Merge branch 'master' into gradient-clipping

16 files changed, 581 insertions, 462 deletions

diff --git a/modules/api/api.py b/modules/api/api.py
index 6e9d6097..49c213ea 100644
--- a/modules/api/api.py
+++ b/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
-
-sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
+from modules.extras import run_extras
 
-class TextToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+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])}")
 
-class ImageToImageResponse(BaseModel):
-    images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.")
-    parameters: Json
-    info: Json
+sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None)
 
+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
 
diff --git a/modules/api/models.py b/modules/api/models.py
index 079e33d9..dd122321 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -1,10 +1,10 @@
-from array import array
-from inflection import underscore
-from typing import Any, Dict, Optional
+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
-import inspect
-
+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()
\ No newline at end of file
+).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.")
\ No newline at end of file
diff --git a/modules/extras.py b/modules/extras.py
index 22c5a1c1..4d51088b 100644
--- a/modules/extras.py
+++ b/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)

+

 

+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):

+

+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)

@@ -56,72 +89,102 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     else:

         outpath = opts.outdir_samples or opts.outdir_extras_samples

 

-    

-    for image, image_name in zip(imageArr, imageNameArr):

-        if image is None:

-            return outputs, "Please select an input image.", ''

-        existing_pnginfo = image.info or {}

+    # Extra operation definitions

 

-        image = image.convert("RGB")

-        info = ""

+    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 > 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)

 

-            if gfpgan_visibility < 1.0:

-                res = Image.blend(image, res, gfpgan_visibility)

+        info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"

+        return (res, info)

 

-            info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"

-            image = res

+    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 > 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)

 

-            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)

 

-            info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"

-            image = res

+    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.", ''

+        existing_pnginfo = image.info or {}

+

+        image = image.convert("RGB")

+        info = ""

+        # Run each operation on each image

+        for op in extras_ops:

+            image, info = op(image, info)

 

-        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:

diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index f73647da..bbaad42e 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/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

 

 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 +26,110 @@ 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 integrate_settings_paste_fields(component_dict):

+    from modules import ui

+

+    settings_map = {

+        'sd_hypernetwork': 'Hypernet',

+        'CLIP_stop_at_last_layers': 'Clip skip',

+        'sd_model_checkpoint': 'Model hash',

+    }

+    settings_paste_fields = [

+        (component_dict[k], lambda d, k=k, v=v: ui.apply_setting(k, d.get(v, None)))

+        for k, v in settings_map.items()

+    ]

+

+    for tabname, info in paste_fields.items():

+        if info["fields"] is not None:

+            info["fields"] += settings_paste_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:

+                if send_generate_info in paste_fields:

+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Size-1', 'Size-2'] + (["Seed"] if shared.opts.send_seed else [])

+

+                    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, paste_fields[tab]["fields"], 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 +178,7 @@ 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 +216,9 @@ 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],

     )

+

+

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 86532063..f45ce199 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/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,

@@ -443,7 +444,9 @@ 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:

@@ -453,9 +456,9 @@ 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)

 

@@ -464,8 +467,8 @@ 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()

diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 2c6c0470..aad09ffc 100644
--- a/modules/hypernetworks/ui.py
+++ b/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.

diff --git a/modules/images.py b/modules/images.py
index 7870b5b7..a0728553 100644
--- a/modules/images.py
+++ b/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)

diff --git a/modules/img2img.py b/modules/img2img.py
index 9c0cf23e..efda26e1 100644
--- a/modules/img2img.py
+++ b/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.")

 

diff --git a/modules/processing.py b/modules/processing.py
index 4efba946..548eec29 100644
--- a/modules/processing.py
+++ b/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

 

@@ -571,37 +638,16 @@ 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]

 

@@ -638,7 +684,12 @@ 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

 

@@ -770,33 +821,7 @@ 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):

diff --git a/modules/scripts.py b/modules/scripts.py
index 9323af3e..a7f36012 100644
--- a/modules/scripts.py
+++ b/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

 

diff --git a/modules/sd_models.py b/modules/sd_models.py
index e697bb72..f86dc3ed 100644
--- a/modules/sd_models.py
+++ b/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)

diff --git a/modules/shared.py b/modules/shared.py
index 1a9b8289..fb84afd8 100644
--- a/modules/shared.py
+++ b/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)]

