Merge branch 'AUTOMATIC1111:master' into 3825-save-hypernet-strength-to-info

modules/api/api.py

@@ -1,46 +1,37 @@
-from modules.api.models import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI
+import uvicorn
+from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
+from fastapi import APIRouter, 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
-from modules.extras import run_pnginfo
-import modules.shared as shared
-import uvicorn
-from fastapi import Body, APIRouter, HTTPException
-from fastapi.responses import JSONResponse
-from pydantic import BaseModel, Field, Json
-from typing import List
-import json
-import io
-import base64
-from PIL import Image
+from modules.extras import run_extras
+
+def upscaler_to_index(name: str):
+    try:
+        return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
+    except:
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
 
 sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
-class TextToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
-
-class ImageToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
-
+def setUpscalers(req: dict):
+    reqDict = vars(req)
+    reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
+    reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
+    reqDict.pop('upscaler_1')
+    reqDict.pop('upscaler_2')
+    return reqDict
 
 class Api:
     def __init__(self, app, queue_lock):
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
-        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"])
-        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"])
-
-    def __base64_to_image(self, base64_string):
-        # if has a comma, deal with prefix
-        if "," in base64_string:
-            base64_string = base64_string.split(",")[1]
-        imgdata = base64.b64decode(base64_string)
-        # convert base64 to PIL image
-        return Image.open(io.BytesIO(imgdata))
+        self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
+        self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
+        self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
 
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
         sampler_index = sampler_to_index(txt2imgreq.sampler_index)
@@ -60,15 +51,9 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = []
-        for i in processed.images:
-            buffer = io.BytesIO()
-            i.save(buffer, format="png")
-            b64images.append(base64.b64encode(buffer.getvalue()))
-
-        return TextToImageResponse(images=b64images, parameters=json.dumps(vars(txt2imgreq)), info=processed.js())
-        
+        b64images = list(map(encode_pil_to_base64, processed.images))
         
+        return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
 
     def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
         sampler_index = sampler_to_index(img2imgreq.sampler_index)
@@ -83,7 +68,7 @@ class Api:
 
         mask = img2imgreq.mask
         if mask:
-            mask = self.__base64_to_image(mask)
+            mask = decode_base64_to_image(mask)
 
         
         populate = img2imgreq.copy(update={ # Override __init__ params
@@ -98,7 +83,7 @@ class Api:
 
         imgs = []
         for img in init_images:
-            img = self.__base64_to_image(img)
+            img = decode_base64_to_image(img)
             imgs = [img] * p.batch_size
 
         p.init_images = imgs
@@ -106,21 +91,40 @@ class Api:
         with self.queue_lock:
             processed = process_images(p)
         
-        b64images = []
-        for i in processed.images:
-            buffer = io.BytesIO()
-            i.save(buffer, format="png")
-            b64images.append(base64.b64encode(buffer.getvalue()))
+        b64images = list(map(encode_pil_to_base64, processed.images))
 
         if (not img2imgreq.include_init_images):
             img2imgreq.init_images = None
             img2imgreq.mask = None
+       
+        return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js())
 
-        return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=processed.js())
+    def extras_single_image_api(self, req: ExtrasSingleImageRequest):
+        reqDict = setUpscalers(req)
 
-    def extrasapi(self):
-        raise NotImplementedError
+        reqDict['image'] = decode_base64_to_image(reqDict['image'])
 
+        with self.queue_lock:
+            result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", **reqDict)
+
+        return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
+
+    def extras_batch_images_api(self, req: ExtrasBatchImagesRequest):
+        reqDict = setUpscalers(req)
+
+        def prepareFiles(file):
+            file = decode_base64_to_file(file.data, file_path=file.name)
+            file.orig_name = file.name
+            return file
+
+        reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList']))
+        reqDict.pop('imageList')
+
+        with self.queue_lock:
+            result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", **reqDict)
+
+        return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
+    
     def pnginfoapi(self):
         raise NotImplementedError
 

modules/api/models.py

@@ -1,10 +1,10 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
-from pydantic import BaseModel, Field, create_model
-from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img
 import inspect
-
+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
 
 API_NOT_ALLOWED = [
     "self",
@@ -105,4 +105,47 @@ StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img", 
     StableDiffusionProcessingImg2Img,
     [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
-).generate_model()
+).generate_model()
+
+class TextToImageResponse(BaseModel):
+    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.")
+    parameters: dict
+    info: str
+
+class ExtrasBaseRequest(BaseModel):
+    resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.")
+    show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?")
+    gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.")
+    codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.")
+    codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.")
+    upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=4, description="By how much to upscale the image, only used when resize_mode=0.")
+    upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.")
+    upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the choosen size?")
+    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.")
+
+class ExtraBaseResponse(BaseModel):
+    html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.")
+
+class ExtrasSingleImageRequest(ExtrasBaseRequest):
+    image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+
+class ExtrasSingleImageResponse(ExtraBaseResponse):
+    image: str = Field(default=None, title="Image", description="The generated image in base64 format.")
+
+class FileData(BaseModel):
+    data: str = Field(title="File data", description="Base64 representation of the file")
+    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")
+
+class ExtrasBatchImagesResponse(ExtraBaseResponse):
+    images: list[str] = Field(title="Images", description="The generated images in base64 format.")

