repair img2img

.eslintrc.js

@@ -74,7 +74,6 @@ module.exports = {
         create_submit_args: "readonly",
         restart_reload: "readonly",
         updateInput: "readonly",
-        onEdit: "readonly",
         //extraNetworks.js
         requestGet: "readonly",
         popup: "readonly",
@@ -91,8 +90,6 @@ module.exports = {
         // localStorage.js
         localSet: "readonly",
         localGet: "readonly",
-        localRemove: "readonly",
-        // resizeHandle.js
-        setupResizeHandle: "writable"
+        localRemove: "readonly"
     }
 };

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,55 +1,35 @@
 name: Bug Report
-description: You think something is broken in the UI
+description: You think somethings is broken in the UI
 title: "[Bug]: "
 labels: ["bug-report"]
 
 body:
-  - type: markdown
-    attributes:
-      value: |
-        > The title of the bug report should be short and descriptive.
-        > Use relevant keywords for searchability.
-        > Do not leave it blank, but also do not put an entire error log in it.
   - type: checkboxes
     attributes:
-      label: Checklist
-      description: |
-        Please perform basic debugging to see if extensions or configuration is the cause of the issue.
-        Basic debug procedure
-        　1. Disable all third-party extensions - check if extension is the cause
-        　2. Update extensions and webui - sometimes things just need to be updated
-        　3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration
-        　4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed
-        　5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue
-        Before making a issue report please, check that the issue hasn't been reported recently.
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
       options:
-        - label: The issue exists after disabling all extensions
-        - label: The issue exists on a clean installation of webui
-        - label: The issue is caused by an extension, but I believe it is caused by a bug in the webui
-        - label: The issue exists in the current version of the webui
-        - label: The issue has not been reported before recently
-        - label: The issue has been reported before but has not been fixed yet
+        - label: I have searched the existing issues and checked the recent builds/commits
+          required: true
   - type: markdown
     attributes:
       value: |
-        > Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible
+        *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
   - type: textarea
     id: what-did
     attributes:
       label: What happened?
       description: Tell us what happened in a very clear and simple way
-      placeholder: |
-        txt2img is not working as intended.
     validations:
       required: true
   - type: textarea
     id: steps
     attributes:
       label: Steps to reproduce the problem
-      description: Please provide us with precise step by step instructions on how to reproduce the bug
-      placeholder: |
-        1. Go to ...
-        2. Press ...
+      description: Please provide us with precise step by step information on how to reproduce the bug
+      value: |
+        1. Go to .... 
+        2. Press ....
         3. ...
     validations:
       required: true
@@ -57,9 +37,64 @@ body:
     id: what-should
     attributes:
       label: What should have happened?
-      description: Tell us what you think the normal behavior should be
-      placeholder: |
-        WebUI should ...
+      description: Tell what you think the normal behavior should be
+    validations:
+      required: true
+  - type: input
+    id: commit
+    attributes:
+      label: Version or Commit where the problem happens
+      description: "Which webui version or 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 **Version: v1.2.3** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)"
+    validations:
+      required: true
+  - type: dropdown
+    id: py-version
+    attributes:
+      label: What Python version are you running on ?
+      multiple: false
+      options:
+        - Python 3.10.x
+        - Python 3.11.x (above, no supported yet)
+        - Python 3.9.x (below, no recommended)
+  - type: dropdown
+    id: platforms
+    attributes:
+      label: What platforms do you use to access the UI ?
+      multiple: true
+      options:
+        - Windows
+        - Linux
+        - MacOS
+        - iOS
+        - Android
+        - Other/Cloud
+  - type: dropdown
+    id: device
+    attributes:
+        label: What device are you running WebUI on?
+        multiple: true
+        options:
+        - Nvidia GPUs (RTX 20 above)
+        - Nvidia GPUs (GTX 16 below)
+        - AMD GPUs (RX 6000 above)
+        - AMD GPUs (RX 5000 below)
+        - CPU
+        - Other GPUs
+  - type: dropdown
+    id: cross_attention_opt
+    attributes:
+      label: Cross attention optimization
+      description: What cross attention optimization are you using, Settings -> Optimizations -> Cross attention optimization
+      multiple: false
+      options:
+        - Automatic
+        - xformers
+        - sdp-no-mem
+        - sdp
+        - Doggettx
+        - V1 
+        - InvokeAI
+        - "None "
     validations:
       required: true
   - type: dropdown
@@ -73,25 +108,26 @@ body:
         - Brave
         - Apple Safari
         - Microsoft Edge
-        - Android
-        - iOS
-        - Other
   - type: textarea
-    id: sysinfo
+    id: cmdargs
     attributes:
-      label: Sysinfo
-      description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
-      placeholder: |
-        1. Go to WebUI Settings -> Sysinfo -> Download system info.
-            If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file
-        2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text.
+      label: Command Line Arguments
+      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 the bug occured. If it's very long, provide a link to pastebin or similar service.
+      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
@@ -99,7 +135,4 @@ body:
     id: misc
     attributes:
       label: Additional information
-      description: | 
-        Please provide us with any relevant additional info or context.
-        Examples:
-        　I have updated my GPU driver recently.
+      description: Please provide us with any relevant additional info or context.

.github/workflows/on_pull_request.yaml

@@ -20,7 +20,7 @@ jobs:
           #     not to have GHA download an (at the time of writing) 4 GB cache
           #     of PyTorch and other dependencies.
       - name: Install Ruff
-        run: pip install ruff==0.1.6
+        run: pip install ruff==0.0.272
       - name: Run Ruff
         run: ruff .
   lint-js:

CHANGELOG.md

@@ -1,163 +1,3 @@
-## 1.6.1
-
-### Bug Fixes:
- * fix an error causing the webui to fail to start ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839))
-
-## 1.6.0
-
-### Features:
- * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371)
- * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards
- * add style editor dialog
- * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181))
- * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227))
- * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542))
- * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers:
-   * makes all of them work with img2img
-   * makes prompt composition posssible (AND)
-   * makes them available for SDXL
- * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808))
- * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599))
- * textual inversion inference support for SDXL
- * extra networks UI: show metadata for SD checkpoints
- * checkpoint merger: add metadata support 
- * prompt editing and attention: add support for whitespace after the number ([ red : green : 0.5 ]) (seed breaking change) ([#12177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12177))
- * VAE: allow selecting own VAE for each checkpoint (in user metadata editor)
- * VAE: add selected VAE to infotext
- * options in main UI: add own separate setting for txt2img and img2img, correctly read values from pasted infotext, add setting for column count ([#12551](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12551))
- * add resize handle to txt2img and img2img tabs, allowing to change the amount of horizontable space given to generation parameters and resulting image gallery ([#12687](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12687), [#12723](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12723))
- * change default behavior for batching cond/uncond -- now it's on by default, and is disabled by an UI setting (Optimizatios -> Batch cond/uncond) - if you are on lowvram/medvram and are getting OOM exceptions, you will need to enable it
- * show current position in queue and make it so that requests are processed in the order of arrival ([#12707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12707))
- * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models
- * prompt editing timeline has separate range for first pass and hires-fix pass (seed breaking change) ([#12457](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12457))
-
-### Minor:
- * img2img batch: RAM savings, VRAM savings, .tif, .tiff in img2img batch ([#12120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12120), [#12514](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12514), [#12515](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12515))
- * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479))
- * XYZ: in the axis labels, remove pathnames from model filenames
- * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298))
- * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491))
- * add gradio version warning
- * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297))
- * use transparent white for mask in inpainting, along with an option to select the color ([#12326](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12326))
- * move some settings to their own section: img2img, VAE
- * add checkbox to show/hide dirs for extra networks
- * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311))
- * gradio theme cache, new gradio themes, along with explanation that the user can input his own values ([#12346](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12346), [#12355](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12355))
- * sampler fixes/tweaks: s_tmax, s_churn, s_noise, s_tmax ([#12354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12354), [#12356](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12356), [#12357](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12357), [#12358](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12358), [#12375](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12375), [#12521](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12521))
- * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352))
- * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338))
- * enable cond cache by default
- * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230))
- * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379))
- * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254))
- * put commonly used samplers on top, make DPM++ 2M Karras the default choice
- * zoom and pan: option to auto-expand a wide image, improved integration ([#12413](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12413), [#12727](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12727))
- * option to cache Lora networks in memory
- * rework hires fix UI to use accordion
- * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back
- * change quicksettings items to have variable width
- * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503))
- * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console
- * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510))
- * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564))
- * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584))
- * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634))
- * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648))
- * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645))
- * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639))
- * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635))
- * make progress bar work independently from live preview display which results in it being updated a lot more often
- * forbid Full live preview method for medvram and add a setting to undo the forbidding
- * make it possible to localize tooltips and placeholders
- * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
- * Restore faces and Tiling generation parameters have been moved to settings out of main UI
-   * if you want to put them back into main UI, use `Options in main UI` setting on the UI page.
-
-### Extensions and API:
- * gradio 3.41.2
- * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd
- * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world')
- * properly clear the total console progressbar when using txt2img and img2img from API
- * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294))
- * shared.py and webui.py split into many files
- * add --loglevel commandline argument for logging
- * add a custom UI element that combines accordion and checkbox
- * avoid importing gradio in tests because it spams warnings
- * put infotext label for setting into OptionInfo definition rather than in a separate list
- * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470))
- * option to make scripts UI without gr.Group
- * add a way for scripts to register a callback for before/after just a single component's creation
- * use dataclass for StableDiffusionProcessing
- * store patches for Lora in a specialized module instead of inside torch
- * support http/https URLs in API ([#12663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12663), [#12698](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12698))
- * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616))
- * dump current stack traces when exiting with SIGINT
- * add type annotations for extra fields of shared.sd_model
-
-### Bug Fixes:
- * Don't crash if out of local storage quota for javascriot localStorage
- * XYZ plot do not fail if an exception occurs
- * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269))
- * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307))
- * fix sdxl model invalid configuration after the hijack
- * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304))
- * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318))
- * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319))
- * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327))
- * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387))
- * fix options in main UI misbehaving when there's just one element
- * make it possible to use a sampler from infotext even if it's hidden in the dropdown
- * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images
- * prevent bogus progress output in console when calculating hires fix dimensions
- * fix --use-textbox-seed
- * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466))
- * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463))
- * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526))
- * add second_order to samplers that mistakenly didn't have it
- * when refreshing cards in extra networks UI, do not discard user's custom resolution
- * fix processing error that happens if batch_size is not a multiple of how many prompts/negative prompts there are ([#12509](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12509))
- * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588))
- * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586))
- * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596))
- * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603))
- * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607))
- * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638))
- * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633))
- * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630))
- * implement missing undo hijack for SDXL
- * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684))
- * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689))
- * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685))
- * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667))
- * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717))
- * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version
- * set devices.dtype_unet correctly
- * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
- * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745))
- * fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
- * fix defaults settings page breaking when any of main UI tabs are hidden
- * fix incorrect save/display of new values in Defaults page in settings
- * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
- * fix an error that prevents VAE being reloaded after an option change if a VAE near the checkpoint exists ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
- * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
- * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
- * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
- * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity) 
- * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
- * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
- * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855))
- * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
- * get progressbar to display correctly in extensions tab
-
-
-## 1.5.2
-
-### Bug Fixes:
- * fix memory leak when generation fails
- * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk
-
-
 ## 1.5.1
 
 ### Minor:

CITATION.cff

@@ -1,7 +0,0 @@
-cff-version: 1.2.0
-message: "If you use this software, please cite it as below."
-authors:
-  - given-names: AUTOMATIC1111
-title: "Stable Diffusion Web UI"
-date-released: 2022-08-22
-url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui"

README.md

@@ -78,7 +78,7 @@ A browser interface based on Gradio library for Stable Diffusion.
 - Clip skip
 - Hypernetworks
 - Loras (same as Hypernetworks but more pretty)
-- A separate 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
@@ -88,23 +88,19 @@ A browser interface based on Gradio library for Stable Diffusion.
 - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
 - Now without any bad letters!
 - Load checkpoints in safetensors format
-- Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64
+- Eased resolution restriction: generated image's dimension must be a multiple of 8 rather than 64
 - Now with a license!
 - Reorder elements in the UI from settings screen
-- [Segmind Stable Diffusion](https://huggingface.co/segmind/SSD-1B) support
 
 ## 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:
-- [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended)
-- [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
-- [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page)
+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.
 
 Alternatively, use online services (like Google Colab):
 
 - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
 
 ### Installation on Windows 10/11 with NVidia-GPUs using release package
-1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract its contents.
+1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
 2. Run `update.bat`.
 3. Run `run.bat`.
 > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
@@ -121,9 +117,7 @@ Alternatively, use online services (like Google Colab):
 # Debian-based:
 sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
 # Red Hat-based:
-sudo dnf install wget git python3 gperftools-libs libglvnd-glx 
-# openSUSE-based:
-sudo zypper install wget git python3 libtcmalloc4 libglvnd
+sudo dnf install wget git python3
 # Arch-based:
 sudo pacman -S wget git python3
 ```
@@ -149,7 +143,7 @@ For the purposes of getting Google and other search engines to crawl the wiki, h
 ## Credits
 Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
 
-- Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers
+- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
 - GFPGAN - https://github.com/TencentARC/GFPGAN.git
 - CodeFormer - https://github.com/sczhou/CodeFormer
@@ -176,6 +170,5 @@ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and al
 - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
 - LyCORIS - KohakuBlueleaf
 - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
-- Hypertile - tfernd - https://github.com/tfernd/HyperTile
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
 - (You)

configs/alt-diffusion-m18-inference.yaml

@@ -1,73 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    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: [ 10000 ]
-        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: 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
-        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: modules.xlmr_m18.BertSeriesModelWithTransformation
-      params:
-        name: "XLMR-Large"

extensions-builtin/Lora/extra_networks_lora.py

@@ -6,14 +6,9 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
     def __init__(self):
         super().__init__('lora')
 
-        self.errors = {}
-        """mapping of network names to the number of errors the network had during operation"""
-
     def activate(self, p, params_list):
         additional = shared.opts.sd_lora
 
-        self.errors.clear()
-
         if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
             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]))
@@ -61,7 +56,4 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
                 p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
 
     def deactivate(self, p):
-        if self.errors:
-            p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
-
-            self.errors.clear()
+        pass

extensions-builtin/Lora/lora_logger.py

@@ -1,33 +0,0 @@
-import sys
-import copy
-import logging
-
-
-class ColoredFormatter(logging.Formatter):
-    COLORS = {
-        "DEBUG": "\033[0;36m",  # CYAN
-        "INFO": "\033[0;32m",  # GREEN
-        "WARNING": "\033[0;33m",  # YELLOW
-        "ERROR": "\033[0;31m",  # RED
-        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
-        "RESET": "\033[0m",  # RESET COLOR
-    }
-
-    def format(self, record):
-        colored_record = copy.copy(record)
-        levelname = colored_record.levelname
-        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
-        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
-        return super().format(colored_record)
-
-
-logger = logging.getLogger("lora")
-logger.propagate = False
-
-
-if not logger.handlers:
-    handler = logging.StreamHandler(sys.stdout)
-    handler.setFormatter(
-        ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s")
-    )
-    logger.addHandler(handler)

extensions-builtin/Lora/lora_patches.py

@@ -1,31 +0,0 @@
-import torch
-
-import networks
-from modules import patches
-
-
-class LoraPatches:
-    def __init__(self):
-        self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
-        self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
-        self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
-        self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
-        self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
-        self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
-        self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward)
-        self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict)
-        self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward)
-        self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict)
-
-    def undo(self):
-        self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward')
-        self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict')
-        self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward')
-        self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict')
-        self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward')
-        self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict')
-        self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward')
-        self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict')
-        self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward')
-        self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict')
-

extensions-builtin/Lora/lyco_helpers.py

@@ -19,50 +19,3 @@ def rebuild_cp_decomposition(up, down, mid):
     up = up.reshape(up.size(0), -1)
     down = down.reshape(down.size(0), -1)
     return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
-
-
-# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
-def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
-    '''
-    return a tuple of two value of input dimension decomposed by the number closest to factor
-    second value is higher or equal than first value.
-
-    In LoRA with Kroneckor Product, first value is a value for weight scale.
-    secon value is a value for weight.
-
-    Becuase of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
-
-    examples)
-    factor
-        -1               2                4               8               16               ...
-    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
-    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
-    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
-    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
-    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
-    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
-    '''
-
-    if factor > 0 and (dimension % factor) == 0:
-        m = factor
-        n = dimension // factor
-        if m > n:
-            n, m = m, n
-        return m, n
-    if factor < 0:
-        factor = dimension
-    m, n = 1, dimension
-    length = m + n
-    while m<n:
-        new_m = m + 1
-        while dimension%new_m != 0:
-            new_m += 1
-        new_n = dimension // new_m
-        if new_m + new_n > length or new_m>factor:
-            break
-        else:
-            m, n = new_m, new_n
-    if m > n:
-        n, m = m, n
-    return m, n
-

extensions-builtin/Lora/network.py

@@ -93,7 +93,6 @@ class Network:  # LoraModule
         self.unet_multiplier = 1.0
         self.dyn_dim = None
         self.modules = {}
-        self.bundle_embeddings = {}
         self.mtime = None
 
         self.mentioned_name = None
@@ -134,7 +133,7 @@ class NetworkModule:
 
         return 1.0
 
-    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
+    def finalize_updown(self, updown, orig_weight, output_shape):
         if self.bias is not None:
             updown = updown.reshape(self.bias.shape)
             updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
@@ -146,10 +145,7 @@ class NetworkModule:
         if orig_weight.size().numel() == updown.size().numel():
             updown = updown.reshape(orig_weight.shape)
 
-        if ex_bias is not None:
-            ex_bias = ex_bias * self.multiplier()
-
-        return updown * self.calc_scale() * self.multiplier(), ex_bias
+        return updown * self.calc_scale() * self.multiplier()
 
     def calc_updown(self, target):
         raise NotImplementedError()

extensions-builtin/Lora/network_full.py

@@ -14,14 +14,9 @@ class NetworkModuleFull(network.NetworkModule):
         super().__init__(net, weights)
 
         self.weight = weights.w.get("diff")
-        self.ex_bias = weights.w.get("diff_b")
 
     def calc_updown(self, orig_weight):
         output_shape = self.weight.shape
         updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
-        if self.ex_bias is not None:
-            ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
-        else:
-            ex_bias = None
 
-        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
+        return self.finalize_updown(updown, orig_weight, output_shape)

extensions-builtin/Lora/network_glora.py

@@ -1,33 +0,0 @@
-
-import network
-
-class ModuleTypeGLora(network.ModuleType):
-    def create_module(self, net: network.Network, weights: network.NetworkWeights):
-        if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):
-            return NetworkModuleGLora(net, weights)
-
-        return None
-
-# adapted from https://github.com/KohakuBlueleaf/LyCORIS
-class NetworkModuleGLora(network.NetworkModule):
-    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
-        super().__init__(net, weights)
-
-        if hasattr(self.sd_module, 'weight'):
-            self.shape = self.sd_module.weight.shape
-
-        self.w1a = weights.w["a1.weight"]
-        self.w1b = weights.w["b1.weight"]
-        self.w2a = weights.w["a2.weight"]
-        self.w2b = weights.w["b2.weight"]
-
-    def calc_updown(self, orig_weight):
-        w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
-        w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
-        w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
-        w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
-
-        output_shape = [w1a.size(0), w1b.size(1)]
-        updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))
-
-        return self.finalize_updown(updown, orig_weight, output_shape)

extensions-builtin/Lora/network_norm.py

@@ -1,28 +0,0 @@
-import network
-
-
-class ModuleTypeNorm(network.ModuleType):
-    def create_module(self, net: network.Network, weights: network.NetworkWeights):
-        if all(x in weights.w for x in ["w_norm", "b_norm"]):
-            return NetworkModuleNorm(net, weights)
-
-        return None
-
-
-class NetworkModuleNorm(network.NetworkModule):
-    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
-        super().__init__(net, weights)
-
-        self.w_norm = weights.w.get("w_norm")
-        self.b_norm = weights.w.get("b_norm")
-
-    def calc_updown(self, orig_weight):
-        output_shape = self.w_norm.shape
-        updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
-
-        if self.b_norm is not None:
-            ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
-        else:
-            ex_bias = None
-
-        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)

extensions-builtin/Lora/network_oft.py

@@ -1,97 +0,0 @@
-import torch
-import network
-from lyco_helpers import factorization
-from einops import rearrange
-
-
-class ModuleTypeOFT(network.ModuleType):
-    def create_module(self, net: network.Network, weights: network.NetworkWeights):
-        if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
-            return NetworkModuleOFT(net, weights)
-
-        return None
-
-# Supports both kohya-ss' implementation of COFT  https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
-# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
-class NetworkModuleOFT(network.NetworkModule):
-    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
-
-        super().__init__(net, weights)
-
-        self.lin_module = None
-        self.org_module: list[torch.Module] = [self.sd_module]
-
-        # kohya-ss
-        if "oft_blocks" in weights.w.keys():
-            self.is_kohya = True
-            self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
-            self.alpha = weights.w["alpha"] # alpha is constraint
-            self.dim = self.oft_blocks.shape[0] # lora dim
-        # LyCORIS
-        elif "oft_diag" in weights.w.keys():
-            self.is_kohya = False
-            self.oft_blocks = weights.w["oft_diag"]
-            # self.alpha is unused
-            self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
-
-        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
-        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
-        is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
-
-        if is_linear:
-            self.out_dim = self.sd_module.out_features
-        elif is_conv:
-            self.out_dim = self.sd_module.out_channels
-        elif is_other_linear:
-            self.out_dim = self.sd_module.embed_dim
-
-        if self.is_kohya:
-            self.constraint = self.alpha * self.out_dim
-            self.num_blocks = self.dim
-            self.block_size = self.out_dim // self.dim
-        else:
-            self.constraint = None
-            self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
-
-    def calc_updown_kb(self, orig_weight, multiplier):
-        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-        oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
-
-        R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-        R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
-
-        # This errors out for MultiheadAttention, might need to be handled up-stream
-        merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
-        merged_weight = torch.einsum(
-            'k n m, k n ... -> k m ...',
-            R,
-            merged_weight
-        )
-        merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
-
-        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
-        output_shape = orig_weight.shape
-        return self.finalize_updown(updown, orig_weight, output_shape)
-
-    def calc_updown(self, orig_weight):
-        # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it
-        multiplier = self.multiplier()
-        return self.calc_updown_kb(orig_weight, multiplier)
-
-    # override to remove the multiplier/scale factor; it's already multiplied in get_weight
-    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
-        if self.bias is not None:
-            updown = updown.reshape(self.bias.shape)
-            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
-            updown = updown.reshape(output_shape)
-
-        if len(output_shape) == 4:
-            updown = updown.reshape(output_shape)
-
-        if orig_weight.size().numel() == updown.size().numel():
-            updown = updown.reshape(orig_weight.shape)
-
-        if ex_bias is not None:
-            ex_bias = ex_bias * self.multiplier()
-
-        return updown, ex_bias

extensions-builtin/Lora/networks.py

@@ -1,25 +1,17 @@
-import logging
 import os
 import re
 
-import lora_patches
 import network
 import network_lora
-import network_glora
 import network_hada
 import network_ia3
 import network_lokr
 import network_full
-import network_norm
-import network_oft
 
 import torch
 from typing import Union
 
 from modules import shared, devices, sd_models, errors, scripts, sd_hijack
-import modules.textual_inversion.textual_inversion as textual_inversion
-
-from lora_logger import logger
 
 module_types = [
     network_lora.ModuleTypeLora(),
@@ -27,9 +19,6 @@ module_types = [
     network_ia3.ModuleTypeIa3(),
     network_lokr.ModuleTypeLokr(),
     network_full.ModuleTypeFull(),
-    network_norm.ModuleTypeNorm(),
-    network_glora.ModuleTypeGLora(),
-    network_oft.ModuleTypeOFT(),
 ]
 
 
@@ -42,8 +31,6 @@ suffix_conversion = {
     "resnets": {
         "conv1": "in_layers_2",
         "conv2": "out_layers_3",
-        "norm1": "in_layers_0",
-        "norm2": "out_layers_0",
         "time_emb_proj": "emb_layers_1",
         "conv_shortcut": "skip_connection",
     }
@@ -156,19 +143,9 @@ def load_network(name, network_on_disk):
     is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
 
     matched_networks = {}
-    bundle_embeddings = {}
 
     for key_network, weight in sd.items():
         key_network_without_network_parts, network_part = key_network.split(".", 1)
-        if key_network_without_network_parts == "bundle_emb":
-            emb_name, vec_name = network_part.split(".", 1)
-            emb_dict = bundle_embeddings.get(emb_name, {})
-            if vec_name.split('.')[0] == 'string_to_param':
-                _, k2 = vec_name.split('.', 1)
-                emb_dict['string_to_param'] = {k2: weight}
-            else:
-                emb_dict[vec_name] = weight
-            bundle_embeddings[emb_name] = emb_dict
 
         key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
         sd_module = shared.sd_model.network_layer_mapping.get(key, None)
@@ -191,17 +168,6 @@ def load_network(name, network_on_disk):
                 key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
                 sd_module = shared.sd_model.network_layer_mapping.get(key, None)
 
-        # kohya_ss OFT module
-        elif sd_module is None and "oft_unet" in key_network_without_network_parts:
-            key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
-            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
-
-        # KohakuBlueLeaf OFT module
-        if sd_module is None and "oft_diag" in key:
-            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
-            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
-            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
-
         if sd_module is None:
             keys_failed_to_match[key_network] = key
             continue
@@ -223,38 +189,18 @@ def load_network(name, network_on_disk):
 
         net.modules[key] = net_module
 
-    embeddings = {}
-    for emb_name, data in bundle_embeddings.items():
-        embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
-        embedding.loaded = None
-        embeddings[emb_name] = embedding
-
-    net.bundle_embeddings = embeddings
-
     if keys_failed_to_match:
-        logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
+        print(f"Failed to match keys when loading network {network_on_disk.filename}: {keys_failed_to_match}")
 
     return net
 
 
-def purge_networks_from_memory():
-    while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
-        name = next(iter(networks_in_memory))
-        networks_in_memory.pop(name, None)
-
-    devices.torch_gc()
-
-
 def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
-    emb_db = sd_hijack.model_hijack.embedding_db
     already_loaded = {}
 
     for net in loaded_networks:
         if net.name in names:
             already_loaded[net.name] = net
-        for emb_name, embedding in net.bundle_embeddings.items():
-            if embedding.loaded:
-                emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)
 
     loaded_networks.clear()
 
@@ -266,19 +212,15 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
 
     failed_to_load_networks = []
 
-    for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
+    for i, name in enumerate(names):
         net = already_loaded.get(name, None)
 
-        if network_on_disk is not None:
-            if net is None:
-                net = networks_in_memory.get(name)
+        network_on_disk = networks_on_disk[i]
 
+        if network_on_disk is not None:
             if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
                 try:
                     net = load_network(name, network_on_disk)
-
-                    networks_in_memory.pop(name, None)
-                    networks_in_memory[name] = net
                 except Exception as e:
                     errors.display(e, f"loading network {network_on_disk.filename}")
                     continue
@@ -289,7 +231,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
 
         if net is None:
             failed_to_load_networks.append(name)
-            logging.info(f"Couldn't find network with name {name}")
+            print(f"Couldn't find network with name {name}")
             continue
 
         net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
@@ -297,54 +239,24 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
         net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
         loaded_networks.append(net)
 
-        for emb_name, embedding in net.bundle_embeddings.items():
-            if embedding.loaded is None and emb_name in emb_db.word_embeddings:
-                logger.warning(
-                    f'Skip bundle embedding: "{emb_name}"'
-                    ' as it was already loaded from embeddings folder'
-                )
-                continue
-
-            embedding.loaded = False
-            if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
-                embedding.loaded = True
-                emb_db.register_embedding(embedding, shared.sd_model)
-            else:
-                emb_db.skipped_embeddings[name] = embedding
-
     if failed_to_load_networks:
-        sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
-
-    purge_networks_from_memory()
+        sd_hijack.model_hijack.comments.append("Failed to find networks: " + ", ".join(failed_to_load_networks))
 
 
-def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
     weights_backup = getattr(self, "network_weights_backup", None)
-    bias_backup = getattr(self, "network_bias_backup", None)
 
-    if weights_backup is None and bias_backup is None:
+    if weights_backup is None:
         return
 
-    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)
-
-    if bias_backup is not None:
-        if isinstance(self, torch.nn.MultiheadAttention):
-            self.out_proj.bias.copy_(bias_backup)
-        else:
-            self.bias.copy_(bias_backup)
+    if isinstance(self, torch.nn.MultiheadAttention):
+        self.in_proj_weight.copy_(weights_backup[0])
+        self.out_proj.weight.copy_(weights_backup[1])
     else:
-        if isinstance(self, torch.nn.MultiheadAttention):
-            self.out_proj.bias = None
-        else:
-            self.bias = None
+        self.weight.copy_(weights_backup)
 
 
-def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
     """
     Applies the currently selected set of networks to the weights of torch layer self.
     If weights already have this particular set of networks applied, does nothing.
@@ -359,10 +271,7 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
     wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
 
     weights_backup = getattr(self, "network_weights_backup", None)
-    if weights_backup is None and wanted_names != ():
-        if current_names != ():
-            raise RuntimeError("no backup weights found and current weights are not unchanged")
-
+    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:
@@ -370,41 +279,21 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
 
         self.network_weights_backup = weights_backup
 
-    bias_backup = getattr(self, "network_bias_backup", None)
-    if bias_backup is None:
-        if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
-            bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
-        elif getattr(self, 'bias', None) is not None:
-            bias_backup = self.bias.to(devices.cpu, copy=True)
-        else:
-            bias_backup = None
-        self.network_bias_backup = bias_backup
-
     if current_names != wanted_names:
         network_restore_weights_from_backup(self)
 
         for net in loaded_networks:
             module = net.modules.get(network_layer_name, None)
             if module is not None and hasattr(self, 'weight'):
-                try:
-                    with torch.no_grad():
-                        updown, ex_bias = module.calc_updown(self.weight)
+                with torch.no_grad():
+                    updown = module.calc_updown(self.weight)
 
-                        if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
-                            # inpainting model. zero pad updown to make channel[1]  4 to 9
-                            updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+                    if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+                        # inpainting model. zero pad updown to make channel[1]  4 to 9
+                        updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
 
-                        self.weight += updown
-                        if ex_bias is not None and hasattr(self, 'bias'):
-                            if self.bias is None:
-                                self.bias = torch.nn.Parameter(ex_bias)
-                            else:
-                                self.bias += ex_bias
-                except RuntimeError as e:
-                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
-                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
-
-                continue
+                    self.weight += updown
+                    continue
 
             module_q = net.modules.get(network_layer_name + "_q_proj", None)
             module_k = net.modules.get(network_layer_name + "_k_proj", None)
@@ -412,33 +301,21 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             module_out = net.modules.get(network_layer_name + "_out_proj", None)
 
             if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
-                try:
-                    with torch.no_grad():
-                        updown_q, _ = module_q.calc_updown(self.in_proj_weight)
-                        updown_k, _ = module_k.calc_updown(self.in_proj_weight)
-                        updown_v, _ = module_v.calc_updown(self.in_proj_weight)
-                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
-                        updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
+                with torch.no_grad():
+                    updown_q = module_q.calc_updown(self.in_proj_weight)
+                    updown_k = module_k.calc_updown(self.in_proj_weight)
+                    updown_v = module_v.calc_updown(self.in_proj_weight)
+                    updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+                    updown_out = module_out.calc_updown(self.out_proj.weight)
 
-                        self.in_proj_weight += updown_qkv
-                        self.out_proj.weight += updown_out
-                    if ex_bias is not None:
-                        if self.out_proj.bias is None:
-                            self.out_proj.bias = torch.nn.Parameter(ex_bias)
-                        else:
-                            self.out_proj.bias += ex_bias
-
-                except RuntimeError as e:
-                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
-                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
-
-                continue
+                    self.in_proj_weight += updown_qkv
+                    self.out_proj.weight += updown_out
+                    continue
 
             if module is None:
                 continue
 
-            logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
-            extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+            print(f'failed to calculate network weights for layer {network_layer_name}')
 
         self.network_current_names = wanted_names
 
@@ -465,7 +342,7 @@ def network_forward(module, input, original_forward):
         if module is None:
             continue
 
-        y = module.forward(input, y)
+        y = module.forward(y, input)
 
     return y
 
@@ -473,79 +350,48 @@ def network_forward(module, input, original_forward):
 def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
     self.network_current_names = ()
     self.network_weights_backup = None
-    self.network_bias_backup = None
 
 
 def network_Linear_forward(self, input):
     if shared.opts.lora_functional:
-        return network_forward(self, input, originals.Linear_forward)
+        return network_forward(self, input, torch.nn.Linear_forward_before_network)
 
     network_apply_weights(self)
 
-    return originals.Linear_forward(self, input)
+    return torch.nn.Linear_forward_before_network(self, input)
 
 
 def network_Linear_load_state_dict(self, *args, **kwargs):
     network_reset_cached_weight(self)
 
-    return originals.Linear_load_state_dict(self, *args, **kwargs)
+    return torch.nn.Linear_load_state_dict_before_network(self, *args, **kwargs)
 
 
 def network_Conv2d_forward(self, input):
     if shared.opts.lora_functional:
-        return network_forward(self, input, originals.Conv2d_forward)
+        return network_forward(self, input, torch.nn.Conv2d_forward_before_network)
 
     network_apply_weights(self)
 
-    return originals.Conv2d_forward(self, input)
+    return torch.nn.Conv2d_forward_before_network(self, input)
 
 
 def network_Conv2d_load_state_dict(self, *args, **kwargs):
     network_reset_cached_weight(self)
 
-    return originals.Conv2d_load_state_dict(self, *args, **kwargs)
-
-
-def network_GroupNorm_forward(self, input):
-    if shared.opts.lora_functional:
-        return network_forward(self, input, originals.GroupNorm_forward)
-
-    network_apply_weights(self)
-
-    return originals.GroupNorm_forward(self, input)
-
-
-def network_GroupNorm_load_state_dict(self, *args, **kwargs):
-    network_reset_cached_weight(self)
-
-    return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
-
-
-def network_LayerNorm_forward(self, input):
-    if shared.opts.lora_functional:
-        return network_forward(self, input, originals.LayerNorm_forward)
-
-    network_apply_weights(self)
-
-    return originals.LayerNorm_forward(self, input)
-
-
-def network_LayerNorm_load_state_dict(self, *args, **kwargs):
-    network_reset_cached_weight(self)
-
-    return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
+    return torch.nn.Conv2d_load_state_dict_before_network(self, *args, **kwargs)
 
 
 def network_MultiheadAttention_forward(self, *args, **kwargs):
     network_apply_weights(self)
 
-    return originals.MultiheadAttention_forward(self, *args, **kwargs)
+    return torch.nn.MultiheadAttention_forward_before_network(self, *args, **kwargs)
 
 
 def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
     network_reset_cached_weight(self)
 
-    return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
+    return torch.nn.MultiheadAttention_load_state_dict_before_network(self, *args, **kwargs)
 
 
 def list_available_networks():
@@ -613,15 +459,9 @@ def infotext_pasted(infotext, params):
         params["Prompt"] += "\n" + "".join(added)
 
 
-originals: lora_patches.LoraPatches = None
-
-extra_network_lora = None
-
 available_networks = {}
 available_network_aliases = {}
 loaded_networks = []
-loaded_bundle_embeddings = {}
-networks_in_memory = {}
 available_network_hash_lookup = {}
 forbidden_network_aliases = {}
 

extensions-builtin/Lora/scripts/lora_script.py

@@ -1,30 +1,57 @@
 import re
 
+import torch
 import gradio as gr
 from fastapi import FastAPI
 
 import network
 import networks
 import lora  # noqa:F401
-import lora_patches
 import extra_networks_lora
 import ui_extra_networks_lora
 from modules import script_callbacks, ui_extra_networks, extra_networks, shared
 
-
 def unload():
-    networks.originals.undo()
+    torch.nn.Linear.forward = torch.nn.Linear_forward_before_network
+    torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_network
+    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_network
+    torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_network
+    torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_network
+    torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_network
 
 
 def before_ui():
     ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
 
-    networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
-    extra_networks.register_extra_network(networks.extra_network_lora)
-    extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
+    extra_network = extra_networks_lora.ExtraNetworkLora()
+    extra_networks.register_extra_network(extra_network)
+    extra_networks.register_extra_network_alias(extra_network, "lyco")
 
 
-networks.originals = lora_patches.LoraPatches()
+if not hasattr(torch.nn, 'Linear_forward_before_network'):
+    torch.nn.Linear_forward_before_network = torch.nn.Linear.forward
+
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_network'):
+    torch.nn.Linear_load_state_dict_before_network = torch.nn.Linear._load_from_state_dict
+
+if not hasattr(torch.nn, 'Conv2d_forward_before_network'):
+    torch.nn.Conv2d_forward_before_network = torch.nn.Conv2d.forward
+
+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_network'):
+    torch.nn.Conv2d_load_state_dict_before_network = torch.nn.Conv2d._load_from_state_dict
+
+if not hasattr(torch.nn, 'MultiheadAttention_forward_before_network'):
+    torch.nn.MultiheadAttention_forward_before_network = torch.nn.MultiheadAttention.forward
+
+if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_network'):
+    torch.nn.MultiheadAttention_load_state_dict_before_network = torch.nn.MultiheadAttention._load_from_state_dict
+
+torch.nn.Linear.forward = networks.network_Linear_forward
+torch.nn.Linear._load_from_state_dict = networks.network_Linear_load_state_dict
+torch.nn.Conv2d.forward = networks.network_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = networks.network_Conv2d_load_state_dict
+torch.nn.MultiheadAttention.forward = networks.network_MultiheadAttention_forward
+torch.nn.MultiheadAttention._load_from_state_dict = networks.network_MultiheadAttention_load_state_dict
 
 script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
 script_callbacks.on_script_unloaded(unload)
@@ -38,7 +65,6 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
     "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
     "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
     "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
-    "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
 }))
 
 
@@ -95,5 +121,3 @@ def infotext_pasted(infotext, d):
 
 
 script_callbacks.on_infotext_pasted(infotext_pasted)
-
-shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)

extensions-builtin/Lora/ui_edit_user_metadata.py

@@ -70,7 +70,6 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
         metadata = item.get("metadata") or {}
 
         keys = {
-            'ss_output_name': "Output name:",
             'ss_sd_model_name': "Model:",
             'ss_clip_skip': "Clip skip:",
             'ss_network_module': "Kohya module:",

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -17,8 +17,6 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
 
     def create_item(self, name, index=None, enable_filter=True):
         lora_on_disk = networks.available_networks.get(name)
-        if lora_on_disk is None:
-            return
 
         path, ext = os.path.splitext(lora_on_disk.filename)
 
@@ -27,10 +25,9 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         item = {
             "name": name,
             "filename": lora_on_disk.filename,
-            "shorthash": lora_on_disk.shorthash,
             "preview": self.find_preview(path),
             "description": self.find_description(path),
-            "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
+            "search_term": self.search_terms_from_path(lora_on_disk.filename),
             "local_preview": f"{path}.{shared.opts.samples_format}",
             "metadata": lora_on_disk.metadata,
             "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
@@ -68,10 +65,9 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         return item
 
     def list_items(self):
-        # instantiate a list to protect against concurrent modification
-        names = list(networks.available_networks)
-        for index, name in enumerate(names):
+        for index, name in enumerate(networks.available_networks):
             item = self.create_item(name, index)
+
             if item is not None:
                 yield item
 

extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js

@@ -12,22 +12,8 @@ onUiLoaded(async() => {
         "Sketch": elementIDs.sketch
     };
 
-
     // Helper functions
     // Get active tab
-
-    /**
-     * Waits for an element to be present in the DOM.
-     */
-    const waitForElement = (id) => new Promise(resolve => {
-        const checkForElement = () => {
-            const element = document.querySelector(id);
-            if (element) return resolve(element);
-            setTimeout(checkForElement, 100);
-        };
-        checkForElement();
-    });
-
     function getActiveTab(elements, all = false) {
         const tabs = elements.img2imgTabs.querySelectorAll("button");
 
@@ -48,7 +34,7 @@ onUiLoaded(async() => {
 
     // Wait until opts loaded
     async function waitForOpts() {
-        for (; ;) {
+        for (;;) {
             if (window.opts && Object.keys(window.opts).length) {
                 return window.opts;
             }
@@ -56,11 +42,6 @@ onUiLoaded(async() => {
         }
     }
 
-    // Detect whether the element has a horizontal scroll bar
-    function hasHorizontalScrollbar(element) {
-        return element.scrollWidth > element.clientWidth;
-    }
-
     // Function for defining the "Ctrl", "Shift" and "Alt" keys
     function isModifierKey(event, key) {
         switch (key) {
@@ -220,8 +201,7 @@ onUiLoaded(async() => {
         canvas_hotkey_overlap: "KeyO",
         canvas_disabled_functions: [],
         canvas_show_tooltip: true,
-        canvas_auto_expand: true,
-        canvas_blur_prompt: false,
+        canvas_blur_prompt: false
     };
 
     const functionMap = {
@@ -269,7 +249,7 @@ onUiLoaded(async() => {
         input?.addEventListener("input", () => restoreImgRedMask(elements));
     }
 
-    function applyZoomAndPan(elemId, isExtension = true) {
+    function applyZoomAndPan(elemId) {
         const targetElement = gradioApp().querySelector(elemId);
 
         if (!targetElement) {
@@ -381,12 +361,6 @@ onUiLoaded(async() => {
                 panY: 0
             };
 
-            if (isExtension) {
-                targetElement.style.overflow = "hidden";
-            }
-
-            targetElement.isZoomed = false;
-
             fixCanvas();
             targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;
 
@@ -397,27 +371,8 @@ onUiLoaded(async() => {
             toggleOverlap("off");
             fullScreenMode = false;
 
-            const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']");
-            if (closeBtn) {
-                closeBtn.addEventListener("click", resetZoom);
-            }
-
-            if (canvas && isExtension) {
-                const parentElement = targetElement.closest('[id^="component-"]');
-                if (
-                    canvas &&
-                    parseFloat(canvas.style.width) > parentElement.offsetWidth &&
-                    parseFloat(targetElement.style.width) > parentElement.offsetWidth
-                ) {
-                    fitToElement();
-                    return;
-                }
-
-            }
-
             if (
                 canvas &&
-                !isExtension &&
                 parseFloat(canvas.style.width) > 865 &&
                 parseFloat(targetElement.style.width) > 865
             ) {
@@ -426,6 +381,9 @@ onUiLoaded(async() => {
             }
 
             targetElement.style.width = "";
+            if (canvas) {
+                targetElement.style.height = canvas.style.height;
+            }
         }
 
         // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
@@ -481,7 +439,7 @@ onUiLoaded(async() => {
 
         // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
         function updateZoom(newZoomLevel, mouseX, mouseY) {
-            newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15));
+            newZoomLevel = Math.max(0.5, Math.min(newZoomLevel, 15));
 
             elemData[elemId].panX +=
                 mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;
@@ -492,10 +450,6 @@ onUiLoaded(async() => {
             targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
 
             toggleOverlap("on");
-            if (isExtension) {
-                targetElement.style.overflow = "visible";
-            }
-
             return newZoomLevel;
         }
 
@@ -518,12 +472,10 @@ onUiLoaded(async() => {
                 fullScreenMode = false;
                 elemData[elemId].zoomLevel = updateZoom(
                     elemData[elemId].zoomLevel +
-                    (operation === "+" ? delta : -delta),
+                        (operation === "+" ? delta : -delta),
                     zoomPosX - targetElement.getBoundingClientRect().left,
                     zoomPosY - targetElement.getBoundingClientRect().top
                 );
-
-                targetElement.isZoomed = true;
             }
         }
 
@@ -537,19 +489,10 @@ onUiLoaded(async() => {
             //Reset Zoom
             targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
 
-            let parentElement;
-
-            if (isExtension) {
-                parentElement = targetElement.closest('[id^="component-"]');
-            } else {
-                parentElement = targetElement.parentElement;
-            }
-
-
             // Get element and screen dimensions
             const elementWidth = targetElement.offsetWidth;
             const elementHeight = targetElement.offsetHeight;
-
+            const parentElement = targetElement.parentElement;
             const screenWidth = parentElement.clientWidth;
             const screenHeight = parentElement.clientHeight;
 
@@ -602,12 +545,8 @@ onUiLoaded(async() => {
 
             if (!canvas) return;
 
-            if (canvas.offsetWidth > 862 || isExtension) {
-                targetElement.style.width = (canvas.offsetWidth + 2) + "px";
-            }
-
-            if (isExtension) {
-                targetElement.style.overflow = "visible";
+            if (canvas.offsetWidth > 862) {
+                targetElement.style.width = canvas.offsetWidth + "px";
             }
 
             if (fullScreenMode) {
@@ -709,48 +648,8 @@ onUiLoaded(async() => {
             mouseY = e.offsetY;
         }
 
-        // Simulation of the function to put a long image into the screen.
-        // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element.
-        // We hide the image and show it to the user when it is ready.
-
-        targetElement.isExpanded = false;
-        function autoExpand() {
-            const canvas = document.querySelector(`${elemId} canvas[key="interface"]`);
-            if (canvas) {
-                if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) {
-                    targetElement.style.visibility = "hidden";
-                    setTimeout(() => {
-                        fitToScreen();
-                        resetZoom();
-                        targetElement.style.visibility = "visible";
-                        targetElement.isExpanded = true;
-                    }, 10);
-                }
-            }
-        }
-
         targetElement.addEventListener("mousemove", getMousePosition);
 
-        //observers
-        // Creating an observer with a callback function to handle DOM changes
-        const observer = new MutationObserver((mutationsList, observer) => {
-            for (let mutation of mutationsList) {
-                // If the style attribute of the canvas has changed, by observation it happens only when the picture changes
-                if (mutation.type === 'attributes' && mutation.attributeName === 'style' &&
-                    mutation.target.tagName.toLowerCase() === 'canvas') {
-                    targetElement.isExpanded = false;
-                    setTimeout(resetZoom, 10);
-                }
-            }
-        });
-
-        // Apply auto expand if enabled
-        if (hotkeysConfig.canvas_auto_expand) {
-            targetElement.addEventListener("mousemove", autoExpand);
-            // Set up an observer to track attribute changes
-            observer.observe(targetElement, {attributes: true, childList: true, subtree: true});
-        }
-
         // Handle events only inside the targetElement
         let isKeyDownHandlerAttached = false;
 
@@ -855,11 +754,6 @@ onUiLoaded(async() => {
             if (isMoving && elemId === activeElement) {
                 updatePanPosition(e.movementX, e.movementY);
                 targetElement.style.pointerEvents = "none";
-
-                if (isExtension) {
-                    targetElement.style.overflow = "visible";
-                }
-
             } else {
                 targetElement.style.pointerEvents = "auto";
             }
@@ -870,93 +764,13 @@ onUiLoaded(async() => {
             isMoving = false;
         };
 
-        // Checks for extension
-        function checkForOutBox() {
-            const parentElement = targetElement.closest('[id^="component-"]');
-            if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) {
-                resetZoom();
-                targetElement.isExpanded = true;
-            }
-
-            if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) {
-                resetZoom();
-            }
-
-            if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) {
-                resetZoom();
-            }
-        }
-
-        if (isExtension) {
-            targetElement.addEventListener("mousemove", checkForOutBox);
-        }
-
-
-        window.addEventListener('resize', (e) => {
-            resetZoom();
-
-            if (isExtension) {
-                targetElement.isExpanded = false;
-                targetElement.isZoomed = false;
-            }
-        });
-
         gradioApp().addEventListener("mousemove", handleMoveByKey);
-
-
     }
 
-    applyZoomAndPan(elementIDs.sketch, false);
-    applyZoomAndPan(elementIDs.inpaint, false);
-    applyZoomAndPan(elementIDs.inpaintSketch, false);
+    applyZoomAndPan(elementIDs.sketch);
+    applyZoomAndPan(elementIDs.inpaint);
+    applyZoomAndPan(elementIDs.inpaintSketch);
 
     // Make the function global so that other extensions can take advantage of this solution
-    const applyZoomAndPanIntegration = async(id, elementIDs) => {
-        const mainEl = document.querySelector(id);
-        if (id.toLocaleLowerCase() === "none") {
-            for (const elementID of elementIDs) {
-                const el = await waitForElement(elementID);
-                if (!el) break;
-                applyZoomAndPan(elementID);
-            }
-            return;
-        }
-
-        if (!mainEl) return;
-        mainEl.addEventListener("click", async() => {
-            for (const elementID of elementIDs) {
-                const el = await waitForElement(elementID);
-                if (!el) break;
-                applyZoomAndPan(elementID);
-            }
-        }, {once: true});
-    };
-
-    window.applyZoomAndPan = applyZoomAndPan; // Only 1 elements, argument elementID, for example applyZoomAndPan("#txt2img_controlnet_ControlNet_input_image")
-
-    window.applyZoomAndPanIntegration = applyZoomAndPanIntegration; // for any extension
-
-    /*
-        The function `applyZoomAndPanIntegration` takes two arguments:
-
-        1. `id`: A string identifier for the element to which zoom and pan functionality will be applied on click.
-        If the `id` value is "none", the functionality will be applied to all elements specified in the second argument without a click event.
-
-        2. `elementIDs`: An array of string identifiers for elements. Zoom and pan functionality will be applied to each of these elements on click of the element specified by the first argument.
-        If "none" is specified in the first argument, the functionality will be applied to each of these elements without a click event.
-
-        Example usage:
-        applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
-        In this example, zoom and pan functionality will be applied to the element with the identifier "txt2img_controlnet_ControlNet_input_image" upon clicking the element with the identifier "txt2img_controlnet".
-    */
-
-    // More examples
-    // Add integration with ControlNet txt2img One TAB
-    // applyZoomAndPanIntegration("#txt2img_controlnet", ["#txt2img_controlnet_ControlNet_input_image"]);
-
-    // Add integration with ControlNet txt2img Tabs
-    // applyZoomAndPanIntegration("#txt2img_controlnet",Array.from({ length: 10 }, (_, i) => `#txt2img_controlnet_ControlNet-${i}_input_image`));
-
-    // Add integration with Inpaint Anything
-    // applyZoomAndPanIntegration("None", ["#ia_sam_image", "#ia_sel_mask"]);
+    window.applyZoomAndPan = applyZoomAndPan;
 });

extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py

@@ -9,7 +9,6 @@ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas
     "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
     "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
     "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
-    "canvas_auto_expand": shared.OptionInfo(True, "Automatically expands an image that does not fit completely in the canvas area, similar to manually pressing the S and R buttons"),
     "canvas_blur_prompt": shared.OptionInfo(False, "Take the focus off the prompt when working with a canvas"),
     "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
 }))

extensions-builtin/canvas-zoom-and-pan/style.css

@@ -61,6 +61,3 @@
   to {opacity: 1;}
 }
 
-.styler {
-  overflow:inherit !important;
-}

extensions-builtin/extra-options-section/scripts/extra_options_section.py

@@ -1,7 +1,5 @@
-import math
-
 import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
+from modules import scripts, shared, ui_components, ui_settings
 from modules.ui_components import FormColumn
 
 
@@ -21,38 +19,18 @@ class ExtraOptionsSection(scripts.Script):
     def ui(self, is_img2img):
         self.comps = []
         self.setting_names = []
-        self.infotext_fields = []
-        extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
-
-        mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
 
         with gr.Blocks() as interface:
-            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
+            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and shared.opts.extra_options else gr.Group(), gr.Row():
+                for setting_name in shared.opts.extra_options:
+                    with FormColumn():
+                        comp = ui_settings.create_setting_component(setting_name)
 
-                row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
-
