22 files changed, 776 insertions, 266 deletions

diff --git a/modules/api/api.py b/modules/api/api.py
index 71c9c160..596a6616 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -2,14 +2,21 @@ import base64
 import io
 import time
 import uvicorn
-from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
-from fastapi import APIRouter, Depends, HTTPException
+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
 import modules.shared as shared
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
-from modules.sd_samplers import all_samplers, sample_to_image, samples_to_image_grid
+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:
@@ -31,13 +38,25 @@ def setUpscalers(req: dict):
 
 
 def encode_pil_to_base64(image):
-    buffer = io.BytesIO()
-    image.save(buffer, format="png")
-    return base64.b64encode(buffer.getvalue())
+    with io.BytesIO() as output_bytes:
+
+        # Copy any text-only metadata
+        use_metadata = False
+        metadata = PngImagePlugin.PngInfo()
+        for key, value in image.info.items():
+            if isinstance(key, str) and isinstance(value, str):
+                metadata.add_text(key, value)
+                use_metadata = True
+
+        image.save(
+            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
+        )
+        bytes_data = output_bytes.getvalue()
+    return base64.b64encode(bytes_data)
 
 
 class Api:
-    def __init__(self, app, queue_lock):
+    def __init__(self, app: FastAPI, queue_lock: Lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
@@ -47,7 +66,20 @@ class Api:
         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])
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -186,11 +218,97 @@ class Api:
 
         return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
 
+    def interrogateapi(self, interrogatereq: InterrogateRequest):
+        image_b64 = interrogatereq.image
+        if image_b64 is None:
+            raise HTTPException(status_code=404, detail="Image not found") 
+
+        img = decode_base64_to_image(image_b64)
+        img = img.convert('RGB')
+
+        # Override object param
+        with self.queue_lock:
+            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.")
+            else:
+                raise HTTPException(status_code=404, detail="Model not found")
+        
+        return InterrogateResponse(caption=processed)
+
     def interruptapi(self):
         shared.state.interrupt()
 
         return {}
 
+    def get_config(self):
+        options = {}
+        for key in shared.opts.data.keys():
+            metadata = shared.opts.data_labels.get(key)
+            if(metadata is not None):
+                options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)})
+            else:
+                options.update({key: shared.opts.data.get(key, None)})
+
+        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])
+
+        shared.opts.save(shared.config_filename)
+        return
+
+    def get_cmd_flags(self):
+        return vars(shared.cmd_opts)
+
+    def get_samplers(self):
+        return [{"name":sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in all_samplers]
+
+    def get_upscalers(self):
+        upscalers = []
+
+        for upscaler in shared.sd_upscalers:
+            u = upscaler.scaler
+            upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
+
+        return upscalers
+
+    def get_sd_models(self):
+        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
+
+    def get_hypernetworks(self):
+        return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
+
+    def get_face_restorers(self):
+        return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers]
+
+    def get_realesrgan_models(self):
+        return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)]
+
+    def get_promp_styles(self):
+        styleList = []
+        for k in shared.prompt_styles.styles:
+            style = shared.prompt_styles.styles[k]
+            styleList.append({"name":style[0], "prompt": style[1], "negative_prompr": style[2]})
+
+        return styleList
+
+    def get_artists_categories(self):
+        return shared.artist_db.cats
+
+    def get_artists(self):
+        return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
+
     def launch(self, server_name, port):
         self.app.include_router(self.router)
         uvicorn.run(self.app, host=server_name, port=port)
diff --git a/modules/api/models.py b/modules/api/models.py
index 9ee42a17..f9cd929e 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,11 +1,11 @@
 import inspect
-from click import prompt
 from pydantic import BaseModel, Field, create_model
 from typing import Any, Optional
 from typing_extensions import Literal
 from inflection import underscore
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
-from modules.shared import sd_upscalers
+from modules.shared import sd_upscalers, opts, parser
+from typing import Dict, List
 
 API_NOT_ALLOWED = [
     "self",
@@ -65,6 +65,7 @@ class PydanticModelGenerator:
 
         self._model_name = model_name
         self._class_data = merge_class_params(class_instance)
+
         self._model_def = [
             ModelDef(
                 field=underscore(k),
@@ -109,12 +110,12 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
 ).generate_model()
 
 class TextToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
 class ImageToImageResponse(BaseModel):
-    images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
+    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
     parameters: dict
     info: str
 
@@ -131,6 +132,7 @@ class ExtrasBaseRequest(BaseModel):
     upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}")
     extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.")
+    upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?")
 
 class ExtraBaseResponse(BaseModel):
     html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
@@ -146,10 +148,10 @@ class FileData(BaseModel):
     name: str = Field(title="File name")
 
 class ExtrasBatchImagesRequest(ExtrasBaseRequest):
-    imageList: list[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
+    imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings")
 
 class ExtrasBatchImagesResponse(ExtraBaseResponse):
-    images: list[str] = Field(title="Images", description="The generated images in base64 format.")
+    images: List[str] = Field(title="Images", description="The generated images in base64 format.")
 
 class PNGInfoRequest(BaseModel):
     image: str = Field(title="Image", description="The base64 encoded PNG image")
@@ -165,3 +167,76 @@ class ProgressResponse(BaseModel):
     eta_relative: float = Field(title="ETA in secs")
     state: dict = Field(title="State", description="The current state snapshot")
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
+
+class InterrogateRequest(BaseModel):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+    model: str = Field(default="clip", title="Model", description="The interrogate model used.")
+
+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))
+
+    if (metadata is not None):
+        fields.update({key: (Optional[optType], Field(
+            default=metadata.default ,description=metadata.label))})
+    else:
+        fields.update({key: (Optional[optType], Field())})
+
+OptionsModel = create_model("Options", **fields)
+
+flags = {}
+_options = vars(parser)['_option_string_actions']
+for key in _options:
+    if(_options[key].dest != 'help'):
+        flag = _options[key]
+        _type = str
+        if _options[key].default is not None: _type = type(_options[key].default)
+        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+
+FlagsModel = create_model("Flags", **flags)
+
+class SamplerItem(BaseModel):
+    name: str = Field(title="Name")
+    aliases: List[str] = Field(title="Aliases")
+    options: Dict[str, str] = Field(title="Options")
+
+class UpscalerItem(BaseModel):
+    name: str = Field(title="Name")
+    model_name: Optional[str] = Field(title="Model Name")
+    model_path: Optional[str] = Field(title="Path")
+    model_url: Optional[str] = Field(title="URL")
+
+class SDModelItem(BaseModel):
+    title: str = Field(title="Title")
+    model_name: str = Field(title="Model Name")
+    hash: str = Field(title="Hash")
+    filename: str = Field(title="Filename")
+    config: str = Field(title="Config file")
+
+class HypernetworkItem(BaseModel):
+    name: str = Field(title="Name")
+    path: Optional[str] = Field(title="Path")
+
+class FaceRestorerItem(BaseModel):
+    name: str = Field(title="Name")
+    cmd_dir: Optional[str] = Field(title="Path")
+
+class RealesrganItem(BaseModel):
+    name: str = Field(title="Name")
+    path: Optional[str] = Field(title="Path")
+    scale: Optional[int] = Field(title="Scale")
+
+class PromptStyleItem(BaseModel):
+    name: str = Field(title="Name")
+    prompt: Optional[str] = Field(title="Prompt")
+    negative_prompt: Optional[str] = Field(title="Negative Prompt")
+
+class ArtistItem(BaseModel):
+    name: str = Field(title="Name")
+    score: float = Field(title="Score")
+    category: str = Field(title="Category")
+
diff --git a/modules/extensions.py b/modules/extensions.py
index 897af96e..8e0977fd 100644
--- a/modules/extensions.py
+++ b/modules/extensions.py
@@ -34,8 +34,11 @@ class Extension:
         if repo is None or repo.bare:

             self.remote = None

         else:

