Merge branch 'AUTOMATIC1111:master' into draft

24 files changed, 1101 insertions, 366 deletions

diff --git a/modules/api/api.py b/modules/api/api.py
index 596a6616..7a567be3 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -5,19 +5,19 @@ import uvicorn
 from threading import Lock
 from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
 from fastapi import APIRouter, Depends, FastAPI, HTTPException
+from fastapi.security import HTTPBasic, HTTPBasicCredentials
+from secrets import compare_digest
+
 import modules.shared as shared
+from modules import sd_samplers, deepbooru
 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
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
 
-if shared.cmd_opts.deepdanbooru:
-    from modules.deepbooru import get_deepbooru_tags
-
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
@@ -25,8 +25,12 @@ 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)
@@ -57,39 +61,53 @@ def encode_pil_to_base64(image):
 
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
+        if shared.cmd_opts.api_auth:
+            self.credenticals = dict()
+            for auth in shared.cmd_opts.api_auth.split(","):
+                user, password = auth.split(":")
+                self.credenticals[user] = password
+
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
-        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
-        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
-        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
-        self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
-        self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
-        self.app.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel)
-        self.app.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel)
-        self.app.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
-        self.app.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
-        self.app.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
-        self.app.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
-        self.app.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
-        self.app.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
-        self.app.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
-        self.app.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
-        self.app.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
+        self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
+        self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
+        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)
+        self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem])
+        self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem])
+        self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem])
+        self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem])
+        self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem])
+        self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem])
+        self.add_api_route("/sdapi/v1/prompt-styles", self.get_promp_styles, methods=["GET"], response_model=List[PromptStyleItem])
+        self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
+        self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+
+    def add_api_route(self, path: str, endpoint, **kwargs):
+        if shared.cmd_opts.api_auth:
+            return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs)
+        return self.app.add_api_route(path, endpoint, **kwargs)
+
+    def auth(self, credenticals: HTTPBasicCredentials = Depends(HTTPBasic())):
+        if credenticals.username in self.credenticals:
+            if compare_digest(credenticals.password, self.credenticals[credenticals.username]):
+                return True
+
+        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
             }
@@ -109,12 +127,6 @@ 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")
@@ -123,10 +135,9 @@ 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
@@ -231,10 +242,7 @@ class Api:
             if interrogatereq.model == "clip":
                 processed = shared.interrogator.interrogate(img)
             elif interrogatereq.model == "deepdanbooru":
-                if shared.cmd_opts.deepdanbooru:
-                    processed = get_deepbooru_tags(img)
-                else:
-                    raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
+                processed = deepbooru.model.tag(img)
             else:
                 raise HTTPException(status_code=404, detail="Model not found")
         
@@ -245,6 +253,9 @@ class Api:
 
         return {}
 
+    def skip(self):
+        shared.state.skip()
+
     def get_config(self):
         options = {}
         for key in shared.opts.data.keys():
@@ -256,14 +267,9 @@ 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 k, v in req.items():
+            shared.opts.set(k, v)
 
         shared.opts.save(shared.config_filename)
         return
@@ -272,7 +278,7 @@ 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 = []
diff --git a/modules/api/models.py b/modules/api/models.py
index f9cd929e..f77951fc 100644
--- a/modules/api/models.py
+++ b/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(
diff --git a/modules/deepbooru.py b/modules/deepbooru.py
index 8bbc90a4..b9066d81 100644
--- a/modules/deepbooru.py
+++ b/modules/deepbooru.py
@@ -1,173 +1,97 @@
-import os.path
-from concurrent.futures import ProcessPoolExecutor
-import multiprocessing
-import time
+import os
 import re
 
+import torch
+from PIL import Image
+import numpy as np
+
+from modules import modelloader, paths, deepbooru_model, devices, images, shared
+
 re_special = re.compile(r'([\\()])')
 
-def get_deepbooru_tags(pil_image):
-    """
-    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
-    """
-    from modules import shared  # prevents circular reference
-
-    try:
-        create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
-        return get_tags_from_process(pil_image)
-    finally:
-        release_process()
-
-
-OPT_INCLUDE_RANKS = "include_ranks"
-def create_deepbooru_opts():
-    from modules import shared
-
-    return {
-        "use_spaces": shared.opts.deepbooru_use_spaces,
-        "use_escape": shared.opts.deepbooru_escape,
-        "alpha_sort": shared.opts.deepbooru_sort_alpha,
-        OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
-    }
-
-
-def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
-    model, tags = get_deepbooru_tags_model()
-    while True: # while process is running, keep monitoring queue for new image
-        pil_image = queue.get()
-        if pil_image == "QUIT":
-            break
-        else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
-
-
-def create_deepbooru_process(threshold, deepbooru_opts):
-    """
-    Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
-    to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
-    dictionary is created to hold the tags created.  To wait for tags to be returned, a value of -1 is assigned
-    to the dictionary and the method adding the image to the queue should wait for this value to be updated with
-    the tags.
-    """
-    from modules import shared  # prevents circular reference
-    context = multiprocessing.get_context("spawn")
-    shared.deepbooru_process_manager = context.Manager()
-    shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
-    shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
-    shared.deepbooru_process.start()
-
-
-def get_tags_from_process(image):
-    from modules import shared
-
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process_queue.put(image)
-    while shared.deepbooru_process_return["value"] == -1:
-        time.sleep(0.2)
-    caption = shared.deepbooru_process_return["value"]
-    shared.deepbooru_process_return["value"] = -1
-
-    return caption
-
-
-def release_process():
-    """
-    Stops the deepbooru process to return used memory
-    """
-    from modules import shared  # prevents circular reference
-    shared.deepbooru_process_queue.put("QUIT")
-    shared.deepbooru_process.join()
-    shared.deepbooru_process_queue = None
-    shared.deepbooru_process = None
-    shared.deepbooru_process_return = None
-    shared.deepbooru_process_manager = None
-
-def get_deepbooru_tags_model():
-    import deepdanbooru as dd
-    import tensorflow as tf
-    import numpy as np
-    this_folder = os.path.dirname(__file__)
-    model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
-    if not os.path.exists(os.path.join(model_path, 'project.json')):
-        # there is no point importing these every time
-        import zipfile
-        from basicsr.utils.download_util import load_file_from_url
-        load_file_from_url(
-            r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
-            model_path)
-        with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
-            zip_ref.extractall(model_path)
-        os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
-
-    tags = dd.project.load_tags_from_project(model_path)
-    model = dd.project.load_model_from_project(
-        model_path, compile_model=False
-    )
-    return model, tags
-
-
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
-    import deepdanbooru as dd
-    import tensorflow as tf
-    import numpy as np
-
-    alpha_sort = deepbooru_opts['alpha_sort']
-    use_spaces = deepbooru_opts['use_spaces']
-    use_escape = deepbooru_opts['use_escape']
-    include_ranks = deepbooru_opts['include_ranks']
-
-    width = model.input_shape[2]
-    height = model.input_shape[1]
-    image = np.array(pil_image)
-    image = tf.image.resize(
-        image,
-        size=(height, width),
-        method=tf.image.ResizeMethod.AREA,
-        preserve_aspect_ratio=True,
-    )
-    image = image.numpy()  # EagerTensor to np.array
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.0
-    image_shape = image.shape
-    image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
-
-    y = model.predict(image)[0]
-
-    result_dict = {}
-
-    for i, tag in enumerate(tags):
-        result_dict[tag] = y[i]
-
-    unsorted_tags_in_theshold = []
-    result_tags_print = []
-    for tag in tags:
-        if result_dict[tag] >= threshold:
+
+class DeepDanbooru:
+    def __init__(self):
+        self.model = None
+
+    def load(self):
+        if self.model is not None:
+            return
+
+        files = modelloader.load_models(
+            model_path=os.path.join(paths.models_path, "torch_deepdanbooru"),
+            model_url='https://github.com/AUTOMATIC1111/TorchDeepDanbooru/releases/download/v1/model-resnet_custom_v3.pt',
+            ext_filter=".pt",
+            download_name='model-resnet_custom_v3.pt',
+        )
+
+        self.model = deepbooru_model.DeepDanbooruModel()
+        self.model.load_state_dict(torch.load(files[0], map_location="cpu"))
+
+        self.model.eval()
+        self.model.to(devices.cpu, devices.dtype)
+
+    def start(self):
+        self.load()
+        self.model.to(devices.device)
+
+    def stop(self):
+        if not shared.opts.interrogate_keep_models_in_memory:
+            self.model.to(devices.cpu)
+            devices.torch_gc()
+
+    def tag(self, pil_image):
+        self.start()
+        res = self.tag_multi(pil_image)
+        self.stop()
+
+        return res
+
+    def tag_multi(self, pil_image, force_disable_ranks=False):
+        threshold = shared.opts.interrogate_deepbooru_score_threshold
+        use_spaces = shared.opts.deepbooru_use_spaces
+        use_escape = shared.opts.deepbooru_escape
+        alpha_sort = shared.opts.deepbooru_sort_alpha
+        include_ranks = shared.opts.interrogate_return_ranks and not force_disable_ranks
+
+        pic = images.resize_image(2, pil_image.convert("RGB"), 512, 512)
+        a = np.expand_dims(np.array(pic, dtype=np.float32), 0) / 255
+
+        with torch.no_grad(), devices.autocast():
+            x = torch.from_numpy(a).cuda()
+            y = self.model(x)[0].detach().cpu().numpy()
+
+        probability_dict = {}
+
+        for tag, probability in zip(self.model.tags, y):
+            if probability < threshold:
+                continue
+
             if tag.startswith("rating:"):
                 continue
-            unsorted_tags_in_theshold.append((result_dict[tag], tag))
-            result_tags_print.append(f'{result_dict[tag]} {tag}')
-
-    # sort tags
-    result_tags_out = []
-    sort_ndx = 0
-    if alpha_sort:
-        sort_ndx = 1
-
-    # sort by reverse by likelihood and normal for alpha, and format tag text as requested
-    unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
-    for weight, tag in unsorted_tags_in_theshold:
-        tag_outformat = tag
-        if use_spaces:
-            tag_outformat = tag_outformat.replace('_', ' ')
-        if use_escape:
-            tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
-        if include_ranks:
-            tag_outformat = f"({tag_outformat}:{weight:.3f})"
-
-        result_tags_out.append(tag_outformat)
-
-    print('\n'.join(sorted(result_tags_print, reverse=True)))
-
-    return ', '.join(result_tags_out)
+
+            probability_dict[tag] = probability
+
+        if alpha_sort:
+            tags = sorted(probability_dict)
+        else:
+            tags = [tag for tag, _ in sorted(probability_dict.items(), key=lambda x: -x[1])]
+
+        res = []
+
+        for tag in tags:
+            probability = probability_dict[tag]
+            tag_outformat = tag
+            if use_spaces:
+                tag_outformat = tag_outformat.replace('_', ' ')
+            if use_escape:
+                tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
+            if include_ranks:
+                tag_outformat = f"({tag_outformat}:{probability:.3f})"
+
+            res.append(tag_outformat)
+
+        return ", ".join(res)
+
+
+model = DeepDanbooru()
diff --git a/modules/deepbooru_model.py b/modules/deepbooru_model.py
new file mode 100644
index 00000000..edd40c81
--- /dev/null
+++ b/modules/deepbooru_model.py
@@ -0,0 +1,676 @@
+import torch

