            cond = cond["c_crossattn"][0]
-            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
-
-        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
-        cond = tensor
-
-        # for DDIM, shapes must match, we can't just process cond and uncond independently;
-        # filling unconditional_conditioning with repeats of the last vector to match length is
-        # not 100% correct but should work well enough
-        if unconditional_conditioning.shape[1] < cond.shape[1]:
-            last_vector = unconditional_conditioning[:, -1:]
-            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
-            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
-        elif unconditional_conditioning.shape[1] > cond.shape[1]:
-            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
-
-        if self.mask is not None:
-            img_orig = self.sampler.model.q_sample(self.init_latent, ts)
-            x_dec = img_orig * self.mask + self.nmask * x_dec
-
-        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
-        # Note that they need to be lists because it just concatenates them later.
-        if image_conditioning is not None:
-            cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
-            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
-
-        if self.mask is not None:
-            self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
-        else:
-            self.last_latent = res[1]
-
-        store_latent(self.last_latent)
-
-        self.step += 1
-        state.sampling_step = self.step
-        shared.total_tqdm.update()
-
-        return res
-
-    def initialize(self, p):
-        self.eta = p.eta if p.eta is not None else opts.eta_ddim
-
-        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
-            if hasattr(self.sampler, fieldname):
-                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
-
-        self.mask = p.mask if hasattr(p, 'mask') else None
-        self.nmask = p.nmask if hasattr(p, 'nmask') else None
-
-    def adjust_steps_if_invalid(self, p, num_steps):
-        if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
-            valid_step = 999 / (1000 // num_steps)
-            if valid_step == floor(valid_step):
-                return int(valid_step) + 1
-        
-        return num_steps
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = setup_img2img_steps(p, steps)
-        steps = self.adjust_steps_if_invalid(p, steps)
-        self.initialize(p)
-
-        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
-        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
-
-        self.init_latent = x
-        self.last_latent = x
-        self.step = 0
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        if image_conditioning is not None:
-            conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
-            unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        self.initialize(p)
-
-        self.init_latent = None
-        self.last_latent = x
-        self.step = 0
-
-        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
-
-        # Wrap the conditioning models with additional image conditioning for inpainting model
-        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
-        if image_conditioning is not None:
-            conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
-            unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
-
-        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
-
-        return samples_ddim
-
-
-class CFGDenoiser(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.step = 0
-
-    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
-        denoised_uncond = x_out[-uncond.shape[0]:]
-        denoised = torch.clone(denoised_uncond)
-
-        for i, conds in enumerate(conds_list):
-            for cond_index, weight in conds:
-                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
-
-        return denoised
-
-    def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
-        if state.interrupted or state.skipped:
-            raise InterruptedException
-
-        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
-        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
-
-        batch_size = len(conds_list)
-        repeats = [len(conds_list[i]) for i in range(batch_size)]
-
-        x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
-        image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
-        sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
-
-        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
-        cfg_denoiser_callback(denoiser_params)
-        x_in = denoiser_params.x
-        image_cond_in = denoiser_params.image_cond
-        sigma_in = denoiser_params.sigma
-
-        if tensor.shape[1] == uncond.shape[1]:
-            cond_in = torch.cat([tensor, uncond])
-
-            if shared.batch_cond_uncond:
-                x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
-            else:
-                x_out = torch.zeros_like(x_in)
-                for batch_offset in range(0, x_out.shape[0], batch_size):
-                    a = batch_offset
-                    b = a + batch_size
-                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]})
-        else:
-            x_out = torch.zeros_like(x_in)
-            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
-            for batch_offset in range(0, tensor.shape[0], batch_size):
-                a = batch_offset
-                b = min(a + batch_size, tensor.shape[0])
-                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]})
-
-            x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
-
-        devices.test_for_nans(x_out, "unet")
-
-        if opts.live_preview_content == "Prompt":
-            store_latent(x_out[0:uncond.shape[0]])
-        elif opts.live_preview_content == "Negative prompt":
-            store_latent(x_out[-uncond.shape[0]:])
-
-        denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
-
-        if self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
-
-        self.step += 1
-
-        return denoised
-
-
-class TorchHijack:
-    def __init__(self, sampler_noises):
-        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
-        # implementation.
-        self.sampler_noises = deque(sampler_noises)
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
-
-    def randn_like(self, x):
-        if self.sampler_noises:
-            noise = self.sampler_noises.popleft()
-            if noise.shape == x.shape:
-                return noise
-
-        if x.device.type == 'mps':
-            return torch.randn_like(x, device=devices.cpu).to(x.device)
-        else:
-            return torch.randn_like(x)
-
-
-# MPS fix for randn in torchsde
-def torchsde_randn(size, dtype, device, seed):
-    if device.type == 'mps':
-        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
-        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
-    else:
-        generator = torch.Generator(device).manual_seed(int(seed))
-        return torch.randn(size, dtype=dtype, device=device, generator=generator)
-
-
-torchsde._brownian.brownian_interval._randn = torchsde_randn
-
-
-class KDiffusionSampler:
-    def __init__(self, funcname, sd_model):
-        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
-        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
-        self.funcname = funcname
-        self.func = getattr(k_diffusion.sampling, self.funcname)
-        self.extra_params = sampler_extra_params.get(funcname, [])
-        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.default_eta = 1.0
-        self.config = None
-        self.last_latent = None
-
-        self.conditioning_key = sd_model.model.conditioning_key
-
-    def callback_state(self, d):
-        step = d['i']
-        latent = d["denoised"]
-        if opts.live_preview_content == "Combined":
-            store_latent(latent)
-        self.last_latent = latent
-
-        if self.stop_at is not None and step > self.stop_at:
-            raise InterruptedException
-
-        state.sampling_step = step
-        shared.total_tqdm.update()
-
-    def launch_sampling(self, steps, func):
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p):
-        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
-        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
-        self.model_wrap.step = 0
-        self.eta = p.eta or opts.eta_ancestral
-
-        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
-
-        if 'eta' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['eta'] = self.eta
-
-        return extra_params_kwargs
-
-    def get_sigmas(self, p, steps):
-        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
-        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
-            discard_next_to_last_sigma = True
-            p.extra_generation_params["Discard penultimate sigma"] = True
-
-        steps += 1 if discard_next_to_last_sigma else 0
-
-        if p.sampler_noise_scheduler_override:
-            sigmas = p.sampler_noise_scheduler_override(steps)
-        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
-        else:
-            sigmas = self.model_wrap.get_sigmas(steps)
-
-        if discard_next_to_last_sigma:
-            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
-        return sigmas
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps, t_enc = setup_img2img_steps(p, steps)
-
-        sigmas = self.get_sigmas(p, steps)
-
-        sigma_sched = sigmas[steps - t_enc - 1:]
-        xi = x + noise * sigma_sched[0]
-        
-        extra_params_kwargs = self.initialize(p)
-        if 'sigma_min' in inspect.signature(self.func).parameters:
-            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
-            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
-        if 'sigma_max' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['sigma_max'] = sigma_sched[0]
-        if 'n' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['n'] = len(sigma_sched) - 1
-        if 'sigma_sched' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['sigma_sched'] = sigma_sched
-        if 'sigmas' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['sigmas'] = sigma_sched
-
-        self.model_wrap_cfg.init_latent = x
-        self.last_latent = x
-
-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale
-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
-        steps = steps or p.steps
-
-        sigmas = self.get_sigmas(p, steps)
-
-        x = x * sigmas[0]
-
-        extra_params_kwargs = self.initialize(p)
-        if 'sigma_min' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
-            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
-            if 'n' in inspect.signature(self.func).parameters:
-                extra_params_kwargs['n'] = steps
-        else:
-            extra_params_kwargs['sigmas'] = sigmas
-
-        self.last_latent = x
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
-            'cond': conditioning, 
-            'image_cond': image_conditioning, 
-            'uncond': unconditional_conditioning, 
-            'cond_scale': p.cfg_scale
-        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        return samples
-
@@ -0,0 +1,72 @@
+from collections import namedtuple
+import numpy as np
+import torch
+from PIL import Image
+from modules import devices, processing, images, sd_vae_approx
+
+from modules.shared import opts, state
+import modules.shared as shared
+
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
+
+
+def setup_img2img_steps(p, steps=None):
+    if opts.img2img_fix_steps or steps is not None:
+        requested_steps = (steps or p.steps)
+        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
+        t_enc = requested_steps - 1
+    else:
+        steps = p.steps
+        t_enc = int(min(p.denoising_strength, 0.999) * steps)
+
+    return steps, t_enc
+
+
+approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2}
+
+
+def single_sample_to_image(sample, approximation=None):
+    if approximation is None:
+        approximation = approximation_indexes.get(opts.show_progress_type, 0)
+
+    if approximation == 2:
+        x_sample = sd_vae_approx.cheap_approximation(sample)
+    elif approximation == 1:
+        x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
+    else:
+        x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
+
+    x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+    x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
+    x_sample = x_sample.astype(np.uint8)
+    return Image.fromarray(x_sample)
+
+
+def sample_to_image(samples, index=0, approximation=None):
+    return single_sample_to_image(samples[index], approximation)
+
+
+def samples_to_image_grid(samples, approximation=None):
+    return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
+
+
+def store_latent(decoded):
+    state.current_latent = decoded
+
+    if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
+        if not shared.parallel_processing_allowed:
+            shared.state.assign_current_image(sample_to_image(decoded))
+
+
+class InterruptedException(BaseException):
+    pass
+
+
+if opts.randn_source == "CPU":
+    import torchsde._brownian.brownian_interval
+
+    def torchsde_randn(size, dtype, device, seed):
+        generator = torch.Generator(devices.cpu).manual_seed(int(seed))
+        return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
+
+    torchsde._brownian.brownian_interval._randn = torchsde_randn
@@ -0,0 +1,220 @@
+import math
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+
+import numpy as np
+import torch
+
+from modules.shared import state
+from modules import sd_samplers_common, prompt_parser, shared
+import modules.models.diffusion.uni_pc
+
+
+samplers_data_compvis = [
+    sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
+    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
+    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {}),
+]
+
+
+class VanillaStableDiffusionSampler:
+    def __init__(self, constructor, sd_model):
+        self.sampler = constructor(sd_model)
+        self.is_ddim = hasattr(self.sampler, 'p_sample_ddim')
+        self.is_plms = hasattr(self.sampler, 'p_sample_plms')
+        self.is_unipc = isinstance(self.sampler, modules.models.diffusion.uni_pc.UniPCSampler)
+        self.orig_p_sample_ddim = None
+        if self.is_plms:
+            self.orig_p_sample_ddim = self.sampler.p_sample_plms
+        elif self.is_ddim:
+            self.orig_p_sample_ddim = self.sampler.p_sample_ddim
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.sampler_noises = None
+        self.step = 0
+        self.stop_at = None
+        self.eta = None
+        self.config = None
+        self.last_latent = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def number_of_needed_noises(self, p):
+        return 0
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except sd_samplers_common.InterruptedException:
+            return self.last_latent
+
+    def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
+        x_dec, ts, cond, unconditional_conditioning = self.before_sample(x_dec, ts, cond, unconditional_conditioning)
+
+        res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
+
+        x_dec, ts, cond, unconditional_conditioning, res = self.after_sample(x_dec, ts, cond, unconditional_conditioning, res)
+
+        return res
+
+    def before_sample(self, x, ts, cond, unconditional_conditioning):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        if self.stop_at is not None and self.step > self.stop_at:
+            raise sd_samplers_common.InterruptedException
+
+        # Have to unwrap the inpainting conditioning here to perform pre-processing
+        image_conditioning = None
+        uc_image_conditioning = None
+        if isinstance(cond, dict):
+            if self.conditioning_key == "crossattn-adm":
+                image_conditioning = cond["c_adm"]
+                uc_image_conditioning = unconditional_conditioning["c_adm"]
+            else:
+                image_conditioning = cond["c_concat"][0]
+            cond = cond["c_crossattn"][0]
+            unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
+
+        assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
+        cond = tensor
+
+        # for DDIM, shapes must match, we can't just process cond and uncond independently;
+        # filling unconditional_conditioning with repeats of the last vector to match length is
+        # not 100% correct but should work well enough
+        if unconditional_conditioning.shape[1] < cond.shape[1]:
+            last_vector = unconditional_conditioning[:, -1:]
+            last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
+            unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
+        elif unconditional_conditioning.shape[1] > cond.shape[1]:
+            unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
+
+        if self.mask is not None:
+            img_orig = self.sampler.model.q_sample(self.init_latent, ts)
+            x = img_orig * self.mask + self.nmask * x
+
+        # Wrap the image conditioning back up since the DDIM code can accept the dict directly.
+        # Note that they need to be lists because it just concatenates them later.
+        if image_conditioning is not None:
+            if self.conditioning_key == "crossattn-adm":
+                cond = {"c_adm": image_conditioning, "c_crossattn": [cond]}
+                unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]}
+            else:
+                cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
+                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+        return x, ts, cond, unconditional_conditioning