modules/extras.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 import math
 import os
 
@@ -7,6 +8,10 @@ from PIL import Image
 import torch
 import tqdm
 
+from typing import Callable, List, OrderedDict, Tuple
+from functools import partial
+from dataclasses import dataclass
+
 from modules import processing, shared, images, devices, sd_models
 from modules.shared import opts
 import modules.gfpgan_model
@@ -17,10 +22,38 @@ import piexif.helper
 import gradio as gr
 
 
-cached_images = {}
+class LruCache(OrderedDict):
+    @dataclass(frozen=True)
+    class Key:
+        image_hash: int
+        info_hash: int
+        args_hash: int
+
+    @dataclass
+    class Value:
+        image: Image.Image
+        info: str
+
+    def __init__(self, max_size: int = 5, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._max_size = max_size
+
+    def get(self, key: LruCache.Key) -> LruCache.Value:
+        ret = super().get(key)
+        if ret is not None:
+            self.move_to_end(key)  # Move to end of eviction list
+        return ret
+
+    def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
+        self[key] = value
+        while len(self) > self._max_size:
+            self.popitem(last=False)
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+cached_images: LruCache = LruCache(max_size=5)
+
+
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool):
     devices.torch_gc()
 
     imageArr = []
@@ -39,7 +72,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         if input_dir == '':
             return outputs, "Please select an input directory.", ''
-        image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)]
+        image_list = shared.listfiles(input_dir)
         for img in image_list:
             try:
                 image = Image.open(img)
@@ -56,7 +89,91 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     else:
         outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    
+    # Extra operation definitions
+
+    def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
+        res = Image.fromarray(restored_img)
+
+        if gfpgan_visibility < 1.0:
+            res = Image.blend(image, res, gfpgan_visibility)
+
+        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
+        return (res, info)
+
+    def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
+        res = Image.fromarray(restored_img)
+
+        if codeformer_visibility < 1.0:
+            res = Image.blend(image, res, codeformer_visibility)
+
+        info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
+        return (res, info)
+
+    def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        upscaler = shared.sd_upscalers[scaler_index]
+        res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
+        if mode == 1 and crop:
+            cropped = Image.new("RGB", (resize_w, resize_h))
+            cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
+            res = cropped
+        return res
+
+    def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
+        nonlocal upscaling_resize
+        if resize_mode == 1:
+            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
+            crop_info = " (crop)" if upscaling_crop else ""
+            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
+        return (image, info)
+
+    @dataclass
+    class UpscaleParams:
+        upscaler_idx: int
+        blend_alpha: float
+
+    def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        blended_result: Image.Image = None
+        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()),
+                                     info_hash=hash(info),
+                                     args_hash=hash(upscale_args))
+            cached_entry = cached_images.get(cache_key)
+            if cached_entry is None:
+                res = upscale(image, *upscale_args)
+                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
+                cached_images.put(cache_key, LruCache.Value(image=res, info=info))
+            else:
+                res, info = cached_entry.image, cached_entry.info
+
+            if blended_result is None:
+                blended_result = res
+            else:
+                blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
+        return (blended_result, info)
+
+    # Build a list of operations to run
+    facefix_ops: List[Callable] = []
+    facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
+    facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
+
+    upscale_ops: List[Callable] = []
+    upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
+
+    if upscaling_resize != 0:
+        step_params: List[UpscaleParams] = []
+        step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
+        if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
+            step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
+
+        upscale_ops.append(partial(run_upscalers_blend, step_params))
+
+    extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
+
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
@@ -64,64 +181,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
         image = image.convert("RGB")
         info = ""
+        # Run each operation on each image
+        for op in extras_ops:
+            image, info = op(image, info)
 
-        if gfpgan_visibility > 0:
-            restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
-            res = Image.fromarray(restored_img)
-
-            if gfpgan_visibility < 1.0:
-                res = Image.blend(image, res, gfpgan_visibility)
-
-            info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
-            image = res
-
-        if codeformer_visibility > 0:
-            restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
-            res = Image.fromarray(restored_img)
-
-            if codeformer_visibility < 1.0:
-                res = Image.blend(image, res, codeformer_visibility)
-
-            info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
-            image = res
-
-        if resize_mode == 1:
-            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
-            crop_info = " (crop)" if upscaling_crop else ""
-            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
-
-        if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
-                small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
-                pixels = tuple(np.array(small).flatten().tolist())
-                key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight, 
-                       resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels
-
-                c = cached_images.get(key)
-                if c is None:
-                    upscaler = shared.sd_upscalers[scaler_index]
-                    c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-                    if mode == 1 and crop:
-                        cropped = Image.new("RGB", (resize_w, resize_h))
-                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
-                        c = cropped
-                    cached_images[key] = c
-
-                return c
-
-            info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-
-            if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
-                info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
-                res = Image.blend(res, res2, extras_upscaler_2_visibility)
-
-            image = res
-
-        while len(cached_images) > 2:
-            del cached_images[next(iter(cached_images.keys()))]
-        
         if opts.use_original_name_batch and image_name != None:
             basename = os.path.splitext(os.path.basename(image_name))[0]
         else:
@@ -141,6 +204,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
 
     return outputs, plaintext_to_html(info), ''
 
