update readme

Fix parsing text encoder blocks in some LoRAs

CHANGELOG.md

@@ -1,3 +1,22 @@
+## 1.5.1
+
+### Minor:
+ * support parsing text encoder blocks in some new LoRAs
+
+### Extensions and API:
+ * add postprocess_batch_list script callback
+
+### Bug Fixes:
+ * fix reload altclip model error
+ * prepend the pythonpath instead of overriding it
+ * fix typo in SD_WEBUI_RESTARTING
+ * if txt2img/img2img raises an exception, finally call state.end()
+ * fix composable diffusion weight parsing
+ * restyle Startup profile for black users
+ * fix webui not launching with --nowebui
+ * catch exception for non git extensions
+
+
 ## 1.5.0
 
 ### Features:
@@ -29,7 +48,8 @@
  * speedup extra networks listing
  * added `[none]` filename token.
  * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)
- * add always_discard_next_to_last_sigma option to XYZ plot 
+ * add always_discard_next_to_last_sigma option to XYZ plot
+ * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.
  
 ### Extensions and API:
  * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop
@@ -58,9 +78,8 @@
  * fix: check fill size none zero when resize  (fixes #11425)
  * use submit and blur for quick settings textbox
  * save img2img batch with images.save_image()
- * 
-
-
+ * prevent running preload.py for disabled extensions
+ * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included
 
 
 ## 1.4.1

extensions-builtin/Lora/extra_networks_lora.py

@@ -25,7 +25,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
             te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0
             te_multiplier = float(params.named.get("te", te_multiplier))
 
-            unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else 1.0
+            unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier
             unet_multiplier = float(params.named.get("unet", unet_multiplier))
 
             dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None

extensions-builtin/Lora/networks.py

@@ -11,7 +11,7 @@ import network_full
 import torch
 from typing import Union
 
-from modules import shared, devices, sd_models, errors, scripts, sd_hijack, paths
+from modules import shared, devices, sd_models, errors, scripts, sd_hijack
 
 module_types = [
     network_lora.ModuleTypeLora(),
@@ -163,6 +163,11 @@ def load_network(name, network_on_disk):
             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)
 
+            # some SD1 Loras also have correct compvis keys
+            if sd_module is None:
+                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)
+
         if sd_module is None:
             keys_failed_to_match[key_network] = key
             continue
@@ -399,7 +404,7 @@ def list_available_networks():
     os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
 
     candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
-    candidates += list(shared.walk_files(os.path.join(paths.models_path, "LyCORIS"), allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
+    candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
     for filename in candidates:
         if os.path.isdir(filename):
             continue

extensions-builtin/Lora/preload.py

@@ -4,3 +4,4 @@ from modules import paths
 
 def preload(parser):
     parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
+    parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))

extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -3,7 +3,7 @@ import os
 import network
 import networks
 
-from modules import shared, ui_extra_networks, paths
+from modules import shared, ui_extra_networks
 from modules.ui_extra_networks import quote_js
 from ui_edit_user_metadata import LoraUserMetadataEditor
 
@@ -72,7 +72,7 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
                 yield item
 
     def allowed_directories_for_previews(self):
-        return [shared.cmd_opts.lora_dir, os.path.join(paths.models_path, "LyCORIS")]
+        return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat]
 
     def create_user_metadata_editor(self, ui, tabname):
         return LoraUserMetadataEditor(ui, tabname, self)

launch.py

@@ -18,6 +18,7 @@ run_pip = launch_utils.run_pip
 check_run_python = launch_utils.check_run_python
 git_clone = launch_utils.git_clone
 git_pull_recursive = launch_utils.git_pull_recursive
+list_extensions = launch_utils.list_extensions
 run_extension_installer = launch_utils.run_extension_installer
 prepare_environment = launch_utils.prepare_environment
 configure_for_tests = launch_utils.configure_for_tests

modules/api/api.py

@@ -333,14 +333,16 @@ class Api:
                 p.outpath_grids = opts.outdir_txt2img_grids
                 p.outpath_samples = opts.outdir_txt2img_samples
 