+import torch.nn as nn

+import torch.nn.functional as F

+

+# see https://github.com/AUTOMATIC1111/TorchDeepDanbooru for more

+

+

+class DeepDanbooruModel(nn.Module):

+    def __init__(self):

+        super(DeepDanbooruModel, self).__init__()

+

+        self.tags = []

+

+        self.n_Conv_0 = nn.Conv2d(kernel_size=(7, 7), in_channels=3, out_channels=64, stride=(2, 2))

+        self.n_MaxPool_0 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2))

+        self.n_Conv_1 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)

+        self.n_Conv_2 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=64)

+        self.n_Conv_3 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)

+        self.n_Conv_4 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)

+        self.n_Conv_5 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)

+        self.n_Conv_6 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)

+        self.n_Conv_7 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)

+        self.n_Conv_8 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=64)

+        self.n_Conv_9 = nn.Conv2d(kernel_size=(3, 3), in_channels=64, out_channels=64)

+        self.n_Conv_10 = nn.Conv2d(kernel_size=(1, 1), in_channels=64, out_channels=256)

+        self.n_Conv_11 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=512, stride=(2, 2))

+        self.n_Conv_12 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=128)

+        self.n_Conv_13 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128, stride=(2, 2))

+        self.n_Conv_14 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_15 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)

+        self.n_Conv_16 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)

+        self.n_Conv_17 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_18 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)

+        self.n_Conv_19 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)

+        self.n_Conv_20 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_21 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)

+        self.n_Conv_22 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)

+        self.n_Conv_23 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_24 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)

+        self.n_Conv_25 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)

+        self.n_Conv_26 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_27 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)

+        self.n_Conv_28 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)

+        self.n_Conv_29 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_30 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)

+        self.n_Conv_31 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)

+        self.n_Conv_32 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_33 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=128)

+        self.n_Conv_34 = nn.Conv2d(kernel_size=(3, 3), in_channels=128, out_channels=128)

+        self.n_Conv_35 = nn.Conv2d(kernel_size=(1, 1), in_channels=128, out_channels=512)

+        self.n_Conv_36 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=1024, stride=(2, 2))

+        self.n_Conv_37 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=256)

+        self.n_Conv_38 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))

+        self.n_Conv_39 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_40 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_41 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_42 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_43 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_44 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_45 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_46 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_47 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_48 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_49 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_50 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_51 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_52 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_53 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_54 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_55 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_56 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_57 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_58 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_59 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_60 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_61 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_62 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_63 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_64 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_65 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_66 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_67 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_68 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_69 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_70 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_71 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_72 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_73 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_74 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_75 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_76 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_77 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_78 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_79 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_80 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_81 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_82 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_83 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_84 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_85 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_86 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_87 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_88 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_89 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_90 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_91 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_92 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_93 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_94 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_95 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_96 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_97 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_98 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256, stride=(2, 2))

+        self.n_Conv_99 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_100 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=1024, stride=(2, 2))

+        self.n_Conv_101 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_102 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_103 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_104 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_105 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_106 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_107 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_108 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_109 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_110 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_111 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_112 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_113 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_114 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_115 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_116 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_117 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_118 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_119 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_120 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_121 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_122 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_123 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_124 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_125 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_126 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_127 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_128 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_129 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_130 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_131 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_132 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_133 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_134 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_135 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_136 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_137 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_138 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_139 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_140 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_141 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_142 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_143 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_144 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_145 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_146 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_147 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_148 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_149 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_150 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_151 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_152 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_153 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_154 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_155 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=256)

+        self.n_Conv_156 = nn.Conv2d(kernel_size=(3, 3), in_channels=256, out_channels=256)

+        self.n_Conv_157 = nn.Conv2d(kernel_size=(1, 1), in_channels=256, out_channels=1024)

+        self.n_Conv_158 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=2048, stride=(2, 2))

+        self.n_Conv_159 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=512)

+        self.n_Conv_160 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512, stride=(2, 2))

+        self.n_Conv_161 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)

+        self.n_Conv_162 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)

+        self.n_Conv_163 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)

+        self.n_Conv_164 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)

+        self.n_Conv_165 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=512)

+        self.n_Conv_166 = nn.Conv2d(kernel_size=(3, 3), in_channels=512, out_channels=512)

+        self.n_Conv_167 = nn.Conv2d(kernel_size=(1, 1), in_channels=512, out_channels=2048)

+        self.n_Conv_168 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=4096, stride=(2, 2))

+        self.n_Conv_169 = nn.Conv2d(kernel_size=(1, 1), in_channels=2048, out_channels=1024)

+        self.n_Conv_170 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024, stride=(2, 2))

+        self.n_Conv_171 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)

+        self.n_Conv_172 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)

+        self.n_Conv_173 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)

+        self.n_Conv_174 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)

+        self.n_Conv_175 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=1024)

+        self.n_Conv_176 = nn.Conv2d(kernel_size=(3, 3), in_channels=1024, out_channels=1024)

+        self.n_Conv_177 = nn.Conv2d(kernel_size=(1, 1), in_channels=1024, out_channels=4096)

+        self.n_Conv_178 = nn.Conv2d(kernel_size=(1, 1), in_channels=4096, out_channels=9176, bias=False)

+

+    def forward(self, *inputs):

+        t_358, = inputs

+        t_359 = t_358.permute(*[0, 3, 1, 2])

+        t_359_padded = F.pad(t_359, [2, 3, 2, 3], value=0)