+def clear_cache():
+    cached_images.clear()
+
 
 def run_pnginfo(image):
     if image is None:

modules/generation_parameters_copypaste.py

@@ -1,14 +1,20 @@
+import base64
+import io
 import os
 import re
 import gradio as gr
 from modules.shared import script_path
 from modules import shared
+import tempfile
+from PIL import Image, PngImagePlugin
 
 re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)'
 re_param = re.compile(re_param_code)
 re_params = re.compile(r"^(?:" + re_param_code + "){3,}$")
 re_imagesize = re.compile(r"^(\d+)x(\d+)$")
 type_of_gr_update = type(gr.update())
+paste_fields = {}
+bind_list = []
 
 
 def quote(text):
@@ -20,6 +26,94 @@ def quote(text):
     text = text.replace('"', '\\"')
     return f'"{text}"'
 
+
+def image_from_url_text(filedata):
+    if type(filedata) == dict and filedata["is_file"]:
+        filename = filedata["name"]
+        tempdir = os.path.normpath(tempfile.gettempdir())
+        normfn = os.path.normpath(filename)
+        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+
+        return Image.open(filename)
+
+    if type(filedata) == list:
+        if len(filedata) == 0:
+            return None
+
+        filedata = filedata[0]
+
+    if filedata.startswith("data:image/png;base64,"):
+        filedata = filedata[len("data:image/png;base64,"):]
+
+    filedata = base64.decodebytes(filedata.encode('utf-8'))
+    image = Image.open(io.BytesIO(filedata))
+    return image
+
+
+def add_paste_fields(tabname, init_img, fields):
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields}
+
+    # backwards compatibility for existing extensions
+    import modules.ui
+    if tabname == 'txt2img':
+        modules.ui.txt2img_paste_fields = fields
+    elif tabname == 'img2img':
+        modules.ui.img2img_paste_fields = fields
+
+
+def create_buttons(tabs_list):
+    buttons = {}
+    for tab in tabs_list:
+        buttons[tab] = gr.Button(f"Send to {tab}")
+    return buttons
+
+
+#if send_generate_info is a tab name, mean generate_info comes from the params fields of the tab
+def bind_buttons(buttons, send_image, send_generate_info):
+    bind_list.append([buttons, send_image, send_generate_info])
+
+
+def run_bind():
+    for buttons, send_image, send_generate_info in bind_list:
+        for tab in buttons:
+            button = buttons[tab]
+            if send_image and paste_fields[tab]["init_img"]:
+                if type(send_image) == gr.Gallery:
+                    button.click(
+                        fn=lambda x: image_from_url_text(x),
+                        _js="extract_image_from_gallery",
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+                else:
+                    button.click(
+                        fn=lambda x:x,
+                        inputs=[send_image],
+                        outputs=[paste_fields[tab]["init_img"]],
+                    )
+
+            if send_generate_info and paste_fields[tab]["fields"] is not None:
+                paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2']
+                if shared.opts.send_seed:
+                    paste_field_names += ["Seed"]
+                if send_generate_info in paste_fields:
+                    button.click(
+                        fn=lambda *x:x,
+                        inputs=[field for field,name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
+                        outputs=[field for field,name in paste_fields[tab]["fields"] if name in paste_field_names],
+                    )
+
+                else:
+                    connect_paste(button, [(field, name) for field, name in paste_fields[tab]["fields"]  if name in paste_field_names], send_generate_info)
+
+            button.click(
+                fn=None,
+                _js=f"switch_to_{tab}",
+                inputs=None,
+                outputs=None,
+            )
+
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -68,7 +162,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
     return res
 
 
-def connect_paste(button, paste_fields, input_comp, js=None):
+def connect_paste(button, paste_fields, input_comp, jsfunc=None):
     def paste_func(prompt):
         if not prompt and not shared.cmd_opts.hide_ui_dir_config:
             filename = os.path.join(script_path, "params.txt")
@@ -106,7 +200,9 @@ def connect_paste(button, paste_fields, input_comp, js=None):
 
     button.click(
         fn=paste_func,
-        _js=js,
+        _js=jsfunc,
         inputs=[input_comp],
         outputs=[x[0] for x in paste_fields],
     )
+
+

modules/hypernetworks/hypernetwork.py

@@ -25,6 +25,7 @@ from statistics import stdev, mean
 class HypernetworkModule(torch.nn.Module):
     multiplier = 1.0
     activation_dict = {
+        "linear": torch.nn.Identity,
         "relu": torch.nn.ReLU,
         "leakyrelu": torch.nn.LeakyReLU,
         "elu": torch.nn.ELU,
@@ -431,7 +432,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 
             optimizer.step()
 
