Merge branch 'master' into api-authorization

modules/api/api.py

@@ -9,9 +9,9 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
+from modules import sd_samplers
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers
 from modules.extras import run_extras, run_pnginfo
 from PIL import PngImagePlugin
 from modules.sd_models import checkpoints_list
@@ -28,8 +28,12 @@ def upscaler_to_index(name: str):
         raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
 
-sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
+def validate_sampler_name(name):
+    config = sd_samplers.all_samplers_map.get(name, None)
+    if config is None:
+        raise HTTPException(status_code=404, detail="Sampler not found")
 
+    return name
 
 def setUpscalers(req: dict):
     reqDict = vars(req)
@@ -77,6 +81,7 @@ class Api:
         self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
         self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
         self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
+        self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"])
         self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=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=FlagsModel)
@@ -103,14 +108,9 @@ class Api:
         raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
-        sampler_index = sampler_to_index(txt2imgreq.sampler_index)
-
-        if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found")
-
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_index": sampler_index[0],
+            "sampler_name": validate_sampler_name(txt2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True
             }
@@ -130,12 +130,6 @@ class Api:
         return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
-        sampler_index = sampler_to_index(img2imgreq.sampler_index)
-
-        if sampler_index is None:
-            raise HTTPException(status_code=404, detail="Sampler not found")
-
-
         init_images = img2imgreq.init_images
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
@@ -144,10 +138,9 @@ class Api:
         if mask:
             mask = decode_base64_to_image(mask)
 
-
         populate = img2imgreq.copy(update={ # Override __init__ params
             "sd_model": shared.sd_model,
-            "sampler_index": sampler_index[0],
+            "sampler_name": validate_sampler_name(img2imgreq.sampler_index),
             "do_not_save_samples": True,
             "do_not_save_grid": True,
             "mask": mask
@@ -266,6 +259,9 @@ class Api:
 
         return {}
 
+    def skip(self):
+        shared.state.skip()
+
     def get_config(self):
         options = {}
         for key in shared.opts.data.keys():
@@ -277,14 +273,10 @@ class Api:
 
         return options
 
-    def set_config(self, req: OptionsModel):
-        # currently req has all options fields even if you send a dict like { "send_seed": false }, which means it will
-        # overwrite all options with default values.
-        raise RuntimeError('Setting options via API is not supported')
-
-        reqDict = vars(req)
-        for o in reqDict:
-            setattr(shared.opts, o, reqDict[o])
+    def set_config(self, req: Dict[str, Any]):
+       
+        for o in req:
+            setattr(shared.opts, o, req[o])
 
         shared.opts.save(shared.config_filename)
         return
@@ -293,7 +285,7 @@ class Api:
         return vars(shared.cmd_opts)
 
     def get_samplers(self):
-        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
 
     def get_upscalers(self):
         upscalers = []

modules/api/models.py

@@ -176,9 +176,9 @@ class InterrogateResponse(BaseModel):
     caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")
 
 fields = {}
-for key, value in opts.data.items():
-    metadata = opts.data_labels.get(key)
-    optType = opts.typemap.get(type(value), type(value))
+for key, metadata in opts.data_labels.items():
+    value = opts.data.get(key)
+    optType = opts.typemap.get(type(metadata.default), type(value))
 
     if (metadata is not None):
         fields.update({key: (Optional[optType], Field(

modules/extensions.py

@@ -65,9 +65,12 @@ class Extension:
         self.can_update = False
         self.status = "latest"
 
-    def pull(self):
+    def fetch_and_reset_hard(self):
         repo = git.Repo(self.path)
-        repo.remotes.origin.pull()
+        # Fix: `error: Your local changes to the following files would be overwritten by merge`,
+        # because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
+        repo.git.fetch('--all')
+        repo.git.reset('--hard', 'origin')
 
 
 def list_extensions():

modules/generation_parameters_copypaste.py

@@ -73,6 +73,7 @@ def integrate_settings_paste_fields(component_dict):
         'sd_hypernetwork': 'Hypernet',
         'sd_hypernetwork_strength': 'Hypernet strength',
         'CLIP_stop_at_last_layers': 'Clip skip',
+        'inpainting_mask_weight': 'Conditional mask weight',
         'sd_model_checkpoint': 'Model hash',
     }
     settings_paste_fields = [

modules/hypernetworks/hypernetwork.py

@@ -12,7 +12,7 @@ import torch
 import tqdm
 from einops import rearrange, repeat
 from ldm.util import default
-from modules import devices, processing, sd_models, shared
+from modules import devices, processing, sd_models, shared, sd_samplers
 from modules.textual_inversion import textual_inversion
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from torch import einsum
@@ -535,7 +535,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
                 p.prompt = preview_prompt
                 p.negative_prompt = preview_negative_prompt
                 p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
+                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