+        t_360 = self.n_Conv_0(t_359_padded)

+        t_361 = F.relu(t_360)

+        t_361 = F.pad(t_361, [0, 1, 0, 1], value=float('-inf'))

+        t_362 = self.n_MaxPool_0(t_361)

+        t_363 = self.n_Conv_1(t_362)

+        t_364 = self.n_Conv_2(t_362)

+        t_365 = F.relu(t_364)

+        t_365_padded = F.pad(t_365, [1, 1, 1, 1], value=0)

+        t_366 = self.n_Conv_3(t_365_padded)

+        t_367 = F.relu(t_366)

+        t_368 = self.n_Conv_4(t_367)

+        t_369 = torch.add(t_368, t_363)

+        t_370 = F.relu(t_369)

+        t_371 = self.n_Conv_5(t_370)

+        t_372 = F.relu(t_371)

+        t_372_padded = F.pad(t_372, [1, 1, 1, 1], value=0)

+        t_373 = self.n_Conv_6(t_372_padded)

+        t_374 = F.relu(t_373)

+        t_375 = self.n_Conv_7(t_374)

+        t_376 = torch.add(t_375, t_370)

+        t_377 = F.relu(t_376)

+        t_378 = self.n_Conv_8(t_377)

+        t_379 = F.relu(t_378)

+        t_379_padded = F.pad(t_379, [1, 1, 1, 1], value=0)

+        t_380 = self.n_Conv_9(t_379_padded)

+        t_381 = F.relu(t_380)

+        t_382 = self.n_Conv_10(t_381)

+        t_383 = torch.add(t_382, t_377)

+        t_384 = F.relu(t_383)

+        t_385 = self.n_Conv_11(t_384)

+        t_386 = self.n_Conv_12(t_384)

+        t_387 = F.relu(t_386)

+        t_387_padded = F.pad(t_387, [0, 1, 0, 1], value=0)

+        t_388 = self.n_Conv_13(t_387_padded)

+        t_389 = F.relu(t_388)

+        t_390 = self.n_Conv_14(t_389)

+        t_391 = torch.add(t_390, t_385)

+        t_392 = F.relu(t_391)

+        t_393 = self.n_Conv_15(t_392)

+        t_394 = F.relu(t_393)

+        t_394_padded = F.pad(t_394, [1, 1, 1, 1], value=0)

+        t_395 = self.n_Conv_16(t_394_padded)

+        t_396 = F.relu(t_395)

+        t_397 = self.n_Conv_17(t_396)

+        t_398 = torch.add(t_397, t_392)

+        t_399 = F.relu(t_398)

+        t_400 = self.n_Conv_18(t_399)

+        t_401 = F.relu(t_400)

+        t_401_padded = F.pad(t_401, [1, 1, 1, 1], value=0)

+        t_402 = self.n_Conv_19(t_401_padded)

+        t_403 = F.relu(t_402)

+        t_404 = self.n_Conv_20(t_403)

+        t_405 = torch.add(t_404, t_399)

+        t_406 = F.relu(t_405)

+        t_407 = self.n_Conv_21(t_406)

+        t_408 = F.relu(t_407)

+        t_408_padded = F.pad(t_408, [1, 1, 1, 1], value=0)

+        t_409 = self.n_Conv_22(t_408_padded)

+        t_410 = F.relu(t_409)

+        t_411 = self.n_Conv_23(t_410)

+        t_412 = torch.add(t_411, t_406)

+        t_413 = F.relu(t_412)

+        t_414 = self.n_Conv_24(t_413)

+        t_415 = F.relu(t_414)

+        t_415_padded = F.pad(t_415, [1, 1, 1, 1], value=0)

+        t_416 = self.n_Conv_25(t_415_padded)

+        t_417 = F.relu(t_416)

+        t_418 = self.n_Conv_26(t_417)

+        t_419 = torch.add(t_418, t_413)

+        t_420 = F.relu(t_419)

+        t_421 = self.n_Conv_27(t_420)

+        t_422 = F.relu(t_421)

+        t_422_padded = F.pad(t_422, [1, 1, 1, 1], value=0)

+        t_423 = self.n_Conv_28(t_422_padded)

+        t_424 = F.relu(t_423)

+        t_425 = self.n_Conv_29(t_424)

+        t_426 = torch.add(t_425, t_420)

+        t_427 = F.relu(t_426)

+        t_428 = self.n_Conv_30(t_427)

+        t_429 = F.relu(t_428)

+        t_429_padded = F.pad(t_429, [1, 1, 1, 1], value=0)

+        t_430 = self.n_Conv_31(t_429_padded)

+        t_431 = F.relu(t_430)

+        t_432 = self.n_Conv_32(t_431)

+        t_433 = torch.add(t_432, t_427)

+        t_434 = F.relu(t_433)

+        t_435 = self.n_Conv_33(t_434)

+        t_436 = F.relu(t_435)

+        t_436_padded = F.pad(t_436, [1, 1, 1, 1], value=0)

+        t_437 = self.n_Conv_34(t_436_padded)

+        t_438 = F.relu(t_437)

+        t_439 = self.n_Conv_35(t_438)

+        t_440 = torch.add(t_439, t_434)

+        t_441 = F.relu(t_440)

+        t_442 = self.n_Conv_36(t_441)

+        t_443 = self.n_Conv_37(t_441)

+        t_444 = F.relu(t_443)

+        t_444_padded = F.pad(t_444, [0, 1, 0, 1], value=0)

+        t_445 = self.n_Conv_38(t_444_padded)

+        t_446 = F.relu(t_445)

+        t_447 = self.n_Conv_39(t_446)

+        t_448 = torch.add(t_447, t_442)

+        t_449 = F.relu(t_448)

+        t_450 = self.n_Conv_40(t_449)

+        t_451 = F.relu(t_450)

+        t_451_padded = F.pad(t_451, [1, 1, 1, 1], value=0)

+        t_452 = self.n_Conv_41(t_451_padded)

+        t_453 = F.relu(t_452)

+        t_454 = self.n_Conv_42(t_453)

+        t_455 = torch.add(t_454, t_449)

+        t_456 = F.relu(t_455)

+        t_457 = self.n_Conv_43(t_456)

+        t_458 = F.relu(t_457)

+        t_458_padded = F.pad(t_458, [1, 1, 1, 1], value=0)

+        t_459 = self.n_Conv_44(t_458_padded)

+        t_460 = F.relu(t_459)

+        t_461 = self.n_Conv_45(t_460)

+        t_462 = torch.add(t_461, t_456)

+        t_463 = F.relu(t_462)

+        t_464 = self.n_Conv_46(t_463)

+        t_465 = F.relu(t_464)

+        t_465_padded = F.pad(t_465, [1, 1, 1, 1], value=0)

+        t_466 = self.n_Conv_47(t_465_padded)

+        t_467 = F.relu(t_466)

+        t_468 = self.n_Conv_48(t_467)

+        t_469 = torch.add(t_468, t_463)

+        t_470 = F.relu(t_469)

+        t_471 = self.n_Conv_49(t_470)

+        t_472 = F.relu(t_471)

+        t_472_padded = F.pad(t_472, [1, 1, 1, 1], value=0)

+        t_473 = self.n_Conv_50(t_472_padded)

+        t_474 = F.relu(t_473)

+        t_475 = self.n_Conv_51(t_474)

+        t_476 = torch.add(t_475, t_470)

+        t_477 = F.relu(t_476)

+        t_478 = self.n_Conv_52(t_477)

+        t_479 = F.relu(t_478)

+        t_479_padded = F.pad(t_479, [1, 1, 1, 1], value=0)

+        t_480 = self.n_Conv_53(t_479_padded)

+        t_481 = F.relu(t_480)

+        t_482 = self.n_Conv_54(t_481)

+        t_483 = torch.add(t_482, t_477)

+        t_484 = F.relu(t_483)

+        t_485 = self.n_Conv_55(t_484)

+        t_486 = F.relu(t_485)

+        t_486_padded = F.pad(t_486, [1, 1, 1, 1], value=0)

+        t_487 = self.n_Conv_56(t_486_padded)

+        t_488 = F.relu(t_487)

+        t_489 = self.n_Conv_57(t_488)

+        t_490 = torch.add(t_489, t_484)

+        t_491 = F.relu(t_490)

+        t_492 = self.n_Conv_58(t_491)

+        t_493 = F.relu(t_492)