-                for row in range(row_count):
-                    with gr.Row():
-                        for col in range(shared.opts.extra_options_cols):
-                            index = row * shared.opts.extra_options_cols + col
-                            if index >= len(extra_options):
-                                break
-
-                            setting_name = extra_options[index]
-
-                            with FormColumn():
-                                comp = ui_settings.create_setting_component(setting_name)
-
-                            self.comps.append(comp)
-                            self.setting_names.append(setting_name)
-
-                            setting_infotext_name = mapping.get(setting_name)
-                            if setting_infotext_name is not None:
-                                self.infotext_fields.append((comp, setting_infotext_name))
+                    self.comps.append(comp)
+                    self.setting_names.append(setting_name)
 
         def get_settings_values():
-            res = [ui_settings.get_value_for_setting(key) for key in self.setting_names]
-            return res[0] if len(res) == 1 else res
+            return [ui_settings.get_value_for_setting(key) for key in self.setting_names]
 
         interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)
 
@@ -65,10 +43,6 @@ class ExtraOptionsSection(scripts.Script):
 
 
 shared.options_templates.update(shared.options_section(('ui', "User interface"), {
-    "extra_options_txt2img": shared.OptionInfo([], "Options in main UI - txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
-    "extra_options_img2img": shared.OptionInfo([], "Options in main UI - img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
-    "extra_options_cols": shared.OptionInfo(1, "Options in main UI - number of columns", gr.Number, {"precision": 0}).needs_reload_ui(),
-    "extra_options_accordion": shared.OptionInfo(False, "Options in main UI - place into an accordion").needs_reload_ui()
+    "extra_options": shared.OptionInfo([], "Options in main UI", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img/img2img interfaces").needs_reload_ui(),
+    "extra_options_accordion": shared.OptionInfo(False, "Place options in main UI into an accordion").needs_restart()
 }))
-
-

extensions-builtin/hypertile/hypertile.py

@@ -1,345 +0,0 @@
-"""
-Hypertile module for splitting attention layers in SD-1.5 U-Net and SD-1.5 VAE
-Warn: The patch works well only if the input image has a width and height that are multiples of 128
-Original author: @tfernd Github: https://github.com/tfernd/HyperTile
-"""
-
-from __future__ import annotations
-
-import functools
-from dataclasses import dataclass
-from typing import Callable
-
-from functools import wraps, cache
-
-import math
-import torch.nn as nn
-import random
-
-from einops import rearrange
-
-
-@dataclass
-class HypertileParams:
-    depth = 0
-    layer_name = ""
-    tile_size: int = 0
-    swap_size: int = 0
-    aspect_ratio: float = 1.0
-    forward = None
-    enabled = False
-
-
-
-# TODO add SD-XL layers
-DEPTH_LAYERS = {
-    0: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.1.1.transformer_blocks.0.attn1",
-        "input_blocks.2.1.transformer_blocks.0.attn1",
-        "output_blocks.9.1.transformer_blocks.0.attn1",
-        "output_blocks.10.1.transformer_blocks.0.attn1",
-        "output_blocks.11.1.transformer_blocks.0.attn1",
-        # SD 1.5 VAE
-        "decoder.mid_block.attentions.0",
-        "decoder.mid.attn_1",
-    ],
-    1: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.1.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.1.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.2.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.2.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.2.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.4.1.transformer_blocks.0.attn1",
-        "input_blocks.5.1.transformer_blocks.0.attn1",
-        "output_blocks.6.1.transformer_blocks.0.attn1",
-        "output_blocks.7.1.transformer_blocks.0.attn1",
-        "output_blocks.8.1.transformer_blocks.0.attn1",
-    ],
-    2: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.2.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.2.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.1.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.1.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.1.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.7.1.transformer_blocks.0.attn1",
-        "input_blocks.8.1.transformer_blocks.0.attn1",
-        "output_blocks.3.1.transformer_blocks.0.attn1",
-        "output_blocks.4.1.transformer_blocks.0.attn1",
-        "output_blocks.5.1.transformer_blocks.0.attn1",
-    ],
-    3: [
-        # SD 1.5 U-Net (diffusers)
-        "mid_block.attentions.0.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "middle_block.1.transformer_blocks.0.attn1",
-    ],
-}
-# XL layers, thanks for GitHub@gel-crabs for the help
-DEPTH_LAYERS_XL = {
-    0: [
-        # SD 1.5 U-Net (diffusers)
-        "down_blocks.0.attentions.0.transformer_blocks.0.attn1",
-        "down_blocks.0.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.0.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.1.transformer_blocks.0.attn1",
-        "up_blocks.3.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.4.1.transformer_blocks.0.attn1",
-        "input_blocks.5.1.transformer_blocks.0.attn1",
-        "output_blocks.3.1.transformer_blocks.0.attn1",
-        "output_blocks.4.1.transformer_blocks.0.attn1",
-        "output_blocks.5.1.transformer_blocks.0.attn1",
-        # SD 1.5 VAE
-        "decoder.mid_block.attentions.0",
-        "decoder.mid.attn_1",
-    ],
-    1: [
-        # SD 1.5 U-Net (diffusers)
-        #"down_blocks.1.attentions.0.transformer_blocks.0.attn1",
-        #"down_blocks.1.attentions.1.transformer_blocks.0.attn1",
-        #"up_blocks.2.attentions.0.transformer_blocks.0.attn1",
-        #"up_blocks.2.attentions.1.transformer_blocks.0.attn1",
-        #"up_blocks.2.attentions.2.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "input_blocks.4.1.transformer_blocks.1.attn1",
-        "input_blocks.5.1.transformer_blocks.1.attn1",
-        "output_blocks.3.1.transformer_blocks.1.attn1",
-        "output_blocks.4.1.transformer_blocks.1.attn1",
-        "output_blocks.5.1.transformer_blocks.1.attn1",
-        "input_blocks.7.1.transformer_blocks.0.attn1",
-        "input_blocks.8.1.transformer_blocks.0.attn1",
-        "output_blocks.0.1.transformer_blocks.0.attn1",
-        "output_blocks.1.1.transformer_blocks.0.attn1",
-        "output_blocks.2.1.transformer_blocks.0.attn1",
-        "input_blocks.7.1.transformer_blocks.1.attn1",
-        "input_blocks.8.1.transformer_blocks.1.attn1",
-        "output_blocks.0.1.transformer_blocks.1.attn1",
-        "output_blocks.1.1.transformer_blocks.1.attn1",
-        "output_blocks.2.1.transformer_blocks.1.attn1",
-        "input_blocks.7.1.transformer_blocks.2.attn1",
-        "input_blocks.8.1.transformer_blocks.2.attn1",
-        "output_blocks.0.1.transformer_blocks.2.attn1",
-        "output_blocks.1.1.transformer_blocks.2.attn1",
-        "output_blocks.2.1.transformer_blocks.2.attn1",
-        "input_blocks.7.1.transformer_blocks.3.attn1",
-        "input_blocks.8.1.transformer_blocks.3.attn1",
-        "output_blocks.0.1.transformer_blocks.3.attn1",
-        "output_blocks.1.1.transformer_blocks.3.attn1",
-        "output_blocks.2.1.transformer_blocks.3.attn1",
-        "input_blocks.7.1.transformer_blocks.4.attn1",
-        "input_blocks.8.1.transformer_blocks.4.attn1",
-        "output_blocks.0.1.transformer_blocks.4.attn1",
-        "output_blocks.1.1.transformer_blocks.4.attn1",
-        "output_blocks.2.1.transformer_blocks.4.attn1",
-        "input_blocks.7.1.transformer_blocks.5.attn1",
-        "input_blocks.8.1.transformer_blocks.5.attn1",
-        "output_blocks.0.1.transformer_blocks.5.attn1",
-        "output_blocks.1.1.transformer_blocks.5.attn1",
-        "output_blocks.2.1.transformer_blocks.5.attn1",
-        "input_blocks.7.1.transformer_blocks.6.attn1",
-        "input_blocks.8.1.transformer_blocks.6.attn1",
-        "output_blocks.0.1.transformer_blocks.6.attn1",
-        "output_blocks.1.1.transformer_blocks.6.attn1",
-        "output_blocks.2.1.transformer_blocks.6.attn1",
-        "input_blocks.7.1.transformer_blocks.7.attn1",
-        "input_blocks.8.1.transformer_blocks.7.attn1",
-        "output_blocks.0.1.transformer_blocks.7.attn1",
-        "output_blocks.1.1.transformer_blocks.7.attn1",
-        "output_blocks.2.1.transformer_blocks.7.attn1",
-        "input_blocks.7.1.transformer_blocks.8.attn1",
-        "input_blocks.8.1.transformer_blocks.8.attn1",
-        "output_blocks.0.1.transformer_blocks.8.attn1",
-        "output_blocks.1.1.transformer_blocks.8.attn1",
-        "output_blocks.2.1.transformer_blocks.8.attn1",
-        "input_blocks.7.1.transformer_blocks.9.attn1",
-        "input_blocks.8.1.transformer_blocks.9.attn1",
-        "output_blocks.0.1.transformer_blocks.9.attn1",
-        "output_blocks.1.1.transformer_blocks.9.attn1",
-        "output_blocks.2.1.transformer_blocks.9.attn1",
-    ],
-    2: [
-        # SD 1.5 U-Net (diffusers)
-        "mid_block.attentions.0.transformer_blocks.0.attn1",
-        # SD 1.5 U-Net (ldm)
-        "middle_block.1.transformer_blocks.0.attn1",
-        "middle_block.1.transformer_blocks.1.attn1",
-        "middle_block.1.transformer_blocks.2.attn1",
-        "middle_block.1.transformer_blocks.3.attn1",
-        "middle_block.1.transformer_blocks.4.attn1",
-        "middle_block.1.transformer_blocks.5.attn1",
-        "middle_block.1.transformer_blocks.6.attn1",
-        "middle_block.1.transformer_blocks.7.attn1",
-        "middle_block.1.transformer_blocks.8.attn1",
-        "middle_block.1.transformer_blocks.9.attn1",
-    ],
-    3 : [] # TODO - separate layers for SD-XL
-}
-
-
-RNG_INSTANCE = random.Random()
-
-
-def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
-    """
-    Returns a random divisor of value that
-        x * min_value <= value
-    if max_options is 1, the behavior is deterministic
-    """
-    min_value = min(min_value, value)
-
-    # All big divisors of value (inclusive)
-    divisors = [i for i in range(min_value, value + 1) if value % i == 0] # divisors in small -> big order
-
-    ns = [value // i for i in divisors[:max_options]]  # has at least 1 element # big -> small order
-
-    idx = RNG_INSTANCE.randint(0, len(ns) - 1)
-
-    return ns[idx]
-
-
-def set_hypertile_seed(seed: int) -> None:
-    RNG_INSTANCE.seed(seed)
-
-
-@functools.cache
-def largest_tile_size_available(width: int, height: int) -> int:
-    """
-    Calculates the largest tile size available for a given width and height
-    Tile size is always a power of 2
-    """
-    gcd = math.gcd(width, height)
-    largest_tile_size_available = 1
-    while gcd % (largest_tile_size_available * 2) == 0:
-        largest_tile_size_available *= 2
-    return largest_tile_size_available
-
-
-def iterative_closest_divisors(hw:int, aspect_ratio:float) -> tuple[int, int]:
-    """
-    Finds h and w such that h*w = hw and h/w = aspect_ratio
-    We check all possible divisors of hw and return the closest to the aspect ratio
-    """
-    divisors = [i for i in range(2, hw + 1) if hw % i == 0] # all divisors of hw
-    pairs = [(i, hw // i) for i in divisors] # all pairs of divisors of hw
-    ratios = [w/h for h, w in pairs] # all ratios of pairs of divisors of hw
-    closest_ratio = min(ratios, key=lambda x: abs(x - aspect_ratio)) # closest ratio to aspect_ratio
-    closest_pair = pairs[ratios.index(closest_ratio)] # closest pair of divisors to aspect_ratio
-    return closest_pair
-
-
-@cache
-def find_hw_candidates(hw:int, aspect_ratio:float) -> tuple[int, int]:
-    """
-    Finds h and w such that h*w = hw and h/w = aspect_ratio
-    """
-    h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio))
-    # find h and w such that h*w = hw and h/w = aspect_ratio
-    if h * w != hw:
-        w_candidate = hw / h
-        # check if w is an integer
-        if not w_candidate.is_integer():
-            h_candidate = hw / w
-            # check if h is an integer
-            if not h_candidate.is_integer():
-                return iterative_closest_divisors(hw, aspect_ratio)
-            else:
-                h = int(h_candidate)
-        else:
-            w = int(w_candidate)
-    return h, w
-
-
-def self_attn_forward(params: HypertileParams, scale_depth=True) -> Callable:
-
-    @wraps(params.forward)
-    def wrapper(*args, **kwargs):
-        if not params.enabled:
-            return params.forward(*args, **kwargs)
-
-        latent_tile_size = max(128, params.tile_size) // 8
-        x = args[0]
-
-        # VAE
-        if x.ndim == 4:
-            b, c, h, w = x.shape
-
-            nh = random_divisor(h, latent_tile_size, params.swap_size)
-            nw = random_divisor(w, latent_tile_size, params.swap_size)
-
-            if nh * nw > 1:
-                x = rearrange(x, "b c (nh h) (nw w) -> (b nh nw) c h w", nh=nh, nw=nw)  # split into nh * nw tiles
-
-            out = params.forward(x, *args[1:], **kwargs)
-
-            if nh * nw > 1:
-                out = rearrange(out, "(b nh nw) c h w -> b c (nh h) (nw w)", nh=nh, nw=nw)
-
-        # U-Net
-        else:
-            hw: int = x.size(1)
-            h, w = find_hw_candidates(hw, params.aspect_ratio)
-            assert h * w == hw, f"Invalid aspect ratio {params.aspect_ratio} for input of shape {x.shape}, hw={hw}, h={h}, w={w}"
-
-            factor = 2 ** params.depth if scale_depth else 1
-            nh = random_divisor(h, latent_tile_size * factor, params.swap_size)
-            nw = random_divisor(w, latent_tile_size * factor, params.swap_size)
-
-            if nh * nw > 1:
-                x = rearrange(x, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
-
-            out = params.forward(x, *args[1:], **kwargs)
-
-            if nh * nw > 1:
-                out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
-                out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
-
-        return out
-
-    return wrapper
-
-
-def hypertile_hook_model(model: nn.Module, width, height, *, enable=False, tile_size_max=128, swap_size=1, max_depth=3, is_sdxl=False):
-    hypertile_layers = getattr(model, "__webui_hypertile_layers", None)
-    if hypertile_layers is None:
-        if not enable:
-            return
-
-        hypertile_layers = {}
-        layers = DEPTH_LAYERS_XL if is_sdxl else DEPTH_LAYERS
-
-        for depth in range(4):
-            for layer_name, module in model.named_modules():
-                if any(layer_name.endswith(try_name) for try_name in layers[depth]):
-                    params = HypertileParams()
-                    module.__webui_hypertile_params = params
-                    params.forward = module.forward
-                    params.depth = depth
-                    params.layer_name = layer_name
-                    module.forward = self_attn_forward(params)
-
-                    hypertile_layers[layer_name] = 1
-
-        model.__webui_hypertile_layers = hypertile_layers
-
-    aspect_ratio = width / height
-    tile_size = min(largest_tile_size_available(width, height), tile_size_max)
-
-    for layer_name, module in model.named_modules():
-        if layer_name in hypertile_layers:
-            params = module.__webui_hypertile_params
-
-            params.tile_size = tile_size
-            params.swap_size = swap_size
-            params.aspect_ratio = aspect_ratio
-            params.enabled = enable and params.depth <= max_depth

extensions-builtin/hypertile/scripts/hypertile_script.py

@@ -1,73 +0,0 @@
-import hypertile
-from modules import scripts, script_callbacks, shared
-
-
-class ScriptHypertile(scripts.Script):
-    name = "Hypertile"
-
-    def title(self):
-        return self.name
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible
-
-    def process(self, p, *args):
-        hypertile.set_hypertile_seed(p.all_seeds[0])
-
-        configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
-
-    def before_hr(self, p, *args):
-        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet)
-
-
-def configure_hypertile(width, height, enable_unet=True):
-    hypertile.hypertile_hook_model(
-        shared.sd_model.first_stage_model,
-        width,
-        height,
-        swap_size=shared.opts.hypertile_swap_size_vae,
-        max_depth=shared.opts.hypertile_max_depth_vae,
-        tile_size_max=shared.opts.hypertile_max_tile_vae,
-        enable=shared.opts.hypertile_enable_vae,
-    )
-
-    hypertile.hypertile_hook_model(
-        shared.sd_model.model,
-        width,
-        height,
-        swap_size=shared.opts.hypertile_swap_size_unet,
-        max_depth=shared.opts.hypertile_max_depth_unet,
-        tile_size_max=shared.opts.hypertile_max_tile_unet,
-        enable=enable_unet,
-        is_sdxl=shared.sd_model.is_sdxl
-    )
-
-
-def on_ui_settings():
-    import gradio as gr
-
-    options = {
-        "hypertile_explanation": shared.OptionHTML("""
-    <a href='https://github.com/tfernd/HyperTile'>Hypertile</a> optimizes the self-attention layer within U-Net and VAE models,
-    resulting in a reduction in computation time ranging from 1 to 4 times. The larger the generated image is, the greater the
-    benefit.
-    """),
-
-        "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net").info("noticeable change in details of the generated picture; if enabled, overrides the setting below"),
-        "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass"),
-        "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
-        "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
-
-        "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE").info("minimal change in the generated picture"),
-        "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
-        "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 6, "step": 1}),
-    }
-
-    for name, opt in options.items():
-        opt.section = ('hypertile', "Hypertile")
-        shared.opts.add_option(name, opt)
-
-
-script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/mobile/javascript/mobile.js

@@ -12,8 +12,6 @@ function isMobile() {
 }
 
 function reportWindowSize() {
-    if (gradioApp().querySelector('.toprow-compact-tools')) return; // not applicable for compact prompt layout
-
     var currentlyMobile = isMobile();
     if (currentlyMobile == isSetupForMobile) return;
     isSetupForMobile = currentlyMobile;
@@ -22,13 +20,7 @@ function reportWindowSize() {
         var button = gradioApp().getElementById(tab + '_generate_box');
         var target = gradioApp().getElementById(currentlyMobile ? tab + '_results' : tab + '_actions_column');
         target.insertBefore(button, target.firstElementChild);
-
-        gradioApp().getElementById(tab + '_results').classList.toggle('mobile', currentlyMobile);
     }
 }
 
 window.addEventListener("resize", reportWindowSize);
-
-onUiLoaded(function() {
-    reportWindowSize();
-});

javascript/dragdrop.js

@@ -119,7 +119,7 @@ window.addEventListener('paste', e => {
     }
 
     const firstFreeImageField = visibleImageFields
-        .filter(el => !el.querySelector('img'))?.[0];
+        .filter(el => el.querySelector('input[type=file]'))?.[0];
 
     dropReplaceImage(
         firstFreeImageField ?

javascript/edit-attention.js

@@ -18,43 +18,37 @@ function keyupEditAttention(event) {
         const before = text.substring(0, selectionStart);
         let beforeParen = before.lastIndexOf(OPEN);
         if (beforeParen == -1) return false;
-
-        let beforeClosingParen = before.lastIndexOf(CLOSE);
-        if (beforeClosingParen != -1 && beforeClosingParen > beforeParen) return false;
+        let beforeParenClose = before.lastIndexOf(CLOSE);
+        while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
+            beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
+            beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
+        }
 
         // Find closing parenthesis around current cursor
         const after = text.substring(selectionStart);
         let afterParen = after.indexOf(CLOSE);
         if (afterParen == -1) return false;
-
-        let afterOpeningParen = after.indexOf(OPEN);
-        if (afterOpeningParen != -1 && afterOpeningParen < afterParen) return false;
+        let afterParenOpen = after.indexOf(OPEN);
+        while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
+            afterParen = after.indexOf(CLOSE, afterParen + 1);
+            afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
+        }
+        if (beforeParen === -1 || afterParen === -1) return false;
 
         // Set the selection to the text between the parenthesis
         const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
-        if (/.*:-?[\d.]+/s.test(parenContent)) {
-            const lastColon = parenContent.lastIndexOf(":");
-            selectionStart = beforeParen + 1;
-            selectionEnd = selectionStart + lastColon;
-        } else {
-            selectionStart = beforeParen + 1;
-            selectionEnd = selectionStart + parenContent.length;
-        }
-
+        const lastColon = parenContent.lastIndexOf(":");
+        selectionStart = beforeParen + 1;
+        selectionEnd = selectionStart + lastColon;
         target.setSelectionRange(selectionStart, selectionEnd);
         return true;
     }
 
     function selectCurrentWord() {
         if (selectionStart !== selectionEnd) return false;
-        const whitespace_delimiters = {"Tab": "\t", "Carriage Return": "\r", "Line Feed": "\n"};
-        let delimiters = opts.keyedit_delimiters;
+        const delimiters = opts.keyedit_delimiters + " \r\n\t";
 
-        for (let i of opts.keyedit_delimiters_whitespace) {
-            delimiters += whitespace_delimiters[i];
-        }
-
-        // seek backward to find beginning
+        // seek backward until to find beggining
         while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
             selectionStart--;
         }
@@ -69,7 +63,7 @@ function keyupEditAttention(event) {
     }
 
     // If the user hasn't selected anything, let's select their current parenthesis block or word
-    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')') && !selectCurrentParenthesisBlock('[', ']')) {
+    if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
         selectCurrentWord();
     }
 
@@ -77,54 +71,33 @@ function keyupEditAttention(event) {
 
     var closeCharacter = ')';
     var delta = opts.keyedit_precision_attention;
-    var start = selectionStart > 0 ? text[selectionStart - 1] : "";
-    var end = text[selectionEnd];
 
-    if (start == '<') {
+    if (selectionStart > 0 && text[selectionStart - 1] == '<') {
         closeCharacter = '>';
         delta = opts.keyedit_precision_extra;
-    } else if (start == '(' && end == ')' || start == '[' && end == ']') { // convert old-style (((emphasis)))
-        let numParen = 0;
+    } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
 
-        while (text[selectionStart - numParen - 1] == start && text[selectionEnd + numParen] == end) {
-            numParen++;
-        }
-
-        if (start == "[") {
-            weight = (1 / 1.1) ** numParen;
-        } else {
-            weight = 1.1 ** numParen;
-        }
-
-        weight = Math.round(weight / opts.keyedit_precision_attention) * opts.keyedit_precision_attention;
-
-        text = text.slice(0, selectionStart - numParen) + "(" + text.slice(selectionStart, selectionEnd) + ":" + weight + ")" + text.slice(selectionEnd + numParen);
-        selectionStart -= numParen - 1;
-        selectionEnd -= numParen - 1;
-    } else if (start != '(') {
         // do not include spaces at the end
         while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') {
-            selectionEnd--;
+            selectionEnd -= 1;
         }
-
         if (selectionStart == selectionEnd) {
             return;
         }
 
         text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
 
-        selectionStart++;
-        selectionEnd++;
+        selectionStart += 1;
+        selectionEnd += 1;
     }
 
-    if (text[selectionEnd] != ':') return;
-    var weightLength = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
-    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + weightLength));
+    var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
+    var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
     if (isNaN(weight)) return;
 
     weight += isPlus ? delta : -delta;
     weight = parseFloat(weight.toPrecision(12));
-    if (Number.isInteger(weight)) weight += ".0";
+    if (String(weight).length == 1) weight += ".0";
 
     if (closeCharacter == ')' && weight == 1) {
         var endParenPos = text.substring(selectionEnd).indexOf(')');
@@ -132,7 +105,7 @@ function keyupEditAttention(event) {
         selectionStart--;
         selectionEnd--;
     } else {
-        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + weightLength);
+        text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end);
     }
 
     target.focus();

javascript/extensions.js

@@ -33,7 +33,7 @@ function extensions_check() {
 
 
     var id = randomId();
-    requestProgress(id, gradioApp().getElementById('extensions_installed_html'), null, function() {
+    requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function() {
 
     });
 

javascript/extraNetworks.js

@@ -26,9 +26,8 @@ function setupExtraNetworksForTab(tabname) {
     var refresh = gradioApp().getElementById(tabname + '_extra_refresh');
     var showDirsDiv = gradioApp().getElementById(tabname + '_extra_show_dirs');
     var showDirs = gradioApp().querySelector('#' + tabname + '_extra_show_dirs input');
-    var promptContainer = gradioApp().querySelector('.prompt-container-compact#' + tabname + '_prompt_container');
-    var negativePrompt = gradioApp().querySelector('#' + tabname + '_neg_prompt');
 
+    sort.dataset.sortkey = 'sortDefault';
     tabs.appendChild(searchDiv);
     tabs.appendChild(sort);
     tabs.appendChild(sortOrder);
@@ -50,23 +49,20 @@ function setupExtraNetworksForTab(tabname) {
 
             elem.style.display = visible ? "" : "none";
         });
-
-        applySort();
     };
 
     var applySort = function() {
-        var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
-
         var reverse = sortOrder.classList.contains("sortReverse");
-        var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim() || "name";
-        sortKey = "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1);
-        var sortKeyStore = sortKey + "-" + (reverse ? "Descending" : "Ascending") + "-" + cards.length;
-
-        if (sortKeyStore == sort.dataset.sortkey) {
+        var sortKey = sort.querySelector("input").value.toLowerCase().replace("sort", "").replaceAll(" ", "_").replace(/_+$/, "").trim();
+        sortKey = sortKey ? "sort" + sortKey.charAt(0).toUpperCase() + sortKey.slice(1) : "";
+        var sortKeyStore = sortKey ? sortKey + (reverse ? "Reverse" : "") : "";
+        if (!sortKey || sortKeyStore == sort.dataset.sortkey) {
             return;
         }
+
         sort.dataset.sortkey = sortKeyStore;
 
+        var cards = gradioApp().querySelectorAll('#' + tabname + '_extra_tabs div.card');
         cards.forEach(function(card) {
             card.originalParentElement = card.parentElement;
         });
@@ -92,13 +88,15 @@ function setupExtraNetworksForTab(tabname) {
     };
 
     search.addEventListener("input", applyFilter);
+    applyFilter();
+    ["change", "blur", "click"].forEach(function(evt) {
+        sort.querySelector("input").addEventListener(evt, applySort);
+    });
     sortOrder.addEventListener("click", function() {
         sortOrder.classList.toggle("sortReverse");
         applySort();
     });
-    applyFilter();
 
-    extraNetworksApplySort[tabname] = applySort;
     extraNetworksApplyFilter[tabname] = applyFilter;
 
     var showDirsUpdate = function() {
@@ -111,51 +109,11 @@ function setupExtraNetworksForTab(tabname) {
     showDirsUpdate();
 }
 
-function extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt) {
-    if (!gradioApp().querySelector('.toprow-compact-tools')) return; // only applicable for compact prompt layout
-
-    var promptContainer = gradioApp().getElementById(tabname + '_prompt_container');
-    var prompt = gradioApp().getElementById(tabname + '_prompt_row');
-    var negPrompt = gradioApp().getElementById(tabname + '_neg_prompt_row');
-    var elem = id ? gradioApp().getElementById(id) : null;
-
-    if (showNegativePrompt && elem) {
-        elem.insertBefore(negPrompt, elem.firstChild);
-    } else {
-        promptContainer.insertBefore(negPrompt, promptContainer.firstChild);
-    }
-
-    if (showPrompt && elem) {
-        elem.insertBefore(prompt, elem.firstChild);
-    } else {
-        promptContainer.insertBefore(prompt, promptContainer.firstChild);
-    }
-
-    if (elem) {
-        elem.classList.toggle('extra-page-prompts-active', showNegativePrompt || showPrompt);
-    }
-}
-
-
-function extraNetworksUrelatedTabSelected(tabname) { // called from python when user selects an unrelated tab (generate)
-    extraNetworksMovePromptToTab(tabname, '', false, false);
-}
-
-function extraNetworksTabSelected(tabname, id, showPrompt, showNegativePrompt) { // called from python when user selects an extra networks tab
-    extraNetworksMovePromptToTab(tabname, id, showPrompt, showNegativePrompt);
-
-}
-
 function applyExtraNetworkFilter(tabname) {
     setTimeout(extraNetworksApplyFilter[tabname], 1);
 }
 
-function applyExtraNetworkSort(tabname) {
-    setTimeout(extraNetworksApplySort[tabname], 1);
-}
-
 var extraNetworksApplyFilter = {};
-var extraNetworksApplySort = {};
 var activePromptTextarea = {};
 
 function setupExtraNetworks() {
@@ -182,15 +140,14 @@ function setupExtraNetworks() {
 
 onUiLoaded(setupExtraNetworks);
 
-var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
-var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
+var re_extranet = /<([^:]+:[^:]+):[\d.]+>(.*)/;
+var re_extranet_g = /\s+<([^:]+:[^:]+):[\d.]+>/g;
 
 function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
     var m = text.match(re_extranet);
     var replaced = false;
     var newTextareaText;
     if (m) {
-        var extraTextBeforeNet = opts.extra_networks_add_text_separator;
         var extraTextAfterNet = m[2];
         var partToSearch = m[1];
         var foundAtPosition = -1;
@@ -204,13 +161,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
             return found;
         });
 
-        if (foundAtPosition >= 0) {
-            if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
-                newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
-            }
-            if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) {
-                newTextareaText = newTextareaText.substr(0, foundAtPosition - extraTextBeforeNet.length) + newTextareaText.substr(foundAtPosition);
-            }
+        if (foundAtPosition >= 0 && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
+            newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
         }
     } else {
         newTextareaText = textarea.value.replaceAll(new RegExp(text, "g"), function(found) {
@@ -264,24 +216,27 @@ function extraNetworksSearchButton(tabs_id, event) {
 
 var globalPopup = null;
 var globalPopupInner = null;
-
 function closePopup() {
     if (!globalPopup) return;
+
     globalPopup.style.display = "none";
 }
-
 function popup(contents) {
     if (!globalPopup) {
         globalPopup = document.createElement('div');
+        globalPopup.onclick = closePopup;
         globalPopup.classList.add('global-popup');
 
         var close = document.createElement('div');
         close.classList.add('global-popup-close');
-        close.addEventListener("click", closePopup);
+        close.onclick = closePopup;
         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);
 
@@ -294,15 +249,6 @@ function popup(contents) {
     globalPopup.style.display = "flex";
 }
 
-var storedPopupIds = {};
-function popupId(id) {
-    if (!storedPopupIds[id]) {
-        storedPopupIds[id] = gradioApp().getElementById(id);
-    }
-
-    popup(storedPopupIds[id]);
-}
-
 function extraNetworksShowMetadata(text) {
     var elem = document.createElement('pre');
     elem.classList.add('popup-metadata');
@@ -380,13 +326,13 @@ function extraNetworksEditUserMetadata(event, tabname, extraPage, cardName) {
 function extraNetworksRefreshSingleCard(page, tabname, name) {
     requestGet("./sd_extra_networks/get-single-card", {page: page, tabname: tabname, name: name}, function(data) {
         if (data && data.html) {
-            var card = gradioApp().querySelector(`#${tabname}_${page.replace(" ", "_")}_cards > .card[data-name="${name}"]`);
+            var card = gradioApp().querySelector('.card[data-name=' + JSON.stringify(name) + ']'); // likely using the wrong stringify function
 
             var newDiv = document.createElement('DIV');
             newDiv.innerHTML = data.html;
             var newCard = newDiv.firstElementChild;
 
-            newCard.style.display = '';
+            newCard.style = '';
             card.parentElement.insertBefore(newCard, card);
             card.parentElement.removeChild(card);
         }

javascript/imageviewer.js

@@ -33,11 +33,8 @@ function updateOnBackgroundChange() {
     const modalImage = gradioApp().getElementById("modalImage");
     if (modalImage && modalImage.offsetParent) {
         let currentButton = selected_gallery_button();
-        let preview = gradioApp().querySelectorAll('.livePreview > img');
-        if (preview.length > 0) {
-            // show preview image if available
-            modalImage.src = preview[preview.length - 1].src;
-        } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
+
+        if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {
             modalImage.src = currentButton.children[0].src;
             if (modalImage.style.display === 'none') {
                 const modal = gradioApp().getElementById("lightboxModal");
@@ -139,11 +136,6 @@ function setupImageForLightbox(e) {
     var event = isFirefox ? 'mousedown' : 'click';
 
     e.addEventListener(event, function(evt) {
-        if (evt.button == 1) {
-            open(evt.target.src);
-            evt.preventDefault();
-            return;
-        }
         if (!opts.js_modal_lightbox || evt.button != 0) return;
 
         modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed);

javascript/inputAccordion.js

@@ -1,68 +0,0 @@
-function inputAccordionChecked(id, checked) {
-    var accordion = gradioApp().getElementById(id);
-    accordion.visibleCheckbox.checked = checked;
-    accordion.onVisibleCheckboxChange();
-}
-
-function setupAccordion(accordion) {
-    var labelWrap = accordion.querySelector('.label-wrap');
-    var gradioCheckbox = gradioApp().querySelector('#' + accordion.id + "-checkbox input");
-    var extra = gradioApp().querySelector('#' + accordion.id + "-extra");
-    var span = labelWrap.querySelector('span');
-    var linked = true;
-
-    var isOpen = function() {
-        return labelWrap.classList.contains('open');
-    };
-
-    var observerAccordionOpen = new MutationObserver(function(mutations) {
-        mutations.forEach(function(mutationRecord) {
-            accordion.classList.toggle('input-accordion-open', isOpen());
-
-            if (linked) {
-                accordion.visibleCheckbox.checked = isOpen();
-                accordion.onVisibleCheckboxChange();
-            }
-        });
-    });
-    observerAccordionOpen.observe(labelWrap, {attributes: true, attributeFilter: ['class']});
-
-    if (extra) {
-        labelWrap.insertBefore(extra, labelWrap.lastElementChild);
-    }
-
-    accordion.onChecked = function(checked) {
-        if (isOpen() != checked) {
-            labelWrap.click();
-        }
-    };
-
-    var visibleCheckbox = document.createElement('INPUT');
-    visibleCheckbox.type = 'checkbox';
-    visibleCheckbox.checked = isOpen();
-    visibleCheckbox.id = accordion.id + "-visible-checkbox";
-    visibleCheckbox.className = gradioCheckbox.className + " input-accordion-checkbox";
-    span.insertBefore(visibleCheckbox, span.firstChild);
-
-    accordion.visibleCheckbox = visibleCheckbox;
-    accordion.onVisibleCheckboxChange = function() {
-        if (linked && isOpen() != visibleCheckbox.checked) {
-            labelWrap.click();
-        }
-
-        gradioCheckbox.checked = visibleCheckbox.checked;
-        updateInput(gradioCheckbox);
-    };
-
-    visibleCheckbox.addEventListener('click', function(event) {
-        linked = false;
-        event.stopPropagation();
-    });
-    visibleCheckbox.addEventListener('input', accordion.onVisibleCheckboxChange);
-}
-
-onUiLoaded(function() {
-    for (var accordion of gradioApp().querySelectorAll('.input-accordion')) {
-        setupAccordion(accordion);
-    }
-});

javascript/localization.js

@@ -107,41 +107,12 @@ function processNode(node) {
     });
 }
 
-function localizeWholePage() {
-    processNode(gradioApp());
-
-    function elem(comp) {
-        var elem_id = comp.props.elem_id ? comp.props.elem_id : "component-" + comp.id;
-        return gradioApp().getElementById(elem_id);
-    }
-
-    for (var comp of window.gradio_config.components) {
-        if (comp.props.webui_tooltip) {
-            let e = elem(comp);
-
-            let tl = e ? getTranslation(e.title) : undefined;
-            if (tl !== undefined) {
-                e.title = tl;
-            }
-        }
-        if (comp.props.placeholder) {
-            let e = elem(comp);
-            let textbox = e ? e.querySelector('[placeholder]') : null;
-
-            let tl = textbox ? getTranslation(textbox.placeholder) : undefined;
-            if (tl !== undefined) {
-                textbox.placeholder = tl;
-            }
-        }
-    }
-}
-
 function dumpTranslations() {
     if (!hasLocalization()) {
         // If we don't have any localization,
         // we will not have traversed the app to find
         // original_lines, so do that now.
-        localizeWholePage();
+        processNode(gradioApp());
     }
     var dumped = {};
     if (localization.rtl) {
@@ -183,7 +154,7 @@ document.addEventListener("DOMContentLoaded", function() {
         });
     });
 
-    localizeWholePage();
+    processNode(gradioApp());
 
     if (localization.rtl) { // if the language is from right to left,
         (new MutationObserver((mutations, observer) => { // wait for the style to load

javascript/notification.js

@@ -15,7 +15,7 @@ onAfterUiUpdate(function() {
         }
     }
 
-    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"] div[id$="_results"] .thumbnail-item > img');
+    const galleryPreviews = gradioApp().querySelectorAll('div[id^="tab_"][style*="display: block"] div[id$="_results"] .thumbnail-item > img');
 
     if (galleryPreviews == null) return;
 
@@ -26,11 +26,7 @@ onAfterUiUpdate(function() {
     lastHeadImg = headImg;
 
     // play notification sound if available
-    const notificationAudio = gradioApp().querySelector('#audio_notification audio');
-    if (notificationAudio) {
-        notificationAudio.volume = opts.notification_volume / 100.0 || 1.0;
-        notificationAudio.play();
-    }
+    gradioApp().querySelector('#audio_notification audio')?.play();
 
     if (document.hasFocus()) return;
 

javascript/progressbar.js

@@ -69,6 +69,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
     var dateStart = new Date();
     var wasEverActive = false;
     var parentProgressbar = progressbarContainer.parentNode;
+    var parentGallery = gallery ? gallery.parentNode : null;
 
     var divProgress = document.createElement('div');
     divProgress.className = 'progressDiv';
@@ -79,26 +80,32 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
     divProgress.appendChild(divInner);
     parentProgressbar.insertBefore(divProgress, progressbarContainer);
 
-    var livePreview = null;
+    if (parentGallery) {
+        var livePreview = document.createElement('div');
+        livePreview.className = 'livePreview';
+        parentGallery.insertBefore(livePreview, gallery);
+    }
 
     var removeProgressBar = function() {
-        if (!divProgress) return;
-
         setTitle("");
         parentProgressbar.removeChild(divProgress);
-        if (gallery && livePreview) gallery.removeChild(livePreview);
+        if (parentGallery) parentGallery.removeChild(livePreview);
         atEnd();
-
-        divProgress = null;
     };
 
-    var funProgress = function(id_task) {
-        request("./internal/progress", {id_task: id_task, live_preview: false}, function(res) {
+    var fun = function(id_task, id_live_preview) {
+        request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
             if (res.completed) {
                 removeProgressBar();
                 return;
             }
 
+            var rect = progressbarContainer.getBoundingClientRect();
+
+            if (rect.width) {
+                divProgress.style.width = rect.width + "px";
+            }
+
             let progressText = "";
 
             divInner.style.width = ((res.progress || 0) * 100.0) + '%';
@@ -112,6 +119,7 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 progressText += " ETA: " + formatTime(res.eta);
             }
 
+
             setTitle(progressText);
 
             if (res.textinfo && res.textinfo.indexOf("\n") == -1) {
@@ -134,33 +142,16 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 return;
             }
 
-            if (onProgress) {
-                onProgress(res);
-            }
-
-            setTimeout(() => {
-                funProgress(id_task, res.id_live_preview);
-            }, opts.live_preview_refresh_period || 500);
-        }, function() {
-            removeProgressBar();
-        });
-    };
-
-    var funLivePreview = function(id_task, id_live_preview) {
-        request("./internal/progress", {id_task: id_task, id_live_preview: id_live_preview}, function(res) {
-            if (!divProgress) {
-                return;
-            }
 
             if (res.live_preview && gallery) {
+                rect = gallery.getBoundingClientRect();
+                if (rect.width) {
+                    livePreview.style.width = rect.width + "px";
+                    livePreview.style.height = rect.height + "px";
+                }
+
                 var img = new Image();
                 img.onload = function() {
-                    if (!livePreview) {
-                        livePreview = document.createElement('div');
-                        livePreview.className = 'livePreview';
-                        gallery.insertBefore(livePreview, gallery.firstElementChild);
-                    }
-
                     livePreview.appendChild(img);
                     if (livePreview.childElementCount > 2) {
                         livePreview.removeChild(livePreview.firstElementChild);
@@ -169,18 +160,18 @@ function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgre
                 img.src = res.live_preview;
             }
 
+
+            if (onProgress) {
+                onProgress(res);
+            }
+
             setTimeout(() => {
-                funLivePreview(id_task, res.id_live_preview);
+                fun(id_task, res.id_live_preview);
             }, opts.live_preview_refresh_period || 500);
         }, function() {
             removeProgressBar();
         });
     };
 
-    funProgress(id_task, 0);
-
-    if (gallery) {
-        funLivePreview(id_task, 0);
-    }
-
+    fun(id_task, 0);
 }

javascript/resizeHandle.js

@@ -1,141 +0,0 @@
-(function() {
-    const GRADIO_MIN_WIDTH = 320;
-    const GRID_TEMPLATE_COLUMNS = '1fr 16px 1fr';
-    const PAD = 16;
-    const DEBOUNCE_TIME = 100;
-
-    const R = {
-        tracking: false,
-        parent: null,
-        parentWidth: null,
-        leftCol: null,
-        leftColStartWidth: null,
-        screenX: null,
-    };
-
-    let resizeTimer;
-    let parents = [];
-
-    function setLeftColGridTemplate(el, width) {
-        el.style.gridTemplateColumns = `${width}px 16px 1fr`;
-    }
-
-    function displayResizeHandle(parent) {
-        if (window.innerWidth < GRADIO_MIN_WIDTH * 2 + PAD * 4) {
-            parent.style.display = 'flex';
-            if (R.handle != null) {
-                R.handle.style.opacity = '0';
-            }
-            return false;
-        } else {
-            parent.style.display = 'grid';
-            if (R.handle != null) {
-                R.handle.style.opacity = '100';
-            }
-            return true;
-        }
-    }
-
-    function afterResize(parent) {
-        if (displayResizeHandle(parent) && parent.style.gridTemplateColumns != GRID_TEMPLATE_COLUMNS) {
-            const oldParentWidth = R.parentWidth;
-            const newParentWidth = parent.offsetWidth;
-            const widthL = parseInt(parent.style.gridTemplateColumns.split(' ')[0]);
-
-            const ratio = newParentWidth / oldParentWidth;
-
-            const newWidthL = Math.max(Math.floor(ratio * widthL), GRADIO_MIN_WIDTH);
-            setLeftColGridTemplate(parent, newWidthL);
-
-            R.parentWidth = newParentWidth;
-        }
-    }
-
-    function setup(parent) {
-        const leftCol = parent.firstElementChild;
-        const rightCol = parent.lastElementChild;
-
-        parents.push(parent);
-
-        parent.style.display = 'grid';
-        parent.style.gap = '0';
-        parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
-
-        const resizeHandle = document.createElement('div');
-        resizeHandle.classList.add('resize-handle');
-        parent.insertBefore(resizeHandle, rightCol);
-
-        resizeHandle.addEventListener('mousedown', (evt) => {
-            if (evt.button !== 0) return;
-
-            evt.preventDefault();
-            evt.stopPropagation();
-
-            document.body.classList.add('resizing');
-
-            R.tracking = true;
-            R.parent = parent;
-            R.parentWidth = parent.offsetWidth;
-            R.handle = resizeHandle;
-            R.leftCol = leftCol;
-            R.leftColStartWidth = leftCol.offsetWidth;
-            R.screenX = evt.screenX;
-        });
-
-        resizeHandle.addEventListener('dblclick', (evt) => {
-            evt.preventDefault();
-            evt.stopPropagation();
-
-            parent.style.gridTemplateColumns = GRID_TEMPLATE_COLUMNS;
-        });
-
-        afterResize(parent);
-    }
-
-    window.addEventListener('mousemove', (evt) => {
-        if (evt.button !== 0) return;
-
-        if (R.tracking) {
-            evt.preventDefault();
-            evt.stopPropagation();
-
-            const delta = R.screenX - evt.screenX;
-            const leftColWidth = Math.max(Math.min(R.leftColStartWidth - delta, R.parent.offsetWidth - GRADIO_MIN_WIDTH - PAD), GRADIO_MIN_WIDTH);
-            setLeftColGridTemplate(R.parent, leftColWidth);
-        }
-    });
-
-    window.addEventListener('mouseup', (evt) => {
-        if (evt.button !== 0) return;
-
-        if (R.tracking) {
-            evt.preventDefault();
-            evt.stopPropagation();
-
-            R.tracking = false;
-
-            document.body.classList.remove('resizing');
-        }
-    });
-
-
-    window.addEventListener('resize', () => {
-        clearTimeout(resizeTimer);
-
-        resizeTimer = setTimeout(function() {
-            for (const parent of parents) {
-                afterResize(parent);
-            }
-        }, DEBOUNCE_TIME);
-    });
-
-    setupResizeHandle = setup;
-})();
-
-onUiLoaded(function() {
-    for (var elem of gradioApp().querySelectorAll('.resize-handle-row')) {
-        if (!elem.querySelector('.resize-handle')) {
-            setupResizeHandle(elem);
-        }
-    }
-});

javascript/settings.js

@@ -1,71 +0,0 @@
-let settingsExcludeTabsFromShowAll = {
-    settings_tab_defaults: 1,
-    settings_tab_sysinfo: 1,
-    settings_tab_actions: 1,
-    settings_tab_licenses: 1,
-};
-
-function settingsShowAllTabs() {
-    gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
-        if (settingsExcludeTabsFromShowAll[elem.id]) return;
-
-        elem.style.display = "block";
-    });
-}
-
-function settingsShowOneTab() {
-    gradioApp().querySelector('#settings_show_one_page').click();
-}
-
-onUiLoaded(function() {
-    var edit = gradioApp().querySelector('#settings_search');
-    var editTextarea = gradioApp().querySelector('#settings_search > label > input');
-    var buttonShowAllPages = gradioApp().getElementById('settings_show_all_pages');
-    var settings_tabs = gradioApp().querySelector('#settings div');
-
-    onEdit('settingsSearch', editTextarea, 250, function() {
-        var searchText = (editTextarea.value || "").trim().toLowerCase();
-
-        gradioApp().querySelectorAll('#settings > div[id^=settings_] div[id^=column_settings_] > *').forEach(function(elem) {
-            var visible = elem.textContent.trim().toLowerCase().indexOf(searchText) != -1;
-            elem.style.display = visible ? "" : "none";
-        });
-
-        if (searchText != "") {
-            settingsShowAllTabs();
-        } else {
-            settingsShowOneTab();
-        }
-    });
-
-    settings_tabs.insertBefore(edit, settings_tabs.firstChild);
-    settings_tabs.appendChild(buttonShowAllPages);
-
-
-    buttonShowAllPages.addEventListener("click", settingsShowAllTabs);
-});
-
-
-onOptionsChanged(function() {
-    if (gradioApp().querySelector('#settings .settings-category')) return;
-
-    var sectionMap = {};
-    gradioApp().querySelectorAll('#settings > div > button').forEach(function(x) {
-        sectionMap[x.textContent.trim()] = x;
-    });
-
-    opts._categories.forEach(function(x) {
-        var section = x[0];
-        var category = x[1];
-
-        var span = document.createElement('SPAN');
-        span.textContent = category;
-        span.className = 'settings-category';
-
-        var sectionElem = sectionMap[section];
-        if (!sectionElem) return;
-
-        sectionElem.parentElement.insertBefore(span, sectionElem);
-    });
-});
-

javascript/token-counters.js

@@ -1,9 +1,10 @@
-let promptTokenCountUpdateFunctions = {};
+let promptTokenCountDebounceTime = 800;
+let promptTokenCountTimeouts = {};
+var promptTokenCountUpdateFunctions = {};
 
 function update_txt2img_tokens(...args) {
     // Called from Gradio
     update_token_counter("txt2img_token_button");
-    update_token_counter("txt2img_negative_token_button");
     if (args.length == 2) {
         return args[0];
     }
@@ -13,7 +14,6 @@ function update_txt2img_tokens(...args) {
 function update_img2img_tokens(...args) {
     // Called from Gradio
     update_token_counter("img2img_token_button");
-    update_token_counter("img2img_negative_token_button");
     if (args.length == 2) {