-            self.remote = next(repo.remote().urls, None)

-            self.status = 'unknown'

+            try:

+                self.remote = next(repo.remote().urls, None)

+                self.status = 'unknown'

+            except Exception:

+                self.remote = None

 

     def list_files(self, subdir, extension):

         from modules import scripts

diff --git a/modules/extras.py b/modules/extras.py
index 8e2ab35c..71b93a06 100644
--- a/modules/extras.py
+++ b/modules/extras.py
@@ -136,12 +136,13 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 

     def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:

         blended_result: Image.Image = None

+        image_hash: str = hash(np.array(image.getdata()).tobytes())

         for upscaler in params:

             upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,

                             upscaling_resize_w, upscaling_resize_h, upscaling_crop)

-            cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),

+            cache_key = LruCache.Key(image_hash=image_hash,

                                      info_hash=hash(info),

-                                     args_hash=hash((upscale_args, upscale_first)))

+                                     args_hash=hash(upscale_args))

             cached_entry = cached_images.get(cache_key)

             if cached_entry is None:

                 res = upscale(image, *upscale_args)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index a11e01d6..7f182712 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -22,6 +22,8 @@ from collections import defaultdict, deque
 from statistics import stdev, mean

 

 

+optimizer_dict = {optim_name : cls_obj for optim_name, cls_obj in inspect.getmembers(torch.optim, inspect.isclass) if optim_name != "Optimizer"}

+

 class HypernetworkModule(torch.nn.Module):

     multiplier = 1.0

     activation_dict = {

@@ -35,7 +37,8 @@ class HypernetworkModule(torch.nn.Module):
     }

     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})

 

-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False):

+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',

+                 add_layer_norm=False, use_dropout=False, activate_output=False, last_layer_dropout=True):

         super().__init__()

 

         assert layer_structure is not None, "layer_structure must not be None"

@@ -48,8 +51,8 @@ class HypernetworkModule(torch.nn.Module):
             # Add a fully-connected layer

             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))

 

-            # Add an activation func

-            if activation_func == "linear" or activation_func is None:

+            # Add an activation func except last layer

+            if activation_func == "linear" or activation_func is None or (i >= len(layer_structure) - 2 and not activate_output):

                 pass

             elif activation_func in self.activation_dict:

                 linears.append(self.activation_dict[activation_func]())

@@ -60,8 +63,8 @@ class HypernetworkModule(torch.nn.Module):
             if add_layer_norm:

                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))

 

-            # Add dropout expect last layer

-            if use_dropout and i < len(layer_structure) - 3:

+            # Add dropout except last layer

+            if use_dropout and (i < len(layer_structure) - 3 or last_layer_dropout and i < len(layer_structure) - 2):

                 linears.append(torch.nn.Dropout(p=0.3))

 

         self.linear = torch.nn.Sequential(*linears)

@@ -75,7 +78,7 @@ class HypernetworkModule(torch.nn.Module):
                     w, b = layer.weight.data, layer.bias.data

                     if weight_init == "Normal" or type(layer) == torch.nn.LayerNorm:

                         normal_(w, mean=0.0, std=0.01)

-                        normal_(b, mean=0.0, std=0.005)

+                        normal_(b, mean=0.0, std=0)

                     elif weight_init == 'XavierUniform':

                         xavier_uniform_(w)

                         zeros_(b)

@@ -127,7 +130,7 @@ class Hypernetwork:
     filename = None

     name = None

 

-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):

+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):

         self.filename = None

         self.name = name

         self.layers = {}

@@ -139,11 +142,17 @@ class Hypernetwork:
         self.weight_init = weight_init

         self.add_layer_norm = add_layer_norm

         self.use_dropout = use_dropout

+        self.activate_output = activate_output

+        self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True

+        self.optimizer_name = None

+        self.optimizer_state_dict = None

 

         for size in enable_sizes or []:

             self.layers[size] = (

-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),

-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),

+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,

+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),

+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,

+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),

             )

 

     def weights(self):

@@ -158,6 +167,7 @@ class Hypernetwork:
 

     def save(self, filename):

         state_dict = {}

+        optimizer_saved_dict = {}

 

         for k, v in self.layers.items():

             state_dict[k] = (v[0].state_dict(), v[1].state_dict())

@@ -171,8 +181,17 @@ class Hypernetwork:
         state_dict['use_dropout'] = self.use_dropout

         state_dict['sd_checkpoint'] = self.sd_checkpoint

         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name

+        state_dict['activate_output'] = self.activate_output

+        state_dict['last_layer_dropout'] = self.last_layer_dropout

+

+        if self.optimizer_name is not None:

+            optimizer_saved_dict['optimizer_name'] = self.optimizer_name

 

         torch.save(state_dict, filename)

+        if shared.opts.save_optimizer_state and self.optimizer_state_dict:

+            optimizer_saved_dict['hash'] = sd_models.model_hash(filename)

+            optimizer_saved_dict['optimizer_state_dict'] = self.optimizer_state_dict

+            torch.save(optimizer_saved_dict, filename + '.optim')

 

     def load(self, filename):

         self.filename = filename

@@ -191,12 +210,29 @@ class Hypernetwork:
         print(f"Layer norm is set to {self.add_layer_norm}")

         self.use_dropout = state_dict.get('use_dropout', False)

         print(f"Dropout usage is set to {self.use_dropout}" )

+        self.activate_output = state_dict.get('activate_output', True)

+        print(f"Activate last layer is set to {self.activate_output}")

+        self.last_layer_dropout = state_dict.get('last_layer_dropout', False)

+

+        optimizer_saved_dict = torch.load(self.filename + '.optim', map_location = 'cpu') if os.path.exists(self.filename + '.optim') else {}

+        self.optimizer_name = optimizer_saved_dict.get('optimizer_name', 'AdamW')

+        print(f"Optimizer name is {self.optimizer_name}")

+        if sd_models.model_hash(filename) == optimizer_saved_dict.get('hash', None):

+            self.optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)

+        else:

+            self.optimizer_state_dict = None

+        if self.optimizer_state_dict:

+            print("Loaded existing optimizer from checkpoint")

+        else:

+            print("No saved optimizer exists in checkpoint")

 

         for size, sd in state_dict.items():

             if type(size) == int:

                 self.layers[size] = (

-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),

-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout),

+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,

+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),

+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,

+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),

                 )

 

         self.name = state_dict.get('name', self.name)

@@ -207,11 +243,11 @@ class Hypernetwork:
 

 def list_hypernetworks(path):

     res = {}

-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):

+    for filename in sorted(glob.iglob(os.path.join(path, '**/*.pt'), recursive=True)):

         name = os.path.splitext(os.path.basename(filename))[0]

         # Prevent a hypothetical "None.pt" from being listed.

         if name != "None":

-            res[name] = filename

+            res[name + f"({sd_models.model_hash(filename)})"] = filename

     return res

 

 

@@ -346,6 +382,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     shared.state.textinfo = "Initializing hypernetwork training..."

     shared.state.job_count = steps

 

+    hypernetwork_name = hypernetwork_name.rsplit('(', 1)[0]

     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

 

     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)

@@ -392,8 +429,22 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
     weights = hypernetwork.weights()

     for weight in weights:

         weight.requires_grad = True

-    # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc...

-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)

+

+    # Here we use optimizer from saved HN, or we can specify as UI option.

+    if hypernetwork.optimizer_name in optimizer_dict:

+        optimizer = optimizer_dict[hypernetwork.optimizer_name](params=weights, lr=scheduler.learn_rate)

+        optimizer_name = hypernetwork.optimizer_name

+    else:

+        print(f"Optimizer type {hypernetwork.optimizer_name} is not defined!")

+        optimizer = torch.optim.AdamW(params=weights, lr=scheduler.learn_rate)

+        optimizer_name = 'AdamW'

+

+    if hypernetwork.optimizer_state_dict:  # This line must be changed if Optimizer type can be different from saved optimizer.