+
+    def update_step(self, last_latent):
+        if self.mask is not None:
+            self.last_latent = self.init_latent * self.mask + self.nmask * last_latent
+        else:
+            self.last_latent = last_latent
+
+        sd_samplers_common.store_latent(self.last_latent)
+
+        self.step += 1
+        state.sampling_step = self.step
+        shared.total_tqdm.update()
+
+    def after_sample(self, x, ts, cond, uncond, res):
+        if not self.is_unipc:
+            self.update_step(res[1])
+
+        return x, ts, cond, uncond, res
+
+    def unipc_after_update(self, x, model_x):
+        self.update_step(x)
+
+    def initialize(self, p):
+        self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+        if self.eta != 0.0:
+            p.extra_generation_params["Eta DDIM"] = self.eta
+
+        if self.is_unipc:
+            keys = [
+                ('UniPC variant', 'uni_pc_variant'),
+                ('UniPC skip type', 'uni_pc_skip_type'),
+                ('UniPC order', 'uni_pc_order'),
+                ('UniPC lower order final', 'uni_pc_lower_order_final'),
+            ]
+
+            for name, key in keys:
+                v = getattr(shared.opts, key)
+                if v != shared.opts.get_default(key):
+                    p.extra_generation_params[name] = v
+
+        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+            if hasattr(self.sampler, fieldname):
+                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+        if self.is_unipc:
+            self.sampler.set_hooks(lambda x, t, c, u: self.before_sample(x, t, c, u), lambda x, t, c, u, r: self.after_sample(x, t, c, u, r), lambda x, mx: self.unipc_after_update(x, mx))
+
+        self.mask = p.mask if hasattr(p, 'mask') else None
+        self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
+
+    def adjust_steps_if_invalid(self, p, num_steps):
+        if ((self.config.name == 'DDIM') and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS') or (self.config.name == 'UniPC'):
+            if self.config.name == 'UniPC' and num_steps < shared.opts.uni_pc_order:
+                num_steps = shared.opts.uni_pc_order
+            valid_step = 999 / (1000 // num_steps)
+            if valid_step == math.floor(valid_step):
+                return int(valid_step) + 1
+
+        return num_steps
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+        steps = self.adjust_steps_if_invalid(p, steps)
+        self.initialize(p)
+
+        self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
+        x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
+
+        self.init_latent = x
+        self.last_latent = x
+        self.step = 0
+
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        if image_conditioning is not None:
+            if self.conditioning_key == "crossattn-adm":
+                conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]}
+                unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]}
+            else:
+                conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
+                unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning))
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        self.initialize(p)
+
+        self.init_latent = None
+        self.last_latent = x
+        self.step = 0
+
+        steps = self.adjust_steps_if_invalid(p, steps or p.steps)
+
+        # Wrap the conditioning models with additional image conditioning for inpainting model
+        # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
+        if image_conditioning is not None:
+            if self.conditioning_key == "crossattn-adm":
+                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning}
+                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)}
+            else:
+                conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
+                unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
+
+        samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
+
+        return samples_ddim
@@ -0,0 +1,386 @@
+from collections import deque
+import torch
+import inspect
+import einops
+import k_diffusion.sampling
+from modules import prompt_parser, devices, sd_samplers_common
+
+from modules.shared import opts, state
+import modules.shared as shared
+from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
+from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
+
+samplers_k_diffusion = [
+    ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {}),
+    ('Euler', 'sample_euler', ['k_euler'], {}),
+    ('LMS', 'sample_lms', ['k_lms'], {}),
+    ('Heun', 'sample_heun', ['k_heun'], {}),
+    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
+    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
+    ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
+    ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
+    ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
+    ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
+    ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras'}),
+]
+
+samplers_data_k_diffusion = [
+    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
+    for label, funcname, aliases, options in samplers_k_diffusion
+    if hasattr(k_diffusion.sampling, funcname)
+]
+
+sampler_extra_params = {
+    'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+    'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
+}
+
+
+class CFGDenoiser(torch.nn.Module):
+    """
+    Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
+    that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
+    instead of one. Originally, the second prompt is just an empty string, but we use non-empty
+    negative prompt.
+    """
+
+    def __init__(self, model):
+        super().__init__()
+        self.inner_model = model
+        self.mask = None
+        self.nmask = None
+        self.init_latent = None
+        self.step = 0
+        self.image_cfg_scale = None
+
+    def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
+        denoised_uncond = x_out[-uncond.shape[0]:]
+        denoised = torch.clone(denoised_uncond)
+
+        for i, conds in enumerate(conds_list):
+            for cond_index, weight in conds:
+                denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
+
+        return denoised
+
+    def combine_denoised_for_edit_model(self, x_out, cond_scale):
+        out_cond, out_img_cond, out_uncond = x_out.chunk(3)
+        denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
+
+        return denoised
+
+    def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
+        if state.interrupted or state.skipped:
+            raise sd_samplers_common.InterruptedException
+
+        # at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
+        # so is_edit_model is set to False to support AND composition.
+        is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
+
+        conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
+        uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
+
+        assert not is_edit_model or all([len(conds) == 1 for conds in conds_list]), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
+
+        batch_size = len(conds_list)
+        repeats = [len(conds_list[i]) for i in range(batch_size)]
+
+        if shared.sd_model.model.conditioning_key == "crossattn-adm":
+            image_uncond = torch.zeros_like(image_cond)
+            make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} 
+        else:
+            image_uncond = image_cond
+            make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} 
+
+        if not is_edit_model:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
+        else:
+            x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
+            sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
+            image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
+
+        denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
+        cfg_denoiser_callback(denoiser_params)
+        x_in = denoiser_params.x
+        image_cond_in = denoiser_params.image_cond
+        sigma_in = denoiser_params.sigma
+        tensor = denoiser_params.text_cond
+        uncond = denoiser_params.text_uncond
+        skip_uncond = False
+
+        # alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
+        if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
+            skip_uncond = True
+            x_in = x_in[:-batch_size]
+            sigma_in = sigma_in[:-batch_size]
+
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+            if is_edit_model:
+                cond_in = torch.cat([tensor, uncond, uncond])
+            elif skip_uncond:
+                cond_in = tensor
+            else:
+                cond_in = torch.cat([tensor, uncond])
+
+            if shared.batch_cond_uncond:
+                x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in))
+            else:
+                x_out = torch.zeros_like(x_in)
+                for batch_offset in range(0, x_out.shape[0], batch_size):
+                    a = batch_offset
+                    b = a + batch_size
+                    x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b]))
+        else:
+            x_out = torch.zeros_like(x_in)
+            batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
+            for batch_offset in range(0, tensor.shape[0], batch_size):
+                a = batch_offset
+                b = min(a + batch_size, tensor.shape[0])
+
+                if not is_edit_model:
+                    c_crossattn = [tensor[a:b]]
+                else:
+                    c_crossattn = torch.cat([tensor[a:b]], uncond)
+
+                x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
+
+            if not skip_uncond:
+                x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:]))
+
+        denoised_image_indexes = [x[0][0] for x in conds_list]
+        if skip_uncond:
+            fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
+            x_out = torch.cat([x_out, fake_uncond])  # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
+
+        denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps)
+        cfg_denoised_callback(denoised_params)
+
+        devices.test_for_nans(x_out, "unet")
+
+        if opts.live_preview_content == "Prompt":
+            sd_samplers_common.store_latent(torch.cat([x_out[i:i+1] for i in denoised_image_indexes]))
+        elif opts.live_preview_content == "Negative prompt":
+            sd_samplers_common.store_latent(x_out[-uncond.shape[0]:])
+
+        if is_edit_model:
+            denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
+        elif skip_uncond:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
+        else:
+            denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
+
+        if self.mask is not None:
+            denoised = self.init_latent * self.mask + self.nmask * denoised
+
+        self.step += 1
+        return denoised
+
+
+class TorchHijack:
+    def __init__(self, sampler_noises):
+        # Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
+        # implementation.
+        self.sampler_noises = deque(sampler_noises)
+
+    def __getattr__(self, item):
+        if item == 'randn_like':
+            return self.randn_like
+
+        if hasattr(torch, item):
+            return getattr(torch, item)
+
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
+
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        if opts.randn_source == "CPU" or x.device.type == 'mps':
+            return torch.randn_like(x, device=devices.cpu).to(x.device)
+        else:
+            return torch.randn_like(x)
+
+
+class KDiffusionSampler:
+    def __init__(self, funcname, sd_model):
+        denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
+
+        self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
+        self.funcname = funcname
+        self.func = getattr(k_diffusion.sampling, self.funcname)
+        self.extra_params = sampler_extra_params.get(funcname, [])
+        self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
+        self.sampler_noises = None
+        self.stop_at = None
+        self.eta = None
+        self.config = None
+        self.last_latent = None
+        self.s_min_uncond = None
+
+        self.conditioning_key = sd_model.model.conditioning_key
+
+    def callback_state(self, d):
+        step = d['i']
+        latent = d["denoised"]
+        if opts.live_preview_content == "Combined":
+            sd_samplers_common.store_latent(latent)
+        self.last_latent = latent
+
+        if self.stop_at is not None and step > self.stop_at:
+            raise sd_samplers_common.InterruptedException
+
+        state.sampling_step = step
+        shared.total_tqdm.update()
+
+    def launch_sampling(self, steps, func):
+        state.sampling_steps = steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except sd_samplers_common.InterruptedException:
+            return self.last_latent
+
+    def number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, p):
+        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
+        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
+        self.model_wrap_cfg.step = 0
+        self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
+        self.eta = p.eta if p.eta is not None else opts.eta_ancestral
+        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
+
+        k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
+
+        extra_params_kwargs = {}
+        for param_name in self.extra_params:
+            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
+                extra_params_kwargs[param_name] = getattr(p, param_name)
+
+        if 'eta' in inspect.signature(self.func).parameters:
+            if self.eta != 1.0:
+                p.extra_generation_params["Eta"] = self.eta
+
+            extra_params_kwargs['eta'] = self.eta
+
+        return extra_params_kwargs
+
+    def get_sigmas(self, p, steps):
+        discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
+        if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
+            discard_next_to_last_sigma = True
+            p.extra_generation_params["Discard penultimate sigma"] = True
+
+        steps += 1 if discard_next_to_last_sigma else 0
+
+        if p.sampler_noise_scheduler_override:
+            sigmas = p.sampler_noise_scheduler_override(steps)
+        elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
+        else:
+            sigmas = self.model_wrap.get_sigmas(steps)
+
+        if discard_next_to_last_sigma:
+            sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+
+        return sigmas
+
+    def create_noise_sampler(self, x, sigmas, p):
+        """For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
+        if shared.opts.no_dpmpp_sde_batch_determinism:
+            return None
+
+        from k_diffusion.sampling import BrownianTreeNoiseSampler
+        sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+        current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
+        return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
+
+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
+
+        sigmas = self.get_sigmas(p, steps)
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
+        xi = x + noise * sigma_sched[0]
+        
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
+            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
+            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
+        if 'sigma_max' in parameters:
+            extra_params_kwargs['sigma_max'] = sigma_sched[0]
+        if 'n' in parameters:
+            extra_params_kwargs['n'] = len(sigma_sched) - 1
+        if 'sigma_sched' in parameters:
+            extra_params_kwargs['sigma_sched'] = sigma_sched
+        if 'sigmas' in parameters:
+            extra_params_kwargs['sigmas'] = sigma_sched
+
+        if self.funcname == 'sample_dpmpp_sde':
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
+        self.model_wrap_cfg.init_latent = x
+        self.last_latent = x
+        extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }
+
+        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        return samples
+
+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
+        steps = steps or p.steps
+
+        sigmas = self.get_sigmas(p, steps)
+
+        x = x * sigmas[0]
+
+        extra_params_kwargs = self.initialize(p)
+        parameters = inspect.signature(self.func).parameters
+
+        if 'sigma_min' in parameters:
+            extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
+            extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
+            if 'n' in parameters:
+                extra_params_kwargs['n'] = steps
+        else:
+            extra_params_kwargs['sigmas'] = sigmas
+
+        if self.funcname == 'sample_dpmpp_sde':
+            noise_sampler = self.create_noise_sampler(x, sigmas, p)
+            extra_params_kwargs['noise_sampler'] = noise_sampler
+
+        self.last_latent = x
+        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
+            'cond': conditioning, 
+            'image_cond': image_conditioning, 
+            'uncond': unconditional_conditioning, 
+            'cond_scale': p.cfg_scale,
+            's_min_uncond': self.s_min_uncond
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
+
+        return samples
+
@@ -3,13 +3,12 @@ import safetensors.torch
 import os
 import collections
 from collections import namedtuple
-from modules import shared, devices, script_callbacks, sd_models
-from modules.paths import models_path
+from modules import paths, shared, devices, script_callbacks, sd_models
 import glob
 from copy import deepcopy