-        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]):
+        steps_done = hypernetwork.step + 1
+
+        if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): 
             raise RuntimeError("Loss diverged.")
         
         if len(previous_mean_losses) > 1:
@@ -441,9 +444,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
         dataset_loss_info = f"dataset loss:{mean(previous_mean_losses):.3f}" + u"\u00B1" + f"({std / (len(previous_mean_losses) ** 0.5):.3f})"
         pbar.set_description(dataset_loss_info)
 
-        if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
+        if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
             # Before saving, change name to match current checkpoint.
-            hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
+            hypernetwork.name = f'{hypernetwork_name}-{steps_done}'
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
             hypernetwork.save(last_saved_file)
 
@@ -452,8 +455,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
             "learn_rate": scheduler.learn_rate
         })
 
-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{hypernetwork_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
 
             optimizer.zero_grad()

modules/hypernetworks/ui.py

@@ -8,7 +8,8 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack, shared
 from modules.hypernetworks import hypernetwork
 
-keys = list(hypernetwork.HypernetworkModule.activation_dict.keys())
+not_available = ["hardswish", "multiheadattention"]
+keys = ["linear"] + 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.

modules/images.py

@@ -300,8 +300,8 @@ class FilenameGenerator:
         'seed': lambda self: self.seed if self.seed is not None else '',
         'steps': lambda self:  self.p and self.p.steps,
         'cfg': lambda self: self.p and self.p.cfg_scale,
-        'width': lambda self: self.p and self.p.width,
-        'height': lambda self: self.p and self.p.height,
+        'width': lambda self: self.image.width,
+        'height': lambda self: self.image.height,
         'styles': lambda self: self.p and sanitize_filename_part(", ".join([style for style in self.p.styles if not style == "None"]) or "None", replace_spaces=False),
         'sampler': lambda self: self.p and sanitize_filename_part(sd_samplers.samplers[self.p.sampler_index].name, replace_spaces=False),
         'model_hash': lambda self: getattr(self.p, "sd_model_hash", shared.sd_model.sd_model_hash),
@@ -315,10 +315,11 @@ class FilenameGenerator:
     }
     default_time_format = '%Y%m%d%H%M%S'
 
-    def __init__(self, p, seed, prompt):
+    def __init__(self, p, seed, prompt, image):
         self.p = p
         self.seed = seed
         self.prompt = prompt
+        self.image = image
 
     def prompt_no_style(self):
         if self.p is None or self.prompt is None:
@@ -449,7 +450,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
         txt_fullfn (`str` or None):
             If a text file is saved for this image, this will be its full path. Otherwise None.
     """
-    namegen = FilenameGenerator(p, seed, prompt)
+    namegen = FilenameGenerator(p, seed, prompt, image)
 
     if save_to_dirs is None:
         save_to_dirs = (grid and opts.grid_save_to_dirs) or (not grid and opts.save_to_dirs and not no_prompt)

modules/img2img.py

@@ -19,7 +19,7 @@ import modules.scripts
 def process_batch(p, input_dir, output_dir, args):
     processing.fix_seed(p)
 
-    images = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+    images = shared.listfiles(input_dir)
 
     print(f"Will process {len(images)} images, creating {p.n_iter * p.batch_size} new images for each.")
 

modules/processing.py

@@ -129,6 +129,73 @@ class StableDiffusionProcessing():
         self.all_seeds = None
         self.all_subseeds = None
 
+    def txt2img_image_conditioning(self, x, width=None, height=None):
+        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
+            # 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
+            )
+
+        height = height or self.height
+        width = width or self.width
+
+        # The "masked-image" in this case will just be all zeros since the entire image is masked.
+        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
+
+        # Add the fake full 1s mask to the first dimension.
+        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+        image_conditioning = image_conditioning.to(x.dtype)            
+
+        return image_conditioning
+
+    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
+            )
+
+        # Handle the different mask inputs
+        if image_mask is not None:
+            if torch.is_tensor(image_mask):
+                conditioning_mask = image_mask
+            else:
+                conditioning_mask = np.array(image_mask.convert("L"))
+                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
+                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
+
+                # 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:])
+
+        # 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_image = torch.lerp(
+            source_image,
+            source_image * (1.0 - conditioning_mask),
+            getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
+        )
+        
+        # Encode the new masked image using first stage of network.
+        conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
+
+        # Create the concatenated conditioning tensor to be fed to `c_concat`
+        conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
+        conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
+        image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
+        image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
+
+        return image_conditioning
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         pass
 