-                shared.state.begin(job="scripts_txt2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_txt2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -390,14 +392,16 @@ class Api:
                 p.outpath_grids = opts.outdir_img2img_grids
                 p.outpath_samples = opts.outdir_img2img_samples
 
-                shared.state.begin(job="scripts_img2img")
-                if selectable_scripts is not None:
-                    p.script_args = script_args
-                    processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
-                else:
-                    p.script_args = tuple(script_args) # Need to pass args as tuple here
-                    processed = process_images(p)
-                shared.state.end()
+                try:
+                    shared.state.begin(job="scripts_img2img")
+                    if selectable_scripts is not None:
+                        p.script_args = script_args
+                        processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
+                    else:
+                        p.script_args = tuple(script_args) # Need to pass args as tuple here
+                        processed = process_images(p)
+                finally:
+                    shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images)) if send_images else []
 
@@ -720,9 +724,9 @@ class Api:
             cuda = {'error': f'{err}'}
         return models.MemoryResponse(ram=ram, cuda=cuda)
 
-    def launch(self, server_name, port):
+    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)
+        uvicorn.run(self.app, host=server_name, port=port, timeout_keep_alive=shared.cmd_opts.timeout_keep_alive, root_path=root_path)
 
     def kill_webui(self):
         restart.stop_program()

modules/extensions.py

@@ -56,9 +56,11 @@ class Extension:
                 self.do_read_info_from_repo()
 
                 return self.to_dict()
-
-        d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
-        self.from_dict(d)
+        try:
+            d = cache.cached_data_for_file('extensions-git', self.name, os.path.join(self.path, ".git"), read_from_repo)
+            self.from_dict(d)
+        except FileNotFoundError:
+            pass
         self.status = 'unknown'
 
     def do_read_info_from_repo(self):

modules/images.py

@@ -363,7 +363,7 @@ class FilenameGenerator:
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(self.p.sampler_name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
-        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.model_name, replace_spaces=False),
+        'model_name': lambda self: sanitize_filename_part(shared.sd_model.sd_checkpoint_info.name_for_extra, replace_spaces=False),
         '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),

modules/launch_utils.py

@@ -196,7 +196,7 @@ def run_extension_installer(extension_dir):
 
     try:
         env = os.environ.copy()
-        env['PYTHONPATH'] = os.path.abspath(".")
+        env['PYTHONPATH'] = f"{os.path.abspath('.')}{os.pathsep}{env.get('PYTHONPATH', '')}"
 
         print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
     except Exception as e:
@@ -233,7 +233,7 @@ def run_extensions_installers(settings_file):
 re_requirement = re.compile(r"\s*([-_a-zA-Z0-9]+)\s*(?:==\s*([-+_.a-zA-Z0-9]+))?\s*")
 
 
-def requrements_met(requirements_file):
+def requirements_met(requirements_file):
     """
     Does a simple parse of a requirements.txt file to determine if all rerqirements in it
     are already installed. Returns True if so, False if not installed or parsing fails.
@@ -293,7 +293,7 @@ def prepare_environment():
     try:
         # 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')
+        os.environ.setdefault('SD_WEBUI_RESTARTING', '1')
     except OSError:
         pass
 
@@ -354,7 +354,7 @@ def prepare_environment():
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
 