-vae_path = os.path.abspath(os.path.join(models_path, "VAE"))
+vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE"))
 vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
 vae_dict = {}

@@ -35,8 +35,11 @@ def model():
    global sd_vae_approx_model

    if sd_vae_approx_model is None:
+        model_path = os.path.join(paths.models_path, "VAE-approx", "model.pt")
        sd_vae_approx_model = VAEApprox()
-        sd_vae_approx_model.load_state_dict(torch.load(os.path.join(paths.models_path, "VAE-approx", "model.pt"), map_location='cpu' if devices.device.type != 'cuda' else None))
+        if not os.path.exists(model_path):
+            model_path = os.path.join(paths.script_path, "models", "VAE-approx", "model.pt")
+        sd_vae_approx_model.load_state_dict(torch.load(model_path, map_location='cpu' if devices.device.type != 'cuda' else None))
        sd_vae_approx_model.eval()
        sd_vae_approx_model.to(devices.device, devices.dtype)

@@ -4,6 +4,7 @@ import json
 import os
 import sys
 import time
+import requests

 from PIL import Image
 import gradio as gr
@@ -13,101 +14,21 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading, errors, ui_components
-from modules.paths import models_path, script_path, sd_path
-
+from modules import localization, script_loading, errors, ui_components, shared_items, cmd_args
+from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir

 demo = None

-sd_default_config = os.path.join(script_path, "configs/v1-inference.yaml")
-sd_model_file = os.path.join(script_path, 'model.ckpt')
-default_sd_model_file = sd_model_file
+parser = cmd_args.parser

-parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
-parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
-parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
-parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")
-parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
-parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
-parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
-parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
-parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
-parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
-parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--textual-inversion-templates-dir", type=str, default=os.path.join(script_path, 'textual_inversion_templates'), help="directory with textual inversion templates")
-parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
-parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory")
-parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
-parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
-parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
-parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
-parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram")
-parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
-parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
-parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
-parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
-parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
-parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")
-parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
-parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
-parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
-parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN'))
-parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN'))
-parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
-parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
-parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
-parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
-parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
-parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
-parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
-parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the percentage of VRAM threshold for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
-parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
-parser.add_argument("--disable-nan-check", action='store_true', help="do not check if produced images/latent spaces have nans; useful for running without a checkpoint in CI")
-parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower)
-parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
-parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
-parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False)
-parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json'))
-parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False)
-parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False)
-parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
-parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
-parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
-parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
-parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
-parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
-parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
-parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None)
-parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
-parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
-parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False)
-parser.add_argument('--vae-path', type=str, help='Checkpoint to use as VAE; setting this argument disables all settings related to VAE', default=None)
-parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
-parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
-parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
-parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
-parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
-parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
-parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
-parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None)
-parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None)
-parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)
-parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)
-parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)
-parser.add_argument("--gradio-queue", action='store_true', help="Uses gradio queue; experimental option; breaks restart UI button")
+script_loading.preload_extensions(extensions_dir, parser)
+script_loading.preload_extensions(extensions_builtin_dir, parser)

+if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
+    cmd_opts = parser.parse_args()
+else:
+    cmd_opts, _ = parser.parse_known_args()

-script_loading.preload_extensions(extensions.extensions_dir, parser)
-script_loading.preload_extensions(extensions.extensions_builtin_dir, parser)
-
-cmd_opts = parser.parse_args()

 restricted_opts = {
    "samples_filename_pattern",
@@ -119,20 +40,37 @@ restricted_opts = {
    "outdir_grids",
    "outdir_txt2img_grids",
    "outdir_save",
+    "outdir_init_images"
 }

 ui_reorder_categories = [
    "inpaint",
    "sampler",
+    "checkboxes",
+    "hires_fix",
    "dimensions",
    "cfg",
    "seed",
-    "checkboxes",
-    "hires_fix",
    "batch",
+    "override_settings",
    "scripts",
 ]

+# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
+gradio_hf_hub_themes = [
+    "gradio/glass",
+    "gradio/monochrome",
+    "gradio/seafoam",
+    "gradio/soft",
+    "freddyaboulton/dracula_revamped",
+    "gradio/dracula_test",
+    "abidlabs/dracula_test",
+    "abidlabs/pakistan",
+    "dawood/microsoft_windows",
+    "ysharma/steampunk"
+]
+
+
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access

 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -263,12 +201,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")

 face_restorers = []

-
-def realesrgan_models_names():
-    import modules.realesrgan_model
-    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
-
-
 class OptionInfo:
    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
        self.default = default
@@ -303,6 +235,7 @@ def list_samplers():


 hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+tab_names = []

 options_templates = {}

@@ -324,13 +257,19 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
    "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
+    "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
+    "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
    "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-    "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
+    "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
+    "export_for_4chan": OptionInfo(True, "If the saved image file size is above the limit, or its either width or height are above the limit, save a downscaled copy as JPG"),
+    "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
+    "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
+    "img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number),

-    "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
+    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
    "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
-    "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
+    "save_init_img": OptionInfo(False, "Save init images when using img2img"),

    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
@@ -346,25 +285,28 @@ options_templates.update(options_section(('saving-paths', "Paths for saving"), {
    "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
    "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
    "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
+    "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
 }))

 options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
-    "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
+    "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
+    "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
-    "directories_filename_pattern": OptionInfo("", "Directory name pattern", component_args=hide_dirs),
+    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
 }))

 options_templates.update(options_section(('upscaling', "Upscaling"), {
    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
 }))

 options_templates.update(options_section(('face-restoration', "Face restoration"), {
-    "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
+    "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
    "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
 }))
@@ -396,7 +338,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
    "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
    "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
@@ -408,13 +350,16 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
    "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
-    "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+    "randn_source": OptionInfo("GPU", "Random number generator source. Changes seeds drastically. Use CPU to produce the same picture across different vidocard vendors.", gr.Radio, {"choices": ["GPU", "CPU"]}),
 }))

 options_templates.update(options_section(('compatibility', "Compatibility"), {
    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
+    "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
+    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
 }))

 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -433,15 +378,22 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 }))

 options_templates.update(options_section(('extra_networks', "Extra Networks"), {
-    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, { "choices": ["cards", "thumbs"] }),
+    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
+    "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
+    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
+    "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
 }))

 options_templates.update(options_section(('ui', "User interface"), {
    "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
+    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
-    "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
    "font": OptionInfo("", "Font for image grids that have text"),
@@ -452,10 +404,13 @@ options_templates.update(options_section(('ui', "User interface"), {
    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
+    "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
    "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
    "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
    "localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
+    "gradio_theme": OptionInfo("Default", "Gradio theme (requires restart)", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes})
 }))

 options_templates.update(options_section(('ui', "Live previews"), {
@@ -474,19 +429,27 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
    "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
+    'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
+    'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
+    'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
+    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
 }))

 options_templates.update(options_section(('postprocessing', "Postprocessing"), {
-    'postprocessing_scipts_order': OptionInfo("upscale, gfpgan, codeformer", "Postprocessing operation order"),
+    'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
+    'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
    'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
 }))

 options_templates.update(options_section((None, "Hidden options"), {
-    "disabled_extensions": OptionInfo([], "Disable those extensions"),
+    "disabled_extensions": OptionInfo([], "Disable these extensions"),
+    "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
+    "restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
 }))

@@ -551,6 +514,15 @@ class Options:

        return True

+    def get_default(self, key):
+        """returns the default value for the key"""
+
+        data_label = self.data_labels.get(key)
+        if data_label is None:
+            return None
+
+        return data_label.default
+
    def save(self, filename):
        assert not cmd_opts.freeze_settings, "saving settings is disabled"

@@ -605,11 +577,37 @@ class Options:

        self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])}

+    def cast_value(self, key, value):
+        """casts an arbitrary to the same type as this setting's value with key
+        Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
+        """
+
+        if value is None:
+            return None
+
+        default_value = self.data_labels[key].default
+        if default_value is None:
+            default_value = getattr(self, key, None)
+        if default_value is None:
+            return None
+
+        expected_type = type(default_value)
+        if expected_type == bool and value == "False":
+            value = False
+        else:
+            value = expected_type(value)
+
+        return value
+
+

 opts = Options()
 if os.path.exists(config_filename):
    opts.load(config_filename)

+settings_components = None
+"""assinged from ui.py, a mapping on setting anmes to gradio components repsponsible for those settings"""
+
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
    "Latent": {"mode": "bilinear", "antialias": False},
@@ -628,6 +626,24 @@ clip_model = None

 progress_print_out = sys.stdout

+gradio_theme = gr.themes.Base()
+
+
+def reload_gradio_theme(theme_name=None):
+    global gradio_theme
+    if not theme_name:
+        theme_name = opts.gradio_theme
+
+    if theme_name == "Default":
+        gradio_theme = gr.themes.Default()
+    else:
+        try:
+            gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+        except requests.exceptions.ConnectionError:
+            print("Can't access HuggingFace Hub, falling back to default Gradio theme")
+            gradio_theme = gr.themes.Default()
+
+

 class TotalTQDM:
    def __init__(self):
@@ -657,6 +673,7 @@ class TotalTQDM:

    def clear(self):
        if self._tqdm is not None:
+            self._tqdm.refresh()
            self._tqdm.close()
            self._tqdm = None

@@ -668,7 +685,7 @@ mem_mon.start()


 def listfiles(dirname):
-    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
    return [file for file in filenames if os.path.isfile(file)]


@@ -0,0 +1,23 @@
+
+
+def realesrgan_models_names():
+    import modules.realesrgan_model
+    return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)]
+
+
+def postprocessing_scripts():
+    import modules.scripts
+
+    return modules.scripts.scripts_postproc.scripts
+
+
+def sd_vae_items():
+    import modules.sd_vae
+
+    return ["Automatic", "None"] + list(modules.sd_vae.vae_dict)
+
+
+def refresh_vae_list():
+    import modules.sd_vae
+
+    modules.sd_vae.refresh_vae_list()
@@ -72,16 +72,14 @@ class StyleDatabase:
        return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])

    def save_styles(self, path: str) -> None:
-        # Write to temporary file first, so we don't nuke the file if something goes wrong
-        fd, temp_path = tempfile.mkstemp(".csv")
+        # Always keep a backup file around
+        if os.path.exists(path):
+            shutil.copy(path, path + ".bak")
+
+        fd = os.open(path, os.O_RDWR|os.O_CREAT)
        with os.fdopen(fd, "w", encoding="utf-8-sig", newline='') as file:
            # _fields is actually part of the public API: typing.NamedTuple is a replacement for collections.NamedTuple,
            # and collections.NamedTuple has explicit documentation for accessing _fields. Same goes for _asdict()
            writer = csv.DictWriter(file, fieldnames=PromptStyle._fields)
            writer.writeheader()
            writer.writerows(style._asdict() for k,     style in self.styles.items())
-
-        # Always keep a backup file around
-        if os.path.exists(path):
-            shutil.move(path, path + ".bak")
-        shutil.move(temp_path, path)
@@ -67,7 +67,7 @@ def _summarize_chunk(
    max_score, _ = torch.max(attn_weights, -1, keepdim=True)
    max_score = max_score.detach()
    exp_weights = torch.exp(attn_weights - max_score)
-    exp_values = torch.bmm(exp_weights, value)
+    exp_values = torch.bmm(exp_weights, value) if query.device.type == 'mps' else torch.bmm(exp_weights, value.to(exp_weights.dtype)).to(value.dtype)
    max_score = max_score.squeeze(-1)
    return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)

@@ -129,7 +129,7 @@ def _get_attention_scores_no_kv_chunking(
    )
    attn_probs = attn_scores.softmax(dim=-1)
    del attn_scores
-    hidden_states_slice = torch.bmm(attn_probs, value)
+    hidden_states_slice = torch.bmm(attn_probs, value) if query.device.type == 'mps' else torch.bmm(attn_probs, value.to(attn_probs.dtype)).to(value.dtype)
    return hidden_states_slice


@@ -19,9 +19,10 @@ re_numbers_at_start = re.compile(r"^[-\d]+\s*")


 class DatasetEntry:
-    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None):
+    def __init__(self, filename=None, filename_text=None, latent_dist=None, latent_sample=None, cond=None, cond_text=None, pixel_values=None, weight=None):
        self.filename = filename
        self.filename_text = filename_text
+        self.weight = weight
        self.latent_dist = latent_dist
        self.latent_sample = latent_sample
        self.cond = cond
@@ -30,7 +31,7 @@ class DatasetEntry:


 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):
        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None

        self.placeholder_token = placeholder_token
@@ -56,10 +57,16 @@ class PersonalizedBase(Dataset):

        print("Preparing dataset...")
        for path in tqdm.tqdm(self.image_paths):
+            alpha_channel = None
            if shared.state.interrupted:
                raise Exception("interrupted")
            try:
-                image = Image.open(path).convert('RGB')
+                image = Image.open(path)
+                #Currently does not work for single color transparency
+                #We would need to read image.info['transparency'] for that
+                if use_weight and 'A' in image.getbands():
+                    alpha_channel = image.getchannel('A')
+                image = image.convert('RGB')
                if not varsize:
                    image = image.resize((width, height), PIL.Image.BICUBIC)
            except Exception:
@@ -87,17 +94,35 @@ class PersonalizedBase(Dataset):
            with devices.autocast():
                latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))

-            if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):
-                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-                latent_sampling_method = "once"
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
-            elif latent_sampling_method == "deterministic":
-                # Works only for DiagonalGaussianDistribution
-                latent_dist.std = 0
-                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample)
-            elif latent_sampling_method == "random":
-                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist)
+            #Perform latent sampling, even for random sampling.
+            #We need the sample dimensions for the weights
+            if latent_sampling_method == "deterministic":
+                if isinstance(latent_dist, DiagonalGaussianDistribution):
+                    # Works only for DiagonalGaussianDistribution
+                    latent_dist.std = 0
+                else:
+                    latent_sampling_method = "once"
+            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+
+            if use_weight and alpha_channel is not None:
+                channels, *latent_size = latent_sample.shape
+                weight_img = alpha_channel.resize(latent_size)
+                npweight = np.array(weight_img).astype(np.float32)
+                #Repeat for every channel in the latent sample
+                weight = torch.tensor([npweight] * channels).reshape([channels] + latent_size)
+                #Normalize the weight to a minimum of 0 and a mean of 1, that way the loss will be comparable to default.
+                weight -= weight.min()
+                weight /= weight.mean()
+            elif use_weight:
+                #If an image does not have a alpha channel, add a ones weight map anyway so we can stack it later
+                weight = torch.ones(latent_sample.shape)
+            else:
+                weight = None
+            
+            if latent_sampling_method == "random":
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)
+            else:
+                entry = DatasetEntry(filename=path, filename_text=filename_text, latent_sample=latent_sample, weight=weight)

            if not (self.tag_drop_out != 0 or self.shuffle_tags):
                entry.cond_text = self.create_text(filename_text)
@@ -110,6 +135,7 @@ class PersonalizedBase(Dataset):
            del torchdata
            del latent_dist
            del latent_sample
+            del weight

        self.length = len(self.dataset)
        self.groups = list(groups.values())
@@ -195,6 +221,10 @@ class BatchLoader:
        self.cond_text = [entry.cond_text for entry in data]
        self.cond = [entry.cond for entry in data]
        self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+        if all(entry.weight is not None for entry in data):
+            self.weight = torch.stack([entry.weight for entry in data]).squeeze(1)
+        else:
+            self.weight = None
        #self.emb_index = [entry.emb_index for entry in data]
        #print(self.latent_sample.device)