@@ -572,37 +639,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
-    def create_dummy_mask(self, x, width=None, height=None):
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            height = height or self.height
-            width = width or self.width
-
-            # The "masked-image" in this case will just be all zeros since the entire image is masked.
-            image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-            image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) 
-
-            # Add the fake full 1s mask to the first dimension.
-            image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-            image_conditioning = image_conditioning.to(x.dtype)
-
-        else:
-            # 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.
-            image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
-
-        return image_conditioning
-
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
         self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
 
         if not self.enable_hr:
             x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x))
+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
         x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height))
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
@@ -639,7 +685,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples))
+        image_conditioning = self.img2img_image_conditioning(
+            decoded_samples, 
+            samples, 
+            decoded_samples.new_ones(decoded_samples.shape[0], 1, decoded_samples.shape[2], decoded_samples.shape[3])
+        )
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
 
         return samples
 
@@ -771,33 +822,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            if self.image_mask is not None:
-                conditioning_mask = np.array(self.image_mask.convert("L"))
-                conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
-                conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
-
-                # 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, *image.shape[-2:])
-
-            # Create another latent image, this time with a masked version of the original input.
-            conditioning_mask = conditioning_mask.to(image.device)
-            conditioning_image = image * (1.0 - conditioning_mask)
-            conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
-
-            # Create the concatenated conditioning tensor to be fed to `c_concat`
-            conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:])
-            conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
-            self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
-            self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
-        else:
-            self.image_conditioning = torch.zeros(
-                self.init_latent.shape[0], 5, 1, 1,
-                dtype=self.init_latent.dtype,
-                device=self.init_latent.device
-            )
+        self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask)
 
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

modules/scripts.py

@@ -236,7 +236,7 @@ class ScriptRunner:
             with gr.Group():
                 create_script_ui(script, inputs, inputs_alwayson)
 
-        dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index")
+        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
 

modules/sd_models.py

@@ -3,6 +3,7 @@ import os.path
 import sys
 from collections import namedtuple
 import torch
+import re
 from omegaconf import OmegaConf
 
 from ldm.util import instantiate_from_config
@@ -35,8 +36,10 @@ def setup_model():
     list_models()
 
 
-def checkpoint_tiles():
-    return sorted([x.title for x in checkpoints_list.values()])
+def checkpoint_tiles(): 
+    convert = lambda name: int(name) if name.isdigit() else name.lower() 
+    alphanumeric_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] 
+    return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
 def list_models():
@@ -170,7 +173,9 @@ def load_model_weights(model, checkpoint_info):
             print(f"Global Step: {pl_sd['global_step']}")
 
         sd = get_state_dict_from_checkpoint(pl_sd)
-        missing, extra = model.load_state_dict(sd, strict=False)
+        del pl_sd
+        model.load_state_dict(sd, strict=False)
+        del sd
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -194,9 +199,10 @@ def load_model_weights(model, checkpoint_info):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-            checkpoints_loaded.popitem(last=False)  # LRU
+        if shared.opts.sd_checkpoint_cache > 0:
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
+            while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
+                checkpoints_loaded.popitem(last=False)  # LRU
     else:
         print(f"Loading weights [{sd_model_hash}] from cache")
         checkpoints_loaded.move_to_end(checkpoint_info)

modules/shared.py

@@ -82,6 +82,7 @@ parser.add_argument("--api", action='store_true', help="use api=True to launch t
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
+parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False)
 
 cmd_opts = parser.parse_args()
 restricted_opts = {
@@ -267,6 +268,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
+    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
     "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
     "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
@@ -303,6 +305,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
     "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"),
     "disable_weights_auto_swap": OptionInfo(False, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."),
+    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
     "font": OptionInfo("", "Font for image grids that have text"),
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
@@ -449,3 +452,8 @@ total_tqdm = TotalTQDM()
 
 mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
 mem_mon.start()
+
+
+def listfiles(dirname):
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname)) if not x.startswith(".")]
+    return [file for file in filenames if os.path.isfile(file)]

modules/textual_inversion/dataset.py

@@ -86,12 +86,12 @@ class PersonalizedBase(Dataset):
         assert len(self.dataset) > 0, "No images have been found in the dataset."
         self.length = len(self.dataset) * repeats // batch_size
 
-        self.initial_indexes = np.arange(len(self.dataset))
+        self.dataset_length = len(self.dataset)
         self.indexes = None
         self.shuffle()
 
     def shuffle(self):
-        self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()]
+        self.indexes = np.random.permutation(self.dataset_length)
 
     def create_text(self, filename_text):
         text = random.choice(self.lines)

modules/textual_inversion/learn_schedule.py

@@ -52,7 +52,7 @@ class LearnRateScheduler:
         self.finished = False
 
     def apply(self, optimizer, step_number):
-        if step_number <= self.end_step:
+        if step_number < self.end_step:
             return
 
         try:

modules/textual_inversion/textual_inversion.py

@@ -184,9 +184,8 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:
         return
 
-    if step % shared.opts.training_write_csv_every != 0:
+    if (step + 1) % shared.opts.training_write_csv_every != 0:
         return
-
     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True
 
     with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
@@ -196,11 +195,11 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()
 
         epoch = step // epoch_len
-        epoch_step = step - epoch * epoch_len
+        epoch_step = step % epoch_len 
 
         csv_writer.writerow({
             "step": step + 1,
-            "epoch": epoch + 1,
+            "epoch": epoch,
             "epoch_step": epoch_step + 1,
             **values,
         })
@@ -282,15 +281,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             loss.backward()
             optimizer.step()
 
+        steps_done = embedding.step + 1
 
         epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step - (epoch_num * len(ds)) + 1
+        epoch_step = embedding.step % len(ds)
 