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 5b1c5002..8bb00d27 100644
--- a/modules/textual_inversion/dataset.py
+++ b/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)

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index ffec3e1b..2627d585 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -52,7 +52,7 @@ class LearnRateScheduler:
         self.finished = False

 

     def step(self, step_number):

-        if step_number <= self.end_step:

+        if step_number < self.end_step:

             return False

 

         try:

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 6b00c6a1..f272e536 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/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,

         })

@@ -297,15 +296,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 

             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

@@ -315,8 +315,8 @@ 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,

@@ -349,7 +349,7 @@ 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)

@@ -365,7 +365,7 @@ 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)

diff --git a/modules/ui.py b/modules/ui.py
index 47d16429..98f9565f 100644
--- a/modules/ui.py
+++ b/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()

@@ -626,6 +590,83 @@ 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

     import modules.txt2img

@@ -675,30 +716,8 @@ 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)

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

 

             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 +775,6 @@ 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",

@@ -784,7 +786,6 @@ def create_ui(wrap_gradio_gpu_call):
                 ]

             )

 

-            global txt2img_paste_fields 

             txt2img_paste_fields = [

                 (txt2img_prompt, "Prompt"),

                 (txt2img_negative_prompt, "Negative prompt"),

@@ -807,6 +808,7 @@ def create_ui(wrap_gradio_gpu_call):
                 (firstphase_height, "First pass size-2"),

                 *modules.scripts.scripts_txt2img.infotext_fields

             ]

+            parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)

 

             txt2img_preview_params = [

                 txt2img_prompt,

@@ -893,30 +895,8 @@ 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)

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

 

             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 +983,9 @@ 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 +1019,8 @@ 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 +1039,8 @@ 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):

@@ -1087,12 +1053,8 @@ def create_ui(wrap_gradio_gpu_call):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")

 

                     with gr.TabItem('Batch from Directory'):

-                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,

-                            placeholder="A directory on the same machine where the server is running."

-                        )

-                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,

-                            placeholder="Leave blank to save images to the default path."

-                        )

+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.")

+                        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)

 

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

@@ -1104,9 +1066,9 @@ 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 +1081,12 @@ 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)

 

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

+                with gr.Group():

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

 

-            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)

+                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(

             fn=wrap_gradio_gpu_call(modules.extras.run_extras),

@@ -1152,6 +1109,7 @@ 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 +1117,11 @@ 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 +1133,16 @@ 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 +1187,7 @@ 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")

@@ -1497,28 +1446,6 @@ 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

 

@@ -1672,10 +1599,6 @@ Requested path was: {f}
         (train_interface, "Train", "ti"),

     ]

 

-    interfaces += script_callbacks.ui_tabs_callback()

-

-    interfaces += [(settings_interface, "Settings", "settings")]

-

     css = ""

 

     for cssfile in modules.scripts.list_files_with_name("style.css"):

@@ -1692,6 +1615,9 @@ Requested path was: {f}
     if not cmd_opts.no_progressbar_hiding:

         css += css_hide_progressbar

 

+    interfaces += script_callbacks.ui_tabs_callback()

+    interfaces += [(settings_interface, "Settings", "settings")]

+

     with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:

         with gr.Row(elem_id="quicksettings"):

             for i, k, item in quicksettings_list:

@@ -1700,6 +1626,9 @@ Requested path was: {f}
 

         settings_interface.gradio_ref = demo

 

+        parameters_copypaste.integrate_settings_paste_fields(component_dict)

+        parameters_copypaste.run_bind()

+

         with gr.Tabs(elem_id="tabs") as tabs:

             for interface, label, ifid in interfaces:

                 with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):

@@ -1753,85 +1682,6 @@ 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',

-            'CLIP_stop_at_last_layers': 'Clip skip',

-            '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')

 

     ui_config_file = cmd_opts.ui_config_file

     ui_settings = {}

@@ -1851,7 +1701,7 @@ Requested path was: {f}
         def apply_field(obj, field, condition=None, init_field=None):

             key = path + "/" + field

 

-            if getattr(obj,'custom_script_source',None) is not None:

+            if getattr(obj, 'custom_script_source', None) is not None:

               key = 'customscript/' + obj.custom_script_source + '/' + key

 

             if getattr(obj, 'do_not_save_to_config', False):

@@ -1911,7 +1761,7 @@ 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>"