@@ -6,13 +6,12 @@ import sys
 import tqdm
 import time

-from modules import shared, images, deepbooru
-from modules.paths import models_path
+from modules import paths, shared, images, deepbooru
 from modules.shared import opts, cmd_opts
 from modules.textual_inversion import autocrop


-def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
    try:
        if process_caption:
            shared.interrogator.load()
@@ -20,7 +19,7 @@ def preprocess(id_task, process_src, process_dst, process_width, process_height,
        if process_caption_deepbooru:
            deepbooru.model.start()

-        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)
+        preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold)

    finally:

@@ -132,7 +131,7 @@ def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, thr
    return wh and center_crop(image, *wh)
    

-def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
+def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):
    width = process_width
    height = process_height
    src = os.path.abspath(process_src)
@@ -162,7 +161,9 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
        params.subindex = 0
        filename = os.path.join(src, imagefile)
        try:
-            img = Image.open(filename).convert("RGB")
+            img = Image.open(filename)
+            img = ImageOps.exif_transpose(img)
+            img = img.convert("RGB")
        except Exception:
            continue

@@ -199,7 +200,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre

            dnn_model_path = None
            try:
-                dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv"))
+                dnn_model_path = autocrop.download_and_cache_models(os.path.join(paths.models_path, "opencv"))
            except Exception as e:
                print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e)

@@ -224,6 +225,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
                print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)")
            process_default_resize = False

+        if process_keep_original_size:
+            save_pic(img, index, params, existing_caption=existing_caption)
+            process_default_resize = False
+
        if process_default_resize:
            img = images.resize_image(1, img, width, height)
            save_pic(img, index, params, existing_caption=existing_caption)
@@ -112,6 +112,7 @@ class EmbeddingDatabase:
        self.skipped_embeddings = {}
        self.expected_shape = -1
        self.embedding_dirs = {}
+        self.previously_displayed_embeddings = ()

    def add_embedding_dir(self, path):
        self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
@@ -151,7 +152,11 @@ class EmbeddingDatabase:
                name = data.get('name', name)
            else:
                data = extract_image_data_embed(embed_image)
-                name = data.get('name', name)
+                if data:
+                    name = data.get('name', name)
+                else:
+                    # if data is None, means this is not an embeding, just a preview image
+                    return
        elif ext in ['.BIN', '.PT']:
            data = torch.load(path, map_location="cpu")
        elif ext in ['.SAFETENSORS']:
@@ -194,7 +199,7 @@ class EmbeddingDatabase:
        if not os.path.isdir(embdir.path):
            return

-        for root, dirs, fns in os.walk(embdir.path):
+        for root, dirs, fns in os.walk(embdir.path, followlinks=True):
            for fn in fns:
                try:
                    fullfn = os.path.join(root, fn)
@@ -228,9 +233,18 @@ class EmbeddingDatabase:
            self.load_from_dir(embdir)
            embdir.update()

-        print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
-        if len(self.skipped_embeddings) > 0:
-            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
+        # re-sort word_embeddings because load_from_dir may not load in alphabetic order.
+        # using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
+        sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
+        self.word_embeddings.clear()
+        self.word_embeddings.update(sorted_word_embeddings)
+
+        displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
+        if self.previously_displayed_embeddings != displayed_embeddings:
+            self.previously_displayed_embeddings = displayed_embeddings
+            print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
+            if len(self.skipped_embeddings) > 0:
+                print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")

    def find_embedding_at_position(self, tokens, offset):
        token = tokens[offset]
@@ -347,7 +361,7 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
        assert log_directory, "Log directory is empty"


-def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
    save_embedding_every = save_embedding_every or 0
    create_image_every = create_image_every or 0
    template_file = textual_inversion_templates.get(template_filename, None)
@@ -406,7 +420,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st

    pin_memory = shared.opts.pin_memory

-    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize)
+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method, varsize=varsize, use_weight=use_weight)

    if shared.opts.save_training_settings_to_txt:
        save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})
@@ -476,6 +490,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
            
                with devices.autocast():
                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
+                    if use_weight:
+                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                    c = shared.sd_model.cond_stage_model(batch.cond_text)

                    if is_training_inpainting_model:
@@ -486,7 +502,11 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                    else:
                        cond = c

-                    loss = shared.sd_model(x, cond)[0] / gradient_step
+                    if use_weight:
+                        loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
+                        del w
+                    else:
+                        loss = shared.sd_model.forward(x, cond)[0] / gradient_step
                    del x

                    _loss_step += loss.item()
@@ -0,0 +1,38 @@
+import time
+
+
+class Timer:
+    def __init__(self):
+        self.start = time.time()
+        self.records = {}
+        self.total = 0
+
+    def elapsed(self):
+        end = time.time()
+        res = end - self.start
+        self.start = end
+        return res
+
+    def record(self, category, extra_time=0):
+        e = self.elapsed()
+        if category not in self.records:
+            self.records[category] = 0
+
+        self.records[category] += e + extra_time
+        self.total += e + extra_time
+
+    def summary(self):
+        res = f"{self.total:.1f}s"
+
+        additions = [x for x in self.records.items() if x[1] >= 0.1]
+        if not additions:
+            return res
+
+        res += " ("
+        res += ", ".join([f"{category}: {time_taken:.1f}s" for category, time_taken in additions])
+        res += ")"
+
+        return res
+
+    def reset(self):
+        self.__init__()
@@ -1,5 +1,6 @@
 import modules.scripts
 from modules import sd_samplers
+from modules.generation_parameters_copypaste import create_override_settings_dict
 from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
    StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
@@ -8,7 +9,9 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html


-def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, *args):
+def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, override_settings_texts, *args):
+    override_settings = create_override_settings_dict(override_settings_texts)
+
    p = StableDiffusionProcessingTxt2Img(
        sd_model=shared.sd_model,
        outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -38,6 +41,7 @@ def txt2img(id_task: str, prompt: str, negative_prompt: str, prompt_styles, step
        hr_second_pass_steps=hr_second_pass_steps,
        hr_resize_x=hr_resize_x,
        hr_resize_y=hr_resize_y,
+        override_settings=override_settings,
    )

    p.scripts = modules.scripts.scripts_txt2img
@@ -19,9 +19,9 @@ import numpy as np
 from PIL import Image, PngImagePlugin
 from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call

-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
-from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
-from modules.paths import script_path
+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing, progress
+from modules.ui_components import FormRow, FormColumn, FormGroup, ToolButton, FormHTML
+from modules.paths import script_path, data_path

 from modules.shared import opts, cmd_opts, restricted_opts

@@ -70,17 +70,6 @@ def gr_show(visible=True):
 sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
 sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None

-css_hide_progressbar = """
-.wrap .m-12 svg { display:none!important; }
-.wrap .m-12::before { content:"Loading..." }
-.wrap .z-20 svg { display:none!important; }
-.wrap .z-20::before { content:"Loading..." }
-.wrap.cover-bg .z-20::before { content:"" }
-.progress-bar { display:none!important; }
-.meta-text { display:none!important; }
-.meta-text-center { display:none!important; }
-"""
-
 # Using constants for these since the variation selector isn't visible.
 # Important that they exactly match script.js for tooltip to work.
 random_symbol = '\U0001f3b2\ufe0f'  # 🎲️
@@ -89,8 +78,10 @@ paste_symbol = '\u2199\ufe0f'  # ↙
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 apply_style_symbol = '\U0001f4cb'  # 📋
-clear_prompt_symbol = '\U0001F5D1'  # 🗑️
+clear_prompt_symbol = '\U0001f5d1\ufe0f'  # 🗑️
 extra_networks_symbol = '\U0001F3B4'  # 🎴
+switch_values_symbol = '\U000021C5' # ⇅
+restore_progress_symbol = '\U0001F300' # 🌀


 def plaintext_to_html(text):
@@ -104,6 +95,9 @@ def send_gradio_gallery_to_image(x):

 def visit(x, func, path=""):
    if hasattr(x, 'children'):
+        if isinstance(x, gr.Tabs) and x.elem_id is not None:
+            # Tabs element can't have a label, have to use elem_id instead
+            func(f"{path}/Tabs@{x.elem_id}", x)
        for c in x.children:
            visit(c, func, path)
    elif x.label is not None:
@@ -137,6 +131,16 @@ def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resiz
    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"


+def resize_from_to_html(width, height, scale_by):
+    target_width = int(width * scale_by)
+    target_height = int(height * scale_by)
+
+    if not target_width or not target_height:
+        return "no image selected"
+
+    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
+
+
 def apply_styles(prompt, prompt_neg, styles):
    prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
    prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
@@ -178,14 +182,13 @@ def interrogate_deepbooru(image):


 def create_seed_inputs(target_interface):
-    with FormRow(elem_id=target_interface + '_seed_row'):
+    with FormRow(elem_id=target_interface + '_seed_row', variant="compact"):
        seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
        seed.style(container=False)
-        random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
-        reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
+        random_seed = ToolButton(random_symbol, elem_id=target_interface + '_random_seed', label='Random seed')
+        reuse_seed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_seed', label='Reuse seed')

-        with gr.Group(elem_id=target_interface + '_subseed_show_box'):
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
+        seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)

    # Components to show/hide based on the 'Extra' checkbox
    seed_extras = []
@@ -194,8 +197,8 @@ def create_seed_inputs(target_interface):
        seed_extras.append(seed_extra_row_1)
        subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
        subseed.style(container=False)
-        random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
-        reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
+        random_subseed = ToolButton(random_symbol, elem_id=target_interface + '_random_subseed')
+        reuse_subseed = ToolButton(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
        subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')

    with FormRow(visible=False) as seed_extra_row_2:
@@ -290,19 +293,19 @@ def create_toprow(is_img2img):
            with gr.Row():
                with gr.Column(scale=80):
                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)")

        button_interrogate = None
        button_deepbooru = None
        if is_img2img:
-            with gr.Column(scale=1, elem_id="interrogate_col"):
+            with gr.Column(scale=1, elem_classes="interrogate-col"):
                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
                button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")

        with gr.Column(scale=1, elem_id=f"{id_part}_actions_column"):
-            with gr.Row(elem_id=f"{id_part}_generate_box"):
-                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
-                skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
+            with gr.Row(elem_id=f"{id_part}_generate_box", elem_classes="generate-box"):
+                interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt", elem_classes="generate-box-interrupt")
+                skip = gr.Button('Skip', elem_id=f"{id_part}_skip", elem_classes="generate-box-skip")
                submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')

                skip.click(
@@ -323,10 +326,11 @@ def create_toprow(is_img2img):
                extra_networks_button = ToolButton(value=extra_networks_symbol, elem_id=f"{id_part}_extra_networks")
                prompt_style_apply = ToolButton(value=apply_style_symbol, elem_id=f"{id_part}_style_apply")
                save_style = ToolButton(value=save_style_symbol, elem_id=f"{id_part}_style_create")
+                restore_progress_button = ToolButton(value=restore_progress_symbol, elem_id=f"{id_part}_restore_progress", visible=False)

-                token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+                token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_token_counter", elem_classes=["token-counter"])
                token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-                negative_token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_negative_token_counter")
+                negative_token_counter = gr.HTML(value="<span>0/75</span>", elem_id=f"{id_part}_negative_token_counter", elem_classes=["token-counter"])
                negative_token_button = gr.Button(visible=False, elem_id=f"{id_part}_negative_token_button")

                clear_prompt_button.click(
@@ -340,7 +344,7 @@ def create_toprow(is_img2img):
                prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
                create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")

-    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button
+    return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button


 def setup_progressbar(*args, **kwargs):
@@ -379,6 +383,7 @@ def apply_setting(key, value):
    opts.save(shared.config_filename)
    return getattr(opts, key)

+
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
    def refresh():
        refresh_method()
@@ -432,6 +437,18 @@ def get_value_for_setting(key):
    return gr.update(value=value, **args)


+def create_override_settings_dropdown(tabname, row):
+    dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
+
+    dropdown.change(
+        fn=lambda x: gr.Dropdown.update(visible=len(x) > 0),
+        inputs=[dropdown],
+        outputs=[dropdown],
+    )
+
+    return dropdown
+
+
 def create_ui():
    import modules.img2img
    import modules.txt2img
@@ -444,7 +461,7 @@ def create_ui():
    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)

    with gr.Blocks(analytics_enabled=False) as txt2img_interface:
-        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=False)
+        txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=False)

        dummy_component = gr.Label(visible=False)
        txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="binary", visible=False)
@@ -465,6 +482,9 @@ def create_ui():
                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")