+        t_493_padded = F.pad(t_493, [1, 1, 1, 1], value=0)

+        t_494 = self.n_Conv_59(t_493_padded)

+        t_495 = F.relu(t_494)

+        t_496 = self.n_Conv_60(t_495)

+        t_497 = torch.add(t_496, t_491)

+        t_498 = F.relu(t_497)

+        t_499 = self.n_Conv_61(t_498)

+        t_500 = F.relu(t_499)

+        t_500_padded = F.pad(t_500, [1, 1, 1, 1], value=0)

+        t_501 = self.n_Conv_62(t_500_padded)

+        t_502 = F.relu(t_501)

+        t_503 = self.n_Conv_63(t_502)

+        t_504 = torch.add(t_503, t_498)

+        t_505 = F.relu(t_504)

+        t_506 = self.n_Conv_64(t_505)

+        t_507 = F.relu(t_506)

+        t_507_padded = F.pad(t_507, [1, 1, 1, 1], value=0)

+        t_508 = self.n_Conv_65(t_507_padded)

+        t_509 = F.relu(t_508)

+        t_510 = self.n_Conv_66(t_509)

+        t_511 = torch.add(t_510, t_505)

+        t_512 = F.relu(t_511)

+        t_513 = self.n_Conv_67(t_512)

+        t_514 = F.relu(t_513)

+        t_514_padded = F.pad(t_514, [1, 1, 1, 1], value=0)

+        t_515 = self.n_Conv_68(t_514_padded)

+        t_516 = F.relu(t_515)

+        t_517 = self.n_Conv_69(t_516)

+        t_518 = torch.add(t_517, t_512)

+        t_519 = F.relu(t_518)

+        t_520 = self.n_Conv_70(t_519)

+        t_521 = F.relu(t_520)

+        t_521_padded = F.pad(t_521, [1, 1, 1, 1], value=0)

+        t_522 = self.n_Conv_71(t_521_padded)

+        t_523 = F.relu(t_522)

+        t_524 = self.n_Conv_72(t_523)

+        t_525 = torch.add(t_524, t_519)

+        t_526 = F.relu(t_525)

+        t_527 = self.n_Conv_73(t_526)

+        t_528 = F.relu(t_527)

+        t_528_padded = F.pad(t_528, [1, 1, 1, 1], value=0)

+        t_529 = self.n_Conv_74(t_528_padded)

+        t_530 = F.relu(t_529)

+        t_531 = self.n_Conv_75(t_530)

+        t_532 = torch.add(t_531, t_526)

+        t_533 = F.relu(t_532)

+        t_534 = self.n_Conv_76(t_533)

+        t_535 = F.relu(t_534)

+        t_535_padded = F.pad(t_535, [1, 1, 1, 1], value=0)

+        t_536 = self.n_Conv_77(t_535_padded)

+        t_537 = F.relu(t_536)

+        t_538 = self.n_Conv_78(t_537)

+        t_539 = torch.add(t_538, t_533)

+        t_540 = F.relu(t_539)

+        t_541 = self.n_Conv_79(t_540)

+        t_542 = F.relu(t_541)

+        t_542_padded = F.pad(t_542, [1, 1, 1, 1], value=0)

+        t_543 = self.n_Conv_80(t_542_padded)

+        t_544 = F.relu(t_543)

+        t_545 = self.n_Conv_81(t_544)

+        t_546 = torch.add(t_545, t_540)

+        t_547 = F.relu(t_546)

+        t_548 = self.n_Conv_82(t_547)

+        t_549 = F.relu(t_548)

+        t_549_padded = F.pad(t_549, [1, 1, 1, 1], value=0)

+        t_550 = self.n_Conv_83(t_549_padded)

+        t_551 = F.relu(t_550)

+        t_552 = self.n_Conv_84(t_551)

+        t_553 = torch.add(t_552, t_547)

+        t_554 = F.relu(t_553)

+        t_555 = self.n_Conv_85(t_554)

+        t_556 = F.relu(t_555)

+        t_556_padded = F.pad(t_556, [1, 1, 1, 1], value=0)

+        t_557 = self.n_Conv_86(t_556_padded)

+        t_558 = F.relu(t_557)

+        t_559 = self.n_Conv_87(t_558)

+        t_560 = torch.add(t_559, t_554)

+        t_561 = F.relu(t_560)

+        t_562 = self.n_Conv_88(t_561)

+        t_563 = F.relu(t_562)

+        t_563_padded = F.pad(t_563, [1, 1, 1, 1], value=0)

+        t_564 = self.n_Conv_89(t_563_padded)

+        t_565 = F.relu(t_564)

+        t_566 = self.n_Conv_90(t_565)

+        t_567 = torch.add(t_566, t_561)

+        t_568 = F.relu(t_567)

+        t_569 = self.n_Conv_91(t_568)

+        t_570 = F.relu(t_569)

+        t_570_padded = F.pad(t_570, [1, 1, 1, 1], value=0)

+        t_571 = self.n_Conv_92(t_570_padded)

+        t_572 = F.relu(t_571)

+        t_573 = self.n_Conv_93(t_572)

+        t_574 = torch.add(t_573, t_568)

+        t_575 = F.relu(t_574)

+        t_576 = self.n_Conv_94(t_575)

+        t_577 = F.relu(t_576)

+        t_577_padded = F.pad(t_577, [1, 1, 1, 1], value=0)

+        t_578 = self.n_Conv_95(t_577_padded)

+        t_579 = F.relu(t_578)

+        t_580 = self.n_Conv_96(t_579)

+        t_581 = torch.add(t_580, t_575)

+        t_582 = F.relu(t_581)

+        t_583 = self.n_Conv_97(t_582)

+        t_584 = F.relu(t_583)

+        t_584_padded = F.pad(t_584, [0, 1, 0, 1], value=0)

+        t_585 = self.n_Conv_98(t_584_padded)

+        t_586 = F.relu(t_585)

+        t_587 = self.n_Conv_99(t_586)

+        t_588 = self.n_Conv_100(t_582)

+        t_589 = torch.add(t_587, t_588)

+        t_590 = F.relu(t_589)

+        t_591 = self.n_Conv_101(t_590)

+        t_592 = F.relu(t_591)

+        t_592_padded = F.pad(t_592, [1, 1, 1, 1], value=0)

+        t_593 = self.n_Conv_102(t_592_padded)

+        t_594 = F.relu(t_593)

+        t_595 = self.n_Conv_103(t_594)

+        t_596 = torch.add(t_595, t_590)

+        t_597 = F.relu(t_596)

+        t_598 = self.n_Conv_104(t_597)

+        t_599 = F.relu(t_598)

+        t_599_padded = F.pad(t_599, [1, 1, 1, 1], value=0)

+        t_600 = self.n_Conv_105(t_599_padded)

+        t_601 = F.relu(t_600)

+        t_602 = self.n_Conv_106(t_601)

+        t_603 = torch.add(t_602, t_597)

+        t_604 = F.relu(t_603)

+        t_605 = self.n_Conv_107(t_604)

+        t_606 = F.relu(t_605)

+        t_606_padded = F.pad(t_606, [1, 1, 1, 1], value=0)

+        t_607 = self.n_Conv_108(t_606_padded)

+        t_608 = F.relu(t_607)

+        t_609 = self.n_Conv_109(t_608)

+        t_610 = torch.add(t_609, t_604)

+        t_611 = F.relu(t_610)

+        t_612 = self.n_Conv_110(t_611)

+        t_613 = F.relu(t_612)

+        t_613_padded = F.pad(t_613, [1, 1, 1, 1], value=0)

+        t_614 = self.n_Conv_111(t_613_padded)

+        t_615 = F.relu(t_614)

+        t_616 = self.n_Conv_112(t_615)

+        t_617 = torch.add(t_616, t_611)

+        t_618 = F.relu(t_617)

+        t_619 = self.n_Conv_113(t_618)

+        t_620 = F.relu(t_619)

+        t_620_padded = F.pad(t_620, [1, 1, 1, 1], value=0)

+        t_621 = self.n_Conv_114(t_620_padded)

+        t_622 = F.relu(t_621)

+        t_623 = self.n_Conv_115(t_622)

+        t_624 = torch.add(t_623, t_618)

+        t_625 = F.relu(t_624)

+        t_626 = self.n_Conv_116(t_625)

+        t_627 = F.relu(t_626)

+        t_627_padded = F.pad(t_627, [1, 1, 1, 1], value=0)