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
 
-        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
+        if embedding_dir is not None and steps_done % save_embedding_every == 0:
             # Before saving, change name to match current checkpoint.
-            embedding.name = f'{embedding_name}-{embedding.step}'
+            embedding.name = f'{embedding_name}-{steps_done}'
             last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')
             embedding.save(last_saved_file)
             embedding_yet_to_be_embedded = True
@@ -300,8 +300,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             "learn_rate": scheduler.learn_rate
         })
 
-        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
-            forced_filename = f'{embedding_name}-{embedding.step}'
+        if images_dir is not None and steps_done % create_image_every == 0:
+            forced_filename = f'{embedding_name}-{steps_done}'
             last_saved_image = os.path.join(images_dir, forced_filename)
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
@@ -334,7 +334,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
 
                 info = PngImagePlugin.PngInfo()
                 data = torch.load(last_saved_file)
@@ -350,7 +350,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}v {}s'.format(vectorSize, embedding.step)
+                footer_right = '{}v {}s'.format(vectorSize, steps_done)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)

modules/ui.py

@@ -1,6 +1,4 @@
-import base64
 import html
-import io
 import json
 import math
 import mimetypes
@@ -18,13 +16,8 @@ import gradio as gr
 import gradio.routes
 import gradio.utils
 import numpy as np
-import piexif
-import torch
 from PIL import Image, PngImagePlugin
 
-import gradio as gr
-import gradio.utils
-import gradio.routes
 
 from modules import sd_hijack, sd_models, localization, script_callbacks
 from modules.paths import script_path
@@ -35,7 +28,7 @@ if cmd_opts.deepdanbooru:
     from modules.deepbooru import get_deepbooru_tags
 
 import modules.codeformer_model
-import modules.generation_parameters_copypaste
+import modules.generation_parameters_copypaste as parameters_copypaste
 import modules.gfpgan_model
 import modules.hypernetworks.ui
 import modules.ldsr_model
@@ -49,13 +42,11 @@ from modules.sd_hijack import model_hijack
 from modules.sd_samplers import samplers, samplers_for_img2img
 import modules.textual_inversion.ui
 import modules.hypernetworks.ui
+from modules.generation_parameters_copypaste import image_from_url_text
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
 mimetypes.add_type('application/javascript', '.js')
-txt2img_paste_fields = []
-img2img_paste_fields = []
-
 
 if not cmd_opts.share and not cmd_opts.listen:
     # fix gradio phoning home
@@ -98,37 +89,11 @@ def plaintext_to_html(text):
     text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
     return text
 
-
-def image_from_url_text(filedata):
-    if type(filedata) == dict and filedata["is_file"]:
-        filename = filedata["name"]
-        tempdir = os.path.normpath(tempfile.gettempdir())
-        normfn = os.path.normpath(filename)
-        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
-
-        return Image.open(filename)
-
-    if type(filedata) == list:
-        if len(filedata) == 0:
-            return None
-
-        filedata = filedata[0]
-
-    if filedata.startswith("data:image/png;base64,"):
-        filedata = filedata[len("data:image/png;base64,"):]
-
-    filedata = base64.decodebytes(filedata.encode('utf-8'))
-    image = Image.open(io.BytesIO(filedata))
-    return image
-
-
 def send_gradio_gallery_to_image(x):
     if len(x) == 0:
         return None
-
     return image_from_url_text(x[0])
 
-
 def save_files(js_data, images, do_make_zip, index):
     import csv
     filenames = []
@@ -192,7 +157,6 @@ def save_files(js_data, images, do_make_zip, index):
 
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
-
 def save_pil_to_file(pil_image, dir=None):
     use_metadata = False
     metadata = PngImagePlugin.PngInfo()
@@ -625,6 +589,82 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
     )
     return refresh_button
 
+def create_output_panel(tabname, outdir):
+    def open_folder(f):
+        if not os.path.exists(f):
+            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
+            return
+        elif not os.path.isdir(f):
+            print(f"""
+WARNING
+An open_folder request was made with an argument that is not a folder.
+This could be an error or a malicious attempt to run code on your computer.
+Requested path was: {f}
+""", file=sys.stderr)
+            return
+
+        if not shared.cmd_opts.hide_ui_dir_config:
+            path = os.path.normpath(f)
+            if platform.system() == "Windows":
+                os.startfile(path)
+            elif platform.system() == "Darwin":
+                sp.Popen(["open", path])
+            else:
+                sp.Popen(["xdg-open", path])
+
+    with gr.Column(variant='panel'):
+            with gr.Group():
+                result_gallery = gr.Gallery(label='Output', show_label=False, elem_id=f"{tabname}_gallery").style(grid=4)
+
+            generation_info = None
+            with gr.Column():
+                with gr.Row():
+                    if tabname != "extras":
+                        save = gr.Button('Save', elem_id=f'save_{tabname}')
+
+                    buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
+                    button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
+                    open_folder_button = gr.Button(folder_symbol, elem_id=button_id)
+
+                open_folder_button.click(
+                    fn=lambda: open_folder(opts.outdir_samples or outdir),
+                    inputs=[],
+                    outputs=[],
+                )
+
+                if tabname != "extras":
+                    with gr.Row():
+                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
+
+                    with gr.Row():
+                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
+
+                    with gr.Group():
+                        html_info = gr.HTML()
+                        generation_info = gr.Textbox(visible=False)
+
+                        save.click(
+                            fn=wrap_gradio_call(save_files),
+                            _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
+                            inputs=[
+                                generation_info,
+                                result_gallery,
+                                do_make_zip,
+                                html_info,
+                            ],
+                            outputs=[
+                                download_files,
+                                html_info,
+                                html_info,
+                                html_info,
+                            ]
+                        )
+                else:
+                    html_info_x = gr.HTML()
+                    html_info = gr.HTML()
+                parameters_copypaste.bind_buttons(buttons, result_gallery, "txt2img" if tabname == "txt2img" else None)
+                return result_gallery, generation_info if tabname != "extras" else html_info_x, html_info
+
 