+                            with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", label="Switch dims")
+
                            if opts.dimensions_and_batch_together:
                                with gr.Column(elem_id="txt2img_column_batch"):
                                    batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
@@ -477,7 +497,7 @@ def create_ui():
                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')

                    elif category == "checkboxes":
-                        with FormRow(elem_id="txt2img_checkboxes", variant="compact"):
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
                            enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
@@ -501,6 +521,10 @@ def create_ui():
                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")

+                    elif category == "override_settings":
+                        with FormRow(elem_id="txt2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('txt2img', row)
+
                    elif category == "scripts":
                        with FormGroup(elem_id="txt2img_script_container"):
                            custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
@@ -522,7 +546,6 @@ def create_ui():
                )

            txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
-            parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)

            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -553,6 +576,7 @@ def create_ui():
                    hr_second_pass_steps,
                    hr_resize_x,
                    hr_resize_y,
+                    override_settings,
                ] + custom_inputs,

                outputs=[
@@ -567,6 +591,21 @@ def create_ui():
            txt2img_prompt.submit(**txt2img_args)
            submit.click(**txt2img_args)

+            res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressTxt2img",
+                inputs=[dummy_component],
+                outputs=[
+                    txt2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )
+
            txt_prompt_img.change(
                fn=modules.images.image_data,
                inputs=[
@@ -610,7 +649,10 @@ def create_ui():
                (hr_resize_y, "Hires resize-2"),
                *modules.scripts.scripts_txt2img.infotext_fields
            ]
-            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=txt2img_paste, tabname="txt2img", source_text_component=txt2img_prompt, source_image_component=None,
+            ))

            txt2img_preview_params = [
                txt2img_prompt,
@@ -632,7 +674,7 @@ def create_ui():
    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)

    with gr.Blocks(analytics_enabled=False) as img2img_interface:
-        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button = create_toprow(is_img2img=True)
+        img2img_prompt, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, extra_networks_button, token_counter, token_button, negative_token_counter, negative_token_button, restore_progress_button = create_toprow(is_img2img=True)

        img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)

@@ -659,6 +701,8 @@ def create_ui():
                            copy_image_buttons.append((button, name, elem))

                with gr.Tabs(elem_id="mode_img2img"):
+                    img2img_selected_tab = gr.State(0)
+
                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
                        add_copy_image_controls('img2img', init_img)
@@ -691,9 +735,21 @@ def create_ui():

                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
                        hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
-                        gr.HTML(f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
+                        gr.HTML(
+                            f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+                            f"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+                            f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
+                            f"{hidden}</p>"
+                        )
                        img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
                        img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
+                        img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
+
+                    img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
+                    img2img_image_inputs = [init_img, sketch, init_img_with_mask, inpaint_color_sketch]
+
+                    for i, tab in enumerate(img2img_tabs):
+                        tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])

                def copy_image(img):
                    if isinstance(img, dict) and 'image' in img:
@@ -724,8 +780,44 @@ def create_ui():
                    elif category == "dimensions":
                        with FormRow():
                            with gr.Column(elem_id="img2img_column_size", scale=4):
-                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
-                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+                                selected_scale_tab = gr.State(value=0)
+
+                                with gr.Tabs():
+                                    with gr.Tab(label="Resize to") as tab_scale_to:
+                                        with FormRow():
+                                            with gr.Column(elem_id="img2img_column_size", scale=4):
+                                                width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
+                                                height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
+                                            with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
+                                                res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn")
+
+                                    with gr.Tab(label="Resize by") as tab_scale_by:
+                                        scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.05, label="Scale", value=1.0, elem_id="img2img_scale")
+
+                                        with FormRow():
+                                            scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
+                                            gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
+                                            button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
+
+                                    on_change_args = dict(
+                                        fn=resize_from_to_html,
+                                        _js="currentImg2imgSourceResolution",
+                                        inputs=[dummy_component, dummy_component, scale_by],
+                                        outputs=scale_by_html,
+                                        show_progress=False,
+                                    )
+
+                                    scale_by.release(**on_change_args)
+                                    button_update_resize_to.click(**on_change_args)
+
+                                    # the code below is meant to update the resolution label after the image in the image selection UI has changed.
+                                    # as it is now the event keeps firing continuously for inpaint edits, which ruins the page with constant requests.
+                                    # I assume this must be a gradio bug and for now we'll just do it for non-inpaint inputs.
+                                    for component in [init_img, sketch]:
+                                        component.change(fn=lambda: None, _js="updateImg2imgResizeToTextAfterChangingImage", inputs=[], outputs=[], show_progress=False)
+
+                            tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
+                            tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])

                            if opts.dimensions_and_batch_together:
                                with gr.Column(elem_id="img2img_column_batch"):
@@ -734,14 +826,16 @@ def create_ui():

                    elif category == "cfg":
                        with FormGroup():
-                            cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                            with FormRow():
+                                cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
+                                image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit")
                            denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")

                    elif category == "seed":
                        seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')

                    elif category == "checkboxes":
-                        with FormRow(elem_id="img2img_checkboxes", variant="compact"):
+                        with FormRow(elem_classes="checkboxes-row", variant="compact"):
                            restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
                            tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")

@@ -751,6 +845,10 @@ def create_ui():
                                batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
                                batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")

+                    elif category == "override_settings":
+                        with FormRow(elem_id="img2img_override_settings_row") as row:
+                            override_settings = create_override_settings_dropdown('img2img', row)
+
                    elif category == "scripts":
                        with FormGroup(elem_id="img2img_script_container"):
                            custom_inputs = modules.scripts.scripts_img2img.setup_ui()
@@ -777,7 +875,7 @@ def create_ui():
                            def select_img2img_tab(tab):
                                return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),

-                            for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]):
+                            for i, elem in enumerate(img2img_tabs):
                                elem.select(
                                    fn=lambda tab=i: select_img2img_tab(tab),
                                    inputs=[],
@@ -785,7 +883,6 @@ def create_ui():
                                )

            img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples)
-            parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)

            connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
            connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -827,17 +924,22 @@ def create_ui():
                    batch_count,
                    batch_size,
                    cfg_scale,
+                    image_cfg_scale,
                    denoising_strength,
                    seed,
                    subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
+                    selected_scale_tab,
                    height,
                    width,
+                    scale_by,
                    resize_mode,
                    inpaint_full_res,
                    inpaint_full_res_padding,
                    inpainting_mask_invert,
                    img2img_batch_input_dir,
                    img2img_batch_output_dir,
+                    img2img_batch_inpaint_mask_dir,
+                    override_settings,
                ] + custom_inputs,
                outputs=[
                    img2img_gallery,
@@ -865,6 +967,20 @@ def create_ui():

            img2img_prompt.submit(**img2img_args)
            submit.click(**img2img_args)
+            res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
+
+            restore_progress_button.click(
+                fn=progress.restore_progress,
+                _js="restoreProgressImg2img",
+                inputs=[dummy_component],
+                outputs=[
+                    img2img_gallery,
+                    generation_info,
+                    html_info,
+                    html_log,
+                ],
+                show_progress=False,
+            )

            img2img_interrogate.click(
                fn=lambda *args: process_interrogate(interrogate, *args),
@@ -899,7 +1015,7 @@ def create_ui():
                )

            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
-            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[txt2img_negative_prompt, steps], outputs=[negative_token_counter])
+            negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[img2img_negative_prompt, steps], outputs=[negative_token_counter])

            ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)

@@ -910,6 +1026,7 @@ def create_ui():
                (sampler_index, "Sampler"),
                (restore_faces, "Face restoration"),
                (cfg_scale, "CFG scale"),
+                (image_cfg_scale, "Image CFG scale"),
                (seed, "Seed"),
                (width, "Size-1"),
                (height, "Size-2"),
@@ -922,8 +1039,11 @@ def create_ui():
                (mask_blur, "Mask blur"),
                *modules.scripts.scripts_img2img.infotext_fields
            ]
-            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
-            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields, override_settings)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields, override_settings)
+            parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                paste_button=img2img_paste, tabname="img2img", source_text_component=img2img_prompt, source_image_component=None,
+            ))

    modules.scripts.scripts_current = None

@@ -941,7 +1061,11 @@ def create_ui():
                html2 = gr.HTML()
                with gr.Row():
                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
-                parameters_copypaste.bind_buttons(buttons, image, generation_info)
+
+                for tabname, button in buttons.items():
+                    parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                        paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
+                    ))

        image.change(
            fn=wrap_gradio_call(modules.extras.run_pnginfo),
@@ -979,8 +1103,9 @@ def create_ui():
                interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])

                with FormRow():
-                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
+                    checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="safetensors", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
                    save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
+                    save_metadata = gr.Checkbox(value=True, label="Save metadata (.safetensors only)", elem_id="modelmerger_save_metadata")

                with FormRow():
                    with gr.Column():
@@ -1008,7 +1133,7 @@ def create_ui():
        with gr.Row(variant="compact").style(equal_height=False):
            with gr.Tabs(elem_id="train_tabs"):

-                with gr.Tab(label="Create embedding"):
+                with gr.Tab(label="Create embedding", id="create_embedding"):
                    new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
                    initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
                    nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
@@ -1021,7 +1146,7 @@ def create_ui():
                        with gr.Column():
                            create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")

-                with gr.Tab(label="Create hypernetwork"):
+                with gr.Tab(label="Create hypernetwork", id="create_hypernetwork"):
                    new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
                    new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
@@ -1039,7 +1164,7 @@ def create_ui():
                        with gr.Column():
                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")

-                with gr.Tab(label="Preprocess images"):
+                with gr.Tab(label="Preprocess images", id="preprocess_images"):
                    process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
                    process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
                    process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
@@ -1047,6 +1172,7 @@ def create_ui():
                    preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")

                    with gr.Row():
+                        process_keep_original_size = gr.Checkbox(label='Keep original size', elem_id="train_process_keep_original_size")
                        process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
                        process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
                        process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
@@ -1106,7 +1232,7 @@ def create_ui():
                def get_textual_inversion_template_names():
                    return sorted([x for x in textual_inversion.textual_inversion_templates])

-                with gr.Tab(label="Train"):
+                with gr.Tab(label="Train", id="train"):
                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
                    with FormRow():
                        train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
@@ -1143,6 +1269,8 @@ def create_ui():
                        create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
                        save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")

+                    use_weight = gr.Checkbox(label="Use PNG alpha channel as loss weight", value=False, elem_id="use_weight")
+
                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
                    preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")

@@ -1162,7 +1290,7 @@ def create_ui():

            with gr.Column(elem_id='ti_gallery_container'):
                ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
+                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
                ti_progress = gr.HTML(elem_id="ti_progress", value="")
                ti_outcome = gr.HTML(elem_id="ti_error", value="")

@@ -1211,6 +1339,7 @@ def create_ui():
                process_width,
                process_height,
                preprocess_txt_action,
+                process_keep_original_size,
                process_flip,
                process_split,
                process_caption,
@@ -1256,6 +1385,7 @@ def create_ui():
                shuffle_tags,
                tag_drop_out,
                latent_sampling_method,
+                use_weight,
                create_image_every,
                save_embedding_every,
                template_file,
@@ -1289,6 +1419,7 @@ def create_ui():
                shuffle_tags,
                tag_drop_out,
                latent_sampling_method,
+                use_weight,
                create_image_every,
                save_embedding_every,
                template_file,
@@ -1350,6 +1481,7 @@ def create_ui():

    components = []
    component_dict = {}
+    shared.settings_components = component_dict

    script_callbacks.ui_settings_callback()
    opts.reorder()
@@ -1434,15 +1566,37 @@ def create_ui():
                current_row.__exit__()
                current_tab.__exit__()

-            with gr.TabItem("Actions"):
+            with gr.TabItem("Actions", id="actions"):
                request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
                download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
                reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
+                with gr.Row():
+                    unload_sd_model = gr.Button(value='Unload SD checkpoint to free VRAM', elem_id="sett_unload_sd_model")
+                    reload_sd_model = gr.Button(value='Reload the last SD checkpoint back into VRAM', elem_id="sett_reload_sd_model")

-            with gr.TabItem("Licenses"):
+            with gr.TabItem("Licenses", id="licenses"):
                gr.HTML(shared.html("licenses.html"), elem_id="licenses")

            gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
+            
+
+        def unload_sd_weights():
+            modules.sd_models.unload_model_weights()
+
+        def reload_sd_weights():
+            modules.sd_models.reload_model_weights()
+
+        unload_sd_model.click(
+            fn=unload_sd_weights,
+            inputs=[],
+            outputs=[]
+        )
+
+        reload_sd_model.click(
+            fn=reload_sd_weights,
+            inputs=[],
+            outputs=[]
+        )

        request_notifications.click(
            fn=lambda: None,
@@ -1488,39 +1642,28 @@ def create_ui():
        (train_interface, "Train", "ti"),
    ]

-    css = ""
-
-    for cssfile in modules.scripts.list_files_with_name("style.css"):
-        if not os.path.isfile(cssfile):
-            continue
-
-        with open(cssfile, "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if os.path.exists(os.path.join(script_path, "user.css")):
-        with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
-            css += file.read() + "\n"
-
-    if not cmd_opts.no_progressbar_hiding:
-        css += css_hide_progressbar
-
    interfaces += script_callbacks.ui_tabs_callback()
    interfaces += [(settings_interface, "Settings", "settings")]

    extensions_interface = ui_extensions.create_ui()
    interfaces += [(extensions_interface, "Extensions", "extensions")]