+        t_628 = self.n_Conv_117(t_627_padded)

+        t_629 = F.relu(t_628)

+        t_630 = self.n_Conv_118(t_629)

+        t_631 = torch.add(t_630, t_625)

+        t_632 = F.relu(t_631)

+        t_633 = self.n_Conv_119(t_632)

+        t_634 = F.relu(t_633)

+        t_634_padded = F.pad(t_634, [1, 1, 1, 1], value=0)

+        t_635 = self.n_Conv_120(t_634_padded)

+        t_636 = F.relu(t_635)

+        t_637 = self.n_Conv_121(t_636)

+        t_638 = torch.add(t_637, t_632)

+        t_639 = F.relu(t_638)

+        t_640 = self.n_Conv_122(t_639)

+        t_641 = F.relu(t_640)

+        t_641_padded = F.pad(t_641, [1, 1, 1, 1], value=0)

+        t_642 = self.n_Conv_123(t_641_padded)

+        t_643 = F.relu(t_642)

+        t_644 = self.n_Conv_124(t_643)

+        t_645 = torch.add(t_644, t_639)

+        t_646 = F.relu(t_645)

+        t_647 = self.n_Conv_125(t_646)

+        t_648 = F.relu(t_647)

+        t_648_padded = F.pad(t_648, [1, 1, 1, 1], value=0)

+        t_649 = self.n_Conv_126(t_648_padded)

+        t_650 = F.relu(t_649)

+        t_651 = self.n_Conv_127(t_650)

+        t_652 = torch.add(t_651, t_646)

+        t_653 = F.relu(t_652)

+        t_654 = self.n_Conv_128(t_653)

+        t_655 = F.relu(t_654)

+        t_655_padded = F.pad(t_655, [1, 1, 1, 1], value=0)

+        t_656 = self.n_Conv_129(t_655_padded)

+        t_657 = F.relu(t_656)

+        t_658 = self.n_Conv_130(t_657)

+        t_659 = torch.add(t_658, t_653)

+        t_660 = F.relu(t_659)

+        t_661 = self.n_Conv_131(t_660)

+        t_662 = F.relu(t_661)

+        t_662_padded = F.pad(t_662, [1, 1, 1, 1], value=0)

+        t_663 = self.n_Conv_132(t_662_padded)

+        t_664 = F.relu(t_663)

+        t_665 = self.n_Conv_133(t_664)

+        t_666 = torch.add(t_665, t_660)

+        t_667 = F.relu(t_666)

+        t_668 = self.n_Conv_134(t_667)

+        t_669 = F.relu(t_668)

+        t_669_padded = F.pad(t_669, [1, 1, 1, 1], value=0)

+        t_670 = self.n_Conv_135(t_669_padded)

+        t_671 = F.relu(t_670)

+        t_672 = self.n_Conv_136(t_671)

+        t_673 = torch.add(t_672, t_667)

+        t_674 = F.relu(t_673)

+        t_675 = self.n_Conv_137(t_674)

+        t_676 = F.relu(t_675)

+        t_676_padded = F.pad(t_676, [1, 1, 1, 1], value=0)

+        t_677 = self.n_Conv_138(t_676_padded)

+        t_678 = F.relu(t_677)

+        t_679 = self.n_Conv_139(t_678)

+        t_680 = torch.add(t_679, t_674)

+        t_681 = F.relu(t_680)

+        t_682 = self.n_Conv_140(t_681)

+        t_683 = F.relu(t_682)

+        t_683_padded = F.pad(t_683, [1, 1, 1, 1], value=0)

+        t_684 = self.n_Conv_141(t_683_padded)

+        t_685 = F.relu(t_684)

+        t_686 = self.n_Conv_142(t_685)

+        t_687 = torch.add(t_686, t_681)

+        t_688 = F.relu(t_687)

+        t_689 = self.n_Conv_143(t_688)

+        t_690 = F.relu(t_689)

+        t_690_padded = F.pad(t_690, [1, 1, 1, 1], value=0)

+        t_691 = self.n_Conv_144(t_690_padded)

+        t_692 = F.relu(t_691)

+        t_693 = self.n_Conv_145(t_692)

+        t_694 = torch.add(t_693, t_688)

+        t_695 = F.relu(t_694)

+        t_696 = self.n_Conv_146(t_695)

+        t_697 = F.relu(t_696)

+        t_697_padded = F.pad(t_697, [1, 1, 1, 1], value=0)

+        t_698 = self.n_Conv_147(t_697_padded)

+        t_699 = F.relu(t_698)

+        t_700 = self.n_Conv_148(t_699)

+        t_701 = torch.add(t_700, t_695)

+        t_702 = F.relu(t_701)

+        t_703 = self.n_Conv_149(t_702)

+        t_704 = F.relu(t_703)

+        t_704_padded = F.pad(t_704, [1, 1, 1, 1], value=0)

+        t_705 = self.n_Conv_150(t_704_padded)

+        t_706 = F.relu(t_705)

+        t_707 = self.n_Conv_151(t_706)

+        t_708 = torch.add(t_707, t_702)

+        t_709 = F.relu(t_708)

+        t_710 = self.n_Conv_152(t_709)

+        t_711 = F.relu(t_710)

+        t_711_padded = F.pad(t_711, [1, 1, 1, 1], value=0)

+        t_712 = self.n_Conv_153(t_711_padded)

+        t_713 = F.relu(t_712)

+        t_714 = self.n_Conv_154(t_713)

+        t_715 = torch.add(t_714, t_709)

+        t_716 = F.relu(t_715)

+        t_717 = self.n_Conv_155(t_716)

+        t_718 = F.relu(t_717)

+        t_718_padded = F.pad(t_718, [1, 1, 1, 1], value=0)

+        t_719 = self.n_Conv_156(t_718_padded)

+        t_720 = F.relu(t_719)

+        t_721 = self.n_Conv_157(t_720)

+        t_722 = torch.add(t_721, t_716)

+        t_723 = F.relu(t_722)

+        t_724 = self.n_Conv_158(t_723)

+        t_725 = self.n_Conv_159(t_723)

+        t_726 = F.relu(t_725)

+        t_726_padded = F.pad(t_726, [0, 1, 0, 1], value=0)

+        t_727 = self.n_Conv_160(t_726_padded)

+        t_728 = F.relu(t_727)

+        t_729 = self.n_Conv_161(t_728)

+        t_730 = torch.add(t_729, t_724)

+        t_731 = F.relu(t_730)

+        t_732 = self.n_Conv_162(t_731)

+        t_733 = F.relu(t_732)

+        t_733_padded = F.pad(t_733, [1, 1, 1, 1], value=0)

+        t_734 = self.n_Conv_163(t_733_padded)

+        t_735 = F.relu(t_734)

+        t_736 = self.n_Conv_164(t_735)

+        t_737 = torch.add(t_736, t_731)

+        t_738 = F.relu(t_737)

+        t_739 = self.n_Conv_165(t_738)

+        t_740 = F.relu(t_739)

+        t_740_padded = F.pad(t_740, [1, 1, 1, 1], value=0)

+        t_741 = self.n_Conv_166(t_740_padded)

+        t_742 = F.relu(t_741)

+        t_743 = self.n_Conv_167(t_742)

+        t_744 = torch.add(t_743, t_738)

+        t_745 = F.relu(t_744)

+        t_746 = self.n_Conv_168(t_745)

+        t_747 = self.n_Conv_169(t_745)

+        t_748 = F.relu(t_747)

+        t_748_padded = F.pad(t_748, [0, 1, 0, 1], value=0)

+        t_749 = self.n_Conv_170(t_748_padded)

+        t_750 = F.relu(t_749)

+        t_751 = self.n_Conv_171(t_750)

+        t_752 = torch.add(t_751, t_746)

+        t_753 = F.relu(t_752)

+        t_754 = self.n_Conv_172(t_753)

+        t_755 = F.relu(t_754)

+        t_755_padded = F.pad(t_755, [1, 1, 1, 1], value=0)

+        t_756 = self.n_Conv_173(t_755_padded)

+        t_757 = F.relu(t_756)

+        t_758 = self.n_Conv_174(t_757)

+        t_759 = torch.add(t_758, t_753)

+        t_760 = F.relu(t_759)

+        t_761 = self.n_Conv_175(t_760)

+        t_762 = F.relu(t_761)

+        t_762_padded = F.pad(t_762, [1, 1, 1, 1], value=0)