         return args[0];
     }
@@ -21,7 +21,16 @@ function update_img2img_tokens(...args) {
 }
 
 function update_token_counter(button_id) {
-    promptTokenCountUpdateFunctions[button_id]?.();
+    if (opts.disable_token_counters) {
+        return;
+    }
+    if (promptTokenCountTimeouts[button_id]) {
+        clearTimeout(promptTokenCountTimeouts[button_id]);
+    }
+    promptTokenCountTimeouts[button_id] = setTimeout(
+        () => gradioApp().getElementById(button_id)?.click(),
+        promptTokenCountDebounceTime,
+    );
 }
 
 
@@ -60,11 +69,10 @@ function setupTokenCounting(id, id_counter, id_button) {
     prompt.parentElement.insertBefore(counter, prompt);
     prompt.parentElement.style.position = "relative";
 
-    var func = onEdit(id, textarea, 800, function() {
-        gradioApp().getElementById(id_button)?.click();
-    });
-    promptTokenCountUpdateFunctions[id] = func;
-    promptTokenCountUpdateFunctions[id_button] = func;
+    promptTokenCountUpdateFunctions[id] = function() {
+        update_token_counter(id_button);
+    };
+    textarea.addEventListener("input", promptTokenCountUpdateFunctions[id]);
 }
 
 function setupTokenCounters() {

javascript/ui.js

@@ -19,11 +19,28 @@ function all_gallery_buttons() {
 }
 
 function selected_gallery_button() {
-    return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null;
+    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() {
-    return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected'));
+    var buttons = all_gallery_buttons();
+    var button = selected_gallery_button();
+
+    var result = -1;
+    buttons.forEach(function(v, i) {
+        if (v == button) {
+            result = i;
+        }
+    });
+
+    return result;
 }
 
 function extract_image_from_gallery(gallery) {
@@ -263,6 +280,21 @@ onAfterUiUpdate(function() {
     json_elem.parentElement.style.display = "none";
 
     setupTokenCounters();
+
+    var show_all_pages = gradioApp().getElementById('settings_show_all_pages');
+    var settings_tabs = gradioApp().querySelector('#settings div');
+    if (show_all_pages && settings_tabs) {
+        settings_tabs.appendChild(show_all_pages);
+        show_all_pages.onclick = function() {
+            gradioApp().querySelectorAll('#settings > div').forEach(function(elem) {
+                if (elem.id == "settings_tab_licenses") {
+                    return;
+                }
+
+                elem.style.display = "block";
+            });
+        };
+    }
 });
 
 onOptionsChanged(function() {
@@ -351,20 +383,3 @@ function switchWidthHeight(tabname) {
     updateInput(height);
     return [];
 }
-
-
-var onEditTimers = {};
-
-// calls func after afterMs milliseconds has passed since the input elem has beed enited by user
-function onEdit(editId, elem, afterMs, func) {
-    var edited = function() {
-        var existingTimer = onEditTimers[editId];
-        if (existingTimer) clearTimeout(existingTimer);
-
-        onEditTimers[editId] = setTimeout(func, afterMs);
-    };
-
-    elem.addEventListener("input", edited);
-
-    return edited;
-}

launch.py

@@ -25,13 +25,6 @@ start = launch_utils.start
 
 
 def main():
-    if args.dump_sysinfo:
-        filename = launch_utils.dump_sysinfo()
-
-        print(f"Sysinfo saved as {filename}. Exiting...")
-
-        exit(0)
-
     launch_utils.startup_timer.record("initial startup")
 
     with launch_utils.startup_timer.subcategory("prepare environment"):

modules/api/api.py

@@ -4,8 +4,6 @@ import os
 import time
 import datetime
 import uvicorn
-import ipaddress
-import requests
 import gradio as gr
 from threading import Lock
 from io import BytesIO
@@ -17,18 +15,20 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
-from PIL import PngImagePlugin, Image
+from PIL import PngImagePlugin,Image
+from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights, checkpoint_aliases
+from modules.sd_vae import vae_dict
 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 Any
+from typing import Dict, List, Any
 import piexif
 import piexif.helper
 from contextlib import closing
@@ -56,41 +56,7 @@ def setUpscalers(req: dict):
     return reqDict
 
 
-def verify_url(url):
-    """Returns True if the url refers to a global resource."""
-
-    import socket
-    from urllib.parse import urlparse
-    try:
-        parsed_url = urlparse(url)
-        domain_name = parsed_url.netloc
-        host = socket.gethostbyname_ex(domain_name)
-        for ip in host[2]:
-            ip_addr = ipaddress.ip_address(ip)
-            if not ip_addr.is_global:
-                return False
-    except Exception:
-        return False
-
-    return True
-
-
 def decode_base64_to_image(encoding):
-    if encoding.startswith("http://") or encoding.startswith("https://"):
-        if not opts.api_enable_requests:
-            raise HTTPException(status_code=500, detail="Requests not allowed")
-
-        if opts.api_forbid_local_requests and not verify_url(encoding):
-            raise HTTPException(status_code=500, detail="Request to local resource not allowed")
-
-        headers = {'user-agent': opts.api_useragent} if opts.api_useragent else {}
-        response = requests.get(encoding, timeout=30, headers=headers)
-        try:
-            image = Image.open(BytesIO(response.content))
-            return image
-        except Exception as e:
-            raise HTTPException(status_code=500, detail="Invalid image url") from e
-
     if encoding.startswith("data:image/"):
         encoding = encoding.split(";")[1].split(",")[1]
     try:
@@ -102,8 +68,7 @@ def decode_base64_to_image(encoding):
 
 def encode_pil_to_base64(image):
     with io.BytesIO() as output_bytes:
-        if isinstance(image, str):
-            return image
+
         if opts.samples_format.lower() == 'png':
             use_metadata = False
             metadata = PngImagePlugin.PngInfo()
@@ -221,15 +186,15 @@ class Api:
         self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=models.OptionsModel)
         self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
         self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=models.FlagsModel)
-        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=list[models.SamplerItem])
-        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=list[models.UpscalerItem])
-        self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=list[models.LatentUpscalerModeItem])
-        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=list[models.SDModelItem])
-        self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=list[models.SDVaeItem])
-        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=list[models.HypernetworkItem])
-        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=list[models.FaceRestorerItem])
-        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=list[models.RealesrganItem])
-        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=list[models.PromptStyleItem])
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
+        self.add_api_route("/sdapi/v1/latent-upscale-modes", self.get_latent_upscale_modes, methods=["GET"], response_model=List[models.LatentUpscalerModeItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
+        self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[models.PromptStyleItem])
         self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=models.EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
         self.add_api_route("/sdapi/v1/refresh-vae", self.refresh_vae, methods=["POST"])
@@ -242,8 +207,7 @@ class Api:
         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=models.ScriptsList)
-        self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=list[models.ScriptInfo])
-        self.add_api_route("/sdapi/v1/extensions", self.get_extensions_list, methods=["GET"], response_model=list[models.ExtensionItem])
+        self.add_api_route("/sdapi/v1/script-info", self.get_script_info, methods=["GET"], response_model=List[models.ScriptInfo])
 
         if shared.cmd_opts.api_server_stop:
             self.add_api_route("/sdapi/v1/server-kill", self.kill_webui, methods=["POST"])
@@ -366,7 +330,6 @@ class Api:
 
         with self.queue_lock:
             with closing(StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)) as p:
-                p.is_api = True
                 p.scripts = script_runner
                 p.outpath_grids = opts.outdir_txt2img_grids
                 p.outpath_samples = opts.outdir_txt2img_samples
@@ -427,7 +390,6 @@ class Api:
         with self.queue_lock:
             with closing(StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)) as p:
                 p.init_images = [decode_base64_to_image(x) for x in init_images]
-                p.is_api = True
                 p.scripts = script_runner
                 p.outpath_grids = opts.outdir_img2img_grids
                 p.outpath_samples = opts.outdir_img2img_samples
@@ -474,6 +436,9 @@ class Api:
         return models.ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
 
     def pnginfoapi(self, req: models.PNGInfoRequest):
+        if(not req.image.strip()):
+            return models.PNGInfoResponse(info="")
+
         image = decode_base64_to_image(req.image.strip())
         if image is None:
             return models.PNGInfoResponse(info="")
@@ -482,10 +447,9 @@ class Api:
         if geninfo is None:
             geninfo = ""
 
-        params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
-        script_callbacks.infotext_pasted_callback(geninfo, params)
+        items = {**{'parameters': geninfo}, **items}
 
-        return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
+        return models.PNGInfoResponse(info=geninfo, items=items)
 
     def progressapi(self, req: models.ProgressRequest = Depends()):
         # copy from check_progress_call of ui.py
@@ -540,12 +504,12 @@ class Api:
         return {}
 
     def unloadapi(self):
-        sd_models.unload_model_weights()
+        unload_model_weights()
 
         return {}
 
     def reloadapi(self):
-        sd_models.send_model_to_device(shared.sd_model)
+        reload_model_weights()
 
         return {}
 
@@ -563,13 +527,13 @@ class Api:
 
         return options
 
-    def set_config(self, req: dict[str, Any]):
+    def set_config(self, req: Dict[str, Any]):
         checkpoint_name = req.get("sd_model_checkpoint", None)
-        if checkpoint_name is not None and checkpoint_name not in sd_models.checkpoint_aliases:
+        if checkpoint_name is not None and checkpoint_name not in checkpoint_aliases:
             raise RuntimeError(f"model {checkpoint_name!r} not found")
 
         for k, v in req.items():
-            shared.opts.set(k, v, is_api=True)
+            shared.opts.set(k, v)
 
         shared.opts.save(shared.config_filename)
         return
@@ -601,12 +565,10 @@ class Api:
         ]
 
     def get_sd_models(self):
-        import modules.sd_models as sd_models
-        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 sd_models.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_sd_vaes(self):
-        import modules.sd_vae as sd_vae
-        return [{"model_name": x, "filename": sd_vae.vae_dict[x]} for x in sd_vae.vae_dict.keys()]
+        return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()]
 
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
@@ -769,25 +731,6 @@ class Api:
             cuda = {'error': f'{err}'}
         return models.MemoryResponse(ram=ram, cuda=cuda)
 
-    def get_extensions_list(self):
-        from modules import extensions
-        extensions.list_extensions()
-        ext_list = []
-        for ext in extensions.extensions:
-            ext: extensions.Extension
-            ext.read_info_from_repo()
-            if ext.remote is not None:
-                ext_list.append({
-                    "name": ext.name,
-                    "remote": ext.remote,
-                    "branch": ext.branch,
-                    "commit_hash":ext.commit_hash,
-                    "commit_date":ext.commit_date,
-                    "version":ext.version,
-                    "enabled":ext.enabled
-                })
-        return ext_list
-
     def launch(self, server_name, port, root_path):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)

modules/api/models.py

@@ -1,10 +1,12 @@
 import inspect
 
 from pydantic import BaseModel, Field, create_model
-from typing import Any, Optional, Literal
+from typing import Any, Optional
+from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 from modules.shared import sd_upscalers, opts, parser
+from typing import Dict, List
 
 API_NOT_ALLOWED = [
     "self",
@@ -48,12 +50,10 @@ class PydanticModelGenerator:
         additional_fields = None,
     ):
         def field_type_generator(k, v):
+            # field_type = str if not overrides.get(k) else overrides[k]["type"]
+            # print(k, v.annotation, v.default)
             field_type = v.annotation
 
-            if field_type == 'Image':
-                # images are sent as base64 strings via API
-                field_type = 'str'
-
             return Optional[field_type]
 
         def merge_class_params(class_):
@@ -63,6 +63,7 @@ class PydanticModelGenerator:
                 parameters = {**parameters, **inspect.signature(classes.__init__).parameters}
             return parameters
 
+
         self._model_name = model_name
         self._class_data = merge_class_params(class_instance)
 
@@ -71,7 +72,7 @@ class PydanticModelGenerator:
                 field=underscore(k),
                 field_alias=k,
                 field_type=field_type_generator(k, v),
-                field_value=None if isinstance(v.default, property) else v.default
+                field_value=v.default
             )
             for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED
         ]
@@ -93,8 +94,8 @@ class PydanticModelGenerator:
             d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def
         }
         DynamicModel = create_model(self._model_name, **fields)
-        DynamicModel.model_config['populate_by_name'] = True
-        DynamicModel.model_config['frozen'] = True
+        DynamicModel.__config__.allow_population_by_field_name = True
+        DynamicModel.__config__.allow_mutation = True
         return DynamicModel
 
 StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
@@ -128,12 +129,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
 ).generate_model()
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
 class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
@@ -166,18 +167,17 @@ class FileData(BaseModel):
     name: str = Field(title="File name")
 
 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+    images: List[str] = Field(title="Images", description="The generated images in base64 format.")
 
 class PNGInfoRequest(BaseModel):
     image: str = Field(title="Image", description="The base64 encoded PNG image")
 
 class PNGInfoResponse(BaseModel):
     info: str = Field(title="Image info", description="A string with the parameters used to generate the image")
-    items: dict = Field(title="Items", description="A dictionary containing all the other fields the image had")
-    parameters: dict = Field(title="Parameters", description="A dictionary with parsed generation info fields")
+    items: dict = Field(title="Items", description="An object containing all the info the image had")
 
 class ProgressRequest(BaseModel):
     skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization")
@@ -231,8 +231,8 @@ FlagsModel = create_model("Flags", **flags)
 
 class SamplerItem(BaseModel):
     name: str = Field(title="Name")
-    aliases: list[str] = Field(title="Aliases")
-    options: dict[str, str] = Field(title="Options")
+    aliases: List[str] = Field(title="Aliases")
+    options: Dict[str, str] = Field(title="Options")
 
 class UpscalerItem(BaseModel):
     name: str = Field(title="Name")
@@ -283,8 +283,8 @@ class EmbeddingItem(BaseModel):
     vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
 
 class EmbeddingsResponse(BaseModel):
-    loaded: dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
-    skipped: dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
+    loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")
 
 class MemoryResponse(BaseModel):
     ram: dict = Field(title="RAM", description="System memory stats")
@@ -302,20 +302,11 @@ class ScriptArg(BaseModel):
     minimum: Optional[Any] = Field(default=None, title="Minimum", description="Minimum allowed value for the argumentin UI")
     maximum: Optional[Any] = Field(default=None, title="Minimum", description="Maximum allowed value for the argumentin UI")
     step: Optional[Any] = Field(default=None, title="Minimum", description="Step for changing value of the argumentin UI")
-    choices: Optional[list[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
+    choices: Optional[List[str]] = Field(default=None, title="Choices", description="Possible values for the argument")
 
 
 class ScriptInfo(BaseModel):
     name: str = Field(default=None, title="Name", description="Script name")
     is_alwayson: bool = Field(default=None, title="IsAlwayson", description="Flag specifying whether this script is an alwayson script")
     is_img2img: bool = Field(default=None, title="IsImg2img", description="Flag specifying whether this script is an img2img script")
-    args: list[ScriptArg] = Field(title="Arguments", description="List of script's arguments")
-
-class ExtensionItem(BaseModel):
-    name: str = Field(title="Name", description="Extension name")
-    remote: str = Field(title="Remote", description="Extension Repository URL")
-    branch: str = Field(title="Branch", description="Extension Repository Branch")
-    commit_hash: str = Field(title="Commit Hash", description="Extension Repository Commit Hash")
-    version: str = Field(title="Version", description="Extension Version")
-    commit_date: str = Field(title="Commit Date", description="Extension Repository Commit Date")
-    enabled: bool = Field(title="Enabled", description="Flag specifying whether this extension is enabled")
+    args: List[ScriptArg] = Field(title="Arguments", description="List of script's arguments")

modules/cache.py

@@ -1,12 +1,11 @@
 import json
-import os
 import os.path
 import threading
 import time
 
 from modules.paths import data_path, script_path
 
-cache_filename = os.environ.get('SD_WEBUI_CACHE_FILE', os.path.join(data_path, "cache.json"))
+cache_filename = os.path.join(data_path, "cache.json")
 cache_data = None
 cache_lock = threading.Lock()
 
@@ -30,11 +29,8 @@ def dump_cache():
             time.sleep(1)
 
         with cache_lock:
-            cache_filename_tmp = cache_filename + "-"
-            with open(cache_filename_tmp, "w", encoding="utf8") as file:
-                json.dump(cache_data, file, indent=4, ensure_ascii=False)
-
-            os.replace(cache_filename_tmp, cache_filename)
+            with open(cache_filename, "w", encoding="utf8") as file:
+                json.dump(cache_data, file, indent=4)
 
             dump_cache_after = None
             dump_cache_thread = None

modules/call_queue.py

@@ -1,10 +1,11 @@
 from functools import wraps
 import html
+import threading
 import time
 
-from modules import shared, progress, errors, devices, fifo_lock
+from modules import shared, progress, errors, devices
 
-queue_lock = fifo_lock.FIFOLock()
+queue_lock = threading.Lock()
 
 
 def wrap_queued_call(func):

modules/cmd_args.py

@@ -16,8 +16,6 @@ parser.add_argument("--test-server", action='store_true', help="launch.py argume
 parser.add_argument("--log-startup", action='store_true', help="launch.py argument: print a detailed log of what's happening at startup")
 parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
 parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
-parser.add_argument("--dump-sysinfo", action='store_true', help="launch.py argument: dump limited sysinfo file (without information about extensions, options) to disk and quit")
-parser.add_argument("--loglevel", type=str, help="log level; one of: CRITICAL, ERROR, WARNING, INFO, DEBUG", default=None)
 parser.add_argument("--do-not-download-clip", action='store_true', help="do not download CLIP model even if it's not included in the checkpoint")
 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",)
@@ -36,10 +34,9 @@ parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_
 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("--medvram-sdxl", action='store_true', help="enable --medvram optimization just for SDXL models")
 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="does not do anything")
+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.")
@@ -83,14 +80,14 @@ parser.add_argument("--gradio-auth", type=str, help='set gradio authentication l
 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("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it", default=[data_path])
+parser.add_argument("--gradio-allowed-path", action='append', help="add path to gradio's allowed_paths, make it possible to serve files from it")
 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="does not do anything", default=False)  # Legacy compatibility, use as default value shared.opts.enable_console_prompts
+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)")
@@ -107,14 +104,13 @@ parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, req
 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 default in earlier versions")
+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)
 parser.add_argument('--subpath', type=str, help='customize the subpath for gradio, use with reverse proxy')
-parser.add_argument('--add-stop-route', action='store_true', help='does not do anything')
+parser.add_argument('--add-stop-route', action='store_true', help='add /_stop route to stop server')
 parser.add_argument('--api-server-stop', action='store_true', help='enable server stop/restart/kill via api')
 parser.add_argument('--timeout-keep-alive', type=int, default=30, help='set timeout_keep_alive for uvicorn')
 parser.add_argument("--disable-all-extensions", action='store_true', help="prevent all extensions from running regardless of any other settings", default=False)
-parser.add_argument("--disable-extra-extensions", action='store_true', help="prevent all extensions except built-in from running regardless of any other settings", default=False)
-parser.add_argument("--skip-load-model-at-start", action='store_true', help="if load a model at web start, only take effect when --nowebui", )
+parser.add_argument("--disable-extra-extensions", action='store_true', help=" prevent all extensions except built-in from running regardless of any other settings", default=False)

modules/config_states.py

@@ -4,15 +4,18 @@ Supports saving and restoring webui and extensions from a known working set of c
 
 import os
 import json
+import time
 import tqdm
 
 from datetime import datetime
+from collections import OrderedDict
 import git
 
 from modules import shared, extensions, errors
 from modules.paths_internal import script_path, config_states_dir
 
-all_config_states = {}
+
+all_config_states = OrderedDict()
 
 
 def list_config_states():
@@ -25,19 +28,15 @@ def list_config_states():
     for filename in os.listdir(config_states_dir):
         if filename.endswith(".json"):
             path = os.path.join(config_states_dir, filename)
-            try:
-                with open(path, "r", encoding="utf-8") as f:
-                    j = json.load(f)
-                    assert "created_at" in j, '"created_at" does not exist'
-                    j["filepath"] = path
-                    config_states.append(j)
-            except Exception as e:
-                print(f'[ERROR]: Config states {path}, {e}')
+            with open(path, "r", encoding="utf-8") as f:
+                j = json.load(f)
+                j["filepath"] = path
+                config_states.append(j)
 
     config_states = sorted(config_states, key=lambda cs: cs["created_at"], reverse=True)
 
     for cs in config_states:
-        timestamp = datetime.fromtimestamp(cs["created_at"]).strftime('%Y-%m-%d %H:%M:%S')
+        timestamp = time.asctime(time.gmtime(cs["created_at"]))
         name = cs.get("name", "Config")
         full_name = f"{name}: {timestamp}"
         all_config_states[full_name] = cs

modules/devices.py

@@ -3,7 +3,7 @@ import contextlib
 from functools import lru_cache
 
 import torch
-from modules import errors, shared
+from modules import errors, rng_philox
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -17,6 +17,8 @@ def has_mps() -> bool:
 
 
 def get_cuda_device_string():
+    from modules import shared
+
     if shared.cmd_opts.device_id is not None:
         return f"cuda:{shared.cmd_opts.device_id}"
 
@@ -38,6 +40,8 @@ def get_optimal_device():
 
 
 def get_device_for(task):
+    from modules import shared
+
     if task in shared.cmd_opts.use_cpu:
         return cpu
 
@@ -60,8 +64,7 @@ def enable_tf32():
 
         # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
         # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
-        device_id = (int(shared.cmd_opts.device_id) if shared.cmd_opts.device_id is not None and shared.cmd_opts.device_id.isdigit() else 0) or torch.cuda.current_device()
-        if torch.cuda.get_device_capability(device_id) == (7, 5) and torch.cuda.get_device_name(device_id).startswith("NVIDIA GeForce GTX 16"):
+        if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())):
             torch.backends.cudnn.benchmark = True
 
         torch.backends.cuda.matmul.allow_tf32 = True
@@ -93,7 +96,87 @@ def cond_cast_float(input):
 nv_rng = None
 
 
+def randn(seed, shape):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
+
+    from modules.shared import opts
+
+    manual_seed(seed)
+
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(shape), device=device)
+
+    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_local(seed, shape):
+    """Generate a tensor with random numbers from a normal distribution using seed.
+
+    Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
+
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        rng = rng_philox.Generator(seed)
+        return torch.asarray(rng.randn(shape), device=device)
+
+    local_device = cpu if opts.randn_source == "CPU" or device.type == 'mps' else device
+    local_generator = torch.Generator(local_device).manual_seed(int(seed))
+    return torch.randn(shape, device=local_device, generator=local_generator).to(device)
+
+
+def randn_like(x):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
+
+    if opts.randn_source == "CPU" or x.device.type == 'mps':
+        return torch.randn_like(x, device=cpu).to(x.device)
+
+    return torch.randn_like(x)
+
+
+def randn_without_seed(shape):
+    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
+
+    Use either randn() or manual_seed() to initialize the generator."""
+
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        return torch.asarray(nv_rng.randn(shape), device=device)
+
+    if opts.randn_source == "CPU" or device.type == 'mps':
+        return torch.randn(shape, device=cpu).to(device)
+
+    return torch.randn(shape, device=device)
+
+
+def manual_seed(seed):
+    """Set up a global random number generator using the specified seed."""
+    from modules.shared import opts
+
+    if opts.randn_source == "NV":
+        global nv_rng
+        nv_rng = rng_philox.Generator(seed)
+        return
+
+    torch.manual_seed(seed)
+
+
 def autocast(disable=False):
+    from modules import shared
+
     if disable:
         return contextlib.nullcontext()
 
@@ -112,6 +195,8 @@ class NansException(Exception):
 
 
 def test_for_nans(x, where):
+    from modules import shared
+
     if shared.cmd_opts.disable_nan_check:
         return
 
@@ -151,4 +236,3 @@ def first_time_calculation():
     x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
     conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
     conv2d(x)
-

modules/errors.py

@@ -6,21 +6,6 @@ import traceback
 exception_records = []
 
 
-def format_traceback(tb):
-    return [[f"{x.filename}, line {x.lineno}, {x.name}", x.line] for x in traceback.extract_tb(tb)]
-
-
-def format_exception(e, tb):
-    return {"exception": str(e), "traceback": format_traceback(tb)}
-
-
-def get_exceptions():
-    try:
-        return list(reversed(exception_records))
-    except Exception as e:
-        return str(e)
-
-
 def record_exception():
     _, e, tb = sys.exc_info()
     if e is None:
@@ -29,7 +14,8 @@ def record_exception():
     if exception_records and exception_records[-1] == e:
         return
 
-    exception_records.append(format_exception(e, tb))
+    from modules import sysinfo
+    exception_records.append(sysinfo.format_exception(e, tb))
 
     if len(exception_records) > 5:
         exception_records.pop(0)
@@ -109,7 +95,7 @@ def check_versions():
 
     expected_torch_version = "2.0.0"
     expected_xformers_version = "0.0.20"
-    expected_gradio_version = "3.41.2"
+    expected_gradio_version = "3.39.0"
 
     if version.parse(torch.__version__) < version.parse(expected_torch_version):
         print_error_explanation(f"""

modules/extensions.py

@@ -1,14 +1,11 @@
-from __future__ import annotations
-
-import configparser
 import os
 import threading
-import re
 
-from modules import shared, errors, cache, scripts
+from modules import shared, errors, cache
 from modules.gitpython_hack import Repo
 from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path  # noqa: F401
 
+extensions = []
 
 os.makedirs(extensions_dir, exist_ok=True)
 
@@ -22,55 +19,11 @@ def active():
         return [x for x in extensions if x.enabled]
 
 
-class ExtensionMetadata:
-    filename = "metadata.ini"
-    config: configparser.ConfigParser
-    canonical_name: str
-    requires: list
-
-    def __init__(self, path, canonical_name):
-        self.config = configparser.ConfigParser()
-
-        filepath = os.path.join(path, self.filename)
-        if os.path.isfile(filepath):
-            try:
-                self.config.read(filepath)
-            except Exception:
-                errors.report(f"Error reading {self.filename} for extension {canonical_name}.", exc_info=True)
-
-        self.canonical_name = self.config.get("Extension", "Name", fallback=canonical_name)
-        self.canonical_name = canonical_name.lower().strip()
-
-        self.requires = self.get_script_requirements("Requires", "Extension")
-
-    def get_script_requirements(self, field, section, extra_section=None):
-        """reads a list of requirements from the config; field is the name of the field in the ini file,
-        like Requires or Before, and section is the name of the [section] in the ini file; additionally,
-        reads more requirements from [extra_section] if specified."""
-
-        x = self.config.get(section, field, fallback='')
-
-        if extra_section:
-            x = x + ', ' + self.config.get(extra_section, field, fallback='')
-
-        return self.parse_list(x.lower())
-
-    def parse_list(self, text):
-        """converts a line from config ("ext1 ext2, ext3  ") into a python list (["ext1", "ext2", "ext3"])"""
-
-        if not text:
-            return []
-
-        # both "," and " " are accepted as separator
-        return [x for x in re.split(r"[,\s]+", text.strip()) if x]
-
-
 class Extension:
     lock = threading.Lock()
     cached_fields = ['remote', 'commit_date', 'branch', 'commit_hash', 'version']
-    metadata: ExtensionMetadata
 
-    def __init__(self, name, path, enabled=True, is_builtin=False, metadata=None):
+    def __init__(self, name, path, enabled=True, is_builtin=False):
         self.name = name
         self.path = path
         self.enabled = enabled
@@ -83,8 +36,6 @@ class Extension:
         self.branch = None
         self.remote = None
         self.have_info_from_repo = False
-        self.metadata = metadata if metadata else ExtensionMetadata(self.path, name.lower())
-        self.canonical_name = metadata.canonical_name
 
     def to_dict(self):
         return {x: getattr(self, x) for x in self.cached_fields}
@@ -105,7 +56,6 @@ class Extension:
                 self.do_read_info_from_repo()
 
                 return self.to_dict()
-
         try:
             d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
             self.from_dict(d)
@@ -140,6 +90,8 @@ class Extension:
         self.have_info_from_repo = True
 
     def list_files(self, subdir, extension):
+        from modules import scripts
+
         dirpath = os.path.join(self.path, subdir)
         if not os.path.isdir(dirpath):
             return []
@@ -186,6 +138,9 @@ class Extension:
 def list_extensions():
     extensions.clear()
 
+    if not os.path.isdir(extensions_dir):
+        return
+
     if shared.cmd_opts.disable_all_extensions:
         print("*** \"--disable-all-extensions\" arg was used, will not load any extensions ***")
     elif shared.opts.disable_all_extensions == "all":
@@ -195,43 +150,18 @@ def list_extensions():
     elif shared.opts.disable_all_extensions == "extra":
         print("*** \"Disable all extensions\" option was set, will only load built-in extensions ***")
 
-    loaded_extensions = {}
-
-    # scan through extensions directory and load metadata
-    for dirname in [extensions_builtin_dir, extensions_dir]:
+    extension_paths = []
+    for dirname in [extensions_dir, extensions_builtin_dir]:
         if not os.path.isdir(dirname):
-            continue
+            return
 
         for extension_dirname in sorted(os.listdir(dirname)):
             path = os.path.join(dirname, extension_dirname)
             if not os.path.isdir(path):
                 continue
 
-            canonical_name = extension_dirname
-            metadata = ExtensionMetadata(path, canonical_name)
+            extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
 
-            # check for duplicated canonical names
-            already_loaded_extension = loaded_extensions.get(metadata.canonical_name)
-            if already_loaded_extension is not None:
-                errors.report(f'Duplicate canonical name "{canonical_name}" found in extensions "{extension_dirname}" and "{already_loaded_extension.name}". Former will be discarded.', exc_info=False)
-                continue
-
-            is_builtin = dirname == extensions_builtin_dir
-            extension = Extension(name=extension_dirname, path=path, enabled=extension_dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin, metadata=metadata)
-            extensions.append(extension)
-            loaded_extensions[canonical_name] = extension
-
-    # check for requirements
-    for extension in extensions:
-        for req in extension.metadata.requires:
-            required_extension = loaded_extensions.get(req)
-            if required_extension is None:
-                errors.report(f'Extension "{extension.name}" requires "{req}" which is not installed.', exc_info=False)
-                continue
-
-            if not extension.enabled:
-                errors.report(f'Extension "{extension.name}" requires "{required_extension.name}" which is disabled.', exc_info=False)
-                continue
-
-
-extensions: list[Extension] = []
+    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)

modules/extra_networks.py

@@ -1,7 +1,6 @@
 import json
 import os
 import re
-import logging
 from collections import defaultdict
 
 from modules import errors
@@ -87,55 +86,27 @@ class ExtraNetwork:
         raise NotImplementedError
 
 
-def lookup_extra_networks(extra_network_data):
-    """returns a dict mapping ExtraNetwork objects to lists of arguments for those extra networks.
-
-    Example input:
-    {
-        'lora': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58310>],
-        'lyco': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58F70>],
-        'hypernet': [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D5A800>]
-    }
-
-    Example output:
-
-    {
-        <extra_networks_lora.ExtraNetworkLora object at 0x0000020581BEECE0>: [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58310>, <modules.extra_networks.ExtraNetworkParams object at 0x0000020690D58F70>],
-        <modules.extra_networks_hypernet.ExtraNetworkHypernet object at 0x0000020581BEEE60>: [<modules.extra_networks.ExtraNetworkParams object at 0x0000020690D5A800>]
-    }
-    """
-
-    res = {}
-
-    for extra_network_name, extra_network_args in list(extra_network_data.items()):
-        extra_network = extra_network_registry.get(extra_network_name, None)
-        alias = extra_network_aliases.get(extra_network_name, None)
-
-        if alias is not None and extra_network is None:
-            extra_network = alias
-
-        if extra_network is None:
-            logging.info(f"Skipping unknown extra network: {extra_network_name}")
-            continue
-
-        res.setdefault(extra_network, []).extend(extra_network_args)
-
-    return res
-
-
 def activate(p, extra_network_data):
     """call activate for extra networks in extra_network_data in specified order, then call
     activate for all remaining registered networks with an empty argument list"""
 
     activated = []
 
-    for extra_network, extra_network_args in lookup_extra_networks(extra_network_data).items():
+    for extra_network_name, extra_network_args in extra_network_data.items():
+        extra_network = extra_network_registry.get(extra_network_name, None)
+
+        if extra_network is None:
+            extra_network = extra_network_aliases.get(extra_network_name, None)
+
+        if extra_network is None:
+            print(f"Skipping unknown extra network: {extra_network_name}")
+            continue
 
         try:
             extra_network.activate(p, extra_network_args)
             activated.append(extra_network)
         except Exception as e:
-            errors.display(e, f"activating extra network {extra_network.name} with arguments {extra_network_args}")
+            errors.display(e, f"activating extra network {extra_network_name} with arguments {extra_network_args}")
 
     for extra_network_name, extra_network in extra_network_registry.items():
         if extra_network in activated:
@@ -154,16 +125,19 @@ def deactivate(p, extra_network_data):
     """call deactivate for extra networks in extra_network_data in specified order, then call
     deactivate for all remaining registered networks"""
 
-    data = lookup_extra_networks(extra_network_data)
+    for extra_network_name in extra_network_data:
+        extra_network = extra_network_registry.get(extra_network_name, None)
+        if extra_network is None:
+            continue
 
-    for extra_network in data:
         try:
             extra_network.deactivate(p)
         except Exception as e:
-            errors.display(e, f"deactivating extra network {extra_network.name}")
+            errors.display(e, f"deactivating extra network {extra_network_name}")
 
     for extra_network_name, extra_network in extra_network_registry.items():
-        if extra_network in data:
+        args = extra_network_data.get(extra_network_name, None)
+        if args is not None:
             continue
 
         try:

modules/fifo_lock.py

@@ -1,37 +0,0 @@
-import threading
-import collections
-
-
-# reference: https://gist.github.com/vitaliyp/6d54dd76ca2c3cdfc1149d33007dc34a
-class FIFOLock(object):
-    def __init__(self):
-        self._lock = threading.Lock()
-        self._inner_lock = threading.Lock()
-        self._pending_threads = collections.deque()
-
-    def acquire(self, blocking=True):
-        with self._inner_lock:
-            lock_acquired = self._lock.acquire(False)
-            if lock_acquired:
-                return True
-            elif not blocking:
-                return False
-
-            release_event = threading.Event()
-            self._pending_threads.append(release_event)
-
-        release_event.wait()
-        return self._lock.acquire()
-
-    def release(self):
-        with self._inner_lock:
-            if self._pending_threads:
-                release_event = self._pending_threads.popleft()
-                release_event.set()
-
-            self._lock.release()
-
-    __enter__ = acquire
-
-    def __exit__(self, t, v, tb):
-        self.release()

modules/generation_parameters_copypaste.py

@@ -6,10 +6,10 @@ import re
 
 import gradio as gr
 from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, processing
+from modules import shared, ui_tempdir, script_callbacks
 from PIL import Image
 
-re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
+re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
@@ -32,7 +32,6 @@ class ParamBinding:
 
 def reset():
     paste_fields.clear()
-    registered_param_bindings.clear()
 
 
 def quote(text):
@@ -199,6 +198,7 @@ def restore_old_hires_fix_params(res):
     height = int(res.get("Size-2", 512))
 
     if firstpass_width == 0 or firstpass_height == 0:
+        from modules import processing
         firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)
 
     res['Size-1'] = firstpass_width
@@ -317,18 +317,36 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
 
 
 infotext_to_setting_name_mapping = [
-
-]
-"""Mapping of infotext labels to setting names. Only left for backwards compatibility - use OptionInfo(..., infotext='...') instead.
-Example content:
-
-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'),
     ('Schedule type', 'k_sched_type'),
+    ('Schedule max sigma', 'sigma_max'),
+    ('Schedule min sigma', 'sigma_min'),
+    ('Schedule rho', 'rho'),
+    ('Noise multiplier', 'initial_noise_multiplier'),
+    ('Eta', 'eta_ancestral'),
+    ('Eta DDIM', 'eta_ddim'),
+    ('Sigma churn', 's_churn'),
+    ('Sigma tmin', 's_tmin'),
+    ('Sigma tmax', 's_tmax'),
+    ('Sigma noise', 's_noise'),
+    ('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'),
+    ('Token merging ratio', 'token_merging_ratio'),
+    ('Token merging ratio hr', 'token_merging_ratio_hr'),
+    ('RNG', 'randn_source'),
+    ('NGMS', 's_min_uncond'),
+    ('Pad conds', 'pad_cond_uncond'),
+    ('VAE Encoder', 'sd_vae_encode_method'),
+    ('VAE Decoder', 'sd_vae_decode_method'),
+    ('Refiner', 'sd_refiner_checkpoint'),
+    ('Refiner switch at', 'sd_refiner_switch_at'),
 ]
-"""
 
 
 def create_override_settings_dict(text_pairs):
@@ -349,8 +367,7 @@ def create_override_settings_dict(text_pairs):
 
         params[k] = v.strip()
 
-    mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
-    for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
+    for param_name, setting_name in infotext_to_setting_name_mapping:
         value = params.get(param_name, None)
 
         if value is None:
@@ -399,16 +416,10 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
         return res
 
     if override_settings_component is not None:
-        already_handled_fields = {key: 1 for _, key in paste_fields}
-
         def paste_settings(params):
             vals = {}
 
-            mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
-            for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
-                if param_name in already_handled_fields:
-                    continue
-
+            for param_name, setting_name in infotext_to_setting_name_mapping:
                 v = params.get(param_name, None)
                 if v is None:
                     continue

modules/gfpgan_model.py

@@ -9,7 +9,6 @@ from modules import paths, shared, devices, modelloader, errors
 model_dir = "GFPGAN"
 user_path = None
 model_path = os.path.join(paths.models_path, model_dir)
-model_file_path = None
 model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
 have_gfpgan = False
 loaded_gfpgan_model = None
@@ -18,7 +17,6 @@ loaded_gfpgan_model = None
 def gfpgann():
     global loaded_gfpgan_model
     global model_path
-    global model_file_path
     if loaded_gfpgan_model is not None:
         loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
         return loaded_gfpgan_model
@@ -26,24 +24,17 @@ def gfpgann():
     if gfpgan_constructor is None:
         return None
 
-    models = modelloader.load_models(model_path, model_url, user_path, ext_filter=['.pth'])
-
+    models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
     if len(models) == 1 and models[0].startswith("http"):
         model_file = models[0]
     elif len(models) != 0:
-        gfp_models = []
-        for item in models:
-            if 'GFPGAN' in os.path.basename(item):
-                gfp_models.append(item)
-        latest_file = max(gfp_models, key=os.path.getctime)
+        latest_file = max(models, key=os.path.getctime)
         model_file = latest_file
     else:
         print("Unable to load gfpgan model!")
         return None
-
     if hasattr(facexlib.detection.retinaface, 'device'):
         facexlib.detection.retinaface.device = devices.device_gfpgan
-    model_file_path = model_file
     model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
     loaded_gfpgan_model = model
 
@@ -86,25 +77,19 @@ def setup_model(dirname):
         global user_path
         global have_gfpgan
         global gfpgan_constructor
-        global model_file_path
-
-        facexlib_path = model_path
-
-        if dirname is not None:
-            facexlib_path = dirname
 
         load_file_from_url_orig = gfpgan.utils.load_file_from_url
         facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
         facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
 
         def my_load_file_from_url(**kwargs):
-            return load_file_from_url_orig(**dict(kwargs, model_dir=model_file_path))
+            return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
 
         def facex_load_file_from_url(**kwargs):
-            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
+            return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
 
         def facex_load_file_from_url2(**kwargs):
-            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=facexlib_path, model_dir=None))
+            return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
 
         gfpgan.utils.load_file_from_url = my_load_file_from_url
         facexlib.detection.load_file_from_url = facex_load_file_from_url

modules/gitpython_hack.py

@@ -23,7 +23,7 @@ class Git(git.Git):
         )
         return self._parse_object_header(ret)
 
-    def stream_object_data(self, ref: str) -> tuple[str, str, int, Git.CatFileContentStream]:
+    def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
         # Not really streaming, per se; this buffers the entire object in memory.
         # Shouldn't be a problem for our use case, since we're only using this for
         # object headers (commit objects).

modules/gradio_extensons.py

@@ -1,9 +1,6 @@
-from inspect import signature
-from functools import wraps
 import gradio as gr
 
-from modules import scripts, ui_tempdir, patches
-
+from modules import scripts
 
 def add_classes_to_gradio_component(comp):
     """
@@ -43,8 +40,6 @@ def Block_get_config(self):
     if webui_tooltip:
         config["webui_tooltip"] = webui_tooltip
 
-    config.pop('example_inputs', None)
-
     return config
 
 
@@ -56,87 +51,10 @@ def BlockContext_init(self, *args, **kwargs):
     return res
 
 
-def Blocks_get_config_file(self, *args, **kwargs):
-    config = original_Blocks_get_config_file(self, *args, **kwargs)
-
-    for comp_config in config["components"]:
-        if "example_inputs" in comp_config:
-            comp_config["example_inputs"] = {"serialized": []}
-
-    return config
-
-
-def gradio_component_compatibility_layer(component_function):
-    @wraps(component_function)
-    def patched_function(*args, **kwargs):
-        original_signature = signature(component_function).parameters
-        valid_kwargs = {k: v for k, v in kwargs.items() if k in original_signature}
-        result = component_function(*args, **valid_kwargs)
-        return result
-
-    return patched_function
-
-
-sub_events = ['then', 'success']
-
-
-def gradio_component_events_compatibility_layer(component_function):
-    @wraps(component_function)
-    def patched_function(*args, **kwargs):
-        kwargs['js'] = kwargs.get('js', kwargs.pop('_js', None))
-        original_signature = signature(component_function).parameters
-        valid_kwargs = {k: v for k, v in kwargs.items() if k in original_signature}
-
-        result = component_function(*args, **valid_kwargs)
-
-        for sub_event in sub_events:
-            component_event_then_function = getattr(result, sub_event, None)
-            if component_event_then_function:
-                patched_component_event_then_function = gradio_component_sub_events_compatibility_layer(component_event_then_function)
-                setattr(result, sub_event, patched_component_event_then_function)
-        # original_component_event_then_function = patches.patch(f'{__name__}.', obj=result, field='then', replacement=patched_component_event_then_function)
-
-        return result
-
-    return patched_function
-
-
-def gradio_component_sub_events_compatibility_layer(component_function):
-    @wraps(component_function)
-    def patched_function(*args, **kwargs):
-        kwargs['js'] = kwargs.get('js', kwargs.pop('_js', None))
-        original_signature = signature(component_function).parameters
-        valid_kwargs = {k: v for k, v in kwargs.items() if k in original_signature}
-        result = component_function(*args, **valid_kwargs)
-        return result
-
-    return patched_function
-
-
-for component_name in set(gr.components.__all__ + gr.layouts.__all__):
-    try:
-        component = getattr(gr, component_name)
-        component_init = getattr(component, '__init__')
-        patched_component_init = gradio_component_compatibility_layer(component_init)
-        original_IOComponent_init = patches.patch(f'{__name__}.{component_name}', obj=component, field="__init__", replacement=patched_component_init)
-
-        component_events = set(getattr(component, 'EVENTS'))
-        for component_event in component_events:
-            component_event_function = getattr(component, component_event)
-            patched_component_event_function = gradio_component_events_compatibility_layer(component_event_function)
-            original_component_event_function = patches.patch(f'{__name__}.{component_name}.{component_event}', obj=component, field=component_event, replacement=patched_component_event_function)
-    except Exception as e:
-        print(e)
-        pass
-
-gr.Box = gr.Group
-
-
-original_IOComponent_init = patches.patch(__name__, obj=gr.components.base.Component, field="__init__", replacement=IOComponent_init)
-original_Block_get_config = patches.patch(__name__, obj=gr.blocks.Block, field="get_config", replacement=Block_get_config)
-original_BlockContext_init = patches.patch(__name__, obj=gr.blocks.BlockContext, field="__init__", replacement=BlockContext_init)