-    with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
+    shared.tab_names = []
+    for _interface, label, _ifid in interfaces:
+        shared.tab_names.append(label)
+
+    with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
        with gr.Row(elem_id="quicksettings", variant="compact"):
            for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                component = create_setting_component(k, is_quicksettings=True)
                component_dict[k] = component

-        parameters_copypaste.integrate_settings_paste_fields(component_dict)
-        parameters_copypaste.run_bind()
+        parameters_copypaste.connect_paste_params_buttons()

        with gr.Tabs(elem_id="tabs") as tabs:
            for interface, label, ifid in interfaces:
+                if label in shared.opts.hidden_tabs:
+                    continue
                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
                    interface.render()

@@ -1540,13 +1683,30 @@ def create_ui():

        for i, k, item in quicksettings_list:
            component = component_dict[k]
+            info = opts.data_labels[k]

-            component.change(
+            change_handler = component.release if hasattr(component, 'release') else component.change
+            change_handler(
                fn=lambda value, k=k: run_settings_single(value, key=k),
                inputs=[component],
                outputs=[component, text_settings],
+                show_progress=info.refresh is not None,
            )

+        text_settings.change(
+            fn=lambda: gr.update(visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit"),
+            inputs=[],
+            outputs=[image_cfg_scale],
+        )
+
+        button_set_checkpoint = gr.Button('Change checkpoint', elem_id='change_checkpoint', visible=False)
+        button_set_checkpoint.click(
+            fn=lambda value, _: run_settings_single(value, key='sd_model_checkpoint'),
+            _js="function(v){ var res = desiredCheckpointName; desiredCheckpointName = ''; return [res || v, null]; }",
+            inputs=[component_dict['sd_model_checkpoint'], dummy_component],
+            outputs=[component_dict['sd_model_checkpoint'], text_settings],
+        )
+
        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]

        def get_settings_values():
@@ -1556,6 +1716,7 @@ def create_ui():
            fn=get_settings_values,
            inputs=[],
            outputs=[component_dict[k] for k in component_keys],
+            queue=False,
        )

        def modelmerger(*args):
@@ -1585,6 +1746,7 @@ def create_ui():
                config_source,
                bake_in_vae,
                discard_weights,
+                save_metadata,
            ],
            outputs=[
                primary_model_name,
@@ -1632,7 +1794,7 @@ def create_ui():
                if init_field is not None:
                    init_field(saved_value)

-        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown] and x.visible:
+        if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number, gr.Dropdown, ToolButton] and x.visible:
            apply_field(x, 'visible')

        if type(x) == gr.Slider:
@@ -1662,12 +1824,27 @@ def create_ui():

            apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))

+        def check_tab_id(tab_id):
+            tab_items = list(filter(lambda e: isinstance(e, gr.TabItem), x.children))
+            if type(tab_id) == str:
+                tab_ids = [t.id for t in tab_items]
+                return tab_id in tab_ids
+            elif type(tab_id) == int:
+                return tab_id >= 0 and tab_id < len(tab_items)
+            else:
+                return False
+
+        if type(x) == gr.Tabs:
+            apply_field(x, 'selected', check_tab_id)
+
    visit(txt2img_interface, loadsave, "txt2img")
    visit(img2img_interface, loadsave, "img2img")
    visit(extras_interface, loadsave, "extras")
    visit(modelmerger_interface, loadsave, "modelmerger")
    visit(train_interface, loadsave, "train")

+    loadsave(f"webui/Tabs@{tabs.elem_id}", tabs)
+
    if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
        with open(ui_config_file, "w", encoding="utf8") as file:
            json.dump(ui_settings, file, indent=4)
@@ -1678,21 +1855,60 @@ def create_ui():
    return demo


-def reload_javascript():
-    head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}"></script>\n'
+def webpath(fn):
+    if fn.startswith(script_path):
+        web_path = os.path.relpath(fn, script_path).replace('\\', '/')
+    else:
+        web_path = os.path.abspath(fn)
+
+    return f'file={web_path}?{os.path.getmtime(fn)}'
+
+
+def javascript_html():
+    script_js = os.path.join(script_path, "script.js")
+    head = f'<script type="text/javascript" src="{webpath(script_js)}"></script>\n'

    inline = f"{localization.localization_js(shared.opts.localization)};"
    if cmd_opts.theme is not None:
        inline += f"set_theme('{cmd_opts.theme}');"

    for script in modules.scripts.list_scripts("javascript", ".js"):
-        head += f'<script type="text/javascript" src="file={script.path}"></script>\n'
+        head += f'<script type="text/javascript" src="{webpath(script.path)}"></script>\n'
+
+    for script in modules.scripts.list_scripts("javascript", ".mjs"):
+        head += f'<script type="module" src="{webpath(script.path)}"></script>\n'

    head += f'<script type="text/javascript">{inline}</script>\n'

+    return head
+
+
+def css_html():
+    head = ""
+
+    def stylesheet(fn):
+        return f'<link rel="stylesheet" property="stylesheet" href="{webpath(fn)}">'
+
+    for cssfile in modules.scripts.list_files_with_name("style.css"):
+        if not os.path.isfile(cssfile):
+            continue
+
+        head += stylesheet(cssfile)
+
+    if os.path.exists(os.path.join(data_path, "user.css")):
+        head += stylesheet(os.path.join(data_path, "user.css"))
+
+    return head
+
+
+def reload_javascript():
+    js = javascript_html()
+    css = css_html()
+
    def template_response(*args, **kwargs):
        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))
+        res.body = res.body.replace(b'</head>', f'{js}</head>'.encode("utf8"))
+        res.body = res.body.replace(b'</body>', f'{css}</body>'.encode("utf8"))
        res.init_headers()
        return res

@@ -1720,7 +1936,7 @@ def versions_html():
    return f"""
 python: <span title="{sys.version}">{python_version}</span>
  • 
-torch: {torch.__version__}
+torch: {getattr(torch, '__long_version__',torch.__version__)}
  • 
 xformers: {xformers_version}
  • 
@@ -125,12 +125,12 @@ Requested path was: {f}

    with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
        with gr.Group(elem_id=f"{tabname}_gallery_container"):
-            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(columns=4)

        generation_info = None
        with gr.Column():
-            with gr.Row(elem_id=f"image_buttons_{tabname}"):
-                open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
+            with gr.Row(elem_id=f"image_buttons_{tabname}", elem_classes="image-buttons"):
+                open_folder_button = gr.Button(folder_symbol, visible=not shared.cmd_opts.hide_ui_dir_config)

                if tabname != "extras":
                    save = gr.Button('Save', elem_id=f'save_{tabname}')
@@ -145,11 +145,10 @@ Requested path was: {f}
            )

            if tabname != "extras":
-                with gr.Row():
-                    download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
+                download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')

                with gr.Group():
-                    html_info = gr.HTML(elem_id=f'html_info_{tabname}')
+                    html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
                    html_log = gr.HTML(elem_id=f'html_log_{tabname}')

                    generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
@@ -160,6 +159,7 @@ Requested path was: {f}
                            _js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
                            inputs=[generation_info, html_info, html_info],
                            outputs=[html_info, html_info],
+                            show_progress=False,
                        )

                    save.click(
@@ -195,8 +195,19 @@ Requested path was: {f}

            else:
                html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
-                html_info = gr.HTML(elem_id=f'html_info_{tabname}')
+                html_info = gr.HTML(elem_id=f'html_info_{tabname}', elem_classes="infotext")
                html_log = gr.HTML(elem_id=f'html_log_{tabname}')

-            parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+            paste_field_names = []
+            if tabname == "txt2img":
+                paste_field_names = modules.scripts.scripts_txt2img.paste_field_names
+            elif tabname == "img2img":
+                paste_field_names = modules.scripts.scripts_img2img.paste_field_names
+
+            for paste_tabname, paste_button in buttons.items():
+                parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
+                    paste_button=paste_button, tabname=paste_tabname, source_tabname="txt2img" if tabname == "txt2img" else None, source_image_component=result_gallery,
+                    paste_field_names=paste_field_names
+                ))
+
            return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info, html_log
@@ -1,50 +1,74 @@
 import gradio as gr


-class ToolButton(gr.Button, gr.components.FormComponent):
+class FormComponent:
+    def get_expected_parent(self):
+        return gr.components.Form
+
+
+gr.Dropdown.get_expected_parent = FormComponent.get_expected_parent
+
+
+class ToolButton(FormComponent, gr.Button):
    """Small button with single emoji as text, fits inside gradio forms"""

-    def __init__(self, **kwargs):
-        super().__init__(variant="tool", **kwargs)
+    def __init__(self, *args, **kwargs):
+        classes = kwargs.pop("elem_classes", [])
+        super().__init__(*args, elem_classes=["tool", *classes], **kwargs)

    def get_block_name(self):
        return "button"


-class ToolButtonTop(gr.Button, gr.components.FormComponent):
-    """Small button with single emoji as text, with extra margin at top, fits inside gradio forms"""
-
-    def __init__(self, **kwargs):
-        super().__init__(variant="tool-top", **kwargs)
-
-    def get_block_name(self):
-        return "button"
-
-
-class FormRow(gr.Row, gr.components.FormComponent):
+class FormRow(FormComponent, gr.Row):
    """Same as gr.Row but fits inside gradio forms"""

    def get_block_name(self):
        return "row"


-class FormGroup(gr.Group, gr.components.FormComponent):
+class FormColumn(FormComponent, gr.Column):
+    """Same as gr.Column but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "column"
+
+
+class FormGroup(FormComponent, gr.Group):
    """Same as gr.Row but fits inside gradio forms"""

    def get_block_name(self):
        return "group"


-class FormHTML(gr.HTML, gr.components.FormComponent):
+class FormHTML(FormComponent, gr.HTML):
    """Same as gr.HTML but fits inside gradio forms"""

    def get_block_name(self):
        return "html"


-class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
+class FormColorPicker(FormComponent, gr.ColorPicker):
    """Same as gr.ColorPicker but fits inside gradio forms"""

    def get_block_name(self):
        return "colorpicker"

+
+class DropdownMulti(FormComponent, gr.Dropdown):
+    """Same as gr.Dropdown but always multiselect"""
+    def __init__(self, **kwargs):
+        super().__init__(multiselect=True, **kwargs)
+
+    def get_block_name(self):
+        return "dropdown"
+
+
+class DropdownEditable(FormComponent, gr.Dropdown):
+    """Same as gr.Dropdown but allows editing value"""
+    def __init__(self, **kwargs):
+        super().__init__(allow_custom_value=True, **kwargs)
+
+    def get_block_name(self):
+        return "dropdown"
+
@@ -1,8 +1,8 @@
 import json
 import os.path
-import shutil
 import sys
 import time
+from datetime import datetime
 import traceback

 import git
@@ -12,17 +12,19 @@ import html
 import shutil
 import errno

-from modules import extensions, shared, paths
-
+from modules import extensions, shared, paths, config_states
+from modules.paths_internal import config_states_dir
+from modules.call_queue import wrap_gradio_gpu_call

 available_extensions = {"extensions": []}
+STYLE_PRIMARY = ' style="color: var(--primary-400)"'


 def check_access():
    assert not shared.cmd_opts.disable_extension_access, "extension access disabled because of command line flags"


-def apply_and_restart(disable_list, update_list):
+def apply_and_restart(disable_list, update_list, disable_all):
    check_access()

    disabled = json.loads(disable_list)
@@ -31,6 +33,9 @@ def apply_and_restart(disable_list, update_list):
    update = json.loads(update_list)
    assert type(update) == list, f"wrong update_list data for apply_and_restart: {update_list}"

+    if update:
+        save_config_state("Backup (pre-update)")
+
    update = set(update)

    for ext in extensions.extensions:
@@ -44,26 +49,87 @@ def apply_and_restart(disable_list, update_list):
            print(traceback.format_exc(), file=sys.stderr)

    shared.opts.disabled_extensions = disabled
+    shared.opts.disable_all_extensions = disable_all
    shared.opts.save(shared.config_filename)

    shared.state.interrupt()
    shared.state.need_restart = True


-def check_updates():
+def save_config_state(name):
+    current_config_state = config_states.get_config()
+    if not name:
+        name = "Config"
+    current_config_state["name"] = name
+    filename = os.path.join(config_states_dir, datetime.now().strftime("%Y_%m_%d-%H_%M_%S") + "_" + name + ".json")
+    print(f"Saving backup of webui/extension state to {filename}.")
+    with open(filename, "w", encoding="utf-8") as f:
+        json.dump(current_config_state, f)
+    config_states.list_config_states()
+    new_value = next(iter(config_states.all_config_states.keys()), "Current")
+    new_choices = ["Current"] + list(config_states.all_config_states.keys())
+    return gr.Dropdown.update(value=new_value, choices=new_choices), f"<span>Saved current webui/extension state to \"{filename}\"</span>"
+
+
+def restore_config_state(confirmed, config_state_name, restore_type):
+    if config_state_name == "Current":
+        return "<span>Select a config to restore from.</span>"
+    if not confirmed:
+        return "<span>Cancelled.</span>"
+
    check_access()