+        t_763 = self.n_Conv_176(t_762_padded)

+        t_764 = F.relu(t_763)

+        t_765 = self.n_Conv_177(t_764)

+        t_766 = torch.add(t_765, t_760)

+        t_767 = F.relu(t_766)

+        t_768 = self.n_Conv_178(t_767)

+        t_769 = F.avg_pool2d(t_768, kernel_size=t_768.shape[-2:])

+        t_770 = torch.squeeze(t_769, 3)

+        t_770 = torch.squeeze(t_770, 2)

+        t_771 = torch.sigmoid(t_770)

+        return t_771

+

+    def load_state_dict(self, state_dict, **kwargs):

+        self.tags = state_dict.get('tags', [])

+

+        super(DeepDanbooruModel, self).load_state_dict({k: v for k, v in state_dict.items() if k != 'tags'})

+

diff --git a/modules/extensions.py b/modules/extensions.py
index 94ce479a..db9c4200 100644
--- a/modules/extensions.py
+++ b/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():

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index 985ec95e..1408ea05 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/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 = [

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7f182712..fbb87dd1 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/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

diff --git a/modules/images.py b/modules/images.py
index ae705cbd..26d5b7a9 100644
--- a/modules/images.py
+++ b/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>]

diff --git a/modules/img2img.py b/modules/img2img.py
index be9f3653..9fc5b693 100644
--- a/modules/img2img.py
+++ b/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,

diff --git a/modules/processing.py b/modules/processing.py
index 03c9143d..c310df6a 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -2,6 +2,7 @@ import json
 import math

 import os

 import sys

+import warnings

 

 import torch

 import numpy as np

@@ -66,19 +67,15 @@ def apply_overlay(image, paste_loc, index, overlays):
 

     return image

 

-def get_correct_sampler(p):

-    if isinstance(p, modules.processing.StableDiffusionProcessingTxt2Img):

-        return sd_samplers.samplers

-    elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):

-        return sd_samplers.samplers_for_img2img

-    elif isinstance(p, modules.api.processing.StableDiffusionProcessingAPI):

-        return sd_samplers.samplers

 

 class StableDiffusionProcessing():

     """

     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing

     """

-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_index: int = 0, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None):

+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, sampler_index: int = None):

+        if sampler_index is not None:

+            warnings.warn("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name")

+

         self.sd_model = sd_model

         self.outpath_samples: str = outpath_samples

         self.outpath_grids: str = outpath_grids

@@ -91,7 +88,7 @@ class StableDiffusionProcessing():
         self.subseed_strength: float = subseed_strength

         self.seed_resize_from_h: int = seed_resize_from_h

         self.seed_resize_from_w: int = seed_resize_from_w

-        self.sampler_index: int = sampler_index

+        self.sampler_name: str = sampler_name

         self.batch_size: int = batch_size

         self.n_iter: int = n_iter

         self.steps: int = steps

@@ -116,6 +113,7 @@ class StableDiffusionProcessing():
         self.s_tmax = s_tmax or float('inf')  # not representable as a standard ui option

         self.s_noise = s_noise or opts.s_noise

         self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}

+        self.is_using_inpainting_conditioning = False

 

         if not seed_enable_extras:

             self.subseed = -1

@@ -126,6 +124,7 @@ class StableDiffusionProcessing():
         self.scripts = None

         self.script_args = None

         self.all_prompts = None

+        self.all_negative_prompts = None

         self.all_seeds = None

         self.all_subseeds = None

 

@@ -136,6 +135,8 @@ class StableDiffusionProcessing():
             # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

             return x.new_zeros(x.shape[0], 5, 1, 1)

 

+        self.is_using_inpainting_conditioning = True

+

         height = height or self.height

         width = width or self.width

 

@@ -154,6 +155,8 @@ class StableDiffusionProcessing():
             # Dummy zero conditioning if we're not using inpainting model.

             return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)

 

+        self.is_using_inpainting_conditioning = True

+

         # Handle the different mask inputs

         if image_mask is not None:

             if torch.is_tensor(image_mask):

@@ -200,7 +203,7 @@ class StableDiffusionProcessing():
 

 

 class Processed:

-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):

+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):

         self.images = images_list

         self.prompt = p.prompt

         self.negative_prompt = p.negative_prompt

@@ -210,8 +213,7 @@ class Processed:
         self.info = info

         self.width = p.width

         self.height = p.height

-        self.sampler_index = p.sampler_index

-        self.sampler = sd_samplers.samplers[p.sampler_index].name

+        self.sampler_name = p.sampler_name

         self.cfg_scale = p.cfg_scale

         self.steps = p.steps

         self.batch_size = p.batch_size

@@ -238,17 +240,20 @@ class Processed:
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]

         self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1

         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1

+        self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning

 

-        self.all_prompts = all_prompts or [self.prompt]

-        self.all_seeds = all_seeds or [self.seed]

-        self.all_subseeds = all_subseeds or [self.subseed]

+        self.all_prompts = all_prompts or p.all_prompts or [self.prompt]

+        self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]

+        self.all_seeds = all_seeds or p.all_seeds or [self.seed]

+        self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]

         self.infotexts = infotexts or [info]

 

     def js(self):

         obj = {

-            "prompt": self.prompt,

+            "prompt": self.all_prompts[0],

             "all_prompts": self.all_prompts,

-            "negative_prompt": self.negative_prompt,

+            "negative_prompt": self.all_negative_prompts[0],

+            "all_negative_prompts": self.all_negative_prompts,

             "seed": self.seed,

             "all_seeds": self.all_seeds,

             "subseed": self.subseed,

@@ -256,8 +261,7 @@ class Processed:
             "subseed_strength": self.subseed_strength,

             "width": self.width,

             "height": self.height,

-            "sampler_index": self.sampler_index,

-            "sampler": self.sampler,

+            "sampler_name": self.sampler_name,

             "cfg_scale": self.cfg_scale,

             "steps": self.steps,

             "batch_size": self.batch_size,

@@ -273,6 +277,7 @@ class Processed:
             "styles": self.styles,

             "job_timestamp": self.job_timestamp,

             "clip_skip": self.clip_skip,

+            "is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,

         }

 

         return json.dumps(obj)

@@ -384,7 +389,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 

     generation_params = {

         "Steps": p.steps,

-        "Sampler": get_correct_sampler(p)[p.sampler_index].name,

+        "Sampler": p.sampler_name,

         "CFG scale": p.cfg_scale,

         "Seed": all_seeds[index],

         "Face restoration": (opts.face_restoration_model if p.restore_faces else None),

@@ -399,6 +404,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),

         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),

         "Denoising strength": getattr(p, 'denoising_strength', None),

+        "Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,

         "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),

         "Clip skip": None if clip_skip <= 1 else clip_skip,

         "ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,

@@ -408,7 +414,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 

     generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])

 

-    negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""

+    negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[0] if  p.all_negative_prompts[0] else ""

 

     return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()

 

@@ -418,13 +424,15 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

     try:

         for k, v in p.override_settings.items():

-            setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model, hypernet impossible

+            setattr(opts, k, v)  # we don't call onchange for simplicity which makes changing model impossible

+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not

 

         res = process_images_inner(p)

 

-    finally:

+    finally:  # restore opts to original state

         for k, v in stored_opts.items():

             setattr(opts, k, v)

+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()

 

     return res

 

@@ -437,10 +445,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     else:

         assert p.prompt is not None

 

-    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:

-        processed = Processed(p, [], p.seed, "")

-        file.write(processed.infotext(p, 0))

-

     devices.torch_gc()

 

     seed = get_fixed_seed(p.seed)

@@ -451,12 +455,15 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 

     comments = {}

 

-    shared.prompt_styles.apply_styles(p)

-

     if type(p.prompt) == list:

-        p.all_prompts = p.prompt

+        p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]

+    else:

+        p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]

+

+    if type(p.negative_prompt) == list:

+        p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]

     else:

-        p.all_prompts = p.batch_size * p.n_iter * [p.prompt]

+        p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]

 

     if type(seed) == list:

         p.all_seeds = seed

@@ -471,6 +478,10 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     def infotext(iteration=0, position_in_batch=0):

         return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)

 

+    with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:

+        processed = Processed(p, [], p.seed, "")

+        file.write(processed.infotext(p, 0))

+

     if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:

         model_hijack.embedding_db.load_textual_inversion_embeddings()

 