+        try:

+            optimizer.load_state_dict(hypernetwork.optimizer_state_dict)

+        except RuntimeError as e:

+            print("Cannot resume from saved optimizer!")

+            print(e)

 

     steps_without_grad = 0

 

@@ -455,7 +506,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             # Before saving, change name to match current checkpoint.

             hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'

             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name_every}.pt')

+            hypernetwork.optimizer_name = optimizer_name

+            if shared.opts.save_optimizer_state:

+                hypernetwork.optimizer_state_dict = optimizer.state_dict()

             save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, last_saved_file)

+            hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.

 

         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {

             "loss": f"{previous_mean_loss:.7f}",

@@ -518,8 +573,12 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     report_statistics(loss_dict)

 

     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')

+    hypernetwork.optimizer_name = optimizer_name

+    if shared.opts.save_optimizer_state:

+        hypernetwork.optimizer_state_dict = optimizer.state_dict()

     save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename)

-

+    del optimizer

+    hypernetwork.optimizer_state_dict = None  # dereference it after saving, to save memory.

     return hypernetwork, filename

 

 def save_hypernetwork(hypernetwork, checkpoint, hypernetwork_name, filename):

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index aad09ffc..c2d4b51c 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -9,7 +9,7 @@ from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork

 

 not_available = ["hardswish", "multiheadattention"]

-keys = ["linear"] + list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)

+keys = list(x for x in hypernetwork.HypernetworkModule.activation_dict.keys() if x not in not_available)

 

 def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False):

     # Remove illegal characters from name.

diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py
index 14db5076..90e0a2f0 100644
--- a/modules/ldsr_model_arch.py
+++ b/modules/ldsr_model_arch.py
@@ -101,8 +101,8 @@ class LDSR:
         down_sample_rate = target_scale / 4
         wd = width_og * down_sample_rate
         hd = height_og * down_sample_rate
-        width_downsampled_pre = int(wd)
-        height_downsampled_pre = int(hd)
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
 
         if down_sample_rate != 1:
             print(
@@ -110,7 +110,12 @@ class LDSR:
             im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
         else:
             print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
-        logs = self.run(model["model"], im_og, diffusion_steps, eta)
+        
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+        
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
 
         sample = logs["sample"]
         sample = sample.detach().cpu()
@@ -120,6 +125,9 @@ class LDSR:
         sample = np.transpose(sample, (0, 2, 3, 1))
         a = Image.fromarray(sample[0])
 
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
         del model
         gc.collect()
         torch.cuda.empty_cache()
diff --git a/modules/localization.py b/modules/localization.py
index b1810cda..f6a6f2fb 100644
--- a/modules/localization.py
+++ b/modules/localization.py
@@ -3,6 +3,7 @@ import os
 import sys

 import traceback

 

+

 localizations = {}

 

 

@@ -16,6 +17,11 @@ def list_localizations(dirname):
 

         localizations[fn] = os.path.join(dirname, file)

 

+    from modules import scripts

+    for file in scripts.list_scripts("localizations", ".json"):

+        fn, ext = os.path.splitext(file.filename)

+        localizations[fn] = file.path

+

 

 def localization_js(current_localization_name):

     fn = localizations.get(current_localization_name, None)

diff --git a/modules/masking.py b/modules/masking.py
index fd8d9241..a5c4d2da 100644
--- a/modules/masking.py
+++ b/modules/masking.py
@@ -49,7 +49,7 @@ def expand_crop_region(crop_region, processing_width, processing_height, image_w
     ratio_processing = processing_width / processing_height

 

     if ratio_crop_region > ratio_processing:

-        desired_height = (x2 - x1) * ratio_processing

+        desired_height = (x2 - x1) / ratio_processing

         desired_height_diff = int(desired_height - (y2-y1))

         y1 -= desired_height_diff//2

         y2 += desired_height_diff - desired_height_diff//2

diff --git a/modules/ngrok.py b/modules/ngrok.py
index 5c5f349a..10d2179f 100644
--- a/modules/ngrok.py
+++ b/modules/ngrok.py
@@ -1,14 +1,23 @@
 from pyngrok import ngrok, conf, exception
 
-
 def connect(token, port, region):
+    account = None
     if token == None:
         token = 'None'
+    else:
+        if ':' in token:
+            # token = authtoken:username:password
+            account = token.split(':')[1] + ':' + token.split(':')[-1]
+            token = token.split(':')[0]
+
     config = conf.PyngrokConfig(
         auth_token=token, region=region
     )
     try:
-        public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        if account == None:
+            public_url = ngrok.connect(port, pyngrok_config=config).public_url
+        else:
+            public_url = ngrok.connect(port, pyngrok_config=config, auth=account).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
diff --git a/modules/processing.py b/modules/processing.py
index 3a364b5f..03c9143d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -134,11 +134,7 @@ class StableDiffusionProcessing():
             # Dummy zero conditioning if we're not using inpainting model.

             # Still takes up a bit of memory, but no encoder call.

             # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.

-            return torch.zeros(

-                x.shape[0], 5, 1, 1, 

-                dtype=x.dtype, 

-                device=x.device

-            )

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

 

         height = height or self.height

         width = width or self.width

@@ -156,11 +152,7 @@ class StableDiffusionProcessing():
     def img2img_image_conditioning(self, source_image, latent_image, image_mask = None):

         if self.sampler.conditioning_key not in {'hybrid', 'concat'}:

             # Dummy zero conditioning if we're not using inpainting model.

-            return torch.zeros(

-                latent_image.shape[0], 5, 1, 1,

-                dtype=latent_image.dtype,

-                device=latent_image.device

-            )

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

 

         # Handle the different mask inputs

         if image_mask is not None:

@@ -174,11 +166,11 @@ class StableDiffusionProcessing():
                 # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0

                 conditioning_mask = torch.round(conditioning_mask)

         else:

-            conditioning_mask = torch.ones(1, 1, *source_image.shape[-2:])

+            conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])

 

         # Create another latent image, this time with a masked version of the original input.

         # Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.

-        conditioning_mask = conditioning_mask.to(source_image.device)

+        conditioning_mask = conditioning_mask.to(source_image.device).to(source_image.dtype)

         conditioning_image = torch.lerp(

             source_image,

             source_image * (1.0 - conditioning_mask),

@@ -426,13 +418,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

     try:

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

-            opts.data[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, hypernet impossible

 

         res = process_images_inner(p)

 

     finally:

         for k, v in stored_opts.items():

-            opts.data[k] = v

+            setattr(opts, k, v)

 

     return res

 

@@ -509,6 +501,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if len(prompts) == 0:

                 break

 

+            if p.scripts is not None:

+                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)

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

@@ -673,10 +668,17 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")

 

         if opts.use_scale_latent_for_hires_fix:

-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

-

             for i in range(samples.shape[0]):

                 save_intermediate(samples, i)

+

+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

+

+            # Avoid making the inpainting conditioning unless necessary as 

+            # this does need some extra compute to decode / encode the image again.