-    for ext in extensions.extensions:
-        if ext.remote is None:
-            continue
+    config_state = config_states.all_config_states[config_state_name]
+
+    print(f"*** Restoring webui state from backup: {restore_type} ***")
+
+    if restore_type == "extensions" or restore_type == "both":
+        shared.opts.restore_config_state_file = config_state["filepath"]
+        shared.opts.save(shared.config_filename)
+
+    if restore_type == "webui" or restore_type == "both":
+        config_states.restore_webui_config(config_state)
+
+    shared.state.interrupt()
+    shared.state.need_restart = True
+
+    return ""
+
+
+def check_updates(id_task, disable_list):
+    check_access()
+
+    disabled = json.loads(disable_list)
+    assert type(disabled) == list, f"wrong disable_list data for apply_and_restart: {disable_list}"
+
+    exts = [ext for ext in extensions.extensions if ext.remote is not None and ext.name not in disabled]
+    shared.state.job_count = len(exts)
+
+    for ext in exts:
+        shared.state.textinfo = ext.name

        try:
            ext.check_updates()
+        except FileNotFoundError as e:
+            if 'FETCH_HEAD' not in str(e):
+                raise
        except Exception:
            print(f"Error checking updates for {ext.name}:", file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)

-    return extension_table()
+        shared.state.nextjob()
+
+    return extension_table(), ""
+
+
+def make_commit_link(commit_hash, remote, text=None):
+    if text is None:
+        text = commit_hash[:8]
+    if remote.startswith("https://github.com/"):
+        href = os.path.join(remote, "commit", commit_hash)
+        return f'<a href="{href}" target="_blank">{text}</a>'
+    else:
+        return text


 def extension_table():
@@ -73,6 +139,7 @@ def extension_table():
            <tr>
                <th><abbr title="Use checkbox to enable the extension; it will be enabled or disabled when you click apply button">Extension</abbr></th>
                <th>URL</th>
+                <th><abbr title="Extension version">Version</abbr></th>
                <th><abbr title="Use checkbox to mark the extension for update; it will be updated when you click apply button">Update</abbr></th>
            </tr>
        </thead>
@@ -80,21 +147,28 @@ def extension_table():
    """

    for ext in extensions.extensions:
-        remote = ""
-        if ext.is_builtin:
-            remote = "built-in"
-        elif ext.remote:
-            remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""
+        ext.read_info_from_repo()
+
+        remote = f"""<a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape("built-in" if ext.is_builtin else ext.remote or '')}</a>"""

        if ext.can_update:
            ext_status = f"""<label><input class="gr-check-radio gr-checkbox" name="update_{html.escape(ext.name)}" checked="checked" type="checkbox">{html.escape(ext.status)}</label>"""
        else:
            ext_status = ext.status

+        style = ""
+        if shared.opts.disable_all_extensions == "extra" and not ext.is_builtin or shared.opts.disable_all_extensions == "all":
+            style = STYLE_PRIMARY
+
+        version_link = ext.version
+        if ext.commit_hash and ext.remote:
+            version_link = make_commit_link(ext.commit_hash, ext.remote, ext.version)
+
        code += f"""
            <tr>
-                <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
+                <td><label{style}><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>
                <td>{remote}</td>
+                <td>{version_link}</td>
                <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>
            </tr>
    """
@@ -107,6 +181,133 @@ def extension_table():
    return code


+def update_config_states_table(state_name):
+    if state_name == "Current":
+        config_state = config_states.get_config()
+    else:
+        config_state = config_states.all_config_states[state_name]
+
+    config_name = config_state.get("name", "Config")
+    created_date = time.asctime(time.gmtime(config_state["created_at"]))
+    filepath = config_state.get("filepath", "<unknown>")
+
+    code = f"""<!-- {time.time()} -->"""
+
+    webui_remote = config_state["webui"]["remote"] or ""
+    webui_branch = config_state["webui"]["branch"]
+    webui_commit_hash = config_state["webui"]["commit_hash"] or "<unknown>"
+    webui_commit_date = config_state["webui"]["commit_date"]
+    if webui_commit_date:
+        webui_commit_date = time.asctime(time.gmtime(webui_commit_date))
+    else:
+        webui_commit_date = "<unknown>"
+
+    remote = f"""<a href="{html.escape(webui_remote)}" target="_blank">{html.escape(webui_remote or '')}</a>"""
+    commit_link = make_commit_link(webui_commit_hash, webui_remote)
+    date_link = make_commit_link(webui_commit_hash, webui_remote, webui_commit_date)
+
+    current_webui = config_states.get_webui_config()
+
+    style_remote = ""
+    style_branch = ""
+    style_commit = ""
+    if current_webui["remote"] != webui_remote:
+        style_remote = STYLE_PRIMARY
+    if current_webui["branch"] != webui_branch:
+        style_branch = STYLE_PRIMARY
+    if current_webui["commit_hash"] != webui_commit_hash:
+        style_commit = STYLE_PRIMARY
+
+    code += f"""<h2>Config Backup: {config_name}</h2>
+      <div><b>Filepath:</b> {filepath}</div>
+      <div><b>Created at:</b> {created_date}</div>"""
+
+    code += f"""<h2>WebUI State</h2>
+      <table id="config_state_webui">
+        <thead>
+            <tr>
+                <th>URL</th>
+                <th>Branch</th>
+                <th>Commit</th>
+                <th>Date</th>
+            </tr>
+        </thead>
+        <tbody>
+            <tr>
+                <td><label{style_remote}>{remote}</label></td>
+                <td><label{style_branch}>{webui_branch}</label></td>
+                <td><label{style_commit}>{commit_link}</label></td>
+                <td><label{style_commit}>{date_link}</label></td>
+            </tr>
+        </tbody>
+      </table>
+    """
+
+    code += """<h2>Extension State</h2>
+      <table id="config_state_extensions">
+        <thead>
+            <tr>
+                <th>Extension</th>
+                <th>URL</th>
+                <th>Branch</th>
+                <th>Commit</th>
+                <th>Date</th>
+            </tr>
+        </thead>
+        <tbody>
+    """
+
+    ext_map = {ext.name: ext for ext in extensions.extensions}
+
+    for ext_name, ext_conf in config_state["extensions"].items():
+        ext_remote = ext_conf["remote"] or ""
+        ext_branch = ext_conf["branch"] or "<unknown>"
+        ext_enabled = ext_conf["enabled"]
+        ext_commit_hash = ext_conf["commit_hash"] or "<unknown>"
+        ext_commit_date = ext_conf["commit_date"]
+        if ext_commit_date:
+            ext_commit_date = time.asctime(time.gmtime(ext_commit_date))
+        else:
+            ext_commit_date = "<unknown>"
+
+        remote = f"""<a href="{html.escape(ext_remote)}" target="_blank">{html.escape(ext_remote or '')}</a>"""
+        commit_link = make_commit_link(ext_commit_hash, ext_remote)
+        date_link = make_commit_link(ext_commit_hash, ext_remote, ext_commit_date)
+
+        style_enabled = ""
+        style_remote = ""
+        style_branch = ""
+        style_commit = ""
+        if ext_name in ext_map:
+            current_ext = ext_map[ext_name]
+            current_ext.read_info_from_repo()
+            if current_ext.enabled != ext_enabled:
+                style_enabled = STYLE_PRIMARY
+            if current_ext.remote != ext_remote:
+                style_remote = STYLE_PRIMARY
+            if current_ext.branch != ext_branch:
+                style_branch = STYLE_PRIMARY
+            if current_ext.commit_hash != ext_commit_hash:
+                style_commit = STYLE_PRIMARY
+
+        code += f"""
+            <tr>
+                <td><label{style_enabled}><input class="gr-check-radio gr-checkbox" type="checkbox" disabled="true" {'checked="checked"' if ext_enabled else ''}>{html.escape(ext_name)}</label></td>
+                <td><label{style_remote}>{remote}</label></td>
+                <td><label{style_branch}>{ext_branch}</label></td>
+                <td><label{style_commit}>{commit_link}</label></td>
+                <td><label{style_commit}>{date_link}</label></td>
+            </tr>
+    """
+
+    code += """
+        </tbody>
+    </table>
+    """
+
+    return code
+
+
 def normalize_git_url(url):
    if url is None:
        return ""
@@ -115,7 +316,7 @@ def normalize_git_url(url):
    return url


-def install_extension_from_url(dirname, url):
+def install_extension_from_url(dirname, url, branch_name=None):
    check_access()

    assert url, 'No URL specified'
@@ -132,26 +333,31 @@ def install_extension_from_url(dirname, url):
    normalized_url = normalize_git_url(url)
    assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'

-    tmpdir = os.path.join(paths.script_path, "tmp", dirname)
+    tmpdir = os.path.join(paths.data_path, "tmp", dirname)

    try:
        shutil.rmtree(tmpdir, True)
-
-        repo = git.Repo.clone_from(url, tmpdir)
-        repo.remote().fetch()
-
+        if not branch_name:
+            # if no branch is specified, use the default branch
+            with git.Repo.clone_from(url, tmpdir) as repo:
+                repo.remote().fetch()
+                for submodule in repo.submodules:
+                    submodule.update()
+        else:
+            with git.Repo.clone_from(url, tmpdir, branch=branch_name) as repo:
+                repo.remote().fetch()
+                for submodule in repo.submodules:
+                    submodule.update()
        try:
            os.rename(tmpdir, target_dir)
        except OSError as err:
-            # TODO what does this do on windows? I think it'll be a different error code but I don't have a system to check it
-            # Shouldn't cause any new issues at least but we probably want to handle it there too.
            if err.errno == errno.EXDEV:
                # Cross device link, typical in docker or when tmp/ and extensions/ are on different file systems
                # Since we can't use a rename, do the slower but more versitile shutil.move()
                shutil.move(tmpdir, target_dir)
            else:
                # Something else, not enough free space, permissions, etc.  rethrow it so that it gets handled.
-                raise(err)
+                raise err

        import launch
        launch.run_extension_installer(target_dir)
@@ -162,12 +368,12 @@ def install_extension_from_url(dirname, url):
        shutil.rmtree(tmpdir, True)


-def install_extension_from_index(url, hide_tags, sort_column):
+def install_extension_from_index(url, hide_tags, sort_column, filter_text):
    ext_table, message = install_extension_from_url(None, url)

-    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
+    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)

-    return code, ext_table, message
+    return code, ext_table, message, ''


 def refresh_available_extensions(url, hide_tags, sort_column):
@@ -181,11 +387,17 @@ def refresh_available_extensions(url, hide_tags, sort_column):

    code, tags = refresh_available_extensions_from_data(hide_tags, sort_column)

-    return url, code, gr.CheckboxGroup.update(choices=tags), ''
+    return url, code, gr.CheckboxGroup.update(choices=tags), '', ''


-def refresh_available_extensions_for_tags(hide_tags, sort_column):
-    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column)
+def refresh_available_extensions_for_tags(hide_tags, sort_column, filter_text):
+    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)
+
+    return code, ''
+
+
+def search_extensions(filter_text, hide_tags, sort_column):
+    code, _ = refresh_available_extensions_from_data(hide_tags, sort_column, filter_text)

    return code, ''

@@ -200,7 +412,7 @@ sort_ordering = [
 ]


-def refresh_available_extensions_from_data(hide_tags, sort_column):
+def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text=""):
    extlist = available_extensions["extensions"]
    installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}

@@ -239,7 +451,12 @@ def refresh_available_extensions_from_data(hide_tags, sort_column):
            hidden += 1
            continue

-        install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
+        if filter_text and filter_text.strip():
+            if filter_text.lower() not in html.escape(name).lower() and filter_text.lower() not in html.escape(description).lower():
+                hidden += 1
+                continue
+
+        install_code = f"""<button onclick="install_extension_from_index(this, '{html.escape(url)}')" {"disabled=disabled" if existing else ""} class="lg secondary gradio-button custom-button">{"Install" if not existing else "Installed"}</button>"""

        tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])

@@ -269,36 +486,47 @@ def refresh_available_extensions_from_data(hide_tags, sort_column):
 def create_ui():
    import modules.ui

+    config_states.list_config_states()
+
    with gr.Blocks(analytics_enabled=False) as ui:
        with gr.Tabs(elem_id="tabs_extensions") as tabs:
-            with gr.TabItem("Installed"):
+            with gr.TabItem("Installed", id="installed"):

-                with gr.Row():
+                with gr.Row(elem_id="extensions_installed_top"):
                    apply = gr.Button(value="Apply and restart UI", variant="primary")
                    check = gr.Button(value="Check for updates")
+                    extensions_disable_all = gr.Radio(label="Disable all extensions", choices=["none", "extra", "all"], value=shared.opts.disable_all_extensions, elem_id="extensions_disable_all")
                    extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
                    extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)

+                html = ""
+                if shared.opts.disable_all_extensions != "none":
+                    html = """
+<span style="color: var(--primary-400);">
+    "Disable all extensions" was set, change it to "none" to load all extensions again
+</span>
+                    """
+                info = gr.HTML(html)
                extensions_table = gr.HTML(lambda: extension_table())

                apply.click(
                    fn=apply_and_restart,
                    _js="extensions_apply",
-                    inputs=[extensions_disabled_list, extensions_update_list],
+                    inputs=[extensions_disabled_list, extensions_update_list, extensions_disable_all],
                    outputs=[],
                )

                check.click(
-                    fn=check_updates,
+                    fn=wrap_gradio_gpu_call(check_updates, extra_outputs=[gr.update()]),
                    _js="extensions_check",
-                    inputs=[],
-                    outputs=[extensions_table],
+                    inputs=[info, extensions_disabled_list],
+                    outputs=[extensions_table, info],
                )

-            with gr.TabItem("Available"):
+            with gr.TabItem("Available", id="available"):
                with gr.Row():
                    refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
-                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
+                    available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui-extensions/master/index.json", label="Extension index URL").style(container=False)
                    extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
                    install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)