@@ -303,7 +303,7 @@ class FilenameGenerator:
         'width': lambda self: self.image.width,
         'height': lambda self: self.image.height,
         '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(sd_samplers.samplers[self.p.sampler_index].name, 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),
         '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>]

modules/img2img.py

@@ -6,7 +6,7 @@ import traceback
 import numpy as np
 from PIL import Image, ImageOps, ImageChops
 
-from modules import devices
+from modules import devices, sd_samplers
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 import modules.shared as shared
@@ -99,7 +99,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sampler_index,
+        sampler_index=sd_samplers.samplers_for_img2img[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,

modules/sd_hijack.py

@@ -96,8 +96,8 @@ class StableDiffusionModelHijack:
         if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
             model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
 
+        self.apply_circular(False)
         self.layers = None
-        self.circular_enabled = False
         self.clip = None
 
     def apply_circular(self, enable):

modules/sd_models.py

@@ -165,16 +165,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
     cache_enabled = shared.opts.sd_checkpoint_cache > 0
 
-    if cache_enabled:
-        sd_vae.restore_base_vae(model)
-
-    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
-
     if cache_enabled and checkpoint_info in checkpoints_loaded:
         # use checkpoint cache
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        print(f"Loading weights [{sd_model_hash}] from cache")
         model.load_state_dict(checkpoints_loaded[checkpoint_info])
     else:
         # load from file
@@ -220,6 +213,7 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
     sd_vae.load_vae(model, vae_file)
 
 

modules/sd_samplers.py

@@ -46,16 +46,23 @@ all_samplers = [
     SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
     SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
 ]
+all_samplers_map = {x.name: x for x in all_samplers}
 
 samplers = []
 samplers_for_img2img = []
 
 
-def create_sampler_with_index(list_of_configs, index, model):
-    config = list_of_configs[index]
+def create_sampler(name, model):
+    if name is not None:
+        config = all_samplers_map.get(name, None)
+    else:
+        config = all_samplers[0]
+
+    assert config is not None, f'bad sampler name: {name}'
+
     sampler = config.constructor(model)
     sampler.config = config
-    
+
     return sampler
 
 

modules/sd_vae.py

@@ -83,47 +83,54 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     return vae_list
 
 
-def resolve_vae(checkpoint_file, vae_file="auto"):
+def get_vae_from_settings(vae_file="auto"):
+    # else, we load from settings, if not set to be default
+    if vae_file == "auto" and shared.opts.sd_vae is not None:
+        # if saved VAE settings isn't recognized, fallback to auto
+        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
+        # if VAE selected but not found, fallback to auto
+        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
+            vae_file = "auto"
+            print(f"Selected VAE doesn't exist: {vae_file}")
+    return vae_file
+
+
+def resolve_vae(checkpoint_file=None, vae_file="auto"):
     global first_load, vae_dict, vae_list
 
     # if vae_file argument is provided, it takes priority, but not saved
     if vae_file and vae_file not in default_vae_list:
         if not os.path.isfile(vae_file):
+            print(f"VAE provided as function argument doesn't exist: {vae_file}")
             vae_file = "auto"
-            print("VAE provided as function argument doesn't exist")
     # for the first load, if vae-path is provided, it takes priority, saved, and failure is reported
     if first_load and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
             shared.opts.data['sd_vae'] = get_filename(vae_file)
         else:
-            print("VAE provided as command line argument doesn't exist")
-    # else, we load from settings
-    if vae_file == "auto" and shared.opts.sd_vae is not None:
-        # if saved VAE settings isn't recognized, fallback to auto
-        vae_file = vae_dict.get(shared.opts.sd_vae, "auto")
-        # if VAE selected but not found, fallback to auto
-        if vae_file not in default_vae_values and not os.path.isfile(vae_file):
-            vae_file = "auto"
-            print("Selected VAE doesn't exist")
+            print(f"VAE provided as command line argument doesn't exist: {vae_file}")
+    # fallback to selector in settings, if vae selector not set to act as default fallback
+    if not shared.opts.sd_vae_as_default:
+        vae_file = get_vae_from_settings(vae_file)
     # vae-path cmd arg takes priority for auto
     if vae_file == "auto" and shared.cmd_opts.vae_path is not None:
         if os.path.isfile(shared.cmd_opts.vae_path):
             vae_file = shared.cmd_opts.vae_path
-            print("Using VAE provided as command line argument")
+            print(f"Using VAE provided as command line argument: {vae_file}")
     # if still not found, try look for ".vae.pt" beside model
     model_path = os.path.splitext(checkpoint_file)[0]
     if vae_file == "auto":
         vae_file_try = model_path + ".vae.pt"
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
-            print("Using VAE found beside selected model")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # if still not found, try look for ".vae.ckpt" beside model
     if vae_file == "auto":
         vae_file_try = model_path + ".vae.ckpt"
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
-            print("Using VAE found beside selected model")
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # No more fallbacks for auto
     if vae_file == "auto":
         vae_file = None
@@ -139,6 +146,7 @@ def load_vae(model, vae_file=None):
     # save_settings = False
 