+            if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:

+                image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)

+            else:

+                image_conditioning = self.txt2img_image_conditioning(samples)

         else:

             decoded_samples = decode_first_stage(self.sd_model, samples)

             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)

@@ -700,14 +702,14 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 

             samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

 

+            image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)

+

         shared.state.nextjob()

 

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

 

-        image_conditioning = self.txt2img_image_conditioning(x)

-

         # GC now before running the next img2img to prevent running out of memory

         x = None

         devices.torch_gc()

diff --git a/modules/safe.py b/modules/safe.py
index 348a24fc..a9209e38 100644
--- a/modules/safe.py
+++ b/modules/safe.py
@@ -23,11 +23,18 @@ def encode(*args):
 

 

 class RestrictedUnpickler(pickle.Unpickler):

+    extra_handler = None

+

     def persistent_load(self, saved_id):

         assert saved_id[0] == 'storage'

         return TypedStorage()

 

     def find_class(self, module, name):

+        if self.extra_handler is not None:

+            res = self.extra_handler(module, name)

+            if res is not None:

+                return res

+

         if module == 'collections' and name == 'OrderedDict':

             return getattr(collections, name)

         if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:

@@ -52,7 +59,7 @@ class RestrictedUnpickler(pickle.Unpickler):
             return set

 

         # Forbid everything else.

-        raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")

+        raise Exception(f"global '{module}/{name}' is forbidden")

 

 

 allowed_zip_names = ["archive/data.pkl", "archive/version"]

@@ -69,7 +76,7 @@ def check_zip_filenames(filename, names):
         raise Exception(f"bad file inside {filename}: {name}")

 

 

-def check_pt(filename):

+def check_pt(filename, extra_handler):

     try:

 

         # new pytorch format is a zip file

@@ -78,6 +85,7 @@ def check_pt(filename):
 

             with z.open('archive/data.pkl') as file:

                 unpickler = RestrictedUnpickler(file)

+                unpickler.extra_handler = extra_handler

                 unpickler.load()

 

     except zipfile.BadZipfile:

@@ -85,16 +93,42 @@ def check_pt(filename):
         # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle

         with open(filename, "rb") as file:

             unpickler = RestrictedUnpickler(file)

+            unpickler.extra_handler = extra_handler

             for i in range(5):

                 unpickler.load()

 

 

 def load(filename, *args, **kwargs):

+    return load_with_extra(filename, *args, **kwargs)

+

+

+def load_with_extra(filename, extra_handler=None, *args, **kwargs):

+    """

+    this functon is intended to be used by extensions that want to load models with

+    some extra classes in them that the usual unpickler would find suspicious.

+

+    Use the extra_handler argument to specify a function that takes module and field name as text,

+    and returns that field's value:

+

+    ```python

+    def extra(module, name):

+        if module == 'collections' and name == 'OrderedDict':

+            return collections.OrderedDict

+

+        return None

+

+    safe.load_with_extra('model.pt', extra_handler=extra)

+    ```

+

+    The alternative to this is just to use safe.unsafe_torch_load('model.pt'), which as the name implies is

+    definitely unsafe.

+    """

+

     from modules import shared

 

     try:

         if not shared.cmd_opts.disable_safe_unpickle:

-            check_pt(filename)

+            check_pt(filename, extra_handler)

 

     except pickle.UnpicklingError:

         print(f"Error verifying pickled file from {filename}:", file=sys.stderr)

diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py
index c28e220e..f19e164c 100644
--- a/modules/script_callbacks.py
+++ b/modules/script_callbacks.py
@@ -7,6 +7,7 @@ from typing import Optional
 from fastapi import FastAPI

 from gradio import Blocks

 

+

 def report_exception(c, job):

     print(f"Error executing callback {job} for {c.script}", file=sys.stderr)

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

@@ -45,26 +46,31 @@ class CFGDenoiserParams:
         """Total number of sampling steps planned"""

 

 

+class UiTrainTabParams:

+    def __init__(self, txt2img_preview_params):

+        self.txt2img_preview_params = txt2img_preview_params

+

+

 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])

-callbacks_app_started = []

-callbacks_model_loaded = []

-callbacks_ui_tabs = []

-callbacks_ui_settings = []

-callbacks_before_image_saved = []

-callbacks_image_saved = []

-callbacks_cfg_denoiser = []

+callback_map = dict(

+    callbacks_app_started=[],

+    callbacks_model_loaded=[],

+    callbacks_ui_tabs=[],

+    callbacks_ui_train_tabs=[],

+    callbacks_ui_settings=[],

+    callbacks_before_image_saved=[],

+    callbacks_image_saved=[],

+    callbacks_cfg_denoiser=[],

+)

 

 

 def clear_callbacks():

-    callbacks_model_loaded.clear()

-    callbacks_ui_tabs.clear()

-    callbacks_ui_settings.clear()

-    callbacks_before_image_saved.clear()

-    callbacks_image_saved.clear()

-    callbacks_cfg_denoiser.clear()

+    for callback_list in callback_map.values():

+        callback_list.clear()

+

 

 def app_started_callback(demo: Optional[Blocks], app: FastAPI):

-    for c in callbacks_app_started:

+    for c in callback_map['callbacks_app_started']:

         try:

             c.callback(demo, app)

         except Exception:

@@ -72,7 +78,7 @@ def app_started_callback(demo: Optional[Blocks], app: FastAPI):
 

 

 def model_loaded_callback(sd_model):

-    for c in callbacks_model_loaded:

+    for c in callback_map['callbacks_model_loaded']:

         try:

             c.callback(sd_model)

         except Exception:

@@ -81,8 +87,8 @@ def model_loaded_callback(sd_model):
 

 def ui_tabs_callback():

     res = []

-    

-    for c in callbacks_ui_tabs:

+

+    for c in callback_map['callbacks_ui_tabs']:

         try:

             res += c.callback() or []

         except Exception:

@@ -91,8 +97,16 @@ def ui_tabs_callback():
     return res

 

 

+def ui_train_tabs_callback(params: UiTrainTabParams):

+    for c in callback_map['callbacks_ui_train_tabs']:

+        try:

+            c.callback(params)

+        except Exception:

+            report_exception(c, 'callbacks_ui_train_tabs')

+

+

 def ui_settings_callback():

-    for c in callbacks_ui_settings:

+    for c in callback_map['callbacks_ui_settings']:

         try:

             c.callback()

         except Exception:

@@ -100,7 +114,7 @@ def ui_settings_callback():
 

 

 def before_image_saved_callback(params: ImageSaveParams):

-    for c in callbacks_before_image_saved:

+    for c in callback_map['callbacks_before_image_saved']:

         try:

             c.callback(params)

         except Exception:

@@ -108,7 +122,7 @@ def before_image_saved_callback(params: ImageSaveParams):
 

 

 def image_saved_callback(params: ImageSaveParams):

-    for c in callbacks_image_saved:

+    for c in callback_map['callbacks_image_saved']:

         try:

             c.callback(params)

         except Exception:

@@ -116,7 +130,7 @@ def image_saved_callback(params: ImageSaveParams):
 

 

 def cfg_denoiser_callback(params: CFGDenoiserParams):

-    for c in callbacks_cfg_denoiser:

+    for c in callback_map['callbacks_cfg_denoiser']:

         try:

             c.callback(params)

         except Exception:

@@ -129,17 +143,33 @@ def add_callback(callbacks, fun):
 

     callbacks.append(ScriptCallback(filename, fun))

 

+    

+def remove_current_script_callbacks():

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

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

+    if filename == 'unknown file':

+        return

+    for callback_list in callback_map.values():

+        for callback_to_remove in [cb for cb in callback_list if cb.script == filename]:

+            callback_list.remove(callback_to_remove)

+

+

+def remove_callbacks_for_function(callback_func):

+    for callback_list in callback_map.values():

+        for callback_to_remove in [cb for cb in callback_list if cb.callback == callback_func]:

+            callback_list.remove(callback_to_remove)

+

 

 def on_app_started(callback):

     """register a function to be called when the webui started, the gradio `Block` component and

     fastapi `FastAPI` object are passed as the arguments"""

-    add_callback(callbacks_app_started, callback)

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

 

 

 def on_model_loaded(callback):

     """register a function to be called when the stable diffusion model is created; the model is

     passed as an argument"""

-    add_callback(callbacks_model_loaded, callback)

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

 

 

 def on_ui_tabs(callback):

@@ -152,13 +182,20 @@ def on_ui_tabs(callback):
     title is tab text displayed to user in the UI

     elem_id is HTML id for the tab

     """

-    add_callback(callbacks_ui_tabs, callback)

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

+

+

+def on_ui_train_tabs(callback):

+    """register a function to be called when the UI is creating new tabs for the train tab.