-original_Blocks_get_config_file = patches.patch(__name__, obj=gr.blocks.Blocks, field="get_config_file", replacement=Blocks_get_config_file)
-
-
-ui_tempdir.install_ui_tempdir_override()
+original_IOComponent_init = gr.components.IOComponent.__init__
+original_Block_get_config = gr.blocks.Block.get_config
+original_BlockContext_init = gr.blocks.BlockContext.__init__
 
+gr.components.IOComponent.__init__ = IOComponent_init
+gr.blocks.Block.get_config = Block_get_config
+gr.blocks.BlockContext.__init__ = BlockContext_init

modules/hypernetworks/hypernetwork.py

@@ -468,7 +468,7 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
     shared.reload_hypernetworks()
 
 
-def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch_size: int, gradient_step: int, data_root: str, log_directory: str, training_width: int, training_height: int, varsize: bool, steps: int, clip_grad_mode: str, clip_grad_value: float, shuffle_tags: bool, tag_drop_out: bool, latent_sampling_method: str, use_weight: bool, create_image_every: int, save_hypernetwork_every: int, template_filename: str, preview_from_txt2img: bool, preview_prompt: str, preview_negative_prompt: str, preview_steps: int, preview_sampler_name: str, preview_cfg_scale: float, preview_seed: int, preview_width: int, preview_height: int):
+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):
     from modules import images, processing
 
     save_hypernetwork_every = save_hypernetwork_every or 0
@@ -698,7 +698,7 @@ def train_hypernetwork(id_task, hypernetwork_name: str, learn_rate: float, batch
                         p.prompt = preview_prompt
                         p.negative_prompt = preview_negative_prompt
                         p.steps = preview_steps
-                        p.sampler_name = sd_samplers.samplers_map[preview_sampler_name.lower()]
+                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
                         p.cfg_scale = preview_cfg_scale
                         p.seed = preview_seed
                         p.width = preview_width

modules/images.py

@@ -21,6 +21,8 @@ from modules import sd_samplers, shared, script_callbacks, errors
 from modules.paths_internal import roboto_ttf_file
 from modules.shared import opts
 
+import modules.sd_vae as sd_vae
+
 LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
 
 
@@ -340,6 +342,16 @@ def sanitize_filename_part(text, replace_spaces=True):
 
 
 class FilenameGenerator:
+    def get_vae_filename(self): #get the name of the VAE file.
+        if sd_vae.loaded_vae_file is None:
+            return "NoneType"
+        file_name = os.path.basename(sd_vae.loaded_vae_file)
+        split_file_name = file_name.split('.')
+        if len(split_file_name) > 1 and split_file_name[0] == '':
+            return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
+        else:
+            return split_file_name[0]
+
     replacements = {
         'seed': lambda self: self.seed if self.seed is not None else '',
         'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
@@ -355,9 +367,7 @@ 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, *args: self.string_hash(self.prompt, *args),
-        'negative_prompt_hash': lambda self, *args: self.string_hash(self.p.negative_prompt, *args),
-        'full_prompt_hash': lambda self, *args: self.string_hash(f"{self.p.prompt} {self.p.negative_prompt}", *args),  # a space in between to create a unique string
+        '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),
@@ -370,8 +380,7 @@ class FilenameGenerator:
         'denoising': lambda self: self.p.denoising_strength if self.p and self.p.denoising_strength else NOTHING_AND_SKIP_PREVIOUS_TEXT,
         'user': lambda self: self.p.user,
         'vae_filename': lambda self: self.get_vae_filename(),
-        'none': lambda self: '',  # Overrides the default, so you can get just the sequence number
-        'image_hash': lambda self, *args: self.image_hash(*args)  # accepts formats: [image_hash<length>] default full hash
+        'none': lambda self: '', # Overrides the default so you can get just the sequence number
     }
     default_time_format = '%Y%m%d%H%M%S'
 
@@ -382,22 +391,6 @@ class FilenameGenerator:
         self.image = image
         self.zip = zip
 
-    def get_vae_filename(self):
-        """Get the name of the VAE file."""
-
-        import modules.sd_vae as sd_vae
-
-        if sd_vae.loaded_vae_file is None:
-            return "NoneType"
-
-        file_name = os.path.basename(sd_vae.loaded_vae_file)
-        split_file_name = file_name.split('.')
-        if len(split_file_name) > 1 and split_file_name[0] == '':
-            return split_file_name[1]  # if the first character of the filename is "." then [1] is obtained.
-        else:
-            return split_file_name[0]
-
-
     def hasprompt(self, *args):
         lower = self.prompt.lower()
         if self.p is None or self.prompt is None:
@@ -451,14 +444,6 @@ class FilenameGenerator:
 
         return sanitize_filename_part(formatted_time, replace_spaces=False)
 
-    def image_hash(self, *args):
-        length = int(args[0]) if (args and args[0] != "") else None
-        return hashlib.sha256(self.image.tobytes()).hexdigest()[0:length]
-
-    def string_hash(self, text, *args):
-        length = int(args[0]) if (args and args[0] != "") else 8
-        return hashlib.sha256(text.encode()).hexdigest()[0:length]
-
     def apply(self, x):
         res = ''
 
@@ -561,8 +546,6 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
             })
 
             piexif.insert(exif_bytes, filename)
-    elif extension.lower() == ".gif":
-        image.save(filename, format=image_format, comment=geninfo)
     else:
         image.save(filename, format=image_format, quality=opts.jpeg_quality)
 
@@ -602,11 +585,6 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
     """
     namegen = FilenameGenerator(p, seed, prompt, image)
 
-    # WebP and JPG formats have maximum dimension limits of 16383 and 65535 respectively. switch to PNG which has a much higher limit
-    if (image.height > 65535 or image.width > 65535) and extension.lower() in ("jpg", "jpeg") or (image.height > 16383 or image.width > 16383) and extension.lower() == "webp":
-        print('Image dimensions too large; saving as PNG')
-        extension = ".png"
-
     if save_to_dirs is None:
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)
 
@@ -663,13 +641,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
 
         save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name)
 
-        filename = filename_without_extension + extension
-        if shared.opts.save_images_replace_action != "Replace":
-            n = 0
-            while os.path.exists(filename):
-                n += 1
-                filename = f"{filename_without_extension}-{n}{extension}"
-        os.replace(temp_file_path, filename)
+        os.replace(temp_file_path, filename_without_extension + extension)
 
     fullfn_without_extension, extension = os.path.splitext(params.filename)
     if hasattr(os, 'statvfs'):
@@ -726,12 +698,7 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
     geninfo = items.pop('parameters', None)
 
     if "exif" in items:
-        exif_data = items["exif"]
-        try:
-            exif = piexif.load(exif_data)
-        except OSError:
-            # memory / exif was not valid so piexif tried to read from a file
-            exif = None
+        exif = piexif.load(items["exif"])
         exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
         try:
             exif_comment = piexif.helper.UserComment.load(exif_comment)
@@ -741,8 +708,6 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]:
         if exif_comment:
             items['exif comment'] = exif_comment
             geninfo = exif_comment
-    elif "comment" in items: # for gif
-        geninfo = items["comment"].decode('utf8', errors="ignore")
 
     for field in IGNORED_INFO_KEYS:
         items.pop(field, None)

modules/img2img.py

@@ -6,11 +6,10 @@ import numpy as np
 from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageError
 import gradio as gr
 
-from modules import images as imgutil
+from modules import sd_samplers, images as imgutil
 from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
-from modules.sd_models import get_closet_checkpoint_match
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
@@ -42,10 +41,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
     cfg_scale = p.cfg_scale
     sampler_name = p.sampler_name
     steps = p.steps
-    override_settings = p.override_settings
-    sd_model_checkpoint_override = get_closet_checkpoint_match(override_settings.get("sd_model_checkpoint", None))
-    batch_results = None
-    discard_further_results = False
+
     for i, image in enumerate(images):
         state.job = f"{i+1} out of {len(images)}"
         if state.skipped:
@@ -108,58 +104,33 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args, to_scale=Fal
             p.sampler_name = parsed_parameters.get("Sampler", sampler_name)
             p.steps = int(parsed_parameters.get("Steps", steps))
 
-            model_info = get_closet_checkpoint_match(parsed_parameters.get("Model hash", None))
-            if model_info is not None:
-                p.override_settings['sd_model_checkpoint'] = model_info.name
-            elif sd_model_checkpoint_override:
-                p.override_settings['sd_model_checkpoint'] = sd_model_checkpoint_override
-            else:
-                p.override_settings.pop("sd_model_checkpoint", None)
-
-        if output_dir:
-            p.outpath_samples = output_dir
-            p.override_settings['save_to_dirs'] = False
-            p.override_settings['save_images_replace_action'] = "Add number suffix"
-            if p.n_iter > 1 or p.batch_size > 1:
-                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
-            else:
-                p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
-
         proc = modules.scripts.scripts_img2img.run(p, *args)
-
         if proc is None:
-            p.override_settings.pop('save_images_replace_action', None)
-            proc = process_images(p)
-
-        if not discard_further_results and proc:
-            if batch_results:
-                batch_results.images.extend(proc.images)
-                batch_results.infotexts.extend(proc.infotexts)
-            else:
-                batch_results = proc
-
-            if 0 <= shared.opts.img2img_batch_show_results_limit < len(batch_results.images):
-                discard_further_results = True
-                batch_results.images = batch_results.images[:int(shared.opts.img2img_batch_show_results_limit)]
-                batch_results.infotexts = batch_results.infotexts[:int(shared.opts.img2img_batch_show_results_limit)]
-
-    return batch_results
+            if output_dir:
+                p.outpath_samples = output_dir
+                p.override_settings['save_to_dirs'] = False
+                if p.n_iter > 1 or p.batch_size > 1:
+                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}-[generation_number]'
+                else:
+                    p.override_settings['samples_filename_pattern'] = f'{image_path.stem}'
+            process_images(p)
 
 
-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_name: str, mask_blur: int, mask_alpha: float, inpainting_fill: int, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, 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, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *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, img2img_batch_use_png_info: bool, img2img_batch_png_info_props: list, img2img_batch_png_info_dir: str, request: gr.Request, *args):
     override_settings = create_override_settings_dict(override_settings_texts)
 
     is_batch = mode == 5
 
     if mode == 0:  # img2img
-        image = init_img
+        image = init_img.convert("RGB")
         mask = None
     elif mode == 1:  # img2img sketch
-        image = sketch
+        image = sketch.convert("RGB")
         mask = None
     elif mode == 2:  # inpaint
         image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
-        mask = processing.create_binary_mask(mask)
+        mask = mask.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+        image = image.convert("RGB")
     elif mode == 3:  # inpaint sketch
         image = inpaint_color_sketch
         orig = inpaint_color_sketch_orig or inpaint_color_sketch
@@ -168,6 +139,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
         blur = ImageFilter.GaussianBlur(mask_blur)
         image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+        image = image.convert("RGB")
     elif mode == 4:  # inpaint upload mask
         image = init_img_inpaint
         mask = init_mask_inpaint
@@ -194,13 +166,21 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
         prompt=prompt,
         negative_prompt=negative_prompt,
         styles=prompt_styles,
-        sampler_name=sampler_name,
+        seed=seed,
+        subseed=subseed,
+        subseed_strength=subseed_strength,
+        seed_resize_from_h=seed_resize_from_h,
+        seed_resize_from_w=seed_resize_from_w,
+        seed_enable_extras=seed_enable_extras,
+        sampler_name=sd_samplers.samplers_for_img2img[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,
         cfg_scale=cfg_scale,
         width=width,
         height=height,
+        restore_faces=restore_faces,
+        tiling=tiling,
         init_images=[image],
         mask=mask,
         mask_blur=mask_blur,
@@ -219,7 +199,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
 
     p.user = request.username
 
-    if shared.opts.enable_console_prompts:
+    if shared.cmd_opts.enable_console_prompts:
         print(f"\nimg2img: {prompt}", file=shared.progress_print_out)
 
     if mask:
@@ -228,10 +208,10 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s
     with closing(p):
         if is_batch:
             assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
-            processed = process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
 
-            if processed is None:
-                processed = Processed(p, [], p.seed, "")
+            process_batch(p, img2img_batch_input_dir, img2img_batch_output_dir, img2img_batch_inpaint_mask_dir, args, to_scale=selected_scale_tab == 1, scale_by=scale_by, use_png_info=img2img_batch_use_png_info, png_info_props=img2img_batch_png_info_props, png_info_dir=img2img_batch_png_info_dir)
+
+            processed = Processed(p, [], p.seed, "")
         else:
             processed = modules.scripts.scripts_img2img.run(p, *args)
             if processed is None:

modules/initialize.py

@@ -1,168 +0,0 @@
-import importlib
-import logging
-import sys
-import warnings
-from threading import Thread
-
-from modules.timer import startup_timer
-
-
-def imports():
-    logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR)  # sshh...
-    logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
-
-    import torch  # noqa: F401
-    startup_timer.record("import torch")
-    import pytorch_lightning  # noqa: F401
-    startup_timer.record("import torch")
-    warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning")
-    warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision")
-
-    import gradio  # noqa: F401
-    startup_timer.record("import gradio")
-
-    from modules import paths, timer, import_hook, errors  # noqa: F401
-    startup_timer.record("setup paths")
-
-    import ldm.modules.encoders.modules  # noqa: F401
-    startup_timer.record("import ldm")
-
-    import sgm.modules.encoders.modules  # noqa: F401
-    startup_timer.record("import sgm")
-
-    from modules import shared_init
-    shared_init.initialize()
-    startup_timer.record("initialize shared")
-
-    from modules import processing, gradio_extensons, ui  # noqa: F401
-    startup_timer.record("other imports")
-
-
-def check_versions():
-    from modules.shared_cmd_options import cmd_opts
-
-    if not cmd_opts.skip_version_check:
-        from modules import errors
-        errors.check_versions()
-
-
-def initialize():
-    from modules import initialize_util
-    initialize_util.fix_torch_version()
-    initialize_util.fix_asyncio_event_loop_policy()
-    initialize_util.validate_tls_options()
-    initialize_util.configure_sigint_handler()
-    initialize_util.configure_opts_onchange()
-
-    from modules import modelloader
-    modelloader.cleanup_models()
-
-    from modules import sd_models
-    sd_models.setup_model()
-    startup_timer.record("setup SD model")
-
-    from modules.shared_cmd_options import cmd_opts
-
-    from modules import codeformer_model
-    warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision.transforms.functional_tensor")
-    codeformer_model.setup_model(cmd_opts.codeformer_models_path)
-    startup_timer.record("setup codeformer")
-
-    from modules import gfpgan_model
-    gfpgan_model.setup_model(cmd_opts.gfpgan_models_path)
-    startup_timer.record("setup gfpgan")
-
-    initialize_rest(reload_script_modules=False)
-
-
-def initialize_rest(*, reload_script_modules=False):
-    """
-    Called both from initialize() and when reloading the webui.
-    """
-    from modules.shared_cmd_options import cmd_opts
-
-    from modules import sd_samplers
-    sd_samplers.set_samplers()
-    startup_timer.record("set samplers")
-
-    from modules import extensions
-    extensions.list_extensions()
-    startup_timer.record("list extensions")
-
-    from modules import initialize_util
-    initialize_util.restore_config_state_file()
-    startup_timer.record("restore config state file")
-
-    from modules import shared, upscaler, scripts
-    if cmd_opts.ui_debug_mode:
-        shared.sd_upscalers = upscaler.UpscalerLanczos().scalers
-        scripts.load_scripts()
-        return
-
-    from modules import sd_models
-    sd_models.list_models()
-    startup_timer.record("list SD models")
-
-    from modules import localization
-    localization.list_localizations(cmd_opts.localizations_dir)
-    startup_timer.record("list localizations")
-
-    with startup_timer.subcategory("load scripts"):
-        scripts.load_scripts()
-
-    if reload_script_modules:
-        for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]:
-            importlib.reload(module)
-        startup_timer.record("reload script modules")
-
-    from modules import modelloader
-    modelloader.load_upscalers()
-    startup_timer.record("load upscalers")
-
-    from modules import sd_vae
-    sd_vae.refresh_vae_list()
-    startup_timer.record("refresh VAE")
-
-    from modules import textual_inversion
-    textual_inversion.textual_inversion.list_textual_inversion_templates()
-    startup_timer.record("refresh textual inversion templates")
-
-    from modules import script_callbacks, sd_hijack_optimizations, sd_hijack
-    script_callbacks.on_list_optimizers(sd_hijack_optimizations.list_optimizers)
-    sd_hijack.list_optimizers()
-    startup_timer.record("scripts list_optimizers")
-
-    from modules import sd_unet
-    sd_unet.list_unets()
-    startup_timer.record("scripts list_unets")
-
-    def load_model():
-        """
-        Accesses shared.sd_model property to load model.
-        After it's available, if it has been loaded before this access by some extension,
-        its optimization may be None because the list of optimizaers has neet been filled
-        by that time, so we apply optimization again.
-        """
-
-        shared.sd_model  # noqa: B018
-
-        if sd_hijack.current_optimizer is None:
-            sd_hijack.apply_optimizations()
-
-        from modules import devices
-        devices.first_time_calculation()
-    if not shared.cmd_opts.skip_load_model_at_start:
-        Thread(target=load_model).start()
-
-    from modules import shared_items
-    shared_items.reload_hypernetworks()
-    startup_timer.record("reload hypernetworks")
-
-    from modules import ui_extra_networks
-    ui_extra_networks.initialize()
-    ui_extra_networks.register_default_pages()
-
-    from modules import extra_networks
-    extra_networks.initialize()
-    extra_networks.register_default_extra_networks()
-    startup_timer.record("initialize extra networks")

modules/initialize_util.py

@@ -1,206 +0,0 @@
-import json
-import os
-import signal
-import sys
-import re
-
-from modules.timer import startup_timer
-
-
-def gradio_server_name():
-    from modules.shared_cmd_options import cmd_opts
-
-    if cmd_opts.server_name:
-        return cmd_opts.server_name
-    else:
-        return "0.0.0.0" if cmd_opts.listen else None
-
-
-def fix_torch_version():
-    import torch
-
-    # Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors
-    if ".dev" in torch.__version__ or "+git" in torch.__version__:
-        torch.__long_version__ = torch.__version__
-        torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
-
-
-def fix_asyncio_event_loop_policy():
-    """
-        The default `asyncio` event loop policy only automatically creates
-        event loops in the main threads. Other threads must create event
-        loops explicitly or `asyncio.get_event_loop` (and therefore
-        `.IOLoop.current`) will fail. Installing this policy allows event
-        loops to be created automatically on any thread, matching the
-        behavior of Tornado versions prior to 5.0 (or 5.0 on Python 2).
-    """
-
-    import asyncio
-
-    if sys.platform == "win32" and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
-        # "Any thread" and "selector" should be orthogonal, but there's not a clean
-        # interface for composing policies so pick the right base.
-        _BasePolicy = asyncio.WindowsSelectorEventLoopPolicy  # type: ignore
-    else:
-        _BasePolicy = asyncio.DefaultEventLoopPolicy
-
-    class AnyThreadEventLoopPolicy(_BasePolicy):  # type: ignore
-        """Event loop policy that allows loop creation on any thread.
-        Usage::
-
-            asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
-        """
-
-        def get_event_loop(self) -> asyncio.AbstractEventLoop:
-            try:
-                return super().get_event_loop()
-            except (RuntimeError, AssertionError):
-                # This was an AssertionError in python 3.4.2 (which ships with debian jessie)
-                # and changed to a RuntimeError in 3.4.3.
-                # "There is no current event loop in thread %r"
-                loop = self.new_event_loop()
-                self.set_event_loop(loop)
-                return loop
-
-    asyncio.set_event_loop_policy(AnyThreadEventLoopPolicy())
-
-
-def restore_config_state_file():
-    from modules import shared, config_states
-
-    config_state_file = shared.opts.restore_config_state_file
-    if config_state_file == "":
-        return
-
-    shared.opts.restore_config_state_file = ""
-    shared.opts.save(shared.config_filename)
-
-    if os.path.isfile(config_state_file):
-        print(f"*** About to restore extension state from file: {config_state_file}")
-        with open(config_state_file, "r", encoding="utf-8") as f:
-            config_state = json.load(f)
-            config_states.restore_extension_config(config_state)
-        startup_timer.record("restore extension config")
-    elif config_state_file:
-        print(f"!!! Config state backup not found: {config_state_file}")
-
-
-def validate_tls_options():
-    from modules.shared_cmd_options import cmd_opts
-
-    if not (cmd_opts.tls_keyfile and cmd_opts.tls_certfile):
-        return
-
-    try:
-        if not os.path.exists(cmd_opts.tls_keyfile):
-            print("Invalid path to TLS keyfile given")
-        if not os.path.exists(cmd_opts.tls_certfile):
-            print(f"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'")
-    except TypeError:
-        cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None
-        print("TLS setup invalid, running webui without TLS")
-    else:
-        print("Running with TLS")
-    startup_timer.record("TLS")
-
-
-def get_gradio_auth_creds():
-    """
-    Convert the gradio_auth and gradio_auth_path commandline arguments into
-    an iterable of (username, password) tuples.
-    """
-    from modules.shared_cmd_options import cmd_opts
-
-    def process_credential_line(s):
-        s = s.strip()
-        if not s:
-            return None
-        return tuple(s.split(':', 1))
-
-    if cmd_opts.gradio_auth:
-        for cred in cmd_opts.gradio_auth.split(','):
-            cred = process_credential_line(cred)
-            if cred:
-                yield cred
-
-    if cmd_opts.gradio_auth_path:
-        with open(cmd_opts.gradio_auth_path, 'r', encoding="utf8") as file:
-            for line in file.readlines():
-                for cred in line.strip().split(','):
-                    cred = process_credential_line(cred)
-                    if cred:
-                        yield cred
-
-
-def dumpstacks():
-    import threading
-    import traceback
-
-    id2name = {th.ident: th.name for th in threading.enumerate()}
-    code = []
-    for threadId, stack in sys._current_frames().items():
-        code.append(f"\n# Thread: {id2name.get(threadId, '')}({threadId})")
-        for filename, lineno, name, line in traceback.extract_stack(stack):
-            code.append(f"""File: "{filename}", line {lineno}, in {name}""")
-            if line:
-                code.append("  " + line.strip())
-
-    print("\n".join(code))
-
-
-def configure_sigint_handler():
-    # make the program just exit at ctrl+c without waiting for anything
-
-    from modules import shared
-
-    def sigint_handler(sig, frame):
-        print(f'Interrupted with signal {sig} in {frame}')
-
-        if shared.opts.dump_stacks_on_signal:
-            dumpstacks()
-
-        os._exit(0)
-
-    if not os.environ.get("COVERAGE_RUN"):
-        # Don't install the immediate-quit handler when running under coverage,
-        # as then the coverage report won't be generated.
-        signal.signal(signal.SIGINT, sigint_handler)
-
-
-def configure_opts_onchange():
-    from modules import shared, sd_models, sd_vae, ui_tempdir, sd_hijack
-    from modules.call_queue import wrap_queued_call
-
-    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
-    shared.opts.onchange("sd_vae", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
-    shared.opts.onchange("sd_vae_overrides_per_model_preferences", wrap_queued_call(lambda: sd_vae.reload_vae_weights()), call=False)
-    shared.opts.onchange("temp_dir", ui_tempdir.on_tmpdir_changed)
-    shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
-    shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)
-    startup_timer.record("opts onchange")
-
-
-def setup_middleware(app):
-    from starlette.middleware.gzip import GZipMiddleware
-
-    app.middleware_stack = None  # reset current middleware to allow modifying user provided list
-    app.add_middleware(GZipMiddleware, minimum_size=1000)
-    configure_cors_middleware(app)
-    app.build_middleware_stack()  # rebuild middleware stack on-the-fly
-
-
-def configure_cors_middleware(app):
-    from starlette.middleware.cors import CORSMiddleware
-    from modules.shared_cmd_options import cmd_opts
-
-    cors_options = {
-        "allow_methods": ["*"],
-        "allow_headers": ["*"],
-        "allow_credentials": True,
-    }
-    if cmd_opts.cors_allow_origins:
-        cors_options["allow_origins"] = cmd_opts.cors_allow_origins.split(',')
-    if cmd_opts.cors_allow_origins_regex:
-        cors_options["allow_origin_regex"] = cmd_opts.cors_allow_origins_regex
-    app.add_middleware(CORSMiddleware, **cors_options)
-

modules/interrogate.py

@@ -186,8 +186,9 @@ class InterrogateModels:
         res = ""
         shared.state.begin(job="interrogate")
         try:
-            lowvram.send_everything_to_cpu()
-            devices.torch_gc()
+            if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+                lowvram.send_everything_to_cpu()
+                devices.torch_gc()
 
             self.load()
 

modules/launch_utils.py

@@ -1,9 +1,7 @@
 # this scripts installs necessary requirements and launches main program in webui.py
-import logging
 import re
 import subprocess
 import os
-import shutil
 import sys
 import importlib.util
 import platform
@@ -13,10 +11,8 @@ from functools import lru_cache
 from modules import cmd_args, errors
 from modules.paths_internal import script_path, extensions_dir
 from modules.timer import startup_timer
-from modules import logging_config
 
 args, _ = cmd_args.parser.parse_known_args()
-logging_config.setup_logging(args.loglevel)
 
 python = sys.executable
 git = os.environ.get('GIT', "git")
@@ -64,7 +60,7 @@ Use --skip-python-version-check to suppress this warning.
 @lru_cache()
 def commit_hash():
     try:
-        return subprocess.check_output([git, "-C", script_path, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
+        return subprocess.check_output([git, "rev-parse", "HEAD"], shell=False, encoding='utf8').strip()
     except Exception:
         return "<none>"
 
@@ -72,7 +68,7 @@ def commit_hash():
 @lru_cache()
 def git_tag():
     try:
-        return subprocess.check_output([git, "-C", script_path, "describe", "--tags"], shell=False, encoding='utf8').strip()
+        return subprocess.check_output([git, "describe", "--tags"], shell=False, encoding='utf8').strip()
     except Exception:
         try:
 
@@ -143,25 +139,6 @@ def check_run_python(code: str) -> bool:
     return result.returncode == 0
 
 
-def git_fix_workspace(dir, name):
-    run(f'"{git}" -C "{dir}" fetch --refetch --no-auto-gc', f"Fetching all contents for {name}", f"Couldn't fetch {name}", live=True)
-    run(f'"{git}" -C "{dir}" gc --aggressive --prune=now', f"Pruning {name}", f"Couldn't prune {name}", live=True)
-    return
-
-
-def run_git(dir, name, command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live, autofix=True):
-    try:
-        return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
-    except RuntimeError:
-        if not autofix:
-            raise
-
-    print(f"{errdesc}, attempting autofix...")
-    git_fix_workspace(dir, name)
-
-    return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
-
-
 def git_clone(url, dir, name, commithash=None):
     # TODO clone into temporary dir and move if successful
 
@@ -169,24 +146,15 @@ def git_clone(url, dir, name, commithash=None):
         if commithash is None:
             return
 
-        current_hash = run_git(dir, name, 'rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
+        current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
         if current_hash == commithash:
             return
 
-        if run_git(dir, name, 'config --get remote.origin.url', None, f"Couldn't determine {name}'s origin URL", live=False).strip() != url:
-            run_git(dir, name, f'remote set-url origin "{url}"', None, f"Failed to set {name}'s origin URL", live=False)
-
-        run_git(dir, name, 'fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}", autofix=False)
-
-        run_git(dir, name, f'checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
-
+        run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
+        run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
         return
 
-    try:
-        run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
-    except RuntimeError:
-        shutil.rmtree(dir, ignore_errors=True)
-        raise
+    run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
 
     if commithash is not None:
         run(f'"{git}" -C "{dir}" checkout {commithash}', None, "Couldn't checkout {name}'s hash: {commithash}")
@@ -228,9 +196,7 @@ def run_extension_installer(extension_dir):
         env = os.environ.copy()
         env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
 
-        stdout = run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env).strip()
-        if stdout:
-            print(stdout)
+        print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
         errors.report(str(e))
 
@@ -248,7 +214,7 @@ def list_extensions(settings_file):
     disabled_extensions = set(settings.get('disabled_extensions', []))
     disable_all_extensions = settings.get('disable_all_extensions', 'none')
 
-    if disable_all_extensions != 'none' or args.disable_extra_extensions or args.disable_all_extensions or not os.path.isdir(extensions_dir):
+    if disable_all_extensions != 'none':
         return []
 
     return [x for x in os.listdir(extensions_dir) if x not in disabled_extensions]
@@ -260,8 +226,6 @@ def run_extensions_installers(settings_file):
 
     with startup_timer.subcategory("run extensions installers"):
         for dirname_extension in list_extensions(settings_file):
-            logging.debug(f"Installing {dirname_extension}")
-
             path = os.path.join(extensions_dir, dirname_extension)
 
             if os.path.isdir(path):
@@ -313,6 +277,7 @@ def prepare_environment():
     requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
 
     xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20')
+    gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "https://github.com/TencentARC/GFPGAN/archive/8d2447a2d918f8eba5a4a01463fd48e45126a379.zip")
     clip_package = os.environ.get('CLIP_PACKAGE', "https://github.com/openai/CLIP/archive/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1.zip")
     openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
 
@@ -323,13 +288,13 @@ def prepare_environment():
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
-    stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
-    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
+    stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "5c10deee76adad0032b412294130090932317a87")
+    k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
     codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
     try:
-        # the existence of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
+        # the existance of this file is a signal to webui.sh/bat that webui needs to be restarted when it stops execution
         os.remove(os.path.join(script_path, "tmp", "restart"))
         os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
     except OSError:
@@ -359,6 +324,11 @@ def prepare_environment():
         )
     startup_timer.record("torch GPU test")
 
+
+    if not is_installed("gfpgan"):
+        run_pip(f"install {gfpgan_package}", "gfpgan")
+        startup_timer.record("install gfpgan")
+
     if not is_installed("clip"):
         run_pip(f"install {clip_package}", "clip")
         startup_timer.record("install clip")
@@ -368,7 +338,17 @@ def prepare_environment():
         startup_timer.record("install open_clip")
 
     if (not is_installed("xformers") or args.reinstall_xformers) and args.xformers:
-        run_pip(f"install -U -I --no-deps {xformers_package}", "xformers")
+        if platform.system() == "Windows":
+            if platform.python_version().startswith("3.10"):
+                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(f"install -U -I --no-deps {xformers_package}", "xformers")
+
         startup_timer.record("install xformers")
 
     if not is_installed("ngrok") and args.ngrok:
@@ -396,8 +376,7 @@ def prepare_environment():
         run_pip(f"install -r \"{requirements_file}\"", "requirements")
         startup_timer.record("install requirements")
 
-    if not args.skip_install:
-        run_extensions_installers(settings_file=args.ui_settings_file)
+    run_extensions_installers(settings_file=args.ui_settings_file)
 
     if args.update_check:
         version_check(commit)
@@ -434,16 +413,3 @@ def start():
         webui.api_only()
     else:
         webui.webui()
-
-
-def dump_sysinfo():
-    from modules import sysinfo
-    import datetime
-
-    text = sysinfo.get()
-    filename = f"sysinfo-{datetime.datetime.utcnow().strftime('%Y-%m-%d-%H-%M')}.json"
-
-    with open(filename, "w", encoding="utf8") as file:
-        file.write(text)
-
-    return filename

modules/localization.py

@@ -1,7 +1,7 @@
 import json
 import os
 
-from modules import errors, scripts
+from modules import errors
 
 localizations = {}
 
@@ -14,24 +14,22 @@ def list_localizations(dirname):
         if ext.lower() != ".json":
             continue
 
-        localizations[fn] = [os.path.join(dirname, file)]
+        localizations[fn] = os.path.join(dirname, file)
 
+    from modules import scripts
     for file in scripts.list_scripts("localizations", ".json"):
         fn, ext = os.path.splitext(file.filename)
-        if fn not in localizations:
-            localizations[fn] = []
-        localizations[fn].append(file.path)
+        localizations[fn] = file.path
 
 
 def localization_js(current_localization_name: str) -> str:
-    fns = localizations.get(current_localization_name, None)
+    fn = localizations.get(current_localization_name, None)
     data = {}
-    if fns is not None:
-        for fn in fns:
-            try:
-                with open(fn, "r", encoding="utf8") as file:
-                    data.update(json.load(file))
-            except Exception:
-                errors.report(f"Error loading localization from {fn}", exc_info=True)
+    if fn is not None:
+        try:
+            with open(fn, "r", encoding="utf8") as file:
+                data = json.load(file)
+        except Exception:
+            errors.report(f"Error loading localization from {fn}", exc_info=True)
 
     return f"window.localization = {json.dumps(data)}"

modules/logging_config.py

@@ -1,41 +0,0 @@
-import os
-import logging
-
-try:
-    from tqdm.auto import tqdm
-
-    class TqdmLoggingHandler(logging.Handler):
-        def __init__(self, level=logging.INFO):
-            super().__init__(level)
-
-        def emit(self, record):
-            try:
-                msg = self.format(record)
-                tqdm.write(msg)
-                self.flush()
-            except Exception:
-                self.handleError(record)
-
-    TQDM_IMPORTED = True
-except ImportError:
-    # tqdm does not exist before first launch
-    # I will import once the UI finishes seting up the enviroment and reloads.
-    TQDM_IMPORTED = False
-
-def setup_logging(loglevel):
-    if loglevel is None:
-        loglevel = os.environ.get("SD_WEBUI_LOG_LEVEL")
-
-    loghandlers = []
-
-    if TQDM_IMPORTED:
-        loghandlers.append(TqdmLoggingHandler())
-
-    if loglevel:
-        log_level = getattr(logging, loglevel.upper(), None) or logging.INFO
-        logging.basicConfig(
-            level=log_level,
-            format='%(asctime)s %(levelname)s [%(name)s] %(message)s',
-            datefmt='%Y-%m-%d %H:%M:%S',
-            handlers=loghandlers
-        )

modules/lowvram.py

@@ -1,5 +1,5 @@
 import torch
-from modules import devices, shared
+from modules import devices
 
 module_in_gpu = None
 cpu = torch.device("cpu")
@@ -14,20 +14,6 @@ def send_everything_to_cpu():
     module_in_gpu = None
 
 
-def is_needed(sd_model):
-    return shared.cmd_opts.lowvram or shared.cmd_opts.medvram or shared.cmd_opts.medvram_sdxl and hasattr(sd_model, 'conditioner')
-
-
-def apply(sd_model):
-    enable = is_needed(sd_model)
-    shared.parallel_processing_allowed = not enable
-
-    if enable:
-        setup_for_low_vram(sd_model, not shared.cmd_opts.lowvram)
-    else:
-        sd_model.lowvram = False
-
-
 def setup_for_low_vram(sd_model, use_medvram):
     if getattr(sd_model, 'lowvram', False):
         return
@@ -144,4 +130,4 @@ def setup_for_low_vram(sd_model, use_medvram):
 
 
 def is_enabled(sd_model):
-    return sd_model.lowvram
+    return getattr(sd_model, 'lowvram', False)

modules/mac_specific.py

@@ -4,7 +4,6 @@ import torch
 import platform
 from modules.sd_hijack_utils import CondFunc
 from packaging import version
-from modules import shared
 
 log = logging.getLogger(__name__)
 
@@ -31,7 +30,8 @@ has_mps = check_for_mps()
 
 def torch_mps_gc() -> None:
     try:
-        if shared.state.current_latent is not None:
+        from modules.shared import state
+        if state.current_latent is not None:
             log.debug("`current_latent` is set, skipping MPS garbage collection")
             return
         from torch.mps import empty_cache
@@ -52,6 +52,9 @@ def cumsum_fix(input, cumsum_func, *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)

modules/options.py

@@ -1,308 +0,0 @@
-import json
-import sys
-from dataclasses import dataclass
-
-import gradio as gr
-
-from modules import errors
-from modules.shared_cmd_options import cmd_opts
-
-
-class OptionInfo:
-    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after='', infotext=None, restrict_api=False, category_id=None):
-        self.default = default
-        self.label = label
-        self.component = component
-        self.component_args = component_args
-        self.onchange = onchange
-        self.section = section
-        self.category_id = category_id
-        self.refresh = refresh
-        self.do_not_save = False
-
-        self.comment_before = comment_before
-        """HTML text that will be added after label in UI"""
-
-        self.comment_after = comment_after
-        """HTML text that will be added before label in UI"""
-
-        self.infotext = infotext
-
-        self.restrict_api = restrict_api
-        """If True, the setting will not be accessible via API"""
-
-    def link(self, label, url):
-        self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
-        return self
-
-    def js(self, label, js_func):
-        self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
-        return self
-
-    def info(self, info):
-        self.comment_after += f"<span class='info'>({info})</span>"
-        return self
-
-    def html(self, html):
-        self.comment_after += html
-        return self
-
-    def needs_restart(self):
-        self.comment_after += " <span class='info'>(requires restart)</span>"
-        return self
-
-    def needs_reload_ui(self):
-        self.comment_after += " <span class='info'>(requires Reload UI)</span>"
-        return self
-
-
-class OptionHTML(OptionInfo):
-    def __init__(self, text):
-        super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))
-
-        self.do_not_save = True
-
-
-def options_section(section_identifier, options_dict):
-    for v in options_dict.values():
-        if len(section_identifier) == 2:
-            v.section = section_identifier
-        elif len(section_identifier) == 3:
-            v.section = section_identifier[0:2]
-            v.category_id = section_identifier[2]
-
-    return options_dict
-
-
-options_builtin_fields = {"data_labels", "data", "restricted_opts", "typemap"}
-
-
-class Options:
-    typemap = {int: float}
-
-    def __init__(self, data_labels: dict[str, OptionInfo], restricted_opts):
-        self.data_labels = data_labels
-        self.data = {k: v.default for k, v in self.data_labels.items() if not v.do_not_save}
-        self.restricted_opts = restricted_opts
-
-    def __setattr__(self, key, value):
-        if key in options_builtin_fields:
-            return super(Options, self).__setattr__(key, value)
-
-        if self.data is not None:
-            if key in self.data or key in self.data_labels:
-                assert not cmd_opts.freeze_settings, "changing settings is disabled"
-
-                info = self.data_labels.get(key, None)
-                if info.do_not_save:
-                    return
-
-                comp_args = info.component_args if info else None
-                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                if cmd_opts.hide_ui_dir_config and key in self.restricted_opts:
-                    raise RuntimeError(f"not possible to set {key} because it is restricted")
-
-                self.data[key] = value
-                return
-
-        return super(Options, self).__setattr__(key, value)
-
-    def __getattr__(self, item):
-        if item in options_builtin_fields:
-            return super(Options, self).__getattribute__(item)
-
-        if self.data is not None:
-            if item in self.data:
-                return self.data[item]
-
-        if item in self.data_labels:
-            return self.data_labels[item].default
-
-        return super(Options, self).__getattribute__(item)
-
-    def set(self, key, value, is_api=False, run_callbacks=True):
-        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
-
-        oldval = self.data.get(key, None)
-        if oldval == value:
-            return False
-
-        option = self.data_labels[key]
-        if option.do_not_save:
-            return False
-
-        if is_api and option.restrict_api:
-            return False
-
-        try:
-            setattr(self, key, value)
-        except RuntimeError:
-            return False
-
-        if run_callbacks and option.onchange is not None:
-            try:
-                option.onchange()
-            except Exception as e:
-                errors.display(e, f"changing setting {key} to {value}")
-                setattr(self, key, oldval)
-                return False
-
-        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"
-
-        with open(filename, "w", encoding="utf8") as file:
-            json.dump(self.data, file, indent=4, ensure_ascii=False)
-
-    def same_type(self, x, y):
-        if x is None or y is None:
-            return True
-
-        type_x = self.typemap.get(type(x), type(x))
-        type_y = self.typemap.get(type(y), type(y))
-
-        return type_x == type_y
-
-    def load(self, filename):
-        with open(filename, "r", encoding="utf8") as file:
-            self.data = json.load(file)
-
-        # 1.6.0 VAE defaults
-        if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
-            self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')
-
-        # 1.1.1 quicksettings list migration
-        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
-            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
-
-        # 1.4.0 ui_reorder
-        if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
-            self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
-
-        bad_settings = 0
-        for k, v in self.data.items():
-            info = self.data_labels.get(k, None)
-            if info is not None and not self.same_type(info.default, v):
-                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
-                bad_settings += 1
-
-        if bad_settings > 0:
-            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
-
-    def onchange(self, key, func, call=True):
-        item = self.data_labels.get(key)
-        item.onchange = func
-
-        if call:
-            func()
-
-    def dumpjson(self):
-        d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
-        d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
-        d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
-
-        item_categories = {}
-        for item in self.data_labels.values():
-            category = categories.mapping.get(item.category_id)
-            category = "Uncategorized" if category is None else category.label
-            if category not in item_categories:
-                item_categories[category] = item.section[1]
-
-        # _categories is a list of pairs: [section, category]. Each section (a setting page) will get a special heading above it with the category as text.
-        d["_categories"] = [[v, k] for k, v in item_categories.items()] + [["Defaults", "Other"]]
-
-        return json.dumps(d)
-
-    def add_option(self, key, info):
-        self.data_labels[key] = info
-        if key not in self.data and not info.do_not_save:
-            self.data[key] = info.default
-
-    def reorder(self):
-        """Reorder settings so that:
-            - all items related to section always go together
-            - all sections belonging to a category go together
-            - sections inside a category are ordered alphabetically
-            - categories are ordered by creation order
-
-        Category is a superset of sections: for category "postprocessing" there could be multiple sections: "face restoration", "upscaling".
-
-        This function also changes items' category_id so that all items belonging to a section have the same category_id.
-        """
-
-        category_ids = {}
-        section_categories = {}
-
-        settings_items = self.data_labels.items()
-        for _, item in settings_items:
-            if item.section not in section_categories:
-                section_categories[item.section] = item.category_id
-
-        for _, item in settings_items:
-            item.category_id = section_categories.get(item.section)
-
-        for category_id in categories.mapping:
-            if category_id not in category_ids:
-                category_ids[category_id] = len(category_ids)
-
-        def sort_key(x):
-            item: OptionInfo = x[1]
-            category_order = category_ids.get(item.category_id, len(category_ids))
-            section_order = item.section[1]
-
-            return category_order, section_order
-
-        self.data_labels = dict(sorted(settings_items, key=sort_key))
-
-    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
-
-
-@dataclass
-class OptionsCategory:
-    id: str
-    label: str
-
-class OptionsCategories:
-    def __init__(self):
-        self.mapping = {}
-
-    def register_category(self, category_id, label):
-        if category_id in self.mapping:
-            return category_id
-
-        self.mapping[category_id] = OptionsCategory(category_id, label)
-
-
-categories = OptionsCategories()

modules/patches.py

@@ -1,64 +0,0 @@
-from collections import defaultdict
-
-
-def patch(key, obj, field, replacement):
-    """Replaces a function in a module or a class.
-
-    Also stores the original function in this module, possible to be retrieved via original(key, obj, field).
-    If the function is already replaced by this caller (key), an exception is raised -- use undo() before that.
-
-    Arguments:
-        key: identifying information for who is doing the replacement. You can use __name__.
-        obj: the module or the class
-        field: name of the function as a string
-        replacement: the new function
-
-    Returns:
-        the original function
-    """
-
-    patch_key = (obj, field)
-    if patch_key in originals[key]:
-        raise RuntimeError(f"patch for {field} is already applied")
-
-    original_func = getattr(obj, field)
-    originals[key][patch_key] = original_func
-
-    setattr(obj, field, replacement)
-
-    return original_func
-
-
-def undo(key, obj, field):
-    """Undoes the peplacement by the patch().
-
-    If the function is not replaced, raises an exception.
-
-    Arguments:
-        key: identifying information for who is doing the replacement. You can use __name__.
-        obj: the module or the class
-        field: name of the function as a string
-
-    Returns:
-        Always None
-    """
-