 def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
@@ -675,30 +715,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
-                    txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        send_to_img2img = gr.Button('Send to img2img')
-                        send_to_inpaint = gr.Button('Send to inpaint')
-                        send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+            txt2img_gallery, generation_info, html_info = create_output_panel("txt2img", opts.outdir_txt2img_samples)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -756,23 +773,6 @@ def create_ui(wrap_gradio_gpu_call):
                 outputs=[hr_options],
             )
 
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    txt2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
-            )
-
             roll.click(
                 fn=roll_artist,
                 _js="update_txt2img_tokens",
@@ -784,8 +784,7 @@ def create_ui(wrap_gradio_gpu_call):
                 ]
             )
 
-            global txt2img_paste_fields 
-            txt2img_paste_fields = [
+            parameters_copypaste.add_paste_fields("txt2img", None, [
                 (txt2img_prompt, "Prompt"),
                 (txt2img_negative_prompt, "Negative prompt"),
                 (steps, "Steps"),
@@ -806,7 +805,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_width, "First pass size-1"),
                 (firstphase_height, "First pass size-2"),
                 *modules.scripts.scripts_txt2img.infotext_fields
-            ]
+            ])
 
             txt2img_preview_params = [
                 txt2img_prompt,
@@ -893,30 +892,7 @@ def create_ui(wrap_gradio_gpu_call):
                 with gr.Group():
                     custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
 
-            with gr.Column(variant='panel'):
-
-                with gr.Group():
-                    img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
-                    img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
-
-                with gr.Column():
-                    with gr.Row():
-                        save = gr.Button('Save')
-                        img2img_send_to_img2img = gr.Button('Send to img2img')
-                        img2img_send_to_inpaint = gr.Button('Send to inpaint')
-                        img2img_send_to_extras = gr.Button('Send to extras')
-                        button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
-                        open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
-
-                    with gr.Row():
-                        do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
-                    with gr.Row():
-                        download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
-
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+            img2img_gallery, generation_info, html_info = create_output_panel("img2img", opts.outdir_img2img_samples)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -1003,25 +979,9 @@ def create_ui(wrap_gradio_gpu_call):
                     fn=interrogate_deepbooru,
                     inputs=[init_img],
                     outputs=[img2img_prompt],
-                )
-
-            save.click(
-                fn=wrap_gradio_call(save_files),
-                _js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
-                inputs=[
-                    generation_info,
-                    img2img_gallery,
-                    do_make_zip,
-                    html_info,
-                ],
-                outputs=[
-                    download_files,
-                    html_info,
-                    html_info,
-                    html_info,
-                ]
             )
 
+
             roll.click(
                 fn=roll_artist,
                 _js="update_img2img_tokens",
@@ -1055,7 +1015,8 @@ def create_ui(wrap_gradio_gpu_call):
                     outputs=[prompt, negative_prompt, style1, style2],
                 )
 
-            global img2img_paste_fields
+            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+
             img2img_paste_fields = [
                 (img2img_prompt, "Prompt"),
                 (img2img_negative_prompt, "Negative prompt"),
@@ -1074,7 +1035,9 @@ def create_ui(wrap_gradio_gpu_call):
                 (denoising_strength, "Denoising strength"),
                 *modules.scripts.scripts_img2img.infotext_fields
             ]
-            token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
+            parameters_copypaste.add_paste_fields("img2img", init_img, img2img_paste_fields)
+            parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask, img2img_paste_fields)
+
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
@@ -1104,9 +1067,9 @@ def create_ui(wrap_gradio_gpu_call):
                                 upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
                                 upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
                             upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
-                
+
                 with gr.Group():
-                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") 
+                    extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
                 with gr.Group():
                     extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -1119,17 +1082,13 @@ def create_ui(wrap_gradio_gpu_call):
                     codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
                     codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
 
+                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')
 
-            with gr.Column(variant='panel'):
-                result_images = gr.Gallery(label="Result", show_label=False)
-                html_info_x = gr.HTML()
-                html_info = gr.HTML()
-                extras_send_to_img2img = gr.Button('Send to img2img')
-                extras_send_to_inpaint = gr.Button('Send to inpaint')
-                button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
-                open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
 