+    Create your new tabs with gr.Tab.

+    """

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

 

 

 def on_ui_settings(callback):

     """register a function to be called before UI settings are populated; add your settings

     by using shared.opts.add_option(shared.OptionInfo(...)) """

-    add_callback(callbacks_ui_settings, callback)

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

 

 

 def on_before_image_saved(callback):

@@ -166,7 +203,7 @@ def on_before_image_saved(callback):
     The callback is called with one argument:

         - params: ImageSaveParams - parameters the image is to be saved with. You can change fields in this object.

     """

-    add_callback(callbacks_before_image_saved, callback)

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

 

 

 def on_image_saved(callback):

@@ -174,7 +211,7 @@ def on_image_saved(callback):
     The callback is called with one argument:

         - params: ImageSaveParams - parameters the image was saved with. Changing fields in this object does nothing.

     """

-    add_callback(callbacks_image_saved, callback)

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

 

 

 def on_cfg_denoiser(callback):

@@ -182,5 +219,4 @@ def on_cfg_denoiser(callback):
     The callback is called with one argument:

         - params: CFGDenoiserParams - parameters to be passed to the inner model and sampling state details.

     """

-    add_callback(callbacks_cfg_denoiser, callback)

-

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

diff --git a/modules/scripts.py b/modules/scripts.py
index 533db45c..22d8908b 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -3,7 +3,6 @@ import sys
 import traceback

 from collections import namedtuple

 

-import modules.ui as ui

 import gradio as gr

 

 from modules.processing import StableDiffusionProcessing

@@ -18,6 +17,9 @@ class Script:
     args_to = None

     alwayson = False

 

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

+    group = None

+

     infotext_fields = None

     """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when

     parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example

@@ -70,6 +72,19 @@ class Script:
 

         pass

 

+    def process_batch(self, p, *args, **kwargs):

+        """

+        Same as process(), but called for every batch.

+

+        **kwargs will have those items:

+          - batch_number - index of current batch, from 0 to number of batches-1

+          - prompts - list of prompts for current batch; you can change contents of this list but changing the number of entries will likely break things

+          - seeds - list of seeds for current batch

+          - subseeds - list of subseeds for current batch

+        """

+

+        pass

+

     def postprocess(self, p, processed, *args):

         """

         This function is called after processing ends for AlwaysVisible scripts.

@@ -125,7 +140,7 @@ def list_files_with_name(filename):
             continue

 

         path = os.path.join(dirpath, filename)

-        if os.path.isfile(filename):

+        if os.path.isfile(path):

             res.append(path)

 

     return res

@@ -218,8 +233,6 @@ class ScriptRunner:
 

             for control in controls:

                 control.custom_script_source = os.path.basename(script.filename)

-                if not script.alwayson:

-                    control.visible = False

 

             if script.infotext_fields is not None:

                 self.infotext_fields += script.infotext_fields

@@ -229,40 +242,41 @@ class ScriptRunner:
             script.args_to = len(inputs)

 

         for script in self.alwayson_scripts:

-            with gr.Group():

+            with gr.Group() as group:

                 create_script_ui(script, inputs, inputs_alwayson)

 

+            script.group = group

+

         dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")

         dropdown.save_to_config = True

         inputs[0] = dropdown

 

         for script in self.selectable_scripts:

-            create_script_ui(script, inputs, inputs_alwayson)

+            with gr.Group(visible=False) as group:

+                create_script_ui(script, inputs, inputs_alwayson)

+

+            script.group = group

 

         def select_script(script_index):

-            if 0 < script_index <= len(self.selectable_scripts):

-                script = self.selectable_scripts[script_index-1]

-                args_from = script.args_from

-                args_to = script.args_to

-            else:

-                args_from = 0

-                args_to = 0

+            selected_script = self.selectable_scripts[script_index - 1] if script_index>0 else None

 

-            return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)]

+            return [gr.update(visible=selected_script == s) for s in self.selectable_scripts]

 

         def init_field(title):

+            """called when an initial value is set from ui-config.json to show script's UI components"""

+

             if title == 'None':

                 return

+

             script_index = self.titles.index(title)

-            script = self.selectable_scripts[script_index]

-            for i in range(script.args_from, script.args_to):

-                inputs[i].visible = True

+            self.selectable_scripts[script_index].group.visible = True

 

         dropdown.init_field = init_field

+

         dropdown.change(

             fn=select_script,

             inputs=[dropdown],

-            outputs=inputs

+            outputs=[script.group for script in self.selectable_scripts]

         )

 

         return inputs

@@ -294,6 +308,15 @@ class ScriptRunner:
                 print(f"Error running process: {script.filename}", file=sys.stderr)

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

 

+    def process_batch(self, p, **kwargs):

+        for script in self.alwayson_scripts:

+            try:

+                script_args = p.script_args[script.args_from:script.args_to]

+                script.process_batch(p, *script_args, **kwargs)

+            except Exception:

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

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

+

     def postprocess(self, p, processed):

         for script in self.alwayson_scripts:

             try:

diff --git a/modules/sd_models.py b/modules/sd_models.py
index 5075fadb..80addf03 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -163,11 +163,21 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename

     sd_model_hash = checkpoint_info.hash

 

-    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)

+    cache_enabled = shared.opts.sd_checkpoint_cache > 0

+

+    if cache_enabled:

+        sd_vae.restore_base_vae(model)

 

-    checkpoint_key = checkpoint_info

+    vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)

 

-    if checkpoint_key not in checkpoints_loaded:

+    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")

+        model.load_state_dict(checkpoints_loaded[checkpoint_info])

+    else:

+        # load from file

         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")

 

         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)

@@ -178,6 +188,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         del pl_sd

         model.load_state_dict(sd, strict=False)

         del sd

+        

+        if cache_enabled:

+            # cache newly loaded model

+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()

 

         if shared.cmd_opts.opt_channelslast:

             model.to(memory_format=torch.channels_last)

@@ -197,17 +211,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 

         model.first_stage_model.to(devices.dtype_vae)

 

-        if shared.opts.sd_checkpoint_cache > 0:

-            # if PR #4035 were to get merged, restore base VAE first before caching

-            checkpoints_loaded[checkpoint_key] = model.state_dict().copy()

-            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:

-                checkpoints_loaded.popitem(last=False)  # LRU

-

-    else:

-        vae_name = sd_vae.get_filename(vae_file)

-        print(f"Loading weights [{sd_model_hash}] with {vae_name} VAE from cache")

-        checkpoints_loaded.move_to_end(checkpoint_key)

-        model.load_state_dict(checkpoints_loaded[checkpoint_key])

+    # clean up cache if limit is reached

+    if cache_enabled:

+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model

+            checkpoints_loaded.popitem(last=False)  # LRU

 

     model.sd_model_hash = sd_model_hash

     model.sd_model_checkpoint = checkpoint_file

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index c7c414ef..783992d2 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -24,11 +24,15 @@ samplers_k_diffusion = [
     ('Heun', 'sample_heun', ['k_heun'], {}),

     ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),

     ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),

+    ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),

+    ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),

     ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),

     ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),

     ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),

     ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),

     ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),

+    ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),

+    ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),

 ]

 

 samplers_data_k_diffusion = [

diff --git a/modules/shared.py b/modules/shared.py
index d8e99f85..caabf078 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -44,6 +44,7 @@ parser.add_argument("--precision", type=str, help="evaluate at this precision",
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")

 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)

 parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")

+parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options")

 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))

 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))

 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))

@@ -85,6 +86,10 @@ parser.add_argument("--nowebui", action='store_true', help="use api=True to laun
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")

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

 parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)

+parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origins", default=None)

+parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None)

+parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None)

+parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None)

 

 cmd_opts = parser.parse_args()

 restricted_opts = {

@@ -99,7 +104,7 @@ restricted_opts = {
     "outdir_save",

 }

 