-    if not requrements_met(requirements_file):
+    if not requirements_met(requirements_file):
         run_pip(f"install -r \"{requirements_file}\"", "requirements")
 
     run_extensions_installers(settings_file=args.ui_settings_file)

modules/lowvram.py

@@ -90,8 +90,12 @@ def setup_for_low_vram(sd_model, use_medvram):
         sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
     elif is_sd2:
         sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
+        sd_model.cond_stage_model.model.token_embedding.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.model] = sd_model.cond_stage_model
+        parents[sd_model.cond_stage_model.model.token_embedding] = sd_model.cond_stage_model
     else:
         sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
+        parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
 
     sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
     sd_model.first_stage_model.encode = first_stage_model_encode_wrap
@@ -101,9 +105,6 @@ def setup_for_low_vram(sd_model, use_medvram):
     if sd_model.embedder:
         sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
 
-    if hasattr(sd_model, 'cond_stage_model'):
-        parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
-
     if use_medvram:
         sd_model.model.register_forward_pre_hook(send_me_to_gpu)
     else:

modules/processing.py

@@ -14,7 +14,7 @@ from skimage import exposure
 from typing import Any, Dict, List
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -538,6 +538,40 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     return x
 
 
+def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
+    samples = []
+
+    for i in range(batch.shape[0]):
+        sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if check_for_nans:
+            try:
+                devices.test_for_nans(sample, "vae")
+            except devices.NansException as e:
+                if devices.dtype_vae == torch.float32 or not shared.opts.auto_vae_precision:
+                    raise e
+
+                errors.print_error_explanation(
+                    "A tensor with all NaNs was produced in VAE.\n"
+                    "Web UI will now convert VAE into 32-bit float and retry.\n"
+                    "To disable this behavior, disable the 'Automaticlly revert VAE to 32-bit floats' setting.\n"
+                    "To always start with 32-bit VAE, use --no-half-vae commandline flag."
+                )
+
+                devices.dtype_vae = torch.float32
+                model.first_stage_model.to(devices.dtype_vae)
+                batch = batch.to(devices.dtype_vae)
+
+                sample = decode_first_stage(model, batch[i:i + 1])[0]
+
+        if target_device is not None:
+            sample = sample.to(target_device)
+
+        samples.append(sample)
+
+    return samples
+
+
 def decode_first_stage(model, x):
     x = model.decode_first_stage(x.to(devices.dtype_vae))
 
@@ -587,7 +621,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),
         "Size": f"{p.width}x{p.height}",
         "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
-        "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
+        "Model": (None if not opts.add_model_name_to_info else shared.sd_model.sd_checkpoint_info.name_for_extra),
         "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w <= 0 or p.seed_resize_from_h <= 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
@@ -683,7 +717,27 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
         p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
 
     def infotext(iteration=0, position_in_batch=0, use_main_prompt=False):
-        return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        all_prompts = p.all_prompts[:]
+        all_negative_prompts = p.all_negative_prompts[:]
+        all_seeds = p.all_seeds[:]
+        all_subseeds = p.all_subseeds[:]
+
+        # apply changes to generation data
+        all_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.prompts
+        all_negative_prompts[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.negative_prompts
+        all_seeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.seeds
+        all_subseeds[iteration * p.batch_size:(iteration + 1) * p.batch_size] = p.subseeds
+
+        # update p.all_negative_prompts in case extensions changed the size of the batch
+        # create_infotext below uses it
+        old_negative_prompts = p.all_negative_prompts
+        p.all_negative_prompts = all_negative_prompts
+
+        try:
+            return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch, use_main_prompt)
+        finally:
+            # restore p.all_negative_prompts in case extensions changed the size of the batch
+            p.all_negative_prompts = old_negative_prompts
 
     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
@@ -758,10 +812,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
-            x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
-            for x in x_samples_ddim:
-                devices.test_for_nans(x, "vae")
-
+            x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)
             x_samples_ddim = torch.stack(x_samples_ddim).float()
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -775,6 +826,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:
                 p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
+                postprocess_batch_list_args = scripts.PostprocessBatchListArgs(list(x_samples_ddim))
+                p.scripts.postprocess_batch_list(p, postprocess_batch_list_args, batch_number=n)
+                x_samples_ddim = postprocess_batch_list_args.images
+
             for i, x_sample in enumerate(x_samples_ddim):
                 p.batch_index = i
 