     if vae_file:
+        assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
         print(f"Loading VAE weights from: {vae_file}")
         vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
         vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}

modules/shared.py

@@ -335,7 +335,8 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": list(sd_vae.vae_list)}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
+    "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),

modules/styles.py

@@ -65,17 +65,6 @@ class StyleDatabase:
     def apply_negative_styles_to_prompt(self, prompt, styles):
         return apply_styles_to_prompt(prompt, [self.styles.get(x, self.no_style).negative_prompt for x in styles])
 
-    def apply_styles(self, p: StableDiffusionProcessing) -> None:
-        if isinstance(p.prompt, list):
-            p.prompt = [self.apply_styles_to_prompt(prompt, p.styles) for prompt in p.prompt]
-        else:
-            p.prompt = self.apply_styles_to_prompt(p.prompt, p.styles)
-
-        if isinstance(p.negative_prompt, list):
-            p.negative_prompt = [self.apply_negative_styles_to_prompt(prompt, p.styles) for prompt in p.negative_prompt]
-        else:
-            p.negative_prompt = self.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
-
     def save_styles(self, path: str) -> None:
         # Write to temporary file first, so we don't nuke the file if something goes wrong
         fd, temp_path = tempfile.mkstemp(".csv")

modules/textual_inversion/textual_inversion.py

@@ -10,7 +10,7 @@ import csv
 
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, processing, sd_models, images
+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -345,7 +345,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 p.prompt = preview_prompt
                 p.negative_prompt = preview_negative_prompt
                 p.steps = preview_steps
-                p.sampler_index = preview_sampler_index
+                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/textual_inversion/ui.py

@@ -18,7 +18,7 @@ def create_embedding(name, initialization_text, nvpt, overwrite_old):
 def preprocess(*args):
     modules.textual_inversion.preprocess.preprocess(*args)
 
-    return "Preprocessing finished.", ""
+    return f"Preprocessing {'interrupted' if shared.state.interrupted else 'finished'}.", ""
 
 
 def train_embedding(*args):

modules/txt2img.py

 import modules.scripts
+from modules import sd_samplers
 from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \
     StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, cmd_opts
@@ -21,7 +22,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         seed_resize_from_h=seed_resize_from_h,
         seed_resize_from_w=seed_resize_from_w,
         seed_enable_extras=seed_enable_extras,
-        sampler_index=sampler_index,
+        sampler_name=sd_samplers.samplers[sampler_index].name,
         batch_size=batch_size,
         n_iter=n_iter,
         steps=steps,

modules/ui.py

@@ -69,8 +69,11 @@ sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
 css_hide_progressbar = """
 .wrap .m-12 svg { display:none!important; }
 .wrap .m-12::before { content:"Loading..." }
+.wrap .z-20 svg { display:none!important; }
+.wrap .z-20::before { content:"Loading..." }
 .progress-bar { display:none!important; }
 .meta-text { display:none!important; }
+.meta-text-center { display:none!important; }
 """
 
 # Using constants for these since the variation selector isn't visible.
@@ -142,7 +145,7 @@ def save_files(js_data, images, do_make_zip, index):
                 filenames.append(os.path.basename(txt_fullfn))
                 fullfns.append(txt_fullfn)
 
-        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
+        writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
 
     # Make Zip
     if do_make_zip:
@@ -1249,7 +1252,9 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            run_preprocess = gr.Button(value="Preprocess", variant='primary')
+                            with gr.Row():
+                                interrupt_preprocessing = gr.Button("Interrupt")
+                                run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
                     process_split.change(
                         fn=lambda show: gr_show(show),
@@ -1422,6 +1427,12 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[],
         )
 