@@ -495,6 +506,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 break

 

             prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]

+            negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]

             seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]

             subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]

 

@@ -505,7 +517,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)

 

             with devices.autocast():

-                uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)

+                uc = prompt_parser.get_learned_conditioning(shared.sd_model, negative_prompts, p.steps)

                 c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)

 

             if len(model_hijack.comments) > 0:

@@ -591,7 +603,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 

     devices.torch_gc()

 

-    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)

+    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)

 

     if p.scripts is not None:

         p.scripts.postprocess(p, res)

@@ -645,7 +657,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f

 

     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):

-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)

 

         if not self.enable_hr:

             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

@@ -706,7 +718,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

         shared.state.nextjob()

 

-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)

+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)

 

         noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

 

@@ -730,7 +742,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.denoising_strength: float = denoising_strength

         self.init_latent = None

         self.image_mask = mask

-        #self.image_unblurred_mask = None

         self.latent_mask = None

         self.mask_for_overlay = None

         self.mask_blur = mask_blur

@@ -743,39 +754,39 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.image_conditioning = None

 

     def init(self, all_prompts, all_seeds, all_subseeds):

-        self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)

+        self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)

         crop_region = None

 

-        if self.image_mask is not None:

-            self.image_mask = self.image_mask.convert('L')

+        image_mask = self.image_mask

 

-            if self.inpainting_mask_invert:

-                self.image_mask = ImageOps.invert(self.image_mask)

+        if image_mask is not None:

+            image_mask = image_mask.convert('L')

 

-            #self.image_unblurred_mask = self.image_mask

+            if self.inpainting_mask_invert:

+                image_mask = ImageOps.invert(image_mask)

 

             if self.mask_blur > 0:

-                self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))

+                image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))

 

             if self.inpaint_full_res:

-                self.mask_for_overlay = self.image_mask

-                mask = self.image_mask.convert('L')

+                self.mask_for_overlay = image_mask

+                mask = image_mask.convert('L')

                 crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)

                 crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)

                 x1, y1, x2, y2 = crop_region

 

                 mask = mask.crop(crop_region)

-                self.image_mask = images.resize_image(2, mask, self.width, self.height)

+                image_mask = images.resize_image(2, mask, self.width, self.height)

                 self.paste_to = (x1, y1, x2-x1, y2-y1)

             else:

-                self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)

-                np_mask = np.array(self.image_mask)

+                image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)

+                np_mask = np.array(image_mask)

                 np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)

                 self.mask_for_overlay = Image.fromarray(np_mask)

 

             self.overlay_images = []

 

-        latent_mask = self.latent_mask if self.latent_mask is not None else self.image_mask

+        latent_mask = self.latent_mask if self.latent_mask is not None else image_mask

 

         add_color_corrections = opts.img2img_color_correction and self.color_corrections is None

         if add_color_corrections:

@@ -787,7 +798,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if crop_region is None:

                 image = images.resize_image(self.resize_mode, image, self.width, self.height)

 

-            if self.image_mask is not None:

+            if image_mask is not None:

                 image_masked = Image.new('RGBa', (image.width, image.height))

                 image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))

 

@@ -797,7 +808,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
                 image = image.crop(crop_region)

                 image = images.resize_image(2, image, self.width, self.height)

 

-            if self.image_mask is not None:

+            if image_mask is not None:

                 if self.inpainting_fill != 1:

                     image = masking.fill(image, latent_mask)

 

@@ -829,7 +840,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 

         self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))

 

-        if self.image_mask is not None:

+        if image_mask is not None:

             init_mask = latent_mask

             latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))

             latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255

@@ -846,7 +857,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:

                 self.init_latent = self.init_latent * self.mask

 

-        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)

+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)

 

     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):

         x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index f19e164c..8e22f875 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -61,6 +61,8 @@ callback_map = dict(
     callbacks_before_image_saved=[],

     callbacks_image_saved=[],

     callbacks_cfg_denoiser=[],

+    callbacks_before_component=[],

+    callbacks_after_component=[],

 )

 

 

@@ -137,6 +139,22 @@ def cfg_denoiser_callback(params: CFGDenoiserParams):
             report_exception(c, 'cfg_denoiser_callback')

 

 

+def before_component_callback(component, **kwargs):

+    for c in callback_map['callbacks_before_component']:

+        try:

+            c.callback(component, **kwargs)

+        except Exception:

+            report_exception(c, 'before_component_callback')

+

+

+def after_component_callback(component, **kwargs):

+    for c in callback_map['callbacks_after_component']:

+        try:

+            c.callback(component, **kwargs)

+        except Exception:

+            report_exception(c, 'after_component_callback')

+

+

 def add_callback(callbacks, fun):

     stack = [x for x in inspect.stack() if x.filename != __file__]

     filename = stack[0].filename if len(stack) > 0 else 'unknown file'

@@ -220,3 +238,20 @@ def on_cfg_denoiser(callback):
         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.

     """

     add_callback(callback_map['callbacks_cfg_denoiser'], callback)

+

+

+def on_before_component(callback):

+    """register a function to be called before a component is created.

+    The callback is called with arguments:

+        - component - gradio component that is about to be created.

+        - **kwargs - args to gradio.components.IOComponent.__init__ function

+

+    Use elem_id/label fields of kwargs to figure out which component it is.

+    This can be useful to inject your own components somewhere in the middle of vanilla UI.

+    """

+    add_callback(callback_map['callbacks_before_component'], callback)

+

+

+def on_after_component(callback):

+    """register a function to be called after a component is created. See on_before_component for more."""

+    add_callback(callback_map['callbacks_after_component'], callback)

diff --git a/modules/scripts.py b/modules/scripts.py
index 986b1914..b934d881 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -17,6 +17,9 @@ class Script:
     args_to = None

     alwayson = False

 

+    is_txt2img = False

+    is_img2img = False

+

     """A gr.Group component that has all script's UI inside it"""

     group = None

 

@@ -93,6 +96,23 @@ class Script:
 

         pass

 

+    def before_component(self, component, **kwargs):

+        """

+        Called before a component is created.

+        Use elem_id/label fields of kwargs to figure out which component it is.

+        This can be useful to inject your own components somewhere in the middle of vanilla UI.

+        You can return created components in the ui() function to add them to the list of arguments for your processing functions

+        """

+

+        pass

+

+    def after_component(self, component, **kwargs):

+        """

+        Called after a component is created. Same as above.

+        """

+

+        pass

+

     def describe(self):

         """unused"""

         return ""

@@ -195,12 +215,18 @@ class ScriptRunner:
         self.titles = []

         self.infotext_fields = []

 

-    def setup_ui(self, is_img2img):

+    def initialize_scripts(self, is_img2img):

+        self.scripts.clear()

+        self.alwayson_scripts.clear()

+        self.selectable_scripts.clear()

+

         for script_class, path, basedir in scripts_data:

             script = script_class()

             script.filename = path

+            script.is_txt2img = not is_img2img

+            script.is_img2img = is_img2img

 

-            visibility = script.show(is_img2img)

+            visibility = script.show(script.is_img2img)

 

             if visibility == AlwaysVisible:

                 self.scripts.append(script)

@@ -211,6 +237,7 @@ class ScriptRunner:
                 self.scripts.append(script)

                 self.selectable_scripts.append(script)

 

+    def setup_ui(self):

         self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts]

 

         inputs = [None]

@@ -220,7 +247,7 @@ class ScriptRunner:
             script.args_from = len(inputs)

             script.args_to = len(inputs)

 

-            controls = wrap_call(script.ui, script.filename, "ui", is_img2img)

+            controls = wrap_call(script.ui, script.filename, "ui", script.is_img2img)

 

             if controls is None:

                 return

@@ -320,6 +347,22 @@ class ScriptRunner:
                 print(f"Error running postprocess: {script.filename}", file=sys.stderr)

                 print(traceback.format_exc(), file=sys.stderr)

 

+    def before_component(self, component, **kwargs):

+        for script in self.scripts:

+            try:

+                script.before_component(component, **kwargs)

+            except Exception:

+                print(f"Error running before_component: {script.filename}", file=sys.stderr)

+                print(traceback.format_exc(), file=sys.stderr)

+

+    def after_component(self, component, **kwargs):

+        for script in self.scripts:

+            try:

+                script.after_component(component, **kwargs)

+            except Exception:

+                print(f"Error running after_component: {script.filename}", file=sys.stderr)