@@ -1029,7 +1084,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 image = sd_samplers.sample_to_image(image, index, approximation=0)
 
             info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
-            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, p=self, suffix="-before-highres-fix")
 
         if latent_scale_mode is not None:
             for i in range(samples.shape[0]):

modules/prompt_parser.py

@@ -178,7 +178,7 @@ def get_learned_conditioning(model, prompts: SdConditioning | list[str], steps):
 
 
 re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
+re_weight = re.compile(r"^((?:\s|.)*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
 
 
 def get_multicond_prompt_list(prompts: SdConditioning | list[str]):

modules/script_loading.py

@@ -12,11 +12,12 @@ def load_module(path):
     return module
 
 
-def preload_extensions(extensions_dir, parser):
+def preload_extensions(extensions_dir, parser, extension_list=None):
     if not os.path.isdir(extensions_dir):
         return
 
-    for dirname in sorted(os.listdir(extensions_dir)):
+    extensions = extension_list if extension_list is not None else os.listdir(extensions_dir)
+    for dirname in sorted(extensions):
         preload_script = os.path.join(extensions_dir, dirname, "preload.py")
         if not os.path.isfile(preload_script):
             continue

modules/scripts.py

@@ -16,6 +16,11 @@ class PostprocessImageArgs:
         self.image = image
 
 
+class PostprocessBatchListArgs:
+    def __init__(self, images):
+        self.images = images
+
+
 class Script:
     name = None
     """script's internal name derived from title"""
@@ -119,7 +124,7 @@ class Script:
 
     def after_extra_networks_activate(self, p, *args, **kwargs):
         """
-        Calledafter extra networks activation, before conds calculation
+        Called after extra networks activation, before conds calculation
         allow modification of the network after extra networks activation been applied
         won't be call if p.disable_extra_networks
 
@@ -156,6 +161,25 @@ class Script:
 
         pass
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, *args, **kwargs):
+        """
+        Same as postprocess_batch(), but receives batch images as a list of 3D tensors instead of a 4D tensor.
+        This is useful when you want to update the entire batch instead of individual images.
+
+        You can modify the postprocessing object (pp) to update the images in the batch, remove images, add images, etc.
+        If the number of images is different from the batch size when returning,
+        then the script has the responsibility to also update the following attributes in the processing object (p):
+          - p.prompts
+          - p.negative_prompts
+          - p.seeds
+          - p.subseeds
+
+        **kwargs will have same items as process_batch, and also:
+          - batch_number - index of current batch, from 0 to number of batches-1
+        """
+
+        pass
+
     def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
         """
         Called for every image after it has been generated.
@@ -536,6 +560,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running postprocess_batch: {script.filename}", exc_info=True)
 
+    def postprocess_batch_list(self, p, pp: PostprocessBatchListArgs, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_batch_list(p, pp, *script_args, **kwargs)
+            except Exception:
+                errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
+
     def postprocess_image(self, p, pp: PostprocessImageArgs):
         for script in self.alwayson_scripts:
             try:

modules/sd_hijack.py

@@ -243,7 +243,7 @@ class StableDiffusionModelHijack:
         ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+        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:

modules/sd_hijack_open_clip.py

@@ -32,7 +32,7 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit
     def encode_embedding_init_text(self, init_text, nvpt):
         ids = tokenizer.encode(init_text)
         ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
-        embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
+        embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
 
         return embedded
 

modules/sd_models_xl.py

@@ -12,8 +12,8 @@ def get_learned_conditioning(self: sgm.models.diffusion.DiffusionEngine, batch:
     for embedder in self.conditioner.embedders:
         embedder.ucg_rate = 0.0
 