-    patch_key = (obj, field)
-
-    if patch_key not in originals[key]:
-        raise RuntimeError(f"there is no patch for {field} to undo")
-
-    original_func = originals[key].pop(patch_key)
-    setattr(obj, field, original_func)
-
-    return None
-
-
-def original(key, obj, field):
-    """Returns the original function for the patch created by the patch() function"""
-    patch_key = (obj, field)
-
-    return originals[key].get(patch_key, None)
-
-
-originals = defaultdict(dict)

modules/paths.py

@@ -1,6 +1,6 @@
 import os
 import sys
-from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir, cwd  # noqa: F401
+from modules.paths_internal import models_path, script_path, data_path, extensions_dir, extensions_builtin_dir  # noqa: F401
 
 import modules.safe  # noqa: F401
 

modules/paths_internal.py

@@ -8,7 +8,6 @@ import shlex
 commandline_args = os.environ.get('COMMANDLINE_ARGS', "")
 sys.argv += shlex.split(commandline_args)
 
-cwd = os.getcwd()
 modules_path = os.path.dirname(os.path.realpath(__file__))
 script_path = os.path.dirname(modules_path)
 

modules/postprocessing.py

@@ -11,32 +11,37 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
     shared.state.begin(job="extras")
 
+    image_data = []
+    image_names = []
     outputs = []
 
-    def get_images(extras_mode, image, image_folder, input_dir):
-        if extras_mode == 1:
-            for img in image_folder:
-                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]
-                yield image, 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'
+    if extras_mode == 1:
+        for img in image_folder:
+            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(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'
 
-            image_list = shared.listfiles(input_dir)
-            for filename in image_list:
-                try:
-                    image = Image.open(filename)
-                except Exception:
-                    continue
-                yield image, filename
-        else:
-            assert image, 'image not selected'
-            yield image, None
+        image_list = shared.listfiles(input_dir)
+        for filename in image_list:
+            try:
+                image = Image.open(filename)
+            except Exception:
+                continue
+            image_data.append(image)
+            image_names.append(filename)
+    else:
+        assert image, 'image not selected'
+
+        image_data.append(image)
+        image_names.append(None)
 
     if extras_mode == 2 and output_dir != '':
         outpath = output_dir
@@ -45,16 +50,14 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
 
     infotext = ''
 
-    for image_data, name in get_images(extras_mode, image, image_folder, input_dir):
-        image_data: Image.Image
-
+    for image, name in zip(image_data, image_names):
         shared.state.textinfo = name
 
-        parameters, existing_pnginfo = images.read_info_from_image(image_data)
+        parameters, existing_pnginfo = images.read_info_from_image(image)
         if parameters:
             existing_pnginfo["parameters"] = parameters
 
-        pp = scripts_postprocessing.PostprocessedImage(image_data.convert("RGB"))
+        pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
 
         scripts.scripts_postproc.run(pp, args)
 
@@ -75,10 +78,8 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
         if extras_mode != 2 or show_extras_results:
             outputs.append(pp.image)
 
-        image_data.close()
-
     devices.torch_gc()
-    shared.state.end()
+
     return outputs, ui_common.plaintext_to_html(infotext), ''
 
 

modules/processing_scripts/refiner.py

@@ -1,49 +0,0 @@
-import gradio as gr
-
-from modules import scripts, sd_models
-from modules.ui_common import create_refresh_button
-from modules.ui_components import InputAccordion
-
-
-class ScriptRefiner(scripts.ScriptBuiltinUI):
-    section = "accordions"
-    create_group = False
-
-    def __init__(self):
-        pass
-
-    def title(self):
-        return "Refiner"
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible
-
-    def ui(self, is_img2img):
-        with InputAccordion(False, label="Refiner", elem_id=self.elem_id("enable")) as enable_refiner:
-            with gr.Row():
-                refiner_checkpoint = gr.Dropdown(label='Checkpoint', elem_id=self.elem_id("checkpoint"), choices=sd_models.checkpoint_tiles(), value='', tooltip="switch to another model in the middle of generation")
-                create_refresh_button(refiner_checkpoint, sd_models.list_models, lambda: {"choices": sd_models.checkpoint_tiles()}, self.elem_id("checkpoint_refresh"))
-
-                refiner_switch_at = gr.Slider(value=0.8, label="Switch at", minimum=0.01, maximum=1.0, step=0.01, elem_id=self.elem_id("switch_at"), tooltip="fraction of sampling steps when the switch to refiner model should happen; 1=never, 0.5=switch in the middle of generation")
-
-        def lookup_checkpoint(title):
-            info = sd_models.get_closet_checkpoint_match(title)
-            return None if info is None else info.title
-
-        self.infotext_fields = [
-            (enable_refiner, lambda d: 'Refiner' in d),
-            (refiner_checkpoint, lambda d: lookup_checkpoint(d.get('Refiner'))),
-            (refiner_switch_at, 'Refiner switch at'),
-        ]
-
-        return enable_refiner, refiner_checkpoint, refiner_switch_at
-
-    def setup(self, p, enable_refiner, refiner_checkpoint, refiner_switch_at):
-        # the actual implementation is in sd_samplers_common.py, apply_refiner
-
-        if not enable_refiner or refiner_checkpoint in (None, "", "None"):
-            p.refiner_checkpoint = None
-            p.refiner_switch_at = None
-        else:
-            p.refiner_checkpoint = refiner_checkpoint
-            p.refiner_switch_at = refiner_switch_at

modules/processing_scripts/seed.py

@@ -1,111 +0,0 @@
-import json
-
-import gradio as gr
-
-from modules import scripts, ui, errors
-from modules.shared import cmd_opts
-from modules.ui_components import ToolButton
-
-
-class ScriptSeed(scripts.ScriptBuiltinUI):
-    section = "seed"
-    create_group = False
-
-    def __init__(self):
-        self.seed = None
-        self.reuse_seed = None
-        self.reuse_subseed = None
-
-    def title(self):
-        return "Seed"
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible
-
-    def ui(self, is_img2img):
-        with gr.Row(elem_id=self.elem_id("seed_row")):
-            if cmd_opts.use_textbox_seed:
-                self.seed = gr.Textbox(label='Seed', value="", elem_id=self.elem_id("seed"), min_width=100)
-            else:
-                self.seed = gr.Number(label='Seed', value=-1, elem_id=self.elem_id("seed"), min_width=100, precision=0)
-
-            random_seed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_seed"), tooltip="Set seed to -1, which will cause a new random number to be used every time")
-            reuse_seed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_seed"), tooltip="Reuse seed from last generation, mostly useful if it was randomized")
-
-            seed_checkbox = gr.Checkbox(label='Extra', elem_id=self.elem_id("subseed_show"), value=False)
-
-        with gr.Group(visible=False, elem_id=self.elem_id("seed_extras")) as seed_extras:
-            with gr.Row(elem_id=self.elem_id("subseed_row")):
-                subseed = gr.Number(label='Variation seed', value=-1, elem_id=self.elem_id("subseed"), precision=0)
-                random_subseed = ToolButton(ui.random_symbol, elem_id=self.elem_id("random_subseed"))
-                reuse_subseed = ToolButton(ui.reuse_symbol, elem_id=self.elem_id("reuse_subseed"))
-                subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=self.elem_id("subseed_strength"))
-
-            with gr.Row(elem_id=self.elem_id("seed_resize_from_row")):
-                seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=self.elem_id("seed_resize_from_w"))
-                seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=self.elem_id("seed_resize_from_h"))
-
-        random_seed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("seed") + "')}", show_progress=False, inputs=[], outputs=[])
-        random_subseed.click(fn=None, _js="function(){setRandomSeed('" + self.elem_id("subseed") + "')}", show_progress=False, inputs=[], outputs=[])
-
-        seed_checkbox.change(lambda x: gr.update(visible=x), show_progress=False, inputs=[seed_checkbox], outputs=[seed_extras])
-
-        self.infotext_fields = [
-            (self.seed, "Seed"),
-            (seed_checkbox, lambda d: "Variation seed" in d or "Seed resize from-1" in d),
-            (subseed, "Variation seed"),
-            (subseed_strength, "Variation seed strength"),
-            (seed_resize_from_w, "Seed resize from-1"),
-            (seed_resize_from_h, "Seed resize from-2"),
-        ]
-
-        self.on_after_component(lambda x: connect_reuse_seed(self.seed, reuse_seed, x.component, False), elem_id=f'generation_info_{self.tabname}')
-        self.on_after_component(lambda x: connect_reuse_seed(subseed, reuse_subseed, x.component, True), elem_id=f'generation_info_{self.tabname}')
-
-        return self.seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h
-
-    def setup(self, p, seed, seed_checkbox, subseed, subseed_strength, seed_resize_from_w, seed_resize_from_h):
-        p.seed = seed
-
-        if seed_checkbox and subseed_strength > 0:
-            p.subseed = subseed
-            p.subseed_strength = subseed_strength
-
-        if seed_checkbox and seed_resize_from_w > 0 and seed_resize_from_h > 0:
-            p.seed_resize_from_w = seed_resize_from_w
-            p.seed_resize_from_h = seed_resize_from_h
-
-
-
-def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, is_subseed):
-    """ Connects a 'reuse (sub)seed' button's click event so that it copies last used
-        (sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
-        was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
-
-    def copy_seed(gen_info_string: str, index):
-        res = -1
-
-        try:
-            gen_info = json.loads(gen_info_string)
-            index -= gen_info.get('index_of_first_image', 0)
-
-            if is_subseed and gen_info.get('subseed_strength', 0) > 0:
-                all_subseeds = gen_info.get('all_subseeds', [-1])
-                res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
-            else:
-                all_seeds = gen_info.get('all_seeds', [-1])
-                res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
-
-        except json.decoder.JSONDecodeError:
-            if gen_info_string:
-                errors.report(f"Error parsing JSON generation info: {gen_info_string}")
-
-        return [res, gr.update()]
-
-    reuse_seed.click(
-        fn=copy_seed,
-        _js="(x, y) => [x, selected_gallery_index()]",
-        show_progress=False,
-        inputs=[generation_info, seed],
-        outputs=[seed, seed]
-    )

modules/progress.py

@@ -48,7 +48,6 @@ def add_task_to_queue(id_job):
 class ProgressRequest(BaseModel):
     id_task: str = Field(default=None, title="Task ID", description="id of the task to get progress for")
     id_live_preview: int = Field(default=-1, title="Live preview image ID", description="id of last received last preview image")
-    live_preview: bool = Field(default=True, title="Include live preview", description="boolean flag indicating whether to include the live preview image")
 
 
 class ProgressResponse(BaseModel):
@@ -72,12 +71,7 @@ def progressapi(req: ProgressRequest):
     completed = req.id_task in finished_tasks
 
     if not active:
-        textinfo = "Waiting..."
-        if queued:
-            sorted_queued = sorted(pending_tasks.keys(), key=lambda x: pending_tasks[x])
-            queue_index = sorted_queued.index(req.id_task)
-            textinfo = "In queue: {}/{}".format(queue_index + 1, len(sorted_queued))
-        return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo=textinfo)
+        return ProgressResponse(active=active, queued=queued, completed=completed, id_live_preview=-1, textinfo="In queue..." if queued else "Waiting...")
 
     progress = 0
 
@@ -95,30 +89,31 @@ def progressapi(req: ProgressRequest):
     predicted_duration = elapsed_since_start / progress if progress > 0 else None
     eta = predicted_duration - elapsed_since_start if predicted_duration is not None else None
 
-    live_preview = None
     id_live_preview = req.id_live_preview
+    shared.state.set_current_image()
+    if opts.live_previews_enable and shared.state.id_live_preview != req.id_live_preview:
+        image = shared.state.current_image
+        if image is not None:
+            buffered = io.BytesIO()
 
-    if opts.live_previews_enable and req.live_preview:
-        shared.state.set_current_image()
-        if shared.state.id_live_preview != req.id_live_preview:
-            image = shared.state.current_image
-            if image is not None:
-                buffered = io.BytesIO()
-
-                if opts.live_previews_image_format == "png":
-                    # using optimize for large images takes an enormous amount of time
-                    if max(*image.size) <= 256:
-                        save_kwargs = {"optimize": True}
-                    else:
-                        save_kwargs = {"optimize": False, "compress_level": 1}
-
+            if opts.live_previews_image_format == "png":
+                # using optimize for large images takes an enormous amount of time
+                if max(*image.size) <= 256:
+                    save_kwargs = {"optimize": True}
                 else:
-                    save_kwargs = {}
+                    save_kwargs = {"optimize": False, "compress_level": 1}
 
-                image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
-                base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
-                live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
-                id_live_preview = shared.state.id_live_preview
+            else:
+                save_kwargs = {}
+
+            image.save(buffered, format=opts.live_previews_image_format, **save_kwargs)
+            base64_image = base64.b64encode(buffered.getvalue()).decode('ascii')
+            live_preview = f"data:image/{opts.live_previews_image_format};base64,{base64_image}"
+            id_live_preview = shared.state.id_live_preview
+        else:
+            live_preview = None
+    else:
+        live_preview = None
 
     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)
 

modules/prompt_parser.py

@@ -2,9 +2,10 @@ from __future__ import annotations
 
 import re
 from collections import namedtuple
+from typing import List
 import lark
 
-# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][: in background:0.25] [shoddy:masterful:0.5]"
+# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
 # will be represented with prompt_schedule like this (assuming steps=100):
 # [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
 # [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
@@ -25,7 +26,7 @@ plain: /([^\\\[\]():|]|\\.)+/
 %import common.SIGNED_NUMBER -> NUMBER
 """)
 
-def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=None, use_old_scheduling=False):
+def get_learned_conditioning_prompt_schedules(prompts, steps):
     """
     >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
     >>> g("test")
@@ -56,39 +57,18 @@ def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=N
     [[1, 'female'], [2, 'male'], [3, 'female'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'female'], [8, 'male'], [9, 'female'], [10, 'male']]
     >>> g("[fe|||]male")
     [[1, 'female'], [2, 'male'], [3, 'male'], [4, 'male'], [5, 'female'], [6, 'male'], [7, 'male'], [8, 'male'], [9, 'female'], [10, 'male']]
-    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10, 10)[0]
-    >>> g("a [b:.5] c")
-    [[10, 'a b c']]
-    >>> g("a [b:1.5] c")
-    [[5, 'a  c'], [10, 'a b c']]
     """
 
-    if hires_steps is None or use_old_scheduling:
-        int_offset = 0
-        flt_offset = 0
-        steps = base_steps
-    else:
-        int_offset = base_steps
-        flt_offset = 1.0
-        steps = hires_steps
-
     def collect_steps(steps, tree):
         res = [steps]
 
         class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
-                s = tree.children[-2]
-                v = float(s)
-                if use_old_scheduling:
-                    v = v*steps if v<1 else v
-                else:
-                    if "." in s:
-                        v = (v - flt_offset) * steps
-                    else:
-                        v = (v - int_offset)
-                tree.children[-2] = min(steps, int(v))
-                if tree.children[-2] >= 1:
-                    res.append(tree.children[-2])
+                tree.children[-2] = float(tree.children[-2])
+                if tree.children[-2] < 1:
+                    tree.children[-2] *= steps
+                tree.children[-2] = min(steps, int(tree.children[-2]))
+                res.append(tree.children[-2])
 
             def alternate(self, tree):
                 res.extend(range(1, steps+1))
@@ -106,7 +86,7 @@ def get_learned_conditioning_prompt_schedules(prompts, base_steps, hires_steps=N
                 yield args[(step - 1) % len(args)]
             def start(self, args):
                 def flatten(x):
-                    if isinstance(x, str):
+                    if type(x) == str:
                         yield x
                     else:
                         for gen in x:
@@ -154,7 +134,7 @@ class SdConditioning(list):
 
 
 
-def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps, hires_steps=None, use_old_scheduling=False):
+def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
     """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
     and the sampling step at which this condition is to be replaced by the next one.
 
@@ -174,7 +154,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps,
     """
     res = []
 
-    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps, hires_steps, use_old_scheduling)
+    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
     cache = {}
 
     for prompt, prompt_schedule in zip(prompts, prompt_schedules):
@@ -239,17 +219,17 @@ def get_multicond_prompt_list(prompts: SdConditioning | list[str]):
 
 class ComposableScheduledPromptConditioning:
     def __init__(self, schedules, weight=1.0):
-        self.schedules: list[ScheduledPromptConditioning] = schedules
+        self.schedules: List[ScheduledPromptConditioning] = schedules
         self.weight: float = weight
 
 
 class MulticondLearnedConditioning:
     def __init__(self, shape, batch):
         self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: list[list[ComposableScheduledPromptConditioning]] = batch
+        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
 
 
-def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None, use_old_scheduling=False) -> MulticondLearnedConditioning:
+def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
     """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
     For each prompt, the list is obtained by splitting the prompt using the AND separator.
 
@@ -258,7 +238,7 @@ def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None,
 
     res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
 
-    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps, hires_steps, use_old_scheduling)
+    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
 
     res = []
     for indexes in res_indexes:
@@ -277,7 +257,7 @@ class DictWithShape(dict):
         return self["crossattn"].shape
 
 
-def reconstruct_cond_batch(c: list[list[ScheduledPromptConditioning]], current_step):
+def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
     param = c[0][0].cond
     is_dict = isinstance(param, dict)
 

modules/realesrgan_model.py

@@ -55,7 +55,6 @@ class UpscalerRealESRGAN(Upscaler):
             half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
             tile=opts.ESRGAN_tile,
             tile_pad=opts.ESRGAN_tile_overlap,
-            device=self.device,
         )
 
         upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]

modules/restart.py

@@ -14,9 +14,7 @@ def is_restartable() -> bool:
 def restart_program() -> None:
     """creates file tmp/restart and immediately stops the process, which webui.bat/webui.sh interpret as a command to start webui again"""
 
-    tmpdir = Path(script_path) / "tmp"
-    tmpdir.mkdir(parents=True, exist_ok=True)
-    (tmpdir / "restart").touch()
+    (Path(script_path) / "tmp" / "restart").touch()
 
     stop_program()
 

modules/rng.py

@@ -1,170 +0,0 @@
-import torch
-
-from modules import devices, rng_philox, shared
-
-
-def randn(seed, shape, generator=None):
-    """Generate a tensor with random numbers from a normal distribution using seed.
-
-    Uses the seed parameter to set the global torch seed; to generate more with that seed, use randn_like/randn_without_seed."""
-
-    manual_seed(seed)
-
-    if shared.opts.randn_source == "NV":
-        return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
-
-    if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
-        return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
-
-    return torch.randn(shape, device=devices.device, generator=generator)
-
-
-def randn_local(seed, shape):
-    """Generate a tensor with random numbers from a normal distribution using seed.
-
-    Does not change the global random number generator. You can only generate the seed's first tensor using this function."""
-
-    if shared.opts.randn_source == "NV":
-        rng = rng_philox.Generator(seed)
-        return torch.asarray(rng.randn(shape), device=devices.device)
-
-    local_device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
-    local_generator = torch.Generator(local_device).manual_seed(int(seed))
-    return torch.randn(shape, device=local_device, generator=local_generator).to(devices.device)
-
-
-def randn_like(x):
-    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
-
-    Use either randn() or manual_seed() to initialize the generator."""
-
-    if shared.opts.randn_source == "NV":
-        return torch.asarray(nv_rng.randn(x.shape), device=x.device, dtype=x.dtype)
-
-    if shared.opts.randn_source == "CPU" or x.device.type == 'mps':
-        return torch.randn_like(x, device=devices.cpu).to(x.device)
-
-    return torch.randn_like(x)
-
-
-def randn_without_seed(shape, generator=None):
-    """Generate a tensor with random numbers from a normal distribution using the previously initialized genrator.
-
-    Use either randn() or manual_seed() to initialize the generator."""
-
-    if shared.opts.randn_source == "NV":
-        return torch.asarray((generator or nv_rng).randn(shape), device=devices.device)
-
-    if shared.opts.randn_source == "CPU" or devices.device.type == 'mps':
-        return torch.randn(shape, device=devices.cpu, generator=generator).to(devices.device)
-
-    return torch.randn(shape, device=devices.device, generator=generator)
-
-
-def manual_seed(seed):
-    """Set up a global random number generator using the specified seed."""
-
-    if shared.opts.randn_source == "NV":
-        global nv_rng
-        nv_rng = rng_philox.Generator(seed)
-        return
-
-    torch.manual_seed(seed)
-
-
-def create_generator(seed):
-    if shared.opts.randn_source == "NV":
-        return rng_philox.Generator(seed)
-
-    device = devices.cpu if shared.opts.randn_source == "CPU" or devices.device.type == 'mps' else devices.device
-    generator = torch.Generator(device).manual_seed(int(seed))
-    return generator
-
-
-# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
-def slerp(val, low, high):
-    low_norm = low/torch.norm(low, dim=1, keepdim=True)
-    high_norm = high/torch.norm(high, dim=1, keepdim=True)
-    dot = (low_norm*high_norm).sum(1)
-
-    if dot.mean() > 0.9995:
-        return low * val + high * (1 - val)
-
-    omega = torch.acos(dot)
-    so = torch.sin(omega)
-    res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
-    return res
-
-
-class ImageRNG:
-    def __init__(self, shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0):
-        self.shape = tuple(map(int, shape))
-        self.seeds = seeds
-        self.subseeds = subseeds
-        self.subseed_strength = subseed_strength
-        self.seed_resize_from_h = seed_resize_from_h
-        self.seed_resize_from_w = seed_resize_from_w
-
-        self.generators = [create_generator(seed) for seed in seeds]
-
-        self.is_first = True
-
-    def first(self):
-        noise_shape = self.shape if self.seed_resize_from_h <= 0 or self.seed_resize_from_w <= 0 else (self.shape[0], int(self.seed_resize_from_h) // 8, int(self.seed_resize_from_w // 8))
-
-        xs = []
-
-        for i, (seed, generator) in enumerate(zip(self.seeds, self.generators)):
-            subnoise = None
-            if self.subseeds is not None and self.subseed_strength != 0:
-                subseed = 0 if i >= len(self.subseeds) else self.subseeds[i]
-                subnoise = randn(subseed, noise_shape)
-
-            if noise_shape != self.shape:
-                noise = randn(seed, noise_shape)
-            else:
-                noise = randn(seed, self.shape, generator=generator)
-
-            if subnoise is not None:
-                noise = slerp(self.subseed_strength, noise, subnoise)
-
-            if noise_shape != self.shape:
-                x = randn(seed, self.shape, generator=generator)
-                dx = (self.shape[2] - noise_shape[2]) // 2
-                dy = (self.shape[1] - noise_shape[1]) // 2
-                w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
-                h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
-                tx = 0 if dx < 0 else dx
-                ty = 0 if dy < 0 else dy
-                dx = max(-dx, 0)
-                dy = max(-dy, 0)
-
-                x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
-                noise = x
-
-            xs.append(noise)
-
-        eta_noise_seed_delta = shared.opts.eta_noise_seed_delta or 0
-        if eta_noise_seed_delta:
-            self.generators = [create_generator(seed + eta_noise_seed_delta) for seed in self.seeds]
-
-        return torch.stack(xs).to(shared.device)
-
-    def next(self):
-        if self.is_first:
-            self.is_first = False
-            return self.first()
-
-        xs = []
-        for generator in self.generators:
-            x = randn_without_seed(self.shape, generator=generator)
-            xs.append(x)
-
-        return torch.stack(xs).to(shared.device)
-
-
-devices.randn = randn
-devices.randn_local = randn_local
-devices.randn_like = randn_like
-devices.randn_without_seed = randn_without_seed
-devices.manual_seed = manual_seed

modules/script_callbacks.py

@@ -1,7 +1,7 @@
 import inspect
 import os
 from collections import namedtuple
-from typing import Optional, Any
+from typing import Optional, Dict, Any
 
 from fastapi import FastAPI
 from gradio import Blocks
@@ -28,18 +28,6 @@ class ImageSaveParams:
         """dictionary with parameters for image's PNG info data; infotext will have the key 'parameters'"""
 
 
-class ExtraNoiseParams:
-    def __init__(self, noise, x, xi):
-        self.noise = noise
-        """Random noise generated by the seed"""
-
-        self.x = x
-        """Latent representation of the image"""
-
-        self.xi = xi
-        """Noisy latent representation of the image"""
-
-
 class CFGDenoiserParams:
     def __init__(self, x, image_cond, sigma, sampling_step, total_sampling_steps, text_cond, text_uncond):
         self.x = x
@@ -112,7 +100,6 @@ callback_map = dict(
     callbacks_ui_settings=[],
     callbacks_before_image_saved=[],
     callbacks_image_saved=[],
-    callbacks_extra_noise=[],
     callbacks_cfg_denoiser=[],
     callbacks_cfg_denoised=[],
     callbacks_cfg_after_cfg=[],
@@ -202,14 +189,6 @@ def image_saved_callback(params: ImageSaveParams):
             report_exception(c, 'image_saved_callback')
 
 
-def extra_noise_callback(params: ExtraNoiseParams):
-    for c in callback_map['callbacks_extra_noise']:
-        try:
-            c.callback(params)
-        except Exception:
-            report_exception(c, 'callbacks_extra_noise')
-
-
 def cfg_denoiser_callback(params: CFGDenoiserParams):
     for c in callback_map['callbacks_cfg_denoiser']:
         try:
@@ -258,7 +237,7 @@ def image_grid_callback(params: ImageGridLoopParams):
             report_exception(c, 'image_grid')
 
 
-def infotext_pasted_callback(infotext: str, params: dict[str, Any]):
+def infotext_pasted_callback(infotext: str, params: Dict[str, Any]):
     for c in callback_map['callbacks_infotext_pasted']:
         try:
             c.callback(infotext, params)
@@ -388,14 +367,6 @@ def on_image_saved(callback):
     add_callback(callback_map['callbacks_image_saved'], callback)
 
 
-def on_extra_noise(callback):
-    """register a function to be called before adding extra noise in img2img or hires fix;
-    The callback is called with one argument:
-        - params: ExtraNoiseParams - contains noise determined by seed and latent representation of image
-    """
-    add_callback(callback_map['callbacks_extra_noise'], callback)
-
-
 def on_cfg_denoiser(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:
@@ -449,7 +420,7 @@ def on_infotext_pasted(callback):
     """register a function to be called before applying an infotext.
     The callback is called with two arguments:
        - infotext: str - raw infotext.
-       - result: dict[str, any] - parsed infotext parameters.
+       - result: Dict[str, any] - parsed infotext parameters.
     """
     add_callback(callback_map['callbacks_infotext_pasted'], callback)
 

modules/scripts.py

@@ -3,7 +3,6 @@ import re
 import sys
 import inspect
 from collections import namedtuple
-from dataclasses import dataclass
 
 import gradio as gr
 
@@ -22,11 +21,6 @@ class PostprocessBatchListArgs:
         self.images = images
 
 
-@dataclass
-class OnComponent:
-    component: gr.blocks.Block
-
-
 class Script:
     name = None
     """script's internal name derived from title"""
@@ -41,13 +35,9 @@ class Script:
 
     is_txt2img = False
     is_img2img = False
-    tabname = None
 
     group = None
-    """A gr.Group component that has all script's UI inside it."""
-
-    create_group = True
-    """If False, for alwayson scripts, a group component will not be created."""
+    """A gr.Group component that has all script's UI inside it"""
 
     infotext_fields = None
     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when
@@ -62,15 +52,6 @@ class Script:
     api_info = None
     """Generated value of type modules.api.models.ScriptInfo with information about the script for API"""
 
-    on_before_component_elem_id = None
-    """list of callbacks to be called before a component with an elem_id is created"""
-
-    on_after_component_elem_id = None
-    """list of callbacks to be called after a component with an elem_id is created"""
-
-    setup_for_ui_only = False
-    """If true, the script setup will only be run in Gradio UI, not in API"""
-
     def title(self):
         """this function should return the title of the script. This is what will be displayed in the dropdown menu."""
 
@@ -109,16 +90,9 @@ class Script:
 
         pass
 
-    def setup(self, p, *args):
-        """For AlwaysVisible scripts, this function is called when the processing object is set up, before any processing starts.
-        args contains all values returned by components from ui().
-        """
-        pass
-
-
     def before_process(self, p, *args):
         """
-        This function is called very early during processing begins for AlwaysVisible scripts.
+        This function is called very early before processing begins for AlwaysVisible scripts.
         You can modify the processing object (p) here, inject hooks, etc.
         args contains all values returned by components from ui()
         """
@@ -238,29 +212,6 @@ class Script:
 
         pass
 
-    def on_before_component(self, callback, *, elem_id):
-        """
-        Calls callback before a component is created. The callback function is called with a single argument of type OnComponent.
-
-        May be called in show() or ui() - but it may be too late in latter as some components may already be created.
-
-        This function is an alternative to before_component in that it also cllows to run before a component is created, but
-        it doesn't require to be called for every created component - just for the one you need.
-        """
-        if self.on_before_component_elem_id is None:
-            self.on_before_component_elem_id = []
-
-        self.on_before_component_elem_id.append((elem_id, callback))
-
-    def on_after_component(self, callback, *, elem_id):
-        """
-        Calls callback after a component is created. The callback function is called with a single argument of type OnComponent.
-        """
-        if self.on_after_component_elem_id is None:
-            self.on_after_component_elem_id = []
-
-        self.on_after_component_elem_id.append((elem_id, callback))
-
     def describe(self):
         """unused"""
         return ""
@@ -269,7 +220,7 @@ class Script:
         """helper function to generate id for a HTML element, constructs final id out of script name, tab and user-supplied item_id"""
 
         need_tabname = self.show(True) == self.show(False)
-        tabkind = 'img2img' if self.is_img2img else 'txt2img'
+        tabkind = 'img2img' if self.is_img2img else 'txt2txt'
         tabname = f"{tabkind}_" if need_tabname else ""
         title = re.sub(r'[^a-z_0-9]', '', re.sub(r'\s', '_', self.title().lower()))
 
@@ -281,19 +232,6 @@ class Script:
         """
         pass
 
-
-class ScriptBuiltinUI(Script):
-    setup_for_ui_only = True
-
-    def elem_id(self, item_id):
-        """helper function to generate id for a HTML element, constructs final id out of tab and user-supplied item_id"""
-
-        need_tabname = self.show(True) == self.show(False)
-        tabname = ('img2img' if self.is_img2img else 'txt2img') + "_" if need_tabname else ""
-
-        return f'{tabname}{item_id}'
-
-
 current_basedir = paths.script_path
 
 
@@ -311,113 +249,19 @@ scripts_data = []
 postprocessing_scripts_data = []
 ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
 
-def topological_sort(dependencies):
-    """Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
-    Ignores errors relating to missing dependeencies or circular dependencies
-    """
 
-    visited = {}
-    result = []
+def list_scripts(scriptdirname, extension):
+    scripts_list = []
 
-    def inner(name):
-        visited[name] = True
+    basedir = os.path.join(paths.script_path, scriptdirname)
+    if os.path.exists(basedir):
+        for filename in sorted(os.listdir(basedir)):
+            scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename)))
 
-        for dep in dependencies.get(name, []):
-            if dep in dependencies and dep not in visited:
-                inner(dep)
+    for ext in extensions.active():
+        scripts_list += ext.list_files(scriptdirname, extension)
 
-        result.append(name)
-
-    for depname in dependencies:
-        if depname not in visited:
-            inner(depname)
-
-    return result
-
-
-@dataclass
-class ScriptWithDependencies:
-    script_canonical_name: str
-    file: ScriptFile
-    requires: list
-    load_before: list
-    load_after: list
-
-
-def list_scripts(scriptdirname, extension, *, include_extensions=True):
-    scripts = {}
-
-    loaded_extensions = {ext.canonical_name: ext for ext in extensions.active()}
-    loaded_extensions_scripts = {ext.canonical_name: [] for ext in extensions.active()}
-
-    # build script dependency map
-    root_script_basedir = os.path.join(paths.script_path, scriptdirname)
-    if os.path.exists(root_script_basedir):
-        for filename in sorted(os.listdir(root_script_basedir)):
-            if not os.path.isfile(os.path.join(root_script_basedir, filename)):
-                continue
-
-            if os.path.splitext(filename)[1].lower() != extension:
-                continue
-
-            script_file = ScriptFile(paths.script_path, filename, os.path.join(root_script_basedir, filename))
-            scripts[filename] = ScriptWithDependencies(filename, script_file, [], [], [])
-
-    if include_extensions:
-        for ext in extensions.active():
-            extension_scripts_list = ext.list_files(scriptdirname, extension)
-            for extension_script in extension_scripts_list:
-                if not os.path.isfile(extension_script.path):
-                    continue
-
-                script_canonical_name = ("builtin/" if ext.is_builtin else "") + ext.canonical_name + "/" + extension_script.filename
-                relative_path = scriptdirname + "/" + extension_script.filename
-
-                script = ScriptWithDependencies(
-                    script_canonical_name=script_canonical_name,
-                    file=extension_script,
-                    requires=ext.metadata.get_script_requirements("Requires", relative_path, scriptdirname),
-                    load_before=ext.metadata.get_script_requirements("Before", relative_path, scriptdirname),
-                    load_after=ext.metadata.get_script_requirements("After", relative_path, scriptdirname),
-                )
-
-                scripts[script_canonical_name] = script
-                loaded_extensions_scripts[ext.canonical_name].append(script)
-
-    for script_canonical_name, script in scripts.items():
-        # load before requires inverse dependency
-        # in this case, append the script name into the load_after list of the specified script
-        for load_before in script.load_before:
-            # if this requires an individual script to be loaded before
-            other_script = scripts.get(load_before)
-            if other_script:
-                other_script.load_after.append(script_canonical_name)
-
-            # if this requires an extension
-            other_extension_scripts = loaded_extensions_scripts.get(load_before)
-            if other_extension_scripts:
-                for other_script in other_extension_scripts:
-                    other_script.load_after.append(script_canonical_name)
-
-        # if After mentions an extension, remove it and instead add all of its scripts
-        for load_after in list(script.load_after):
-            if load_after not in scripts and load_after in loaded_extensions_scripts:
-                script.load_after.remove(load_after)
-
-                for other_script in loaded_extensions_scripts.get(load_after, []):
-                    script.load_after.append(other_script.script_canonical_name)
-
-    dependencies = {}
-
-    for script_canonical_name, script in scripts.items():
-        for required_script in script.requires:
-            if required_script not in scripts and required_script not in loaded_extensions:
-                errors.report(f'Script "{script_canonical_name}" requires "{required_script}" to be loaded, but it is not.', exc_info=False)
-
-        dependencies[script_canonical_name] = script.load_after
-
-    ordered_scripts = topological_sort(dependencies)
-    scripts_list = [scripts[script_canonical_name].file for script_canonical_name in ordered_scripts]
+    scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)]
 
     return scripts_list
 
@@ -444,7 +288,7 @@ def load_scripts():
     postprocessing_scripts_data.clear()
     script_callbacks.clear_callbacks()
 
-    scripts_list = list_scripts("scripts", ".py") + list_scripts("modules/processing_scripts", ".py", include_extensions=False)
+    scripts_list = list_scripts("scripts", ".py")
 
     syspath = sys.path
 
@@ -458,9 +302,15 @@ def load_scripts():
             elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
                 postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
 
-    # here the scripts_list is already ordered
-    # processing_script is not considered though
-    for scriptfile in 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
@@ -499,17 +349,10 @@ class ScriptRunner:
         self.selectable_scripts = []
         self.alwayson_scripts = []
         self.titles = []
-        self.title_map = {}
         self.infotext_fields = []
         self.paste_field_names = []
         self.inputs = [None]
 
-        self.on_before_component_elem_id = {}
-        """dict of callbacks to be called before an element is created; key=elem_id, value=list of callbacks"""
-
-        self.on_after_component_elem_id = {}
-        """dict of callbacks to be called after an element is created; key=elem_id, value=list of callbacks"""
-
     def initialize_scripts(self, is_img2img):
         from modules import scripts_auto_postprocessing
 
@@ -524,7 +367,6 @@ class ScriptRunner:
             script.filename = script_data.path
             script.is_txt2img = not is_img2img
             script.is_img2img = is_img2img
-            script.tabname = "img2img" if is_img2img else "txt2img"
 
             visibility = script.show(script.is_img2img)
 
@@ -537,28 +379,6 @@ class ScriptRunner:
                 self.scripts.append(script)
                 self.selectable_scripts.append(script)
 
-        self.apply_on_before_component_callbacks()
-
-    def apply_on_before_component_callbacks(self):
-        for script in self.scripts:
-            on_before = script.on_before_component_elem_id or []
-            on_after = script.on_after_component_elem_id or []
-
-            for elem_id, callback in on_before:
-                if elem_id not in self.on_before_component_elem_id:
-                    self.on_before_component_elem_id[elem_id] = []
-
-                self.on_before_component_elem_id[elem_id].append((callback, script))
-
-            for elem_id, callback in on_after:
-                if elem_id not in self.on_after_component_elem_id:
-                    self.on_after_component_elem_id[elem_id] = []
-
-                self.on_after_component_elem_id[elem_id].append((callback, script))
-
-            on_before.clear()
-            on_after.clear()
-
     def create_script_ui(self, script):
         import modules.api.models as api_models
 
@@ -578,15 +398,11 @@ class ScriptRunner:
 
             arg_info = api_models.ScriptArg(label=control.label or "")
 
-            for field in ("value", "minimum", "maximum", "step"):
+            for field in ("value", "minimum", "maximum", "step", "choices"):
                 v = getattr(control, field, None)
                 if v is not None:
                     setattr(arg_info, field, v)
 
-            choices = getattr(control, 'choices', None)  # as of gradio 3.41, some items in choices are strings, and some are tuples where the first elem is the string
-            if choices is not None:
-                arg_info.choices = [x[0] if isinstance(x, tuple) else x for x in choices]
-
             api_args.append(arg_info)
 
         script.api_info = api_models.ScriptInfo(
@@ -613,20 +429,15 @@ class ScriptRunner:
             if script.alwayson and script.section != section:
                 continue
 
-            if script.create_group:
-                with gr.Group(visible=script.alwayson) as group:
-                    self.create_script_ui(script)
-
-                script.group = group
-            else:
+            with gr.Group(visible=script.alwayson) as group:
                 self.create_script_ui(script)
 
+            script.group = group
+
     def prepare_ui(self):
         self.inputs = [None]
 
     def setup_ui(self):
-        all_titles = [wrap_call(script.title, script.filename, "title") or script.filename for script in self.scripts]
-        self.title_map = {title.lower(): script for title, script in zip(all_titles, self.scripts)}
         self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]
 
         self.setup_ui_for_section(None)
@@ -673,8 +484,6 @@ class ScriptRunner:
         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])
 
-        self.apply_on_before_component_callbacks()
-
         return self.inputs
 
     def run(self, p, *args):
@@ -768,12 +577,6 @@ class ScriptRunner:
                 errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
 
     def before_component(self, component, **kwargs):
-        for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
-            try:
-                callback(OnComponent(component=component))
-            except Exception:
-                errors.report(f"Error running on_before_component: {script.filename}", exc_info=True)
-
         for script in self.scripts:
             try:
                 script.before_component(component, **kwargs)
@@ -781,21 +584,12 @@ class ScriptRunner:
                 errors.report(f"Error running before_component: {script.filename}", exc_info=True)
 
     def after_component(self, component, **kwargs):
-        for callback, script in self.on_after_component_elem_id.get(component.elem_id, []):
-            try:
-                callback(OnComponent(component=component))
-            except Exception:
-                errors.report(f"Error running on_after_component: {script.filename}", exc_info=True)
-
         for script in self.scripts:
             try:
                 script.after_component(component, **kwargs)
             except Exception:
                 errors.report(f"Error running after_component: {script.filename}", exc_info=True)
 
-    def script(self, title):
-        return self.title_map.get(title.lower())
-
     def reload_sources(self, cache):
         for si, script in list(enumerate(self.scripts)):
             args_from = script.args_from
@@ -814,6 +608,7 @@ class ScriptRunner:
                     self.scripts[si].args_from = args_from
                     self.scripts[si].args_to = args_to
 
+
     def before_hr(self, p):
         for script in self.alwayson_scripts:
             try:
@@ -822,17 +617,6 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running before_hr: {script.filename}", exc_info=True)
 
-    def setup_scrips(self, p, *, is_ui=True):
-        for script in self.alwayson_scripts:
-            if not is_ui and script.setup_for_ui_only:
-                continue
-
-            try:
-                script_args = p.script_args[script.args_from:script.args_to]
-                script.setup(p, *script_args)
-            except Exception:
-                errors.report(f"Error running setup: {script.filename}", exc_info=True)
-
 
 scripts_txt2img: ScriptRunner = None
 scripts_img2img: ScriptRunner = None

modules/sd_disable_initialization.py

@@ -155,16 +155,10 @@ class LoadStateDictOnMeta(ReplaceHelper):
     ```
     """
 
-    def __init__(self, state_dict, device, weight_dtype_conversion=None):
+    def __init__(self, state_dict, device):
         super().__init__()
         self.state_dict = state_dict
         self.device = device
-        self.weight_dtype_conversion = weight_dtype_conversion or {}
-        self.default_dtype = self.weight_dtype_conversion.get('')
-
-    def get_weight_dtype(self, key):
-        key_first_term, _ = key.split('.', 1)
-        return self.weight_dtype_conversion.get(key_first_term, self.default_dtype)
 
     def __enter__(self):
         if shared.cmd_opts.disable_model_loading_ram_optimization:
@@ -173,60 +167,23 @@ class LoadStateDictOnMeta(ReplaceHelper):
         sd = self.state_dict
         device = self.device
 
-        def load_from_state_dict(original, module, state_dict, prefix, *args, **kwargs):
-            used_param_keys = []
-
-            for name, param in module._parameters.items():
-                if param is None:
-                    continue
-
-                key = prefix + name
-                sd_param = sd.pop(key, None)
-                if sd_param is not None:
-                    state_dict[key] = sd_param.to(dtype=self.get_weight_dtype(key))
-                    used_param_keys.append(key)
+        def load_from_state_dict(original, self, state_dict, prefix, *args, **kwargs):
+            params = [(name, param) for name, param in self._parameters.items() if param is not None and param.is_meta]
 
+            for name, param in params:
                 if param.is_meta:
-                    dtype = sd_param.dtype if sd_param is not None else param.dtype
-                    module._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device, dtype=dtype), requires_grad=param.requires_grad)
+                    self._parameters[name] = torch.nn.parameter.Parameter(torch.zeros_like(param, device=device), requires_grad=param.requires_grad)
 
-            for name in module._buffers:
+            original(self, state_dict, prefix, *args, **kwargs)
+
+            for name, _ in params:
                 key = prefix + name
+                if key in sd:
+                    del sd[key]
 
-                sd_param = sd.pop(key, None)
-                if sd_param is not None:
-                    state_dict[key] = sd_param
-                    used_param_keys.append(key)
-
-            original(module, state_dict, prefix, *args, **kwargs)
-
-            for key in used_param_keys:
-                state_dict.pop(key, None)
-
-        def load_state_dict(original, module, state_dict, strict=True):
-            """torch makes a lot of copies of the dictionary with weights, so just deleting entries from state_dict does not help
-            because the same values are stored in multiple copies of the dict. The trick used here is to give torch a dict with
-            all weights on meta device, i.e. deleted, and then it doesn't matter how many copies torch makes.
-
-            In _load_from_state_dict, the correct weight will be obtained from a single dict with the right weights (sd).
-
-            The dangerous thing about this is if _load_from_state_dict is not called, (if some exotic module overloads
-            the function and does not call the original) the state dict will just fail to load because weights
-            would be on the meta device.
-            """
-
-            if state_dict == sd:
-                state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
-
-            original(module, state_dict, strict=strict)
-
-        module_load_state_dict = self.replace(torch.nn.Module, 'load_state_dict', lambda *args, **kwargs: load_state_dict(module_load_state_dict, *args, **kwargs))
-        module_load_from_state_dict = self.replace(torch.nn.Module, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(module_load_from_state_dict, *args, **kwargs))
         linear_load_from_state_dict = self.replace(torch.nn.Linear, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(linear_load_from_state_dict, *args, **kwargs))
         conv2d_load_from_state_dict = self.replace(torch.nn.Conv2d, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(conv2d_load_from_state_dict, *args, **kwargs))