+                print(traceback.format_exc(), file=sys.stderr)

+

     def reload_sources(self, cache):

         for si, script in list(enumerate(self.scripts)):

             args_from = script.args_from

@@ -341,6 +384,7 @@ class ScriptRunner:
 

 scripts_txt2img = ScriptRunner()

 scripts_img2img = ScriptRunner()

+scripts_current: ScriptRunner = None

 

 

 def reload_script_body_only():

@@ -357,3 +401,22 @@ def reload_scripts():
     scripts_txt2img = ScriptRunner()

     scripts_img2img = ScriptRunner()

 

+

+def IOComponent_init(self, *args, **kwargs):

+    if scripts_current is not None:

+        scripts_current.before_component(self, **kwargs)

+

+    script_callbacks.before_component_callback(self, **kwargs)

+

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

+

+    script_callbacks.after_component_callback(self, **kwargs)

+

+    if scripts_current is not None:

+        scripts_current.after_component(self, **kwargs)

+

+    return res

+

+

+original_IOComponent_init = gr.components.IOComponent.__init__

+gr.components.IOComponent.__init__ = IOComponent_init

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 97979d05..eaedac13 100644
--- a/modules/sd_hijack.py
+++ b/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):

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 80addf03..c59151e0 100644
--- a/modules/sd_models.py
+++ b/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)

 

 

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index 783992d2..4fe67854 100644
--- a/modules/sd_samplers.py
+++ b/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

 

 

diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 71e7a6e6..9c120975 100644
--- a/modules/sd_vae.py
+++ b/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}
diff --git a/modules/shared.py b/modules/shared.py
index c46c29f7..25dd611a 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -55,7 +55,7 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)

 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")

 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")

-parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")

+parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")

 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")

 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")

 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")

@@ -81,6 +81,7 @@ parser.add_argument("--enable-console-prompts", action='store_true', help="print
 parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', 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 with the webui")

+parser.add_argument("--api-auth", type=str, help='Set authentication for api like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)

 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")

 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")

 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)

@@ -106,7 +107,7 @@ restricted_opts = {
     "outdir_save",

 }

 

-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) and not cmd_opts.enable_insecure_extension_access

+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_swinir, devices.device_esrgan, devices.device_scunet, 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', 'swinir', 'esrgan', 'scunet', 'codeformer'])

@@ -334,7 +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}),

@@ -436,6 +438,23 @@ class Options:
 

         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

+

+        try:

+            setattr(self, key, value)

+        except RuntimeError:

+            return False

+

+        if self.data_labels[key].onchange is not None:

+            self.data_labels[key].onchange()

+

+        return True

+

     def save(self, filename):

         assert not cmd_opts.freeze_settings, "saving settings is disabled"

 

diff --git a/modules/styles.py b/modules/styles.py
index 3bf5c5b6..ce6e71ca 100644
--- a/modules/styles.py
+++ b/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")

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index 488aa5b5..56b9b2eb 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -6,12 +6,10 @@ import sys
 import tqdm

 import time

 

-from modules import shared, images

+from modules import shared, images, deepbooru

 from modules.paths import models_path

 from modules.shared import opts, cmd_opts

 from modules.textual_inversion import autocrop

-if cmd_opts.deepdanbooru:

-    import modules.deepbooru as deepbooru

 

 

 def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False):

@@ -20,9 +18,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             shared.interrogator.load()

 

         if process_caption_deepbooru:

-            db_opts = deepbooru.create_deepbooru_opts()

-            db_opts[deepbooru.OPT_INCLUDE_RANKS] = False

-            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)

+            deepbooru.model.start()

 

         preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug)

 

@@ -32,7 +28,7 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             shared.interrogator.send_blip_to_ram()

 

         if process_caption_deepbooru:

-            deepbooru.release_process()

+            deepbooru.model.stop()

 

 

 def listfiles(dirname):

@@ -58,7 +54,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
     if params.process_caption_deepbooru:

         if len(caption) > 0:

             caption += ", "

-        caption += deepbooru.get_tags_from_process(image)

+        caption += deepbooru.model.tag_multi(image)

 

     filename_part = params.src

     filename_part = os.path.splitext(filename_part)[0]

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0aeb0459..5e4d8688 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/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

diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py
index d679e6f4..35c4feef 100644
--- a/modules/textual_inversion/ui.py
+++ b/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):

diff --git a/modules/txt2img.py b/modules/txt2img.py
index 8e4e8677..c8f81176 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -1,4 +1,5 @@
 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,

diff --git a/modules/ui.py b/modules/ui.py
index 5dce7f3b..e6da1b2a 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -19,14 +19,11 @@ import numpy as np
 from PIL import Image, PngImagePlugin

 

 

-from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions

+from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru

 from modules.paths import script_path

 

 from modules.shared import opts, cmd_opts, restricted_opts

 

-if cmd_opts.deepdanbooru:

-    from modules.deepbooru import get_deepbooru_tags

-

 import modules.codeformer_model

 import modules.generation_parameters_copypaste as parameters_copypaste

 import modules.gfpgan_model

@@ -69,8 +66,11 @@ 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 +142,7 @@ 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:

@@ -349,7 +349,7 @@ def interrogate(image):
 

 

 def interrogate_deepbooru(image):

-    prompt = get_deepbooru_tags(image)

+    prompt = deepbooru.model.tag(image)

     return gr_show(True) if prompt is None else prompt

 

 

@@ -692,6 +692,9 @@ def create_ui(wrap_gradio_gpu_call):
 

     parameters_copypaste.reset()

 

+    modules.scripts.scripts_current = modules.scripts.scripts_txt2img

+    modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)

+

     with gr.Blocks(analytics_enabled=False) as txt2img_interface:

         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)

         dummy_component = gr.Label(visible=False)

@@ -734,7 +737,7 @@ def create_ui(wrap_gradio_gpu_call):
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()

 

                 with gr.Group():

-                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)

+                    custom_inputs = modules.scripts.scripts_txt2img.setup_ui()

 

             txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)

             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)

@@ -843,6 +846,9 @@ def create_ui(wrap_gradio_gpu_call):
 

             token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])

 

+    modules.scripts.scripts_current = modules.scripts.scripts_img2img

+    modules.scripts.scripts_img2img.initialize_scripts(is_img2img=True)

+

     with gr.Blocks(analytics_enabled=False) as img2img_interface:

         img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)

 

@@ -913,7 +919,7 @@ def create_ui(wrap_gradio_gpu_call):
                 seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()

 

                 with gr.Group():

-                    custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)

+                    custom_inputs = modules.scripts.scripts_img2img.setup_ui()

 

             img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)

             parameters_copypaste.bind_buttons({"img2img": img2img_paste}, None, img2img_prompt)

@@ -1062,6 +1068,8 @@ def create_ui(wrap_gradio_gpu_call):
             parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)

             parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)

 

+    modules.scripts.scripts_current = None

+

     with gr.Blocks(analytics_enabled=False) as extras_interface:

         with gr.Row().style(equal_height=False):

             with gr.Column(variant='panel'):

@@ -1249,7 +1257,9 @@ 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 +1432,12 @@ 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

@@ -1473,16 +1489,9 @@ def create_ui(wrap_gradio_gpu_call):
             if comp == dummy_component:

                 continue

 

-            oldval = opts.data.get(key, None)

-            try:

-                setattr(opts, key, value)

-            except RuntimeError:

-                continue

-            if oldval != value:

-                if opts.data_labels[key].onchange is not None:

-                    opts.data_labels[key].onchange()

-

+            if opts.set(key, value):

                 changed.append(key)

+

         try:

             opts.save(shared.config_filename)

         except RuntimeError:

@@ -1493,15 +1502,8 @@ def create_ui(wrap_gradio_gpu_call):
         if not opts.same_type(value, opts.data_labels[key].default):

             return gr.update(visible=True), opts.dumpjson()

 

-        oldval = opts.data.get(key, None)

-        try:

-            setattr(opts, key, value)

-        except Exception:

-            return gr.update(value=oldval), opts.dumpjson()

-

-        if oldval != value:

-            if opts.data_labels[key].onchange is not None:

-                opts.data_labels[key].onchange()

+        if not opts.set(key, value):

+            return gr.update(value=getattr(opts, key)), opts.dumpjson()

 

         opts.save(shared.config_filename)

 

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index 6671cb60..030f011e 100644
--- a/modules/ui_extensions.py
+++ b/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