-cmd_opts.disable_extension_access = cmd_opts.share or cmd_opts.listen

+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen) 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'])

@@ -146,6 +151,9 @@ class State:
         self.interrupted = True

 

     def nextjob(self):

+        if opts.show_progress_every_n_steps == -1:

+            self.do_set_current_image()

+

         self.job_no += 1

         self.sampling_step = 0

         self.current_image_sampling_step = 0

@@ -186,17 +194,21 @@ class State:
 

     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""

     def set_current_image(self):

+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:

+            self.do_set_current_image()

+

+    def do_set_current_image(self):

         if not parallel_processing_allowed:

             return

+        if self.current_latent is None:

+            return

 

-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and self.current_latent is not None:

-            if opts.show_progress_grid:

-                self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)

-            else:

-                self.current_image = sd_samplers.sample_to_image(self.current_latent)

-

-            self.current_image_sampling_step = self.sampling_step

+        if opts.show_progress_grid:

+            self.current_image = sd_samplers.samples_to_image_grid(self.current_latent)

+        else:

+            self.current_image = sd_samplers.sample_to_image(self.current_latent)

 

+        self.current_image_sampling_step = self.sampling_step

 

 state = State()

 

@@ -209,8 +221,6 @@ interrogator = modules.interrogate.InterrogateModels("interrogate")
 

 face_restorers = []

 

-localization.list_localizations(cmd_opts.localizations_dir)

-

 

 def realesrgan_models_names():

     import modules.realesrgan_model

@@ -309,6 +319,9 @@ options_templates.update(options_section(('system', "System"), {
 

 options_templates.update(options_section(('training', "Training"), {

     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),

+    "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),

+    "tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),

+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),

     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),

     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

@@ -351,7 +364,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
 

 options_templates.update(options_section(('ui', "User interface"), {

     "show_progressbar": OptionInfo(True, "Show progressbar"),

-    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}),

+    "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),

     "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),

     "return_grid": OptionInfo(True, "Show grid in results for web"),

     "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),

@@ -396,6 +409,16 @@ class Options:
     def __setattr__(self, key, value):

         if self.data is not None:

             if key in self.data or key in self.data_labels:

+                assert not cmd_opts.freeze_settings, "changing settings is disabled"

+

+                info = opts.data_labels.get(key, None)

+                comp_args = info.component_args if info else None

+                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:

+                    raise RuntimeError(f"not possible to set {key} because it is restricted")

+

+                if cmd_opts.hide_ui_dir_config and key in restricted_opts:

+                    raise RuntimeError(f"not possible to set {key} because it is restricted")

+

                 self.data[key] = value

                 return

 

@@ -412,6 +435,8 @@ class Options:
         return super(Options, self).__getattribute__(item)

 

     def save(self, filename):

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

+

         with open(filename, "w", encoding="utf8") as file:

             json.dump(self.data, file, indent=4)

 

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index ad726577..eb75c376 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -98,7 +98,12 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):

         text = random.choice(self.lines)

         text = text.replace("[name]", self.placeholder_token)

-        text = text.replace("[filewords]", filename_text)

+        tags = filename_text.split(',')

+        if shared.opts.tag_drop_out != 0:

+            tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]

+        if shared.opts.shuffle_tags:

+            random.shuffle(tags)

+        text = text.replace("[filewords]", ','.join(tags))

         return text

 

     def __len__(self):

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index e13b1894..488aa5b5 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -35,6 +35,84 @@ def preprocess(process_src, process_dst, process_width, process_height, preproce
             deepbooru.release_process()

 

 

+def listfiles(dirname):

+    return os.listdir(dirname)

+

+

+class PreprocessParams:

+    src = None

+    dstdir = None

+    subindex = 0

+    flip = False

+    process_caption = False

+    process_caption_deepbooru = False

+    preprocess_txt_action = None

+

+

+def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None):

+    caption = ""

+

+    if params.process_caption:

+        caption += shared.interrogator.generate_caption(image)

+

+    if params.process_caption_deepbooru:

+        if len(caption) > 0:

+            caption += ", "

+        caption += deepbooru.get_tags_from_process(image)

+

+    filename_part = params.src

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

+    filename_part = os.path.basename(filename_part)

+

+    basename = f"{index:05}-{params.subindex}-{filename_part}"

+    image.save(os.path.join(params.dstdir, f"{basename}.png"))

+

+    if params.preprocess_txt_action == 'prepend' and existing_caption:

+        caption = existing_caption + ' ' + caption

+    elif params.preprocess_txt_action == 'append' and existing_caption:

+        caption = caption + ' ' + existing_caption

+    elif params.preprocess_txt_action == 'copy' and existing_caption:

+        caption = existing_caption

+

+    caption = caption.strip()

+

+    if len(caption) > 0:

+        with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:

+            file.write(caption)

+

+    params.subindex += 1

+

+

+def save_pic(image, index, params, existing_caption=None):

+    save_pic_with_caption(image, index, params, existing_caption=existing_caption)

+

+    if params.flip:

+        save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption)

+

+

+def split_pic(image, inverse_xy, width, height, overlap_ratio):

+    if inverse_xy:

+        from_w, from_h = image.height, image.width

+        to_w, to_h = height, width

+    else:

+        from_w, from_h = image.width, image.height

+        to_w, to_h = width, height

+    h = from_h * to_w // from_w

+    if inverse_xy:

+        image = image.resize((h, to_w))

+    else:

+        image = image.resize((to_w, h))

+

+    split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))

+    y_step = (h - to_h) / (split_count - 1)

+    for i in range(split_count):

+        y = int(y_step * i)

+        if inverse_xy:

+            splitted = image.crop((y, 0, y + to_h, to_w))

+        else:

+            splitted = image.crop((0, y, to_w, y + to_h))

+        yield splitted

+

 

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

     width = process_width

@@ -48,82 +126,28 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 

     os.makedirs(dst, exist_ok=True)

 

-    files = os.listdir(src)

+    files = listfiles(src)

 

     shared.state.textinfo = "Preprocessing..."

     shared.state.job_count = len(files)

 

-    def save_pic_with_caption(image, index, existing_caption=None):

-        caption = ""

-

-        if process_caption:

-            caption += shared.interrogator.generate_caption(image)

-

-        if process_caption_deepbooru:

-            if len(caption) > 0:

-                caption += ", "

-            caption += deepbooru.get_tags_from_process(image)

-

-        filename_part = filename

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

-        filename_part = os.path.basename(filename_part)

-

-        basename = f"{index:05}-{subindex[0]}-{filename_part}"

-        image.save(os.path.join(dst, f"{basename}.png"))

-

-        if preprocess_txt_action == 'prepend' and existing_caption:

-            caption = existing_caption + ' ' + caption

-        elif preprocess_txt_action == 'append' and existing_caption:

-            caption = caption + ' ' + existing_caption

-        elif preprocess_txt_action == 'copy' and existing_caption:

-            caption = existing_caption

-

-        caption = caption.strip()

-        

-        if len(caption) > 0:

-            with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:

-                file.write(caption)

-

-        subindex[0] += 1

-

-    def save_pic(image, index, existing_caption=None):

-        save_pic_with_caption(image, index, existing_caption=existing_caption)

-

-        if process_flip:

-            save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)

-

-    def split_pic(image, inverse_xy):

-        if inverse_xy:

-            from_w, from_h = image.height, image.width

-            to_w, to_h = height, width

-        else:

-            from_w, from_h = image.width, image.height

-            to_w, to_h = width, height

-        h = from_h * to_w // from_w

-        if inverse_xy:

-            image = image.resize((h, to_w))

-        else:

-            image = image.resize((to_w, h))

-

-        split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio)))

-        y_step = (h - to_h) / (split_count - 1)

-        for i in range(split_count):

-            y = int(y_step * i)