         mha_load_from_state_dict = self.replace(torch.nn.MultiheadAttention, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(mha_load_from_state_dict, *args, **kwargs))
-        layer_norm_load_from_state_dict = self.replace(torch.nn.LayerNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(layer_norm_load_from_state_dict, *args, **kwargs))
-        group_norm_load_from_state_dict = self.replace(torch.nn.GroupNorm, '_load_from_state_dict', lambda *args, **kwargs: load_from_state_dict(group_norm_load_from_state_dict, *args, **kwargs))
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         self.restore()

modules/sd_hijack.py

@@ -2,15 +2,14 @@ import torch
 from torch.nn.functional import silu
 from types import MethodType
 
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet, patches
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, xlmr_m18
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr, sd_hijack_inpainting
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.modules.diffusionmodules.openaimodel
-import ldm.models.diffusion.ddpm
 import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
@@ -35,11 +34,11 @@ ldm.modules.diffusionmodules.model.print = shared.ldm_print
 ldm.util.print = shared.ldm_print
 ldm.models.diffusion.ddpm.print = shared.ldm_print
 
+sd_hijack_inpainting.do_inpainting_hijack()
+
 optimizers = []
 current_optimizer: sd_hijack_optimizations.SdOptimization = None
 
-ldm_original_forward = patches.patch(__file__, ldm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
-sgm_original_forward = patches.patch(__file__, sgm.modules.diffusionmodules.openaimodel.UNetModel, "forward", sd_unet.UNetModel_forward)
 
 def list_optimizers():
     new_optimizers = script_callbacks.list_optimizers_callback()
@@ -184,20 +183,6 @@ class StableDiffusionModelHijack:
             errors.display(e, "applying cross attention optimization")
             undo_optimizations()
 
-    def convert_sdxl_to_ssd(self, m):
-        """Converts an SDXL model to a Segmind Stable Diffusion model (see https://huggingface.co/segmind/SSD-1B)"""
-
-        delattr(m.model.diffusion_model.middle_block, '1')
-        delattr(m.model.diffusion_model.middle_block, '2')
-        for i in ['9', '8', '7', '6', '5', '4']:
-            delattr(m.model.diffusion_model.input_blocks[7][1].transformer_blocks, i)
-            delattr(m.model.diffusion_model.input_blocks[8][1].transformer_blocks, i)
-            delattr(m.model.diffusion_model.output_blocks[0][1].transformer_blocks, i)
-            delattr(m.model.diffusion_model.output_blocks[1][1].transformer_blocks, i)
-        delattr(m.model.diffusion_model.output_blocks[4][1].transformer_blocks, '1')
-        delattr(m.model.diffusion_model.output_blocks[5][1].transformer_blocks, '1')
-        devices.torch_gc()
-
     def hijack(self, m):
         conditioner = getattr(m, 'conditioner', None)
         if conditioner:
@@ -225,7 +210,7 @@ class StableDiffusionModelHijack:
             else:
                 m.cond_stage_model = conditioner
 
-        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation or type(m.cond_stage_model) == xlmr_m18.BertSeriesModelWithTransformation:
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             model_embeddings = m.cond_stage_model.roberta.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
             m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
@@ -256,34 +241,13 @@ class StableDiffusionModelHijack:
 
         self.layers = flatten(m)
 
-        import modules.models.diffusion.ddpm_edit
-
-        if isinstance(m, ldm.models.diffusion.ddpm.LatentDiffusion):
-            sd_unet.original_forward = ldm_original_forward
-        elif isinstance(m, modules.models.diffusion.ddpm_edit.LatentDiffusion):
-            sd_unet.original_forward = ldm_original_forward
-        elif isinstance(m, sgm.models.diffusion.DiffusionEngine):
-            sd_unet.original_forward = sgm_original_forward
-        else:
-            sd_unet.original_forward = None
+        if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+            ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
 
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
 
     def undo_hijack(self, m):
-        conditioner = getattr(m, 'conditioner', None)
-        if conditioner:
-            for i in range(len(conditioner.embedders)):
-                embedder = conditioner.embedders[i]
-                if isinstance(embedder, (sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords, sd_hijack_open_clip.FrozenOpenCLIPEmbedder2WithCustomWords)):
-                    embedder.wrapped.model.token_embedding = embedder.wrapped.model.token_embedding.wrapped
-                    conditioner.embedders[i] = embedder.wrapped
-                if isinstance(embedder, sd_hijack_clip.FrozenCLIPEmbedderForSDXLWithCustomWords):
-                    embedder.wrapped.transformer.text_model.embeddings.token_embedding = embedder.wrapped.transformer.text_model.embeddings.token_embedding.wrapped
-                    conditioner.embedders[i] = embedder.wrapped
-
-            if hasattr(m, 'cond_stage_model'):
-                delattr(m, 'cond_stage_model')
-
-        elif type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
+        if type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
@@ -303,8 +267,7 @@ class StableDiffusionModelHijack:
         self.layers = None
         self.clip = None
 
-        sd_unet.original_forward = None
-
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
 
     def apply_circular(self, enable):
         if self.circular_enabled == enable:

modules/sd_hijack_inpainting.py

@@ -0,0 +1,95 @@
+import torch
+
+import ldm.models.diffusion.ddpm
+import ldm.models.diffusion.ddim
+import ldm.models.diffusion.plms
+
+from ldm.models.diffusion.ddim import noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding
+
+
+@torch.no_grad()
+def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None, dynamic_threshold=None):
+    b, *_, device = *x.shape, x.device
+
+    def get_model_output(x, t):
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            e_t = self.model.apply_model(x, t, c)
+        else:
+            x_in = torch.cat([x] * 2)
+            t_in = torch.cat([t] * 2)
+
+            if isinstance(c, dict):
+                assert isinstance(unconditional_conditioning, dict)
+                c_in = {}
+                for k in c:
+                    if isinstance(c[k], list):
+                        c_in[k] = [
+                            torch.cat([unconditional_conditioning[k][i], c[k][i]])
+                            for i in range(len(c[k]))
+                        ]
+                    else:
+                        c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
+            else:
+                c_in = torch.cat([unconditional_conditioning, c])
+
+            e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+            e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+        if score_corrector is not None:
+            assert self.model.parameterization == "eps"
+            e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+        return e_t
+
+    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+    def get_x_prev_and_pred_x0(e_t, index):
+        # select parameters corresponding to the currently considered timestep
+        a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+        a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+        sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+        sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+        # current prediction for x_0
+        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+        if quantize_denoised:
+            pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+        if dynamic_threshold is not None:
+            pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
+        # direction pointing to x_t
+        dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+        noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+        return x_prev, pred_x0
+
+    e_t = get_model_output(x, t)
+    if len(old_eps) == 0:
+        # Pseudo Improved Euler (2nd order)
+        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+        e_t_next = get_model_output(x_prev, t_next)
+        e_t_prime = (e_t + e_t_next) / 2
+    elif len(old_eps) == 1:
+        # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (3 * e_t - old_eps[-1]) / 2
+    elif len(old_eps) == 2:
+        # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+    elif len(old_eps) >= 3:
+        # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+        e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+    x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+    return x_prev, pred_x0, e_t
+
+
+def do_inpainting_hijack():
+    ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms

modules/sd_hijack_optimizations.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 import math
 import psutil
-import platform
 
 import torch
 from torch import einsum
@@ -95,10 +94,7 @@ class SdOptimizationSdp(SdOptimizationSdpNoMem):
 class SdOptimizationSubQuad(SdOptimization):
     name = "sub-quadratic"
     cmd_opt = "opt_sub_quad_attention"
-
-    @property
-    def priority(self):
-        return 1000 if shared.device.type == 'mps' else 10
+    priority = 10
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = sub_quad_attention_forward
@@ -124,7 +120,7 @@ class SdOptimizationInvokeAI(SdOptimization):
 
     @property
     def priority(self):
-        return 1000 if shared.device.type != 'mps' and not torch.cuda.is_available() else 10
+        return 1000 if not torch.cuda.is_available() else 10
 
     def apply(self):
         ldm.modules.attention.CrossAttention.forward = split_cross_attention_forward_invokeAI
@@ -431,10 +427,7 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
     qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
 
     if chunk_threshold is None:
-        if q.device.type == 'mps':
-            chunk_threshold_bytes = 268435456 * (2 if platform.processor() == 'i386' else bytes_per_token)
-        else:
-            chunk_threshold_bytes = int(get_available_vram() * 0.7)
+        chunk_threshold_bytes = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
     elif chunk_threshold == 0:
         chunk_threshold_bytes = None
     else:

modules/sd_models.py

@@ -1,22 +1,22 @@
 import collections
 import os.path
 import sys
+import gc
 import threading
 
 import torch
 import re
 import safetensors.torch
-from omegaconf import OmegaConf, ListConfig
+from omegaconf import OmegaConf
 from os import mkdir
 from urllib import request
 import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack, patches
+from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache
 from modules.timer import Timer
 import tomesd
-import numpy as np
 
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
@@ -27,34 +27,15 @@ checkpoint_alisases = checkpoint_aliases  # for compatibility with old name
 checkpoints_loaded = collections.OrderedDict()
 
 
-def replace_key(d, key, new_key, value):
-    keys = list(d.keys())
-
-    d[new_key] = value
-
-    if key not in keys:
-        return d
-
-    index = keys.index(key)
-    keys[index] = new_key
-
-    new_d = {k: d[k] for k in keys}
-
-    d.clear()
-    d.update(new_d)
-    return d
-
-
 class CheckpointInfo:
     def __init__(self, filename):
         self.filename = filename
         abspath = os.path.abspath(filename)
-        abs_ckpt_dir = os.path.abspath(shared.cmd_opts.ckpt_dir) if shared.cmd_opts.ckpt_dir is not None else None
 
         self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
 
-        if abs_ckpt_dir and abspath.startswith(abs_ckpt_dir):
-            name = abspath.replace(abs_ckpt_dir, '')
+        if shared.cmd_opts.ckpt_dir is not None and abspath.startswith(shared.cmd_opts.ckpt_dir):
+            name = abspath.replace(shared.cmd_opts.ckpt_dir, '')
         elif abspath.startswith(model_path):
             name = abspath.replace(model_path, '')
         else:
@@ -87,9 +68,7 @@ class CheckpointInfo:
         self.title = name if self.shorthash is None else f'{name} [{self.shorthash}]'
         self.short_title = self.name_for_extra if self.shorthash is None else f'{self.name_for_extra} [{self.shorthash}]'
 
-        self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]']
-        if self.shorthash:
-            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
+        self.ids = [self.hash, self.model_name, self.title, name, self.name_for_extra, f'{name} [{self.hash}]'] + ([self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]'] if self.shorthash else [])
 
     def register(self):
         checkpoints_list[self.title] = self
@@ -101,20 +80,14 @@ class CheckpointInfo:
         if self.sha256 is None:
             return
 
-        shorthash = self.sha256[0:10]
-        if self.shorthash == self.sha256[0:10]:
-            return self.shorthash
-
-        self.shorthash = shorthash
+        self.shorthash = self.sha256[0:10]
 
         if self.shorthash not in self.ids:
-            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]', f'{self.name_for_extra} [{self.shorthash}]']
+            self.ids += [self.shorthash, self.sha256, f'{self.name} [{self.shorthash}]']
 
-        old_title = self.title
+        checkpoints_list.pop(self.title, None)
         self.title = f'{self.name} [{self.shorthash}]'
         self.short_title = f'{self.name_for_extra} [{self.shorthash}]'
-
-        replace_key(checkpoints_list, old_title, self.title, self)
         self.register()
 
         return self.shorthash
@@ -130,12 +103,9 @@ except Exception:
 
 
 def setup_model():
-    """called once at startup to do various one-time tasks related to SD models"""
-
     os.makedirs(model_path, exist_ok=True)
 
     enable_midas_autodownload()
-    patch_given_betas()
 
 
 def checkpoint_tiles(use_short=False):
@@ -171,9 +141,6 @@ re_strip_checksum = re.compile(r"\s*\[[^]]+]\s*$")
 
 
 def get_closet_checkpoint_match(search_string):
-    if not search_string:
-        return None
-
     checkpoint_info = checkpoint_aliases.get(search_string, None)
     if checkpoint_info is not None:
         return checkpoint_info
@@ -313,8 +280,6 @@ def get_checkpoint_state_dict(checkpoint_info: CheckpointInfo, timer):
     if checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
         print(f"Loading weights [{sd_model_hash}] from cache")
-        # move to end as latest
-        checkpoints_loaded.move_to_end(checkpoint_info)
         return checkpoints_loaded[checkpoint_info]
 
     print(f"Loading weights [{sd_model_hash}] from {checkpoint_info.filename}")
@@ -352,19 +317,16 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
     model.is_sdxl = hasattr(model, 'conditioner')
     model.is_sd2 = not model.is_sdxl and hasattr(model.cond_stage_model, 'model')
     model.is_sd1 = not model.is_sdxl and not model.is_sd2
-    model.is_ssd = model.is_sdxl and 'model.diffusion_model.middle_block.1.transformer_blocks.0.attn1.to_q.weight' not in state_dict.keys()
+
     if model.is_sdxl:
         sd_models_xl.extend_sdxl(model)
 
-    if model.is_ssd:
-        sd_hijack.model_hijack.convert_sdxl_to_ssd(model)
+    model.load_state_dict(state_dict, strict=False)
+    timer.record("apply weights to model")
 
     if shared.opts.sd_checkpoint_cache > 0:
         # cache newly loaded model
-        checkpoints_loaded[checkpoint_info] = state_dict.copy()
-
-    model.load_state_dict(state_dict, strict=False)
-    timer.record("apply weights to model")
+        checkpoints_loaded[checkpoint_info] = state_dict
 
     del state_dict
 
@@ -372,11 +334,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
         model.to(memory_format=torch.channels_last)
         timer.record("apply channels_last")
 
-    if shared.cmd_opts.no_half:
-        model.float()
-        devices.dtype_unet = torch.float32
-        timer.record("apply float()")
-    else:
+    if not shared.cmd_opts.no_half:
         vae = model.first_stage_model
         depth_model = getattr(model, 'depth_model', None)
 
@@ -392,9 +350,9 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
         if depth_model:
             model.depth_model = depth_model
 
-        devices.dtype_unet = torch.float16
         timer.record("apply half()")
 
+    devices.dtype_unet = torch.float16 if model.is_sdxl and not shared.cmd_opts.no_half else 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)
@@ -414,7 +372,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
 
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
-    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
+    vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
     sd_vae.load_vae(model, vae_file, vae_source)
     timer.record("load VAE")
 
@@ -462,20 +420,6 @@ def enable_midas_autodownload():
     midas.api.load_model = load_model_wrapper
 
 
-def patch_given_betas():
-    import ldm.models.diffusion.ddpm
-
-    def patched_register_schedule(*args, **kwargs):
-        """a modified version of register_schedule function that converts plain list from Omegaconf into numpy"""
-
-        if isinstance(args[1], ListConfig):
-            args = (args[0], np.array(args[1]), *args[2:])
-
-        original_register_schedule(*args, **kwargs)
-
-    original_register_schedule = patches.patch(__name__, ldm.models.diffusion.ddpm.DDPM, 'register_schedule', patched_register_schedule)
-
-
 def repair_config(sd_config):
 
     if not hasattr(sd_config.model.params, "use_ema"):
@@ -529,12 +473,8 @@ class SdModelData:
 
         return self.sd_model
 
-    def set_sd_model(self, v, already_loaded=False):
+    def set_sd_model(self, v):
         self.sd_model = v
-        if already_loaded:
-            sd_vae.base_vae = getattr(v, "base_vae", None)
-            sd_vae.loaded_vae_file = getattr(v, "loaded_vae_file", None)
-            sd_vae.checkpoint_info = v.sd_checkpoint_info
 
         try:
             self.loaded_sd_models.remove(v)
@@ -549,6 +489,7 @@ model_data = SdModelData()
 
 
 def get_empty_cond(sd_model):
+    from modules import extra_networks, processing
 
     p = processing.StableDiffusionProcessingTxt2Img()
     extra_networks.activate(p, {})
@@ -561,7 +502,9 @@ def get_empty_cond(sd_model):
 
 
 def send_model_to_cpu(m):
-    if m.lowvram:
+    from modules import lowvram
+
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.send_everything_to_cpu()
     else:
         m.to(devices.cpu)
@@ -569,17 +512,12 @@ def send_model_to_cpu(m):
     devices.torch_gc()
 
 
-def model_target_device(m):
-    if lowvram.is_needed(m):
-        return devices.cpu
-    else:
-        return devices.device
-
-
 def send_model_to_device(m):
-    lowvram.apply(m)
+    from modules import lowvram
 
-    if not m.lowvram:
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
+        lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
+    else:
         m.to(shared.device)
 
 
@@ -637,15 +575,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
 
     timer.record("create model")
 
-    if shared.cmd_opts.no_half:
-        weight_dtype_conversion = None
-    else:
-        weight_dtype_conversion = {
-            'first_stage_model': None,
-            '': torch.float16,
-        }
-
-    with sd_disable_initialization.LoadStateDictOnMeta(state_dict, device=model_target_device(sd_model), weight_dtype_conversion=weight_dtype_conversion):
+    with sd_disable_initialization.LoadStateDictOnMeta(state_dict, devices.cpu):
         load_model_weights(sd_model, checkpoint_info, state_dict, timer)
     timer.record("load weights from state dict")
 
@@ -708,14 +638,8 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
         send_model_to_device(already_loaded)
         timer.record("send model to device")
 
-        model_data.set_sd_model(already_loaded, already_loaded=True)
-
-        if not SkipWritingToConfig.skip:
-            shared.opts.data["sd_model_checkpoint"] = already_loaded.sd_checkpoint_info.title
-            shared.opts.data["sd_checkpoint_hash"] = already_loaded.sd_checkpoint_info.sha256
-
+        model_data.set_sd_model(already_loaded)
         print(f"Using already loaded model {already_loaded.sd_checkpoint_info.title}: done in {timer.summary()}")
-        sd_vae.reload_vae_weights(already_loaded)
         return model_data.sd_model
     elif shared.opts.sd_checkpoints_limit > 1 and len(model_data.loaded_sd_models) < shared.opts.sd_checkpoints_limit:
         print(f"Loading model {checkpoint_info.title} ({len(model_data.loaded_sd_models) + 1} out of {shared.opts.sd_checkpoints_limit})")
@@ -727,10 +651,6 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
         sd_model = model_data.loaded_sd_models.pop()
         model_data.sd_model = sd_model
 
-        sd_vae.base_vae = getattr(sd_model, "base_vae", None)
-        sd_vae.loaded_vae_file = getattr(sd_model, "loaded_vae_file", None)
-        sd_vae.checkpoint_info = sd_model.sd_checkpoint_info
-
         print(f"Reusing loaded model {sd_model.sd_checkpoint_info.title} to load {checkpoint_info.title}")
         return sd_model
     else:
@@ -738,6 +658,7 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
 
 
 def reload_model_weights(sd_model=None, info=None):
+    from modules import devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
 
     timer = Timer()
@@ -787,7 +708,7 @@ def reload_model_weights(sd_model=None, info=None):
         script_callbacks.model_loaded_callback(sd_model)
         timer.record("script callbacks")
 
-        if not sd_model.lowvram:
+        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
             sd_model.to(devices.device)
             timer.record("move model to device")
 
@@ -800,7 +721,18 @@ def reload_model_weights(sd_model=None, info=None):
 
 
 def unload_model_weights(sd_model=None, info=None):
-    send_model_to_cpu(sd_model or shared.sd_model)
+    from modules import devices, sd_hijack
+    timer = Timer()
+
+    if model_data.sd_model:
+        model_data.sd_model.to(devices.cpu)
+        sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
+        model_data.sd_model = None
+        sd_model = None
+        gc.collect()
+        devices.torch_gc()
+
+    print(f"Unloaded weights {timer.summary()}.")
 
     return sd_model
 

modules/sd_models_config.py

@@ -2,7 +2,7 @@ import os
 
 import torch
 
-from modules import shared, paths, sd_disable_initialization, devices
+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")
@@ -21,7 +21,7 @@ config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inf
 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")
-config_alt_diffusion_m18 = os.path.join(sd_configs_path, "alt-diffusion-m18-inference.yaml")
+
 
 def is_using_v_parameterization_for_sd2(state_dict):
     """
@@ -29,6 +29,7 @@ def is_using_v_parameterization_for_sd2(state_dict):
     """
 
     import ldm.modules.diffusionmodules.openaimodel
+    from modules import devices
 
     device = devices.cpu
 
@@ -95,10 +96,7 @@ def guess_model_config_from_state_dict(sd, filename):
         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:
-        if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
-            return config_alt_diffusion_m18
         return config_alt_diffusion
 
     return config_default

modules/sd_models_types.py

@@ -1,34 +0,0 @@
-from ldm.models.diffusion.ddpm import LatentDiffusion
-from typing import TYPE_CHECKING
-
-
-if TYPE_CHECKING:
-    from modules.sd_models import CheckpointInfo
-
-
-class WebuiSdModel(LatentDiffusion):
-    """This class is not actually instantinated, but its fields are created and fieeld by webui"""
-
-    lowvram: bool
-    """True if lowvram/medvram optimizations are enabled -- see modules.lowvram for more info"""
-
-    sd_model_hash: str
-    """short hash, 10 first characters of SHA1 hash of the model file; may be None if --no-hashing flag is used"""
-
-    sd_model_checkpoint: str
-    """path to the file on disk that model weights were obtained from"""
-
-    sd_checkpoint_info: 'CheckpointInfo'
-    """structure with additional information about the file with model's weights"""
-
-    is_sdxl: bool
-    """True if the model's architecture is SDXL or SSD"""
-
-    is_ssd: bool
-    """True if the model is SSD"""
-
-    is_sd2: bool
-    """True if the model's architecture is SD 2.x"""
-
-    is_sd1: bool
-    """True if the model's architecture is SD 1.x"""

modules/sd_samplers.py

@@ -1,18 +1,17 @@
-from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, shared
+from modules import sd_samplers_compvis, sd_samplers_kdiffusion, shared
 
 # 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  # noqa: F401
 
 all_samplers = [
     *sd_samplers_kdiffusion.samplers_data_k_diffusion,
-    *sd_samplers_timesteps.samplers_data_timesteps,
+    *sd_samplers_compvis.samplers_data_compvis,
 ]
 all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
 samplers_map = {}
-samplers_hidden = {}
 
 
 def find_sampler_config(name):
@@ -39,11 +38,13 @@ def create_sampler(name, model):
 
 
 def set_samplers():
-    global samplers, samplers_for_img2img, samplers_hidden
+    global samplers, samplers_for_img2img
 
-    samplers_hidden = set(shared.opts.hide_samplers)
-    samplers = all_samplers
-    samplers_for_img2img = all_samplers
+    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]
 
     samplers_map.clear()
     for sampler in all_samplers:
@@ -52,8 +53,4 @@ def set_samplers():
             samplers_map[alias.lower()] = sampler.name
 
 
-def visible_sampler_names():
-    return [x.name for x in samplers if x.name not in samplers_hidden]
-
-
 set_samplers()

modules/sd_samplers_cfg_denoiser.py

@@ -1,230 +0,0 @@
-import torch
-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
-from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
-
-
-def catenate_conds(conds):
-    if not isinstance(conds[0], dict):
-        return torch.cat(conds)
-
-    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
-
-
-def subscript_cond(cond, a, b):
-    if not isinstance(cond, dict):
-        return cond[a:b]
-
-    return {key: vec[a:b] for key, vec in cond.items()}
-
-
-def pad_cond(tensor, repeats, empty):
-    if not isinstance(tensor, dict):
-        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
-
-    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
-    return tensor
-
-
-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, sampler):
-        super().__init__()
-        self.model_wrap = None
-        self.mask = None
-        self.nmask = None
-        self.init_latent = None
-        self.steps = None
-        """number of steps as specified by user in UI"""
-
-        self.total_steps = None
-        """expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
-
-        self.step = 0
-        self.image_cfg_scale = None
-        self.padded_cond_uncond = False
-        self.sampler = sampler
-        self.model_wrap = None
-        self.p = None
-        self.mask_before_denoising = False
-
-    @property
-    def inner_model(self):
-        raise NotImplementedError()
-
-    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 get_pred_x0(self, x_in, x_out, sigma):
-        return x_out
-
-    def update_inner_model(self):
-        self.model_wrap = None
-
-        c, uc = self.p.get_conds()
-        self.sampler.sampler_extra_args['cond'] = c
-        self.sampler.sampler_extra_args['uncond'] = uc
-
-    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
-
-        if sd_samplers_common.apply_refiner(self):
-            cond = self.sampler.sampler_extra_args['cond']
-            uncond = self.sampler.sampler_extra_args['uncond']
-
-        # 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)"
-
-        if self.mask_before_denoising and self.mask is not None:
-            x = self.init_latent * self.mask + self.nmask * x
-
-        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
-            if isinstance(uncond, dict):
-                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
-            else:
-                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]
-
-        self.padded_cond_uncond = False
-        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
-            empty = shared.sd_model.cond_stage_model_empty_prompt
-            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
-
-            if num_repeats < 0:
-                tensor = pad_cond(tensor, -num_repeats, empty)
-                self.padded_cond_uncond = True
-            elif num_repeats > 0:
-                uncond = pad_cond(uncond, num_repeats, empty)
-                self.padded_cond_uncond = True
-
-        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
-            if is_edit_model:
-                cond_in = catenate_conds([tensor, uncond, uncond])
-            elif skip_uncond:
-                cond_in = tensor
-            else:
-                cond_in = catenate_conds([tensor, uncond])
-
-            if shared.opts.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(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
-        else:
-            x_out = torch.zeros_like(x_in)
-            batch_size = batch_size*2 if shared.opts.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 = subscript_cond(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, self.inner_model)
-        cfg_denoised_callback(denoised_params)
-
-        devices.test_for_nans(x_out, "unet")
-
-        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 not self.mask_before_denoising and self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
-
-        self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
-
-        if opts.live_preview_content == "Prompt":
-            preview = self.sampler.last_latent
-        elif opts.live_preview_content == "Negative prompt":
-            preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
-        else:
-            preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
-
-        sd_samplers_common.store_latent(preview)
-
-        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
-        cfg_after_cfg_callback(after_cfg_callback_params)
-        denoised = after_cfg_callback_params.x
-
-        self.step += 1
-        return denoised
-

modules/sd_samplers_common.py

@@ -1,22 +1,11 @@
-import inspect
 from collections import namedtuple
 import numpy as np
 import torch
 from PIL import Image
-from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
+from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared
 from modules.shared import opts, state
-import k_diffusion.sampling
 
-
-SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
-
-
-class SamplerData(SamplerDataTuple):
-    def total_steps(self, steps):
-        if self.options.get("second_order", False):
-            steps = steps * 2
-
-        return steps
+SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
 
 
 def setup_img2img_steps(p, steps=None):
@@ -35,27 +24,22 @@ approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD":
 
 
 def samples_to_images_tensor(sample, approximation=None, model=None):
-    """Transforms 4-channel latent space images into 3-channel RGB image tensors, with values in range [-1, 1]."""
-
-    if approximation is None or (shared.state.interrupted and opts.live_preview_fast_interrupt):
+    '''latents -> images [-1, 1]'''
+    if approximation is None:
         approximation = approximation_indexes.get(opts.show_progress_type, 0)
 
-        from modules import lowvram
-        if approximation == 0 and lowvram.is_enabled(shared.sd_model) and not shared.opts.live_preview_allow_lowvram_full:
-            approximation = 1
-
     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)).detach()
     elif approximation == 3:
-        x_sample = sd_vae_taesd.decoder_model()(sample.to(devices.device, devices.dtype)).detach()
+        x_sample = sample * 1.5
+        x_sample = sd_vae_taesd.decoder_model()(x_sample.to(devices.device, devices.dtype)).detach()
         x_sample = x_sample * 2 - 1
     else:
         if model is None:
             model = shared.sd_model
-        with devices.without_autocast(): # fixes an issue with unstable VAEs that are flaky even in fp32
-            x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype))
+        x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype))
 
     return x_sample
 
@@ -95,19 +79,9 @@ def images_tensor_to_samples(image, approximation=None, model=None):
     else:
         if model is None:
             model = shared.sd_model
-        model.first_stage_model.to(devices.dtype_vae)
-
         image = image.to(shared.device, dtype=devices.dtype_vae)
         image = image * 2 - 1
-        if len(image) > 1:
-            x_latent = torch.stack([
-                model.get_first_stage_encoding(
-                    model.encode_first_stage(torch.unsqueeze(img, 0))
-                )[0]
-                for img in image
-            ])
-        else:
-            x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
+        x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
 
     return x_latent
 
@@ -153,185 +127,3 @@ def replace_torchsde_browinan():
 
 
 replace_torchsde_browinan()
-
-
-def apply_refiner(cfg_denoiser):
-    completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps
-    refiner_switch_at = cfg_denoiser.p.refiner_switch_at
-    refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info
-
-    if refiner_switch_at is not None and completed_ratio < refiner_switch_at:
-        return False
-
-    if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info:
-        return False
-
-    if getattr(cfg_denoiser.p, "enable_hr", False):
-        is_second_pass = cfg_denoiser.p.is_hr_pass
-
-        if opts.hires_fix_refiner_pass == "first pass" and is_second_pass:
-            return False
-
-        if opts.hires_fix_refiner_pass == "second pass" and not is_second_pass:
-            return False
-
-        if opts.hires_fix_refiner_pass != "second pass":
-            cfg_denoiser.p.extra_generation_params['Hires refiner'] = opts.hires_fix_refiner_pass
-
-    cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
-    cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at
-
-    with sd_models.SkipWritingToConfig():
-        sd_models.reload_model_weights(info=refiner_checkpoint_info)
-
-    devices.torch_gc()
-    cfg_denoiser.p.setup_conds()
-    cfg_denoiser.update_inner_model()
-
-    return True
-
-
-class TorchHijack:
-    """This is here to replace torch.randn_like of k-diffusion.
-
-    k-diffusion has random_sampler argument for most samplers, but not for all, so
-    this is needed to properly replace every use of torch.randn_like.
-
-    We need to replace to make images generated in batches to be same as images generated individually."""
-
-    def __init__(self, p):
-        self.rng = p.rng
-
-    def __getattr__(self, item):
-        if item == 'randn_like':
-            return self.randn_like
-
-        if hasattr(torch, item):
-            return getattr(torch, item)
-
-        raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
-
-    def randn_like(self, x):
-        return self.rng.next()
-
-
-class Sampler:
-    def __init__(self, funcname):
-        self.funcname = funcname
-        self.func = funcname
-        self.extra_params = []
-        self.sampler_noises = None
-        self.stop_at = None
-        self.eta = None
-        self.config: SamplerData = None  # set by the function calling the constructor
-        self.last_latent = None
-        self.s_min_uncond = None
-        self.s_churn = 0.0
-        self.s_tmin = 0.0
-        self.s_tmax = float('inf')
-        self.s_noise = 1.0
-
-        self.eta_option_field = 'eta_ancestral'
-        self.eta_infotext_field = 'Eta'
-        self.eta_default = 1.0
-
-        self.conditioning_key = shared.sd_model.model.conditioning_key
-
-        self.p = None
-        self.model_wrap_cfg = None
-        self.sampler_extra_args = None
-        self.options = {}
-
-    def callback_state(self, d):
-        step = d['i']
-
-        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):
-        self.model_wrap_cfg.steps = steps
-        self.model_wrap_cfg.total_steps = self.config.total_steps(steps)
-        state.sampling_steps = steps
-        state.sampling_step = 0
-
-        try:
-            return func()
-        except RecursionError:
-            print(
-                'Encountered RecursionError during sampling, returning last latent. '
-                'rho >5 with a polyexponential scheduler may cause this error. '
-                'You should try to use a smaller rho value instead.'
-            )
-            return self.last_latent
-        except InterruptedException:
-            return self.last_latent
-
-    def number_of_needed_noises(self, p):
-        return p.steps
-
-    def initialize(self, p) -> dict:
-        self.p = p
-        self.model_wrap_cfg.p = 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 getattr(opts, self.eta_option_field, 0.0)
-        self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
-        k_diffusion.sampling.torch = TorchHijack(p)
-
-        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 != self.eta_default:
-                p.extra_generation_params[self.eta_infotext_field] = self.eta
-
-            extra_params_kwargs['eta'] = self.eta
-
-        if len(self.extra_params) > 0:
-            s_churn = getattr(opts, 's_churn', p.s_churn)
-            s_tmin = getattr(opts, 's_tmin', p.s_tmin)
-            s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
-            s_noise = getattr(opts, 's_noise', p.s_noise)
-
-            if 's_churn' in extra_params_kwargs and s_churn != self.s_churn:
-                extra_params_kwargs['s_churn'] = s_churn
-                p.s_churn = s_churn
-                p.extra_generation_params['Sigma churn'] = s_churn
-            if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin:
-                extra_params_kwargs['s_tmin'] = s_tmin
-                p.s_tmin = s_tmin
-                p.extra_generation_params['Sigma tmin'] = s_tmin
-            if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax:
-                extra_params_kwargs['s_tmax'] = s_tmax
-                p.s_tmax = s_tmax
-                p.extra_generation_params['Sigma tmax'] = s_tmax
-            if 's_noise' in extra_params_kwargs and s_noise != self.s_noise:
-                extra_params_kwargs['s_noise'] = s_noise
-                p.s_noise = s_noise
-                p.extra_generation_params['Sigma noise'] = s_noise
-
-        return extra_params_kwargs
-
-    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(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        raise NotImplementedError()
-
-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        raise NotImplementedError()

modules/sd_samplers_compvis.py

@@ -0,0 +1,224 @@
+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), [], {"default_eta_is_0": True, "uses_ensd": True, "no_sdxl": True}),
+    sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {"no_sdxl": True}),
+    sd_samplers_common.SamplerData('UniPC', lambda model: VanillaStableDiffusionSampler(modules.models.diffusion.uni_pc.UniPCSampler, model), [], {"no_sdxl": True}),
+]
+
+
+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 = self.stop_at if self.stop_at is not None else 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, *args, unconditional_conditioning=unconditional_conditioning, **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):
+        if self.is_ddim:
+            self.eta = p.eta if p.eta is not None else shared.opts.eta_ddim
+        else:
+            self.eta = 0.0
+
+        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

modules/sd_samplers_extra.py

@@ -60,7 +60,7 @@ def restart_sampler(model, x, sigmas, extra_args=None, callback=None, disable=No
                 sigma_restart = get_sigmas_karras(restart_steps, sigmas[min_idx].item(), sigmas[max_idx].item(), device=sigmas.device)[:-1]
                 while restart_times > 0:
                     restart_times -= 1
-                    step_list.extend(zip(sigma_restart[:-1], sigma_restart[1:]))
+                    step_list.extend([(old_sigma, new_sigma) for (old_sigma, new_sigma) in zip(sigma_restart[:-1], sigma_restart[1:])])
 
     last_sigma = None
     for old_sigma, new_sigma in tqdm.tqdm(step_list, disable=disable):

modules/sd_samplers_kdiffusion.py

@@ -1,41 +1,38 @@
+from collections import deque
 import torch
 import inspect
 import k_diffusion.sampling
-from modules import sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
-from modules.sd_samplers_cfg_denoiser import CFGDenoiser  # noqa: F401
-from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
+from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
 
-from modules.shared import opts
+from modules.processing import StableDiffusionProcessing
+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
+from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
 
 samplers_k_diffusion = [
-    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
-    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
-    ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
-    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
     ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
     ('Euler', 'sample_euler', ['k_euler'], {}),
     ('LMS', 'sample_lms', ['k_lms'], {}),
     ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
-    ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True, "second_order": True}),
-    ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
+    ('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, "uses_ensd": True}),
     ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
     ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
     ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
     ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
-    ('DPM++ 2M SDE Heun', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun'], {"brownian_noise": True, "solver_type": "heun"}),
-    ('DPM++ 2M SDE Heun Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_ka'], {'scheduler': 'karras', "brownian_noise": True, "solver_type": "heun"}),
-    ('DPM++ 2M SDE Heun Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_heun_exp'], {'scheduler': 'exponential', "brownian_noise": True, "solver_type": "heun"}),
-    ('DPM++ 3M SDE', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde'], {'discard_next_to_last_sigma': True, "brownian_noise": True}),
-    ('DPM++ 3M SDE Karras', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
-    ('DPM++ 3M SDE Exponential', 'sample_dpmpp_3m_sde', ['k_dpmpp_3m_sde_exp'], {'scheduler': 'exponential', 'discard_next_to_last_sigma': True, "brownian_noise": True}),
     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
     ('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, "uses_ensd": True, "second_order": True}),
     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
     ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
-    ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras', "second_order": True}),
+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
+    ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
+    ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
+    ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
+    ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
 ]
 
 
@@ -49,12 +46,6 @@ 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'],
-    'sample_dpm_fast': ['s_noise'],
-    'sample_dpm_2_ancestral': ['s_noise'],
-    'sample_dpmpp_2s_ancestral': ['s_noise'],
-    'sample_dpmpp_sde': ['s_noise'],
-    'sample_dpmpp_2m_sde': ['s_noise'],
-    'sample_dpmpp_3m_sde': ['s_noise'],
 }
 
 k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
@@ -66,27 +57,319 @@ k_diffusion_scheduler = {
 }
 
 
-class CFGDenoiserKDiffusion(sd_samplers_cfg_denoiser.CFGDenoiser):
-    @property
-    def inner_model(self):
-        if self.model_wrap is None:
-            denoiser = k_diffusion.external.CompVisVDenoiser if shared.sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-            self.model_wrap = denoiser(shared.sd_model, quantize=shared.opts.enable_quantization)
+def catenate_conds(conds):
+    if not isinstance(conds[0], dict):
+        return torch.cat(conds)
 
-        return self.model_wrap
+    return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
 
 
-class KDiffusionSampler(sd_samplers_common.Sampler):
-    def __init__(self, funcname, sd_model, options=None):
-        super().__init__(funcname)
+def subscript_cond(cond, a, b):
+    if not isinstance(cond, dict):
+        return cond[a:b]
 
-        self.extra_params = sampler_extra_params.get(funcname, [])
+    return {key: vec[a:b] for key, vec in cond.items()}
 
-        self.options = options or {}
+
+def pad_cond(tensor, repeats, empty):
+    if not isinstance(tensor, dict):
+        return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
+
+    tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
+    return tensor
+
+
+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
+        self.padded_cond_uncond = False
+
+    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
+            if isinstance(uncond, dict):
+                make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
+            else:
+                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]
+
+        self.padded_cond_uncond = False
+        if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
+            empty = shared.sd_model.cond_stage_model_empty_prompt
+            num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
+
+            if num_repeats < 0:
+                tensor = pad_cond(tensor, -num_repeats, empty)
+                self.padded_cond_uncond = True
+            elif num_repeats > 0:
+                uncond = pad_cond(uncond, num_repeats, empty)
+                self.padded_cond_uncond = True
+
+        if tensor.shape[1] == uncond.shape[1] or skip_uncond:
+            if is_edit_model:
+                cond_in = catenate_conds([tensor, uncond, uncond])
+            elif skip_uncond:
+                cond_in = tensor
+            else:
+                cond_in = catenate_conds([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(subscript_cond(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 = subscript_cond(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, self.inner_model)
+        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
+
+        after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
+        cfg_after_cfg_callback(after_cfg_callback_params)
+        denoised = after_cfg_callback_params.x
+
+        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(f"'{type(self).__name__}' object has no attribute '{item}'")
+
+    def randn_like(self, x):
+        if self.sampler_noises:
+            noise = self.sampler_noises.popleft()
+            if noise.shape == x.shape:
+                return noise
+
+        return devices.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 = funcname if callable(funcname) else 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.noisy_output = None
+        self.eta = None
+        self.config = None  # set by the function calling the constructor
+        self.last_latent = None
+        self.s_min_uncond = None
 
-        self.model_wrap_cfg = CFGDenoiserKDiffusion(self)
-        self.model_wrap = self.model_wrap_cfg.inner_model
+        # NOTE: These are also defined in the StableDiffusionProcessing class.
+        # They should have been here to begin with but we're going to
+        # leave that class __init__ signature alone.
+        self.s_churn = 0.0
+        self.s_tmin = 0.0
+        self.s_tmax = float('inf')
+        self.s_noise = 1.0
+
+        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
+        self.noisy_output = d['x']
+
+        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 = self.stop_at if self.stop_at is not None else steps
+        state.sampling_step = 0
+
+        try:
+            return func()
+        except RecursionError:
+            print(
+                'Encountered RecursionError during sampling, returning last latent. '
+                'rho >5 with a polyexponential scheduler may cause this error. '
+                'You should try to use a smaller rho value instead.'
+            )
+            return self.last_latent
+        except sd_samplers_common.InterruptedException:
+            return self.last_latent
+
+    def number_of_needed_noises(self, p):
+        return p.steps
+
+    def initialize(self, p: StableDiffusionProcessing):
+        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
+
+        if len(self.extra_params) > 0:
+            s_churn = getattr(opts, 's_churn', p.s_churn)
+            s_tmin = getattr(opts, 's_tmin', p.s_tmin)
+            s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
+            s_noise = getattr(opts, 's_noise', p.s_noise)
+
+            if s_churn != self.s_churn:
+                extra_params_kwargs['s_churn'] = s_churn
+                p.s_churn = s_churn
+                p.extra_generation_params['Sigma churn'] = s_churn
+            if s_tmin != self.s_tmin:
+                extra_params_kwargs['s_tmin'] = s_tmin
+                p.s_tmin = s_tmin
+                p.extra_generation_params['Sigma tmin'] = s_tmin
+            if s_tmax != self.s_tmax:
+                extra_params_kwargs['s_tmax'] = s_tmax
+                p.s_tmax = s_tmax
+                p.extra_generation_params['Sigma tmax'] = s_tmax
+            if s_noise != self.s_noise:
+                extra_params_kwargs['s_noise'] = s_noise
+                p.s_noise = s_noise
+                p.extra_generation_params['Sigma noise'] = s_noise
+
+        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)
@@ -138,21 +421,24 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
 
         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]
 
-        if opts.img2img_extra_noise > 0:
-            p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
-            extra_noise_params = ExtraNoiseParams(noise, x, xi)
-            extra_noise_callback(extra_noise_params)
-            noise = extra_noise_params.noise
-            xi += noise * opts.img2img_extra_noise
-
         extra_params_kwargs = self.initialize(p)
         parameters = inspect.signature(self.func).parameters
 
@@ -172,12 +458,9 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 
-        if self.config.options.get('solver_type', None) == 'heun':
-            extra_params_kwargs['solver_type'] = 'heun'
-
         self.model_wrap_cfg.init_latent = x
         self.last_latent = x
-        self.sampler_extra_args = {
+        extra_args = {
             'cond': conditioning,
             'image_cond': image_conditioning,