+        interrupt_preprocessing.click(
+            fn=lambda: shared.state.interrupt(),
+            inputs=[],
+            outputs=[],
+        )
+
     def create_setting_component(key, is_quicksettings=False):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default

modules/ui_extensions.py

@@ -36,9 +36,9 @@ def apply_and_restart(disable_list, update_list):
             continue
 
         try:
-            ext.pull()
+            ext.fetch_and_reset_hard()
         except Exception:
-            print(f"Error pulling updates for {ext.name}:", file=sys.stderr)
+            print(f"Error getting updates for {ext.name}:", file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
     shared.opts.disabled_extensions = disabled

requirements.txt

+accelerate
 basicsr
 diffusers
 fairscale==0.4.4

requirements_versions.txt

 transformers==4.19.2
 diffusers==0.3.0
+accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
 gradio==3.9

scripts/img2imgalt.py

@@ -157,7 +157,7 @@ class Script(scripts.Script):
     def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
         # Override
         if override_sampler:
-            p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
+            p.sampler_name = "Euler"
         if override_prompt:
             p.prompt = original_prompt
             p.negative_prompt = original_negative_prompt
@@ -191,7 +191,7 @@ class Script(scripts.Script):
             
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
             
-            sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
+            sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
 
             sigmas = sampler.model_wrap.get_sigmas(p.steps)
             

scripts/xy_grid.py

@@ -10,9 +10,9 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images
+from modules import images, sd_samplers
 from modules.hypernetworks import hypernetwork
-from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
+from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
@@ -60,9 +60,9 @@ def apply_order(p, x, xs):
     p.prompt = prompt_tmp + p.prompt
     
 
-def build_samplers_dict(p):
+def build_samplers_dict():
     samplers_dict = {}
-    for i, sampler in enumerate(get_correct_sampler(p)):
+    for i, sampler in enumerate(sd_samplers.all_samplers):
         samplers_dict[sampler.name.lower()] = i
         for alias in sampler.aliases:
             samplers_dict[alias.lower()] = i
@@ -70,7 +70,7 @@ def build_samplers_dict(p):
 
 
 def apply_sampler(p, x, xs):
-    sampler_index = build_samplers_dict(p).get(x.lower(), None)
+    sampler_index = build_samplers_dict().get(x.lower(), None)
     if sampler_index is None:
         raise RuntimeError(f"Unknown sampler: {x}")
 
@@ -78,7 +78,7 @@ def apply_sampler(p, x, xs):
 
 
 def confirm_samplers(p, xs):
-    samplers_dict = build_samplers_dict(p)
+    samplers_dict = build_samplers_dict()
     for x in xs:
         if x.lower() not in samplers_dict.keys():
             raise RuntimeError(f"Unknown sampler: {x}")

webui-user.bat

@@ -4,5 +4,6 @@ set PYTHON=
 set GIT=
 set VENV_DIR=
 set COMMANDLINE_ARGS=
+set ACCELERATE=
 
 call webui.bat

webui-user.sh

@@ -40,4 +40,7 @@ export COMMANDLINE_ARGS=""
 #export CODEFORMER_COMMIT_HASH=""
 #export BLIP_COMMIT_HASH=""
 
+# Uncomment to enable accelerated launch
+#export ACCELERATE="True"
+
 ###########################################

webui.bat

@@ -28,15 +28,27 @@ goto :show_stdout_stderr
 :activate_venv
 set PYTHON="%~dp0%VENV_DIR%\Scripts\Python.exe"
 echo venv %PYTHON%
+if [%ACCELERATE%] == ["True"] goto :accelerate
 goto :launch
 
 :skip_venv
 
+:accelerate
+echo "Checking for accelerate"
+set ACCELERATE="%~dp0%VENV_DIR%\Scripts\accelerate.exe"
+if EXIST %ACCELERATE% goto :accelerate_launch
+
 :launch
 %PYTHON% launch.py %*
 pause
 exit /b
 
+:accelerate_launch
+echo "Accelerating"
+%ACCELERATE% launch --num_cpu_threads_per_process=6 launch.py
+pause
+exit /b
+
 :show_stdout_stderr
 
 echo.

webui.py

@@ -82,6 +82,7 @@ def initialize():
     modules.sd_models.load_model()
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
+    shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
     shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
 

webui.sh

@@ -134,7 +134,15 @@ else
     exit 1
 fi
 
-printf "\n%s\n" "${delimiter}"
-printf "Launching launch.py..."
-printf "\n%s\n" "${delimiter}"
-"${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
+if [[ ! -z "${ACCELERATE}" ]] && [ ${ACCELERATE}="True" ] && [ -x "$(command -v accelerate)" ]
+then
+    printf "\n%s\n" "${delimiter}"
+    printf "Accelerating launch.py..."
+    printf "\n%s\n" "${delimiter}"
+    accelerate launch --num_cpu_threads_per_process=6 "${LAUNCH_SCRIPT}" "$@"
+else
+    printf "\n%s\n" "${delimiter}"
+    printf "Launching launch.py..."
+    printf "\n%s\n" "${delimiter}"
+    "${python_cmd}" "${LAUNCH_SCRIPT}" "$@"
+fi