@@ -306,43 +534,77 @@ def create_ui():
                    hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
                    sort_column = gr.Radio(value="newest first", label="Order", choices=["newest first", "oldest first", "a-z", "z-a", "internal order", ], type="index")

+                with gr.Row(): 
+                    search_extensions_text = gr.Text(label="Search").style(container=False)
+                   
                install_result = gr.HTML()
                available_extensions_table = gr.HTML()

                refresh_available_extensions_button.click(
                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),
                    inputs=[available_extensions_index, hide_tags, sort_column],
-                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],
+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result, search_extensions_text],
                )

                install_extension_button.click(
                    fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
-                    inputs=[extension_to_install, hide_tags, sort_column],
+                    inputs=[extension_to_install, hide_tags, sort_column, search_extensions_text],
                    outputs=[available_extensions_table, extensions_table, install_result],
                )

+                search_extensions_text.change(
+                    fn=modules.ui.wrap_gradio_call(search_extensions, extra_outputs=[gr.update()]),
+                    inputs=[search_extensions_text, hide_tags, sort_column],
+                    outputs=[available_extensions_table, install_result],
+                )
+
                hide_tags.change(
                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
-                    inputs=[hide_tags, sort_column],
+                    inputs=[hide_tags, sort_column, search_extensions_text],
                    outputs=[available_extensions_table, install_result]
                )

                sort_column.change(
                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),
-                    inputs=[hide_tags, sort_column],
+                    inputs=[hide_tags, sort_column, search_extensions_text],
                    outputs=[available_extensions_table, install_result]
                )

-            with gr.TabItem("Install from URL"):
+            with gr.TabItem("Install from URL", id="install_from_url"):
                install_url = gr.Text(label="URL for extension's git repository")
+                install_branch = gr.Text(label="Specific branch name", placeholder="Leave empty for default main branch")
                install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
                install_button = gr.Button(value="Install", variant="primary")
                install_result = gr.HTML(elem_id="extension_install_result")

                install_button.click(
                    fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
-                    inputs=[install_dirname, install_url],
+                    inputs=[install_dirname, install_url, install_branch],
                    outputs=[extensions_table, install_result],
                )

+            with gr.TabItem("Backup/Restore"):
+                with gr.Row(elem_id="extensions_backup_top_row"):
+                    config_states_list = gr.Dropdown(label="Saved Configs", elem_id="extension_backup_saved_configs", value="Current", choices=["Current"] + list(config_states.all_config_states.keys()))
+                    modules.ui.create_refresh_button(config_states_list, config_states.list_config_states, lambda: {"choices": ["Current"] + list(config_states.all_config_states.keys())}, "refresh_config_states")
+                    config_restore_type = gr.Radio(label="State to restore", choices=["extensions", "webui", "both"], value="extensions", elem_id="extension_backup_restore_type")
+                    config_restore_button = gr.Button(value="Restore Selected Config", variant="primary", elem_id="extension_backup_restore")
+                with gr.Row(elem_id="extensions_backup_top_row2"):
+                    config_save_name = gr.Textbox("", placeholder="Config Name", show_label=False)
+                    config_save_button = gr.Button(value="Save Current Config")
+
+                config_states_info = gr.HTML("")
+                config_states_table = gr.HTML(lambda: update_config_states_table("Current"))
+
+                config_save_button.click(fn=save_config_state, inputs=[config_save_name], outputs=[config_states_list, config_states_info])
+
+                dummy_component = gr.Label(visible=False)
+                config_restore_button.click(fn=restore_config_state, _js="config_state_confirm_restore", inputs=[dummy_component, config_states_list, config_restore_type], outputs=[config_states_info])
+
+                config_states_list.change(
+                    fn=update_config_states_table,
+                    inputs=[config_states_list],
+                    outputs=[config_states_table],
+                )
+
    return ui
@@ -1,6 +1,11 @@
+import glob
 import os.path
+import urllib.parse
+from pathlib import Path
+from PIL import PngImagePlugin

 from modules import shared
+from modules.images import read_info_from_image
 import gradio as gr
 import json
 import html
@@ -8,12 +13,48 @@ import html
 from modules.generation_parameters_copypaste import image_from_url_text

 extra_pages = []
+allowed_dirs = set()


 def register_page(page):
    """registers extra networks page for the UI; recommend doing it in on_before_ui() callback for extensions"""

    extra_pages.append(page)
+    allowed_dirs.clear()
+    allowed_dirs.update(set(sum([x.allowed_directories_for_previews() for x in extra_pages], [])))
+
+
+def fetch_file(filename: str = ""):
+    from starlette.responses import FileResponse
+
+    if not any([Path(x).absolute() in Path(filename).absolute().parents for x in allowed_dirs]):
+        raise ValueError(f"File cannot be fetched: {filename}. Must be in one of directories registered by extra pages.")
+
+    ext = os.path.splitext(filename)[1].lower()
+    if ext not in (".png", ".jpg", ".webp"):
+        raise ValueError(f"File cannot be fetched: {filename}. Only png and jpg and webp.")
+
+    # would profit from returning 304
+    return FileResponse(filename, headers={"Accept-Ranges": "bytes"})
+
+
+def get_metadata(page: str = "", item: str = ""):
+    from starlette.responses import JSONResponse
+
+    page = next(iter([x for x in extra_pages if x.name == page]), None)
+    if page is None:
+        return JSONResponse({})
+
+    metadata = page.metadata.get(item)
+    if metadata is None:
+        return JSONResponse({})
+
+    return JSONResponse({"metadata": metadata})
+
+
+def add_pages_to_demo(app):
+    app.add_api_route("/sd_extra_networks/thumb", fetch_file, methods=["GET"])
+    app.add_api_route("/sd_extra_networks/metadata", get_metadata, methods=["GET"])


 class ExtraNetworksPage:
@@ -22,23 +63,73 @@ class ExtraNetworksPage:
        self.name = title.lower()
        self.card_page = shared.html("extra-networks-card.html")
        self.allow_negative_prompt = False
+        self.metadata = {}

    def refresh(self):
        pass

+    def link_preview(self, filename):
+        return "./sd_extra_networks/thumb?filename=" + urllib.parse.quote(filename.replace('\\', '/')) + "&mtime=" + str(os.path.getmtime(filename))
+
+    def search_terms_from_path(self, filename, possible_directories=None):
+        abspath = os.path.abspath(filename)
+
+        for parentdir in (possible_directories if possible_directories is not None else self.allowed_directories_for_previews()):
+            parentdir = os.path.abspath(parentdir)
+            if abspath.startswith(parentdir):
+                return abspath[len(parentdir):].replace('\\', '/')
+
+        return ""
+
    def create_html(self, tabname):
        view = shared.opts.extra_networks_default_view
        items_html = ''

+        self.metadata = {}
+
+        subdirs = {}
+        for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
+            for x in glob.glob(os.path.join(parentdir, '**/*'), recursive=True):
+                if not os.path.isdir(x):
+                    continue
+
+                subdir = os.path.abspath(x)[len(parentdir):].replace("\\", "/")
+                while subdir.startswith("/"):
+                    subdir = subdir[1:]
+
+                is_empty = len(os.listdir(x)) == 0
+                if not is_empty and not subdir.endswith("/"):
+                    subdir = subdir + "/"
+
+                subdirs[subdir] = 1
+
+        if subdirs:
+            subdirs = {"": 1, **subdirs}
+
+        subdirs_html = "".join([f"""
+<button class='lg secondary gradio-button custom-button{" search-all" if subdir=="" else ""}' onclick='extraNetworksSearchButton("{tabname}_extra_tabs", event)'>
+{html.escape(subdir if subdir!="" else "all")}
+</button>
+""" for subdir in subdirs])
+
        for item in self.list_items():
+            metadata = item.get("metadata")
+            if metadata:
+                self.metadata[item["name"]] = metadata
+
            items_html += self.create_html_for_item(item, tabname)

        if items_html == '':
            dirs = "".join([f"<li>{x}</li>" for x in self.allowed_directories_for_previews()])
            items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)

+        self_name_id = self.name.replace(" ", "_")
+
        res = f"""
-<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
+<div id='{tabname}_{self_name_id}_subdirs' class='extra-network-subdirs extra-network-subdirs-{view}'>
+{subdirs_html}
+</div>
+<div id='{tabname}_{self_name_id}_cards' class='extra-network-{view}'>
 {items_html}
 </div>
 """
@@ -54,18 +145,62 @@ class ExtraNetworksPage:
    def create_html_for_item(self, item, tabname):
        preview = item.get("preview", None)

+        onclick = item.get("onclick", None)
+        if onclick is None:
+            onclick = '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
+
+        height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
+        width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
+        background_image = f"background-image: url(\"{html.escape(preview)}\");" if preview else ''
+        metadata_button = ""
+        metadata = item.get("metadata")
+        if metadata:
+            metadata_button = f"<div class='metadata-button' title='Show metadata' onclick='extraNetworksRequestMetadata(event, {json.dumps(self.name)}, {json.dumps(item['name'])})'></div>"
+
        args = {
-            "preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
-            "prompt": item["prompt"],
+            "style": f"'{height}{width}{background_image}'",
+            "prompt": item.get("prompt", None),
            "tabname": json.dumps(tabname),
            "local_preview": json.dumps(item["local_preview"]),
            "name": item["name"],
-            "card_clicked": '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"',
+            "description": (item.get("description") or ""),
+            "card_clicked": onclick,
            "save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
+            "search_term": item.get("search_term", ""),
+            "metadata_button": metadata_button,
        }

        return self.card_page.format(**args)

+    def find_preview(self, path):
+        """
+        Find a preview PNG for a given path (without extension) and call link_preview on it.
+        """
+
+        preview_extensions = ["png", "jpg", "webp"]
+        if shared.opts.samples_format not in preview_extensions:
+            preview_extensions.append(shared.opts.samples_format)
+
+        potential_files = sum([[path + "." + ext, path + ".preview." + ext] for ext in preview_extensions], [])
+
+        for file in potential_files:
+            if os.path.isfile(file):
+                return self.link_preview(file)
+
+        return None
+
+    def find_description(self, path):
+        """
+        Find and read a description file for a given path (without extension).
+        """
+        for file in [f"{path}.txt", f"{path}.description.txt"]:
+            try:
+                with open(file, "r", encoding="utf-8", errors="replace") as f:
+                    return f.read()
+            except OSError:
+                pass
+        return None
+

 def intialize():
    extra_pages.clear()
@@ -106,19 +241,23 @@ def create_ui(container, button, tabname):

    with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
        for page in ui.stored_extra_pages:
-            with gr.Tab(page.title):
+            with gr.Tab(page.title, id=page.title.lower().replace(" ", "_")):
+
                page_elem = gr.HTML(page.create_html(ui.tabname))
                ui.pages.append(page_elem)

    filter = gr.Textbox('', show_label=False, elem_id=tabname+"_extra_search", placeholder="Search...", visible=False)
    button_refresh = gr.Button('Refresh', elem_id=tabname+"_extra_refresh")
-    button_close = gr.Button('Close', elem_id=tabname+"_extra_close")

    ui.button_save_preview = gr.Button('Save preview', elem_id=tabname+"_save_preview", visible=False)
    ui.preview_target_filename = gr.Textbox('Preview save filename', elem_id=tabname+"_preview_filename", visible=False)

-    button.click(fn=lambda: gr.update(visible=True), inputs=[], outputs=[container])
-    button_close.click(fn=lambda: gr.update(visible=False), inputs=[], outputs=[container])
+    def toggle_visibility(is_visible):
+        is_visible = not is_visible
+        return is_visible, gr.update(visible=is_visible), gr.update(variant=("secondary-down" if is_visible else "secondary"))
+
+    state_visible = gr.State(value=False)
+    button.click(fn=toggle_visibility, inputs=[state_visible], outputs=[state_visible, container, button])

    def refresh():
        res = []
@@ -138,7 +277,7 @@ def path_is_parent(parent_path, child_path):
    parent_path = os.path.abspath(parent_path)
    child_path = os.path.abspath(child_path)

-    return os.path.commonpath([parent_path]) == os.path.commonpath([parent_path, child_path])
+    return child_path.startswith(parent_path)


 def setup_ui(ui, gallery):
@@ -153,6 +292,7 @@ def setup_ui(ui, gallery):

        img_info = images[index if index >= 0 else 0]
        image = image_from_url_text(img_info)
+        geninfo, items = read_info_from_image(image)

        is_allowed = False
        for extra_page in ui.stored_extra_pages:
@@ -162,13 +302,19 @@ def setup_ui(ui, gallery):

        assert is_allowed, f'writing to {filename} is not allowed'

-        image.save(filename)
+        if geninfo:
+            pnginfo_data = PngImagePlugin.PngInfo()
+            pnginfo_data.add_text('parameters', geninfo)
+            image.save(filename, pnginfo=pnginfo_data)
+        else:
+            image.save(filename)

        return [page.create_html(ui.tabname) for page in ui.stored_extra_pages]

    ui.button_save_preview.click(
        fn=save_preview,
-        _js="function(x, y, z){console.log(x, y, z); return [selected_gallery_index(), y, z]}",
+        _js="function(x, y, z){return [selected_gallery_index(), y, z]}",
        inputs=[ui.preview_target_filename, gallery, ui.preview_target_filename],
        outputs=[*ui.pages]
    )
+
--- a/Show More
+++ b/Show More