-            if inverse_xy:

-                splitted = image.crop((y, 0, y + to_h, to_w))

-            else:

-                splitted = image.crop((0, y, to_w, y + to_h))

-            yield splitted

-

+    params = PreprocessParams()

+    params.dstdir = dst

+    params.flip = process_flip

+    params.process_caption = process_caption

+    params.process_caption_deepbooru = process_caption_deepbooru

+    params.preprocess_txt_action = preprocess_txt_action

 

     for index, imagefile in enumerate(tqdm.tqdm(files)):

-        subindex = [0]

+        params.subindex = 0

         filename = os.path.join(src, imagefile)

         try:

             img = Image.open(filename).convert("RGB")

         except Exception:

             continue

 

+        params.src = filename

+

         existing_caption = None

         existing_caption_filename = os.path.splitext(filename)[0] + '.txt'

         if os.path.exists(existing_caption_filename):

@@ -143,8 +167,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         process_default_resize = True

 

         if process_split and ratio < 1.0 and ratio <= split_threshold:

-            for splitted in split_pic(img, inverse_xy):

-                save_pic(splitted, index, existing_caption=existing_caption)

+            for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio):

+                save_pic(splitted, index, params, existing_caption=existing_caption)

             process_default_resize = False

 

         if process_focal_crop and img.height != img.width:

@@ -165,11 +189,11 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
                 dnn_model_path = dnn_model_path,

             )

             for focal in autocrop.crop_image(img, autocrop_settings):

-                save_pic(focal, index, existing_caption=existing_caption)

+                save_pic(focal, index, params, existing_caption=existing_caption)

             process_default_resize = False

 

         if process_default_resize:

             img = images.resize_image(1, img, width, height)

-            save_pic(img, index, existing_caption=existing_caption)

+            save_pic(img, index, params, existing_caption=existing_caption)

 

-        shared.state.nextjob()
\ No newline at end of file
+        shared.state.nextjob()

diff --git a/modules/ui.py b/modules/ui.py
index 2609857e..5dce7f3b 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -174,9 +174,9 @@ def save_pil_to_file(pil_image, dir=None):
 gr.processing_utils.save_pil_to_file = save_pil_to_file

 

 

-def wrap_gradio_call(func, extra_outputs=None):

+def wrap_gradio_call(func, extra_outputs=None, add_stats=False):

     def f(*args, extra_outputs_array=extra_outputs, **kwargs):

-        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled

+        run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats

         if run_memmon:

             shared.mem_mon.monitor()

         t = time.perf_counter()

@@ -203,11 +203,18 @@ def wrap_gradio_call(func, extra_outputs=None):
 

             res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]

 

+        shared.state.skipped = False

+        shared.state.interrupted = False

+        shared.state.job_count = 0

+

+        if not add_stats:

+            return tuple(res)

+

         elapsed = time.perf_counter() - t

         elapsed_m = int(elapsed // 60)

         elapsed_s = elapsed % 60

         elapsed_text = f"{elapsed_s:.2f}s"

-        if (elapsed_m > 0):

+        if elapsed_m > 0:

             elapsed_text = f"{elapsed_m}m "+elapsed_text

 

         if run_memmon:

@@ -225,10 +232,6 @@ def wrap_gradio_call(func, extra_outputs=None):
         # last item is always HTML

         res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"

 

-        shared.state.skipped = False

-        shared.state.interrupted = False

-        shared.state.job_count = 0

-

         return tuple(res)

 

     return f

@@ -276,7 +279,7 @@ def check_progress_call(id_part):
     image = gr_show(False)

     preview_visibility = gr_show(False)

 

-    if opts.show_progress_every_n_steps > 0:

+    if opts.show_progress_every_n_steps != 0:

         shared.state.set_current_image()

         image = shared.state.current_image

 

@@ -563,6 +566,19 @@ def apply_setting(key, value):
     return value

 

 

+def update_generation_info(args):

+    generation_info, html_info, img_index = args

+    try:

+        generation_info = json.loads(generation_info)

+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):

+            return html_info

+        return plaintext_to_html(generation_info["infotexts"][img_index])

+    except Exception:

+        pass

+    # if the json parse or anything else fails, just return the old html_info

+    return html_info

+

+

 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):

     def refresh():

         refresh_method()

@@ -635,6 +651,15 @@ Requested path was: {f}
                     with gr.Group():

                         html_info = gr.HTML()

                         generation_info = gr.Textbox(visible=False)

+                        if tabname == 'txt2img' or tabname == 'img2img':

+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")

+                            generation_info_button.click(

+                                fn=update_generation_info,

+                                _js="(x, y) => [x, y, selected_gallery_index()]",

+                                inputs=[generation_info, html_info],

+                                outputs=[html_info],

+                                preprocess=False

+                            )

 

                         save.click(

                             fn=wrap_gradio_call(save_files),

@@ -1052,6 +1077,8 @@ def create_ui(wrap_gradio_gpu_call):
                         extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.")

                         show_extras_results = gr.Checkbox(label='Show result images', value=True)

 

+                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')

+

                 with gr.Tabs(elem_id="extras_resize_mode"):

                     with gr.TabItem('Scale by'):

                         upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4)

@@ -1079,8 +1106,6 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():

                     upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False)

 

-                submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')

-

             result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)

 

         submit.click(

@@ -1138,7 +1163,7 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[html, generation_info, html2],

         )

 

-    with gr.Blocks() as modelmerger_interface:

+    with gr.Blocks(analytics_enabled=False) as modelmerger_interface:

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

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

                 gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")

@@ -1158,7 +1183,7 @@ def create_ui(wrap_gradio_gpu_call):
 

     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()

 

-    with gr.Blocks() as train_interface:

+    with gr.Blocks(analytics_enabled=False) as train_interface:

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

             gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")

 

@@ -1182,8 +1207,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")

                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])

                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")

-                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)

-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])

+                    new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)

+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])

                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")

                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")

                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")

@@ -1267,6 +1292,10 @@ def create_ui(wrap_gradio_gpu_call):
                         train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')

                         train_embedding = gr.Button(value="Train Embedding", variant='primary')

 

+                params = script_callbacks.UiTrainTabParams(txt2img_preview_params)

+

+                script_callbacks.ui_train_tabs_callback(params)

+

             with gr.Column():

                 progressbar = gr.HTML(elem_id="ti_progressbar")

                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)

@@ -1417,15 +1446,14 @@ def create_ui(wrap_gradio_gpu_call):
 

         if info.refresh is not None:

             if is_quicksettings:

-                res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))

+                res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

                 create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)

             else:

                 with gr.Row(variant="compact"):

-                    res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))

+                    res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

                     create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)

         else:

-            res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))

-

+            res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {}))

 

         return res

 

@@ -1436,50 +1464,41 @@ def create_ui(wrap_gradio_gpu_call):
     opts.reorder()

 

     def run_settings(*args):

-        changed = 0

-

-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"

+        changed = []

 

         for key, value, comp in zip(opts.data_labels.keys(), args, components):

-            if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):

-                return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()

+            assert comp == dummy_component or opts.same_type(value, opts.data_labels[key].default), f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}"

 

         for key, value, comp in zip(opts.data_labels.keys(), args, components):

             if comp == dummy_component:

                 continue

 

-            comp_args = opts.data_labels[key].component_args

-            if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:

-                continue

-

-            if cmd_opts.hide_ui_dir_config and key in restricted_opts:

-                continue

-

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

-            opts.data[key] = value

-

+            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()

 

-                changed += 1

-

-        opts.save(shared.config_filename)

-

-        return f'{changed} settings changed.', opts.dumpjson()

+                changed.append(key)

+        try:

+            opts.save(shared.config_filename)

+        except RuntimeError:

+            return opts.dumpjson(), f'{len(changed)} settings changed without save: {", ".join(changed)}.'

+        return opts.dumpjson(), f'{len(changed)} settings changed: {", ".join(changed)}.'