+            result_images, html_info_x, html_info = create_output_panel("extras", opts.outdir_extras_samples)
 
         submit.click(
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
@@ -1152,6 +1111,7 @@ def create_ui(wrap_gradio_gpu_call):
                 extras_upscaler_1,
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
+                upscale_before_face_fix,
             ],
             outputs=[
                 result_images,
@@ -1159,19 +1119,12 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
+        parameters_copypaste.add_paste_fields("extras", extras_image, None)
 
-        extras_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[result_images],
-            outputs=[init_img],
-        )
 
-        extras_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[result_images],
-            outputs=[init_img_with_mask],
+        extras_image.change(
+            fn=modules.extras.clear_cache,
+            inputs=[], outputs=[]
         )
 
     with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
@@ -1183,17 +1136,16 @@ def create_ui(wrap_gradio_gpu_call):
                 html = gr.HTML()
                 generation_info = gr.Textbox(visible=False)
                 html2 = gr.HTML()
-
                 with gr.Row():
-                    pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
-                    pnginfo_send_to_img2img = gr.Button('Send to img2img')
+                    buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
+                parameters_copypaste.bind_buttons(buttons, image, generation_info)
 
         image.change(
             fn=wrap_gradio_call(modules.extras.run_pnginfo),
             inputs=[image],
             outputs=[html, generation_info, html2],
         )
-   
+
     with gr.Blocks() as modelmerger_interface:
         with gr.Row().style(equal_height=False):
             with gr.Column(variant='panel'):
@@ -1238,7 +1190,7 @@ 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="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
+                    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_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
@@ -1491,28 +1443,6 @@ def create_ui(wrap_gradio_gpu_call):
     script_callbacks.ui_settings_callback()
     opts.reorder()
 
-    def open_folder(f):
-        if not os.path.exists(f):
-            print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
-            return
-        elif not os.path.isdir(f):
-            print(f"""
-WARNING
-An open_folder request was made with an argument that is not a folder.
-This could be an error or a malicious attempt to run code on your computer.
-Requested path was: {f}
-""", file=sys.stderr)
-            return
-
-        if not shared.cmd_opts.hide_ui_dir_config:
-            path = os.path.normpath(f)
-            if platform.system() == "Windows":
-                os.startfile(path)
-            elif platform.system() == "Darwin":
-                sp.Popen(["open", path])
-            else:
-                sp.Popen(["xdg-open", path])
-
     def run_settings(*args):
         changed = 0
 
@@ -1657,6 +1587,9 @@ Requested path was: {f}
         if column is not None:
             column.__exit__()
 
+
+
+
     interfaces = [
         (txt2img_interface, "txt2img", "txt2img"),
         (img2img_interface, "img2img", "img2img"),
@@ -1747,68 +1680,7 @@ Requested path was: {f}
                 component_dict['sd_model_checkpoint'],
             ]
         )
-        paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
-        txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
-        img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
-        send_to_img2img.click(
-            fn=lambda img, *args: (image_from_url_text(img),*args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img] + img2img_fields,
-        )
 
-        send_to_inpaint.click(
-            fn=lambda x, *args: (image_from_url_text(x), *args),
-            _js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]",
-            inputs=[txt2img_gallery] + txt2img_fields,
-            outputs=[init_img_with_mask] + img2img_fields,
-        )
-
-        img2img_send_to_img2img.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_img2img",
-            inputs=[img2img_gallery],
-            outputs=[init_img],
-        )
-
-        img2img_send_to_inpaint.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_inpaint",
-            inputs=[img2img_gallery],
-            outputs=[init_img_with_mask],
-        )
-
-        send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[txt2img_gallery],
-            outputs=[extras_image],
-        )
-
-        open_txt2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_img2img_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        open_extras_folder.click(
-            fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
-            inputs=[],
-            outputs=[],
-        )
-
-        img2img_send_to_extras.click(
-            fn=lambda x: image_from_url_text(x),
-            _js="extract_image_from_gallery_extras",
-            inputs=[img2img_gallery],
-            outputs=[extras_image],
-        )
 
         settings_map = {
             'sd_hypernetwork': 'Hypernet',
@@ -1817,16 +1689,7 @@ Requested path was: {f}
             'sd_model_checkpoint': 'Model hash',
         }
 
-        settings_paste_fields = [
-            (component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
-            for k, v in settings_map.items()
-        ]
-
-        modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
-        modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
-
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
-        modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
+        parameters_copypaste.run_bind()
 
     ui_config_file = cmd_opts.ui_config_file
     ui_settings = {}
@@ -1906,7 +1769,7 @@ def load_javascript(raw_response):
         javascript = f'<script>{jsfile.read()}</script>'
 
     scripts_list = modules.scripts.list_scripts("javascript", ".js")
-    
+
     for basedir, filename, path in scripts_list:
         with open(path, "r", encoding="utf8") as jsfile:
             javascript += f"\n<!-- {filename} --><script>{jsfile.read()}</script>"