             'uncond': unconditional_conditioning,
@@ -185,7 +468,7 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
             's_min_uncond': self.s_min_uncond
         }
 
-        samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        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))
 
         if self.model_wrap_cfg.padded_cond_uncond:
             p.extra_generation_params["Pad conds"] = True
@@ -197,46 +480,34 @@ class KDiffusionSampler(sd_samplers_common.Sampler):
 
         sigmas = self.get_sigmas(p, steps)
 
-        if opts.sgm_noise_multiplier:
-            p.extra_generation_params["SGM noise multiplier"] = True
-            x = x * torch.sqrt(1.0 + sigmas[0] ** 2.0)
-        else:
-            x = x * sigmas[0]
+        x = x * sigmas[0]
 
         extra_params_kwargs = self.initialize(p)
         parameters = inspect.signature(self.func).parameters
 
-        if 'n' in parameters:
-            extra_params_kwargs['n'] = steps
-
         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 'sigmas' in parameters:
+            if 'n' in parameters:
+                extra_params_kwargs['n'] = steps
+        else:
             extra_params_kwargs['sigmas'] = sigmas
 
         if self.config.options.get('brownian_noise', False):
             noise_sampler = self.create_noise_sampler(x, sigmas, p)
             extra_params_kwargs['noise_sampler'] = noise_sampler
 
-        if self.config.options.get('solver_type', None) == 'heun':
-            extra_params_kwargs['solver_type'] = 'heun'
-
         self.last_latent = x
-        self.sampler_extra_args = {
+        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
-        }
-
-        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
+        }, disable=False, callback=self.callback_state, **extra_params_kwargs))
 
         if self.model_wrap_cfg.padded_cond_uncond:
             p.extra_generation_params["Pad conds"] = True
 
         return samples
 
-

modules/sd_samplers_timesteps.py

@@ -1,167 +0,0 @@
-import torch
-import inspect
-import sys
-from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
-from modules.sd_samplers_cfg_denoiser import CFGDenoiser
-from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
-
-from modules.shared import opts
-import modules.shared as shared
-
-samplers_timesteps = [
-    ('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
-    ('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
-    ('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}),
-]
-
-
-samplers_data_timesteps = [
-    sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options)
-    for label, funcname, aliases, options in samplers_timesteps
-]
-
-
-class CompVisTimestepsDenoiser(torch.nn.Module):
-    def __init__(self, model, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.inner_model = model
-
-    def forward(self, input, timesteps, **kwargs):
-        return self.inner_model.apply_model(input, timesteps, **kwargs)
-
-
-class CompVisTimestepsVDenoiser(torch.nn.Module):
-    def __init__(self, model, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.inner_model = model
-
-    def predict_eps_from_z_and_v(self, x_t, t, v):
-        return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
-
-    def forward(self, input, timesteps, **kwargs):
-        model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
-        e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
-        return e_t
-
-
-class CFGDenoiserTimesteps(CFGDenoiser):
-
-    def __init__(self, sampler):
-        super().__init__(sampler)
-
-        self.alphas = shared.sd_model.alphas_cumprod
-        self.mask_before_denoising = True
-
-    def get_pred_x0(self, x_in, x_out, sigma):
-        ts = sigma.to(dtype=int)
-
-        a_t = self.alphas[ts][:, None, None, None]
-        sqrt_one_minus_at = (1 - a_t).sqrt()
-
-        pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
-
-        return pred_x0
-
-    @property
-    def inner_model(self):
-        if self.model_wrap is None:
-            denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
-            self.model_wrap = denoiser(shared.sd_model)
-
-        return self.model_wrap
-
-
-class CompVisSampler(sd_samplers_common.Sampler):
-    def __init__(self, funcname, sd_model):
-        super().__init__(funcname)
-
-        self.eta_option_field = 'eta_ddim'
-        self.eta_infotext_field = 'Eta DDIM'
-        self.eta_default = 0.0
-
-        self.model_wrap_cfg = CFGDenoiserTimesteps(self)
-
-    def get_timesteps(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
-
-        timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999)
-
-        return timesteps
-
-    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)
-
-        timesteps = self.get_timesteps(p, steps)
-        timesteps_sched = timesteps[:t_enc]
-
-        alphas_cumprod = shared.sd_model.alphas_cumprod
-        sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
-        sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
-
-        xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
-
-        if opts.img2img_extra_noise > 0:
-            p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
-            extra_noise_params = ExtraNoiseParams(noise, x, xi)
-            extra_noise_callback(extra_noise_params)
-            noise = extra_noise_params.noise
-            xi += noise * opts.img2img_extra_noise * sqrt_alpha_cumprod
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'timesteps' in parameters:
-            extra_params_kwargs['timesteps'] = timesteps_sched
-        if 'is_img2img' in parameters:
-            extra_params_kwargs['is_img2img'] = True
-
-        self.model_wrap_cfg.init_latent = x
-        self.last_latent = x
-        self.sampler_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=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        if self.model_wrap_cfg.padded_cond_uncond:
-            p.extra_generation_params["Pad conds"] = True
-
-        return samples
-
-    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
-        steps = steps or p.steps
-        timesteps = self.get_timesteps(p, steps)
-
-        extra_params_kwargs = self.initialize(p)
-        parameters = inspect.signature(self.func).parameters
-
-        if 'timesteps' in parameters:
-            extra_params_kwargs['timesteps'] = timesteps
-
-        self.last_latent = x
-        self.sampler_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(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
-        if self.model_wrap_cfg.padded_cond_uncond:
-            p.extra_generation_params["Pad conds"] = True
-
-        return samples
-
-
-sys.modules['modules.sd_samplers_compvis'] = sys.modules[__name__]
-VanillaStableDiffusionSampler = CompVisSampler  # temp. compatibility with older extensions

modules/sd_samplers_timesteps_impl.py

@@ -1,137 +0,0 @@
-import torch
-import tqdm
-import k_diffusion.sampling
-import numpy as np
-
-from modules import shared
-from modules.models.diffusion.uni_pc import uni_pc
-
-
-@torch.no_grad()
-def ddim(model, x, timesteps, extra_args=None, callback=None, disable=None, eta=0.0):
-    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
-    alphas = alphas_cumprod[timesteps]
-    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
-    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
-    sigmas = eta * np.sqrt((1 - alphas_prev.cpu().numpy()) / (1 - alphas.cpu()) * (1 - alphas.cpu() / alphas_prev.cpu().numpy()))
-
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones((x.shape[0]))
-    s_x = x.new_ones((x.shape[0], 1, 1, 1))
-    for i in tqdm.trange(len(timesteps) - 1, disable=disable):
-        index = len(timesteps) - 1 - i
-
-        e_t = model(x, timesteps[index].item() * s_in, **extra_args)
-
-        a_t = alphas[index].item() * s_x
-        a_prev = alphas_prev[index].item() * s_x
-        sigma_t = sigmas[index].item() * s_x
-        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
-
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-        dir_xt = (1. - a_prev - sigma_t ** 2).sqrt() * e_t
-        noise = sigma_t * k_diffusion.sampling.torch.randn_like(x)
-        x = a_prev.sqrt() * pred_x0 + dir_xt + noise
-
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
-
-    return x
-
-
-@torch.no_grad()
-def plms(model, x, timesteps, extra_args=None, callback=None, disable=None):
-    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
-    alphas = alphas_cumprod[timesteps]
-    alphas_prev = alphas_cumprod[torch.nn.functional.pad(timesteps[:-1], pad=(1, 0))].to(torch.float64 if x.device.type != 'mps' else torch.float32)
-    sqrt_one_minus_alphas = torch.sqrt(1 - alphas)
-
-    extra_args = {} if extra_args is None else extra_args
-    s_in = x.new_ones([x.shape[0]])
-    s_x = x.new_ones((x.shape[0], 1, 1, 1))
-    old_eps = []
-
-    def get_x_prev_and_pred_x0(e_t, index):
-        # select parameters corresponding to the currently considered timestep
-        a_t = alphas[index].item() * s_x
-        a_prev = alphas_prev[index].item() * s_x
-        sqrt_one_minus_at = sqrt_one_minus_alphas[index].item() * s_x
-
-        # current prediction for x_0
-        pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-
-        # direction pointing to x_t
-        dir_xt = (1. - a_prev).sqrt() * e_t
-        x_prev = a_prev.sqrt() * pred_x0 + dir_xt
-        return x_prev, pred_x0
-
-    for i in tqdm.trange(len(timesteps) - 1, disable=disable):
-        index = len(timesteps) - 1 - i
-        ts = timesteps[index].item() * s_in
-        t_next = timesteps[max(index - 1, 0)].item() * s_in
-
-        e_t = model(x, ts, **extra_args)
-
-        if len(old_eps) == 0:
-            # Pseudo Improved Euler (2nd order)
-            x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
-            e_t_next = model(x_prev, t_next, **extra_args)
-            e_t_prime = (e_t + e_t_next) / 2
-        elif len(old_eps) == 1:
-            # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (3 * e_t - old_eps[-1]) / 2
-        elif len(old_eps) == 2:
-            # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
-        else:
-            # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
-            e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
-
-        x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
-
-        old_eps.append(e_t)
-        if len(old_eps) >= 4:
-            old_eps.pop(0)
-
-        x = x_prev
-
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': 0, 'sigma_hat': 0, 'denoised': pred_x0})
-
-    return x
-
-
-class UniPCCFG(uni_pc.UniPC):
-    def __init__(self, cfg_model, extra_args, callback, *args, **kwargs):
-        super().__init__(None, *args, **kwargs)
-
-        def after_update(x, model_x):
-            callback({'x': x, 'i': self.index, 'sigma': 0, 'sigma_hat': 0, 'denoised': model_x})
-            self.index += 1
-
-        self.cfg_model = cfg_model
-        self.extra_args = extra_args
-        self.callback = callback
-        self.index = 0
-        self.after_update = after_update
-
-    def get_model_input_time(self, t_continuous):
-        return (t_continuous - 1. / self.noise_schedule.total_N) * 1000.
-
-    def model(self, x, t):
-        t_input = self.get_model_input_time(t)
-
-        res = self.cfg_model(x, t_input, **self.extra_args)
-
-        return res
-
-
-def unipc(model, x, timesteps, extra_args=None, callback=None, disable=None, is_img2img=False):
-    alphas_cumprod = model.inner_model.inner_model.alphas_cumprod
-
-    ns = uni_pc.NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-    t_start = timesteps[-1] / 1000 + 1 / 1000 if is_img2img else None  # this is likely off by a bit - if someone wants to fix it please by all means
-    unipc_sampler = UniPCCFG(model, extra_args, callback, ns, predict_x0=True, thresholding=False, variant=shared.opts.uni_pc_variant)
-    x = unipc_sampler.sample(x, steps=len(timesteps), t_start=t_start, 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

modules/sd_unet.py

@@ -1,11 +1,11 @@
 import torch.nn
+import ldm.modules.diffusionmodules.openaimodel
 
 from modules import script_callbacks, shared, devices
 
 unet_options = []
 current_unet_option = None
 current_unet = None
-original_forward = None
 
 
 def list_unets():
@@ -47,7 +47,7 @@ def apply_unet(option=None):
     if current_unet_option is None:
         current_unet = None
 
-        if not shared.sd_model.lowvram:
+        if not (shared.cmd_opts.lowvram or shared.cmd_opts.medvram):
             shared.sd_model.model.diffusion_model.to(devices.device)
 
         return
@@ -88,5 +88,5 @@ def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
     if current_unet is not None:
         return current_unet.forward(x, timesteps, context, *args, **kwargs)
 
-    return original_forward(self, x, timesteps, context, *args, **kwargs)
+    return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
 

modules/sd_vae.py

@@ -1,9 +1,6 @@
 import os
 import collections
-from dataclasses import dataclass
-
-from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks, lowvram, sd_hijack, hashes
-
+from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks
 import glob
 from copy import deepcopy
 
@@ -20,22 +17,6 @@ checkpoint_info = None
 checkpoints_loaded = collections.OrderedDict()
 
 
-def get_loaded_vae_name():
-    if loaded_vae_file is None:
-        return None
-
-    return os.path.basename(loaded_vae_file)
-
-
-def get_loaded_vae_hash():
-    if loaded_vae_file is None:
-        return None
-
-    sha256 = hashes.sha256(loaded_vae_file, 'vae')
-
-    return sha256[0:10] if sha256 else None
-
-
 def get_base_vae(model):
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
         return base_vae
@@ -70,6 +51,7 @@ def get_filename(filepath):
 
 
 def refresh_vae_list():
+    global vae_dict
     vae_dict.clear()
 
     paths = [
@@ -103,7 +85,7 @@ def refresh_vae_list():
         name = get_filename(filepath)
         vae_dict[name] = filepath
 
-    vae_dict.update(dict(sorted(vae_dict.items(), key=lambda item: shared.natural_sort_key(item[0]))))
+    vae_dict = dict(sorted(vae_dict.items(), key=lambda item: shared.natural_sort_key(item[0])))
 
 
 def find_vae_near_checkpoint(checkpoint_file):
@@ -115,74 +97,37 @@ def find_vae_near_checkpoint(checkpoint_file):
     return None
 
 
-@dataclass
-class VaeResolution:
-    vae: str = None
-    source: str = None
-    resolved: bool = True
+def resolve_vae(checkpoint_file):
+    if shared.cmd_opts.vae_path is not None:
+        return shared.cmd_opts.vae_path, 'from commandline argument'
 
-    def tuple(self):
-        return self.vae, self.source
-
-
-def is_automatic():
-    return shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config
-
-
-def resolve_vae_from_setting() -> VaeResolution:
-    if shared.opts.sd_vae == "None":
-        return VaeResolution()
-
-    vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
-    if vae_from_options is not None:
-        return VaeResolution(vae_from_options, 'specified in settings')
-
-    if not is_automatic():
-        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
-
-    return VaeResolution(resolved=False)
-
-
-def resolve_vae_from_user_metadata(checkpoint_file) -> VaeResolution:
     metadata = extra_networks.get_user_metadata(checkpoint_file)
     vae_metadata = metadata.get("vae", None)
     if vae_metadata is not None and vae_metadata != "Automatic":
         if vae_metadata == "None":
-            return VaeResolution()
+            return None, None
 
         vae_from_metadata = vae_dict.get(vae_metadata, None)
         if vae_from_metadata is not None:
-            return VaeResolution(vae_from_metadata, "from user metadata")
+            return vae_from_metadata, "from user metadata"
 
-    return VaeResolution(resolved=False)
+    is_automatic = shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config
 
-
-def resolve_vae_near_checkpoint(checkpoint_file) -> VaeResolution:
     vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
-    if vae_near_checkpoint is not None and (not shared.opts.sd_vae_overrides_per_model_preferences or is_automatic()):
-        return VaeResolution(vae_near_checkpoint, 'found near the checkpoint')
+    if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or is_automatic):
+        return vae_near_checkpoint, 'found near the checkpoint'
 
-    return VaeResolution(resolved=False)
+    if shared.opts.sd_vae == "None":
+        return None, None
 
+    vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
+    if vae_from_options is not None:
+        return vae_from_options, 'specified in settings'
 
-def resolve_vae(checkpoint_file) -> VaeResolution:
-    if shared.cmd_opts.vae_path is not None:
-        return VaeResolution(shared.cmd_opts.vae_path, 'from commandline argument')
+    if not is_automatic:
+        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
 
-    if shared.opts.sd_vae_overrides_per_model_preferences and not is_automatic():
-        return resolve_vae_from_setting()
-
-    res = resolve_vae_from_user_metadata(checkpoint_file)
-    if res.resolved:
-        return res
-
-    res = resolve_vae_near_checkpoint(checkpoint_file)
-    if res.resolved:
-        return res
-
-    res = resolve_vae_from_setting()
-
-    return res
+    return None, None
 
 
 def load_vae_dict(filename, map_location):
@@ -192,7 +137,7 @@ def load_vae_dict(filename, map_location):
 
 
 def load_vae(model, vae_file=None, vae_source="from unknown source"):
-    global vae_dict, base_vae, loaded_vae_file
+    global vae_dict, loaded_vae_file
     # save_settings = False
 
     cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
@@ -230,8 +175,6 @@ def load_vae(model, vae_file=None, vae_source="from unknown source"):
         restore_base_vae(model)
 
     loaded_vae_file = vae_file
-    model.base_vae = base_vae
-    model.loaded_vae_file = loaded_vae_file
 
 
 # don't call this from outside
@@ -249,6 +192,8 @@ unspecified = object()
 
 
 def reload_vae_weights(sd_model=None, vae_file=unspecified):
+    from modules import lowvram, devices, sd_hijack
+
     if not sd_model:
         sd_model = shared.sd_model
 
@@ -256,14 +201,14 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
     checkpoint_file = checkpoint_info.filename
 
     if vae_file == unspecified:
-        vae_file, vae_source = resolve_vae(checkpoint_file).tuple()
+        vae_file, vae_source = resolve_vae(checkpoint_file)
     else:
         vae_source = "from function argument"
 
     if loaded_vae_file == vae_file:
         return
 
-    if sd_model.lowvram:
+    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.send_everything_to_cpu()
     else:
         sd_model.to(devices.cpu)
@@ -275,7 +220,7 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
     sd_hijack.model_hijack.hijack(sd_model)
     script_callbacks.model_loaded_callback(sd_model)
 
-    if not sd_model.lowvram:
+    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
         sd_model.to(devices.device)
 
     print("VAE weights loaded.")

modules/shared.py

@@ -1,51 +1,840 @@
+import datetime
+import json
+import os
+import re
 import sys
+import threading
+import time
+import logging
 
 import gradio as gr
+import torch
+import tqdm
 
-from modules import shared_cmd_options, shared_gradio_themes, options, shared_items, sd_models_types
+import launch
+import modules.interrogate
+import modules.memmon
+import modules.styles
+import modules.devices as devices
+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  # noqa: F401
-from modules import util
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from typing import Optional
 
-cmd_opts = shared_cmd_options.cmd_opts
-parser = shared_cmd_options.parser
-
-batch_cond_uncond = True  # old field, unused now in favor of shared.opts.batch_cond_uncond
-parallel_processing_allowed = True
-styles_filename = cmd_opts.styles_file
-config_filename = cmd_opts.ui_settings_file
-hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+log = logging.getLogger(__name__)
 
 demo = None
 
-device = None
+parser = cmd_args.parser
 
-weight_load_location = None
+script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
+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()
+
+
+restricted_opts = {
+    "samples_filename_pattern",
+    "directories_filename_pattern",
+    "outdir_samples",
+    "outdir_txt2img_samples",
+    "outdir_img2img_samples",
+    "outdir_extras_samples",
+    "outdir_grids",
+    "outdir_txt2img_grids",
+    "outdir_save",
+    "outdir_init_images"
+}
+
+# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
+gradio_hf_hub_themes = [
+    "gradio/base",
+    "gradio/glass",
+    "gradio/monochrome",
+    "gradio/seafoam",
+    "gradio/soft",
+    "gradio/dracula_test",
+    "abidlabs/dracula_test",
+    "abidlabs/Lime",
+    "abidlabs/pakistan",
+    "Ama434/neutral-barlow",
+    "dawood/microsoft_windows",
+    "finlaymacklon/smooth_slate",
+    "Franklisi/darkmode",
+    "freddyaboulton/dracula_revamped",
+    "freddyaboulton/test-blue",
+    "gstaff/xkcd",
+    "Insuz/Mocha",
+    "Insuz/SimpleIndigo",
+    "JohnSmith9982/small_and_pretty",
+    "nota-ai/theme",
+    "nuttea/Softblue",
+    "ParityError/Anime",
+    "reilnuud/polite",
+    "remilia/Ghostly",
+    "rottenlittlecreature/Moon_Goblin",
+    "step-3-profit/Midnight-Deep",
+    "Taithrah/Minimal",
+    "ysharma/huggingface",
+    "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 = \
+    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
+
+devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
+devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
+
+device = devices.device
+weight_load_location = None if cmd_opts.lowram else "cpu"
+
+batch_cond_uncond = cmd_opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
+parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
+config_filename = cmd_opts.ui_settings_file
 
+os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
 hypernetworks = {}
-
 loaded_hypernetworks = []
 
-state = None
 
-prompt_styles = None
+def reload_hypernetworks():
+    from modules.hypernetworks import hypernetwork
+    global hypernetworks
 
-interrogator = None
+    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+
+
+class State:
+    skipped = False
+    interrupted = False
+    job = ""
+    job_no = 0
+    job_count = 0
+    processing_has_refined_job_count = False
+    job_timestamp = '0'
+    sampling_step = 0
+    sampling_steps = 0
+    current_latent = None
+    current_image = None
+    current_image_sampling_step = 0
+    id_live_preview = 0
+    textinfo = None
+    time_start = None
+    server_start = None
+    _server_command_signal = threading.Event()
+    _server_command: Optional[str] = None
+
+    @property
+    def need_restart(self) -> bool:
+        # Compatibility getter for need_restart.
+        return self.server_command == "restart"
+
+    @need_restart.setter
+    def need_restart(self, value: bool) -> None:
+        # Compatibility setter for need_restart.
+        if value:
+            self.server_command = "restart"
+
+    @property
+    def server_command(self):
+        return self._server_command
+
+    @server_command.setter
+    def server_command(self, value: Optional[str]) -> None:
+        """
+        Set the server command to `value` and signal that it's been set.
+        """
+        self._server_command = value
+        self._server_command_signal.set()
+
+    def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
+        """
+        Wait for server command to get set; return and clear the value and signal.
+        """
+        if self._server_command_signal.wait(timeout):
+            self._server_command_signal.clear()
+            req = self._server_command
+            self._server_command = None
+            return req
+        return None
+
+    def request_restart(self) -> None:
+        self.interrupt()
+        self.server_command = "restart"
+        log.info("Received restart request")
+
+    def skip(self):
+        self.skipped = True
+        log.info("Received skip request")
+
+    def interrupt(self):
+        self.interrupted = True
+        log.info("Received interrupt request")
+
+    def nextjob(self):
+        if opts.live_previews_enable and opts.show_progress_every_n_steps == -1:
+            self.do_set_current_image()
+
+        self.job_no += 1
+        self.sampling_step = 0
+        self.current_image_sampling_step = 0
+
+    def dict(self):
+        obj = {
+            "skipped": self.skipped,
+            "interrupted": self.interrupted,
+            "job": self.job,
+            "job_count": self.job_count,
+            "job_timestamp": self.job_timestamp,
+            "job_no": self.job_no,
+            "sampling_step": self.sampling_step,
+            "sampling_steps": self.sampling_steps,
+        }
+
+        return obj
+
+    def begin(self, job: str = "(unknown)"):
+        self.sampling_step = 0
+        self.job_count = -1
+        self.processing_has_refined_job_count = False
+        self.job_no = 0
+        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+        self.current_latent = None
+        self.current_image = None
+        self.current_image_sampling_step = 0
+        self.id_live_preview = 0
+        self.skipped = False
+        self.interrupted = False
+        self.textinfo = None
+        self.time_start = time.time()
+        self.job = job
+        devices.torch_gc()
+        log.info("Starting job %s", job)
+
+    def end(self):
+        duration = time.time() - self.time_start
+        log.info("Ending job %s (%.2f seconds)", self.job, duration)
+        self.job = ""
+        self.job_count = 0
+
+        devices.torch_gc()
+
+    def set_current_image(self):
+        """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
+        if not parallel_processing_allowed:
+            return
+
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps != -1:
+            self.do_set_current_image()
+
+    def do_set_current_image(self):
+        if self.current_latent is None:
+            return
+
+        import modules.sd_samplers
+
+        try:
+            if opts.show_progress_grid:
+                self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
+            else:
+                self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
+
+            self.current_image_sampling_step = self.sampling_step
+
+        except Exception:
+            # when switching models during genration, VAE would be on CPU, so creating an image will fail.
+            # we silently ignore this error
+            errors.record_exception()
+
+    def assign_current_image(self, image):
+        self.current_image = image
+        self.id_live_preview += 1
+
+
+state = State()
+state.server_start = time.time()
+
+styles_filename = cmd_opts.styles_file
+prompt_styles = modules.styles.StyleDatabase(styles_filename)
+
+interrogator = modules.interrogate.InterrogateModels("interrogate")
 
 face_restorers = []
 
-options_templates = None
-opts = None
-restricted_opts = None
 
-sd_model: sd_models_types.WebuiSdModel = None
+class OptionInfo:
+    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
+        self.default = default
+        self.label = label
+        self.component = component
+        self.component_args = component_args
+        self.onchange = onchange
+        self.section = section
+        self.refresh = refresh
+        self.do_not_save = False
+
+        self.comment_before = comment_before
+        """HTML text that will be added after label in UI"""
+
+        self.comment_after = comment_after
+        """HTML text that will be added before label in UI"""
+
+    def link(self, label, url):
+        self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
+        return self
+
+    def js(self, label, js_func):
+        self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
+        return self
+
+    def info(self, info):
+        self.comment_after += f"<span class='info'>({info})</span>"
+        return self
+
+    def html(self, html):
+        self.comment_after += html
+        return self
+
+    def needs_restart(self):
+        self.comment_after += " <span class='info'>(requires restart)</span>"
+        return self
+
+    def needs_reload_ui(self):
+        self.comment_after += " <span class='info'>(requires Reload UI)</span>"
+        return self
+
+
+class OptionHTML(OptionInfo):
+    def __init__(self, text):
+        super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))
+
+        self.do_not_save = True
+
+
+def options_section(section_identifier, options_dict):
+    for v in options_dict.values():
+        v.section = section_identifier
+
+    return options_dict
+
+
+def list_checkpoint_tiles():
+    import modules.sd_models
+    return modules.sd_models.checkpoint_tiles()
+
+
+def refresh_checkpoints():
+    import modules.sd_models
+    return modules.sd_models.list_models()
+
+
+def list_samplers():
+    import modules.sd_samplers
+    return modules.sd_samplers.all_samplers
+
+
+hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+tab_names = []
+
+options_templates = {}
+
+options_templates.update(options_section(('saving-images', "Saving images/grids"), {
+    "samples_save": OptionInfo(True, "Always save all generated images"),
+    "samples_format": OptionInfo('png', 'File format for images'),
+    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
+    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
+
+    "grid_save": OptionInfo(True, "Always save all generated image grids"),
+    "grid_format": OptionInfo('png', 'File format for grids'),
+    "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
+    "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
+    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
+    "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
+    "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
+    "font": OptionInfo("", "Font for image grids that have text"),
+    "grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
+    "grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
+    "grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
+
+    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
+    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
+    "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}),
+    "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
+    "export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
+    "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", gr.Number).info("in megapixels"),
+
+    "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"),
+    "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"),
+
+    "save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
+}))
+
+options_templates.update(options_section(('saving-paths', "Paths for saving"), {
+    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
+    "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
+    "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
+    "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
+    "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
+    "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(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("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
+    "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.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
+    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", 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]}),
+}))
+
+options_templates.update(options_section(('face-restoration', "Face restoration"), {
+    "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", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
+    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
+}))
+
+options_templates.update(options_section(('system', "System"), {
+    "show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
+    "show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
+    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
+    "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
+    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
+    "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
+    "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
+    "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
+    "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
+}))
+
+options_templates.update(options_section(('training', "Training"), {
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
+    "save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
+    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
+    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
+    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
+    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
+    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
+    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
+    "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
+}))
+
+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_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
+    "sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
+    "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
+    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
+    "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
+    "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, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
+    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
+    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
+    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
+    "sd_refiner_checkpoint": OptionInfo(None, "Refiner checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints).info("switch to another model in the middle of generation"),
+    "sd_refiner_switch_at": OptionInfo(1.0, "Refiner switch at", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}).info("fraction of sampling steps when the swtch to refiner model should happen; 1=never, 0.5=switch in the middle of generation"),
+}))
+
+options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
+    "sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
+    "sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
+    "sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
+    "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
+}))
+
+options_templates.update(options_section(('vae', "VAE"), {
+    "sd_vae_explanation": OptionHTML("""
+<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
+image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
+(i.e. when the progress bar is between empty and full). For txt2img, VAE is used to create a resulting image after the sampling is finished.
+For img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.
+"""),
+    "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": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
+    "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
+    "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
+    "sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
+    "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to decode latent to image"),
+}))
+
+options_templates.update(options_section(('img2img', "img2img"), {
+    "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}),
+    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
+    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
+    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
+    "img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_reload_ui(),
+    "img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_reload_ui(),
+    "img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker,  {}).info("brush color of inpaint mask").needs_reload_ui(),
+    "img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
+    "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"),
+}))
+
+options_templates.update(options_section(('optimizations', "Optimizations"), {
+    "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
+    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+    "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
+    "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
+    "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
+    "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
+    "persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("Do not recalculate conds from prompts if prompts have not changed since previous calculation"),
+}))
+
+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."),
+    "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
+}))
+
+options_templates.update(options_section(('interrogate', "Interrogate"), {
+    "interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
+    "interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
+    "interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+    "interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+    "interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
+    "interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
+    "deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
+    "deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
+    "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
+}))
+
+options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+    "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
+    "extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
+    "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
+    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
+    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
+    "extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
+    "extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
+    "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
+    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
+    "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
+    "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
+}))
+
+options_templates.update(options_section(('ui', "User interface"), {
+    "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
+    "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
+    "gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
+    "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
+    "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"),
+    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
+    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
+    "js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
+    "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
+    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
+    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
+    "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"),
+    "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
+    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
+    "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_reload_ui(),
+    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_reload_ui(),
+    "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
+    "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
+    "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
+    "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
+}))
+
+
+options_templates.update(options_section(('infotext', "Infotext"), {
+    "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"),
+    "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
+    "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
+    "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
+    "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
+<li>Ignore: keep prompt and styles dropdown as it is.</li>
+<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
+<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
+<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
+</ul>"""),
+
+}))
+
+options_templates.update(options_section(('ui', "Live previews"), {
+    "show_progressbar": OptionInfo(True, "Show progressbar"),
+    "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
+    "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
+    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
+    "show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
+    "show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
+    "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
+    "live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
+}))
+
+options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
+    "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_reload_ui(),
+    "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
+    "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
+    "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": 100.0, "step": 0.01}),
+    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_tmax':  OptionInfo(0.0, "sigma tmax",  gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}).info("0 = inf"),
+    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    'k_sched_type':  OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
+    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
+    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
+    'rho':  OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
+    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
+    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
+    '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", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
+    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
+}))
+
+options_templates.update(options_section(('postprocessing', "Postprocessing"), {
+    '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 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"),
+}))
+
+
+options_templates.update()
+
+
+class Options:
+    data = None
+    data_labels = options_templates
+    typemap = {int: float}
+
+    def __init__(self):
+        self.data = {k: v.default for k, v in self.data_labels.items()}
+
+    def __setattr__(self, key, value):
+        if self.data is not None:
+            if key in self.data or key in self.data_labels:
+                assert not cmd_opts.freeze_settings, "changing settings is disabled"
+
+                info = opts.data_labels.get(key, None)
+                if info.do_not_save:
+                    return
+
+                comp_args = info.component_args if info else None
+                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                    raise RuntimeError(f"not possible to set {key} because it is restricted")
+
+                self.data[key] = value
+                return
+
+        return super(Options, self).__setattr__(key, value)
+
+    def __getattr__(self, item):
+        if self.data is not None:
+            if item in self.data:
+                return self.data[item]
+
+        if item in self.data_labels:
+            return self.data_labels[item].default
+
+        return super(Options, self).__getattribute__(item)
+
+    def set(self, key, value):
+        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
+
+        oldval = self.data.get(key, None)
+        if oldval == value:
+            return False
+
+        if self.data_labels[key].do_not_save:
+            return False
+
+        try:
+            setattr(self, key, value)
+        except RuntimeError:
+            return False
+
+        if self.data_labels[key].onchange is not None:
+            try:
+                self.data_labels[key].onchange()
+            except Exception as e:
+                errors.display(e, f"changing setting {key} to {value}")
+                setattr(self, key, oldval)
+                return False
+
+        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"
+
+        with open(filename, "w", encoding="utf8") as file:
+            json.dump(self.data, file, indent=4)
+
+    def same_type(self, x, y):
+        if x is None or y is None:
+            return True
+
+        type_x = self.typemap.get(type(x), type(x))
+        type_y = self.typemap.get(type(y), type(y))
+
+        return type_x == type_y
+
+    def load(self, filename):
+        with open(filename, "r", encoding="utf8") as file:
+            self.data = json.load(file)
+
+        # 1.1.1 quicksettings list migration
+        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
+            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
+
+        # 1.4.0 ui_reorder
+        if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
+            self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
+
+        bad_settings = 0
+        for k, v in self.data.items():
+            info = self.data_labels.get(k, None)
+            if info is not None and not self.same_type(info.default, v):
+                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
+                bad_settings += 1
+
+        if bad_settings > 0:
+            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
+
+    def onchange(self, key, func, call=True):
+        item = self.data_labels.get(key)
+        item.onchange = func
+
+        if call:
+            func()
+
+    def dumpjson(self):
+        d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
+        d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
+        d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
+        return json.dumps(d)
+
+    def add_option(self, key, info):
+        self.data_labels[key] = info
+
+    def reorder(self):
+        """reorder settings so that all items related to section always go together"""
+
+        section_ids = {}
+        settings_items = self.data_labels.items()
+        for _, item in settings_items:
+            if item.section not in section_ids:
+                section_ids[item.section] = len(section_ids)
+
+        self.data_labels = dict(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)
+
+
+class Shared(sys.modules[__name__].__class__):
+    """
+    this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
+    at program startup.
+    """
+
+    sd_model_val = None
+
+    @property
+    def sd_model(self):
+        import modules.sd_models
+
+        return modules.sd_models.model_data.get_sd_model()
+
+    @sd_model.setter
+    def sd_model(self, value):
+        import modules.sd_models
+
+        modules.sd_models.model_data.set_sd_model(value)
+
+
+sd_model: LatentDiffusion = None  # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
+sys.modules[__name__].__class__ = Shared
 
 settings_components = None
 """assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
 
-tab_names = []
-
 latent_upscale_default_mode = "Latent"
 latent_upscale_modes = {
     "Latent": {"mode": "bilinear", "antialias": False},
@@ -64,24 +853,121 @@ progress_print_out = sys.stdout
 
 gradio_theme = gr.themes.Base()
 
-total_tqdm = None
 
-mem_mon = None
+def reload_gradio_theme(theme_name=None):
+    global gradio_theme
+    if not theme_name:
+        theme_name = opts.gradio_theme
 
-options_section = options.options_section
-OptionInfo = options.OptionInfo
-OptionHTML = options.OptionHTML
+    default_theme_args = dict(
+        font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
+        font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
+    )
 
-natural_sort_key = util.natural_sort_key
-listfiles = util.listfiles
-html_path = util.html_path
-html = util.html
-walk_files = util.walk_files
-ldm_print = util.ldm_print
+    if theme_name == "Default":
+        gradio_theme = gr.themes.Default(**default_theme_args)
+    else:
+        try:
+            theme_cache_dir = os.path.join(script_path, 'tmp', 'gradio_themes')
+            theme_cache_path = os.path.join(theme_cache_dir, f'{theme_name.replace("/", "_")}.json')
+            if opts.gradio_themes_cache and os.path.exists(theme_cache_path):
+                gradio_theme = gr.themes.ThemeClass.load(theme_cache_path)
+            else:
+                os.makedirs(theme_cache_dir, exist_ok=True)
+                gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+                gradio_theme.dump(theme_cache_path)
+        except Exception as e:
+            errors.display(e, "changing gradio theme")
+            gradio_theme = gr.themes.Default(**default_theme_args)
 
-reload_gradio_theme = shared_gradio_themes.reload_gradio_theme
 
-list_checkpoint_tiles = shared_items.list_checkpoint_tiles
-refresh_checkpoints = shared_items.refresh_checkpoints
-list_samplers = shared_items.list_samplers
-reload_hypernetworks = shared_items.reload_hypernetworks
+class TotalTQDM:
+    def __init__(self):
+        self._tqdm = None
+
+    def reset(self):
+        self._tqdm = tqdm.tqdm(
+            desc="Total progress",
+            total=state.job_count * state.sampling_steps,
+            position=1,
+            file=progress_print_out
+        )
+
+    def update(self):
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
+            return
+        if self._tqdm is None:
+            self.reset()
+        self._tqdm.update()
+
+    def updateTotal(self, new_total):
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
+            return
+        if self._tqdm is None:
+            self.reset()
+        self._tqdm.total = new_total
+
+    def clear(self):
+        if self._tqdm is not None:
+            self._tqdm.refresh()
+            self._tqdm.close()
+            self._tqdm = None
+
+
+total_tqdm = TotalTQDM()
+
+mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
+mem_mon.start()
+
+
+def natural_sort_key(s, regex=re.compile('([0-9]+)')):
+    return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]
+
+
+def listfiles(dirname):
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
+    return [file for file in filenames if os.path.isfile(file)]
+
+
+def html_path(filename):
+    return os.path.join(script_path, "html", filename)
+
+
+def html(filename):
+    path = html_path(filename)
+
+    if os.path.exists(path):
+        with open(path, encoding="utf8") as file:
+            return file.read()
+
+    return ""
+
+
+def walk_files(path, allowed_extensions=None):
+    if not os.path.exists(path):
+        return
+
+    if allowed_extensions is not None:
+        allowed_extensions = set(allowed_extensions)
+
+    items = list(os.walk(path, followlinks=True))
+    items = sorted(items, key=lambda x: natural_sort_key(x[0]))
+
+    for root, _, files in items:
+        for filename in sorted(files, key=natural_sort_key):
+            if allowed_extensions is not None:
+                _, ext = os.path.splitext(filename)
+                if ext not in allowed_extensions:
+                    continue
+
+            if not opts.list_hidden_files and ("/." in root or "\\." in root):
+                continue
+
+            yield os.path.join(root, filename)
+
+
+def ldm_print(*args, **kwargs):
+    if opts.hide_ldm_prints:
+        return
+
+    print(*args, **kwargs)