 

     def run_settings_single(value, key):

-        assert not shared.cmd_opts.freeze_settings, "changing settings is disabled"

-

         if not opts.same_type(value, opts.data_labels[key].default):

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

 

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

-        if cmd_opts.hide_ui_dir_config and key in restricted_opts:

+        try:

+            setattr(opts, key, value)

+        except Exception:

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

 

-        opts.data[key] = value

-

         if oldval != value:

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

                 opts.data_labels[key].onchange()

@@ -1572,11 +1591,10 @@ def create_ui(wrap_gradio_gpu_call):
             shared.state.need_restart = True

 

         restart_gradio.click(

-

             fn=request_restart,

+            _js='restart_reload',

             inputs=[],

             outputs=[],

-            _js='restart_reload'

         )

 

         if column is not None:

@@ -1632,9 +1650,9 @@ def create_ui(wrap_gradio_gpu_call):
 

         text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)

         settings_submit.click(

-            fn=run_settings,

+            fn=wrap_gradio_call(run_settings, extra_outputs=[gr.update()]),

             inputs=components,

-            outputs=[result, text_settings],

+            outputs=[text_settings, result],

         )

 

         for i, k, item in quicksettings_list:

@@ -1646,6 +1664,17 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[component, text_settings],

             )

 

+        component_keys = [k for k in opts.data_labels.keys() if k in component_dict]

+

+        def get_settings_values():

+            return [getattr(opts, key) for key in component_keys]

+

+        demo.load(

+            fn=get_settings_values,

+            inputs=[],

+            outputs=[component_dict[k] for k in component_keys],

+        )

+

         def modelmerger(*args):

             try:

                 results = modules.extras.run_modelmerger(*args)

@@ -1749,7 +1778,7 @@ def create_ui(wrap_gradio_gpu_call):
     return demo

 

 

-def load_javascript(raw_response):

+def reload_javascript():

     with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:

         javascript = f'<script>{jsfile.read()}</script>'

 

@@ -1765,7 +1794,7 @@ def load_javascript(raw_response):
     javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"

 

     def template_response(*args, **kwargs):

-        res = raw_response(*args, **kwargs)

+        res = shared.GradioTemplateResponseOriginal(*args, **kwargs)

         res.body = res.body.replace(

             b'</head>', f'{javascript}</head>'.encode("utf8"))

         res.init_headers()

@@ -1774,4 +1803,5 @@ def load_javascript(raw_response):
     gradio.routes.templates.TemplateResponse = template_response

 

 

-reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse)

+if not hasattr(shared, 'GradioTemplateResponseOriginal'):

+    shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse

diff --git a/modules/ui_extensions.py b/modules/ui_extensions.py
index ab807722..02ab9643 100644
--- a/modules/ui_extensions.py
+++ b/modules/ui_extensions.py
@@ -86,7 +86,7 @@ def extension_table():
         code += f"""

             <tr>

                 <td><label><input class="gr-check-radio gr-checkbox" name="enable_{html.escape(ext.name)}" type="checkbox" {'checked="checked"' if ext.enabled else ''}>{html.escape(ext.name)}</label></td>

-                <td><a href="{html.escape(ext.remote or '')}">{html.escape(ext.remote or '')}</a></td>

+                <td><a href="{html.escape(ext.remote or '')}" target="_blank">{html.escape(ext.remote or '')}</a></td>

                 <td{' class="extension_status"' if ext.remote is not None else ''}>{ext_status}</td>

             </tr>

     """

@@ -140,13 +140,15 @@ def install_extension_from_url(dirname, url):
         shutil.rmtree(tmpdir, True)

 

 

-def install_extension_from_index(url):

+def install_extension_from_index(url, hide_tags):

     ext_table, message = install_extension_from_url(None, url)

 

-    return refresh_available_extensions_from_data(), ext_table, message

+    code, _ = refresh_available_extensions_from_data(hide_tags)

 

+    return code, ext_table, message

 

-def refresh_available_extensions(url):

+

+def refresh_available_extensions(url, hide_tags):

     global available_extensions

 

     import urllib.request

@@ -155,13 +157,25 @@ def refresh_available_extensions(url):
 

     available_extensions = json.loads(text)

 

-    return url, refresh_available_extensions_from_data(), ''

+    code, tags = refresh_available_extensions_from_data(hide_tags)

+

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

+

+

+def refresh_available_extensions_for_tags(hide_tags):

+    code, _ = refresh_available_extensions_from_data(hide_tags)

 

+    return code, ''

 

-def refresh_available_extensions_from_data():

+

+def refresh_available_extensions_from_data(hide_tags):

     extlist = available_extensions["extensions"]

     installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}

 

+    tags = available_extensions.get("tags", {})

+    tags_to_hide = set(hide_tags)

+    hidden = 0

+

     code = f"""<!-- {time.time()} -->

     <table id="available_extensions">

         <thead>

@@ -178,17 +192,24 @@ def refresh_available_extensions_from_data():
         name = ext.get("name", "noname")

         url = ext.get("url", None)

         description = ext.get("description", "")

+        extension_tags = ext.get("tags", [])

 

         if url is None:

             continue

 

+        if len([x for x in extension_tags if x in tags_to_hide]) > 0:

+            hidden += 1

+            continue

+

         existing = installed_extension_urls.get(normalize_git_url(url), None)

 

         install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""

 

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

+

         code += f"""

             <tr>

-                <td><a href="{html.escape(url)}">{html.escape(name)}</a></td>

+                <td><a href="{html.escape(url)}" target="_blank">{html.escape(name)}</a><br />{tags_text}</td>

                 <td>{html.escape(description)}</td>

                 <td>{install_code}</td>

             </tr>

@@ -199,7 +220,10 @@ def refresh_available_extensions_from_data():
     </table>

     """

 

-    return code

+    if hidden > 0:

+        code += f"<p>Extension hidden: {hidden}</p>"

+

+    return code, list(tags)

 

 

 def create_ui():

@@ -238,21 +262,30 @@ def create_ui():
                     extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)

                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)

 

+                with gr.Row():

+                    hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])

+

                 install_result = gr.HTML()

                 available_extensions_table = gr.HTML()

 

                 refresh_available_extensions_button.click(

-                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),

-                    inputs=[available_extensions_index],

-                    outputs=[available_extensions_index, available_extensions_table, install_result],

+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update(), gr.update()]),

+                    inputs=[available_extensions_index, hide_tags],

+                    outputs=[available_extensions_index, available_extensions_table, hide_tags, install_result],

                 )

 

                 install_extension_button.click(

                     fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),

-                    inputs=[extension_to_install],

+                    inputs=[extension_to_install, hide_tags],

                     outputs=[available_extensions_table, extensions_table, install_result],

                 )

 

+                hide_tags.change(

+                    fn=modules.ui.wrap_gradio_call(refresh_available_extensions_for_tags, extra_outputs=[gr.update()]),

+                    inputs=[hide_tags],

+                    outputs=[available_extensions_table, install_result]

+                )

+

             with gr.TabItem("Install from URL"):

                 install_url = gr.Text(label="URL for extension's git repository")

                 install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")

diff --git a/modules/upscaler.py b/modules/upscaler.py
index 83fde7ca..c4e6e6bd 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -57,10 +57,18 @@ class Upscaler:
         self.scale = scale
         dest_w = img.width * scale
         dest_h = img.height * scale
+
         for i in range(3):
-            if img.width > dest_w and img.height > dest_h:
-                break
+            shape = (img.width, img.height)
+
             img = self.do_upscale(img, selected_model)
+
+            if shape == (img.width, img.height):
+                break
+
+            if img.width >= dest_w and img.height >= dest_h:
+                break
+
         if img.width != dest_w or img.height != dest_h:
             img = img.resize((int(dest_w), int(dest_h)), resample=LANCZOS)