-    width = getattr(self, 'target_width', 1024)
-    height = getattr(self, 'target_height', 1024)
+    width = getattr(batch, 'width', 1024)
+    height = getattr(batch, 'height', 1024)
     is_negative_prompt = getattr(batch, 'is_negative_prompt', False)
     aesthetic_score = shared.opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else shared.opts.sdxl_refiner_high_aesthetic_score
 

modules/shared.py

@@ -11,6 +11,7 @@ import gradio as gr
 import torch
 import tqdm
 
+import launch
 import modules.interrogate
 import modules.memmon
 import modules.styles
@@ -26,7 +27,7 @@ demo = None
 
 parser = cmd_args.parser
 
-script_loading.preload_extensions(extensions_dir, parser)
+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:
@@ -426,6 +427,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "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"),
+    "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"),
     "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
 }))
 

style.css

@@ -423,15 +423,16 @@ div#extras_scale_to_tab div.form{
 }
 
 table.popup-table{
-    background: white;
+    background: var(--body-background-fill);
+    color: var(--body-text-color);
     border-collapse: collapse;
     margin: 1em;
-    border: 4px solid white;
+    border: 4px solid var(--body-background-fill);
 }
 
 table.popup-table td{
     padding: 0.4em;
-    border: 1px solid #ccc;
+    border: 1px solid rgba(128, 128, 128, 0.5);
     max-width: 36em;
 }
 

webui.py

@@ -374,7 +374,7 @@ def api_only():
     api.launch(
         server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1",
         port=cmd_opts.port if cmd_opts.port else 7861,
-        root_path = f"/{cmd_opts.subpath}"
+        root_path=f"/{cmd_opts.subpath}" if cmd_opts.subpath else ""
     )
 
 
@@ -407,7 +407,7 @@ def webui():
             ssl_verify=cmd_opts.disable_tls_verify,
             debug=cmd_opts.gradio_debug,
             auth=gradio_auth_creds,
-            inbrowser=cmd_opts.autolaunch and os.getenv('SD_WEBUI_RESTARTING ') != '1',
+            inbrowser=cmd_opts.autolaunch and os.getenv('SD_WEBUI_RESTARTING') != '1',
             prevent_thread_lock=True,
             allowed_paths=cmd_opts.gradio_allowed_path,
             app_kwargs={

webui.sh

@@ -4,8 +4,15 @@
 # change the variables in webui-user.sh instead #
 #################################################
 
+
+use_venv=1
+if [[ $venv_dir == "-" ]]; then
+  use_venv=0
+fi
+
 SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
 
+
 # If run from macOS, load defaults from webui-macos-env.sh
 if [[ "$OSTYPE" == "darwin"* ]]; then
     if [[ -f "$SCRIPT_DIR"/webui-macos-env.sh ]]
@@ -47,7 +54,7 @@ then
 fi
 
 # python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
-if [[ -z "${venv_dir}" ]]
+if [[ -z "${venv_dir}" ]] && [[ $use_venv -eq 1 ]]
 then
     venv_dir="venv"
 fi
@@ -164,7 +171,7 @@ do
     fi
 done
 
-if ! "${python_cmd}" -c "import venv" &>/dev/null
+if [[ $use_venv -eq 1 ]] && ! "${python_cmd}" -c "import venv" &>/dev/null
 then
     printf "\n%s\n" "${delimiter}"
     printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m"
@@ -184,7 +191,7 @@ else
     cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; }
 fi
 
-if [[ -z "${VIRTUAL_ENV}" ]];
+if [[ $use_venv -eq 1 ]] && [[ -z "${VIRTUAL_ENV}" ]];
 then
     printf "\n%s\n" "${delimiter}"
     printf "Create and activate python venv"
@@ -207,7 +214,7 @@ then
     fi
 else
     printf "\n%s\n" "${delimiter}"
-    printf "python venv already activate: ${VIRTUAL_ENV}"
+    printf "python venv already activate or run without venv: ${VIRTUAL_ENV}"
     printf "\n%s\n" "${delimiter}"
 fi