mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2025-12-24 19:01:06 -08:00
Merge branch 'AUTOMATIC1111:master' into master
This commit is contained in:
InvincibleDude
GitHub
commit
44c0e6b993
39 changed files with 1199 additions and 676 deletions
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@ -11,10 +11,9 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
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from secrets import compare_digest
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import modules.shared as shared
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from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui
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from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
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from modules.api.models import *
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from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
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from modules.extras import run_extras
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from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
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from modules.textual_inversion.preprocess import preprocess
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from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
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@ -23,6 +22,8 @@ from modules.sd_models import checkpoints_list, find_checkpoint_config
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from modules.realesrgan_model import get_realesrgan_models
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from modules import devices
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from typing import List
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import piexif
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import piexif.helper
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def upscaler_to_index(name: str):
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try:
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@ -45,32 +46,46 @@ def validate_sampler_name(name):
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def setUpscalers(req: dict):
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reqDict = vars(req)
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reqDict['extras_upscaler_1'] = upscaler_to_index(req.upscaler_1)
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reqDict['extras_upscaler_2'] = upscaler_to_index(req.upscaler_2)
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reqDict.pop('upscaler_1')
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reqDict.pop('upscaler_2')
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reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None)
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reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None)
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return reqDict
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def decode_base64_to_image(encoding):
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if encoding.startswith("data:image/"):
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encoding = encoding.split(";")[1].split(",")[1]
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return Image.open(BytesIO(base64.b64decode(encoding)))
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try:
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image = Image.open(BytesIO(base64.b64decode(encoding)))
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return image
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except Exception as err:
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raise HTTPException(status_code=500, detail="Invalid encoded image")
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def encode_pil_to_base64(image):
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with io.BytesIO() as output_bytes:
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# Copy any text-only metadata
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use_metadata = False
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metadata = PngImagePlugin.PngInfo()
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for key, value in image.info.items():
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if isinstance(key, str) and isinstance(value, str):
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metadata.add_text(key, value)
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use_metadata = True
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if opts.samples_format.lower() == 'png':
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use_metadata = False
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metadata = PngImagePlugin.PngInfo()
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for key, value in image.info.items():
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if isinstance(key, str) and isinstance(value, str):
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metadata.add_text(key, value)
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use_metadata = True
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image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality)
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elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"):
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parameters = image.info.get('parameters', None)
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exif_bytes = piexif.dump({
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"Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") }
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})
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if opts.samples_format.lower() in ("jpg", "jpeg"):
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image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality)
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else:
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image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality)
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else:
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raise HTTPException(status_code=500, detail="Invalid image format")
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image.save(
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output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
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)
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bytes_data = output_bytes.getvalue()
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return base64.b64encode(bytes_data)
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def api_middleware(app: FastAPI):
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@ -244,7 +259,7 @@ class Api:
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reqDict['image'] = decode_base64_to_image(reqDict['image'])
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with self.queue_lock:
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result = run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
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result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict)
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return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1])
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@ -260,7 +275,7 @@ class Api:
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reqDict.pop('imageList')
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with self.queue_lock:
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result = run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
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result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict)
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return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1])
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@ -360,13 +375,16 @@ class Api:
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return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers]
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def get_upscalers(self):
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upscalers = []
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for upscaler in shared.sd_upscalers:
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u = upscaler.scaler
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upscalers.append({"name":u.name, "model_name":u.model_name, "model_path":u.model_path, "model_url":u.model_url})
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return upscalers
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return [
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{
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"name": upscaler.name,
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"model_name": upscaler.scaler.model_name,
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"model_path": upscaler.data_path,
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"model_url": None,
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"scale": upscaler.scale,
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}
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for upscaler in shared.sd_upscalers
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]
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def get_sd_models(self):
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return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
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@ -220,6 +220,7 @@ class UpscalerItem(BaseModel):
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model_name: Optional[str] = Field(title="Model Name")
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model_path: Optional[str] = Field(title="Path")
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model_url: Optional[str] = Field(title="URL")
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scale: Optional[float] = Field(title="Scale")
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class SDModelItem(BaseModel):
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title: str = Field(title="Title")
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@ -24,6 +24,18 @@ See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable
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""")
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already_displayed = {}
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def display_once(e: Exception, task):
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if task in already_displayed:
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return
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display(e, task)
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already_displayed[task] = 1
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def run(code, task):
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try:
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code()
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@ -1,230 +1,16 @@
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from __future__ import annotations
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import math
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import os
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import sys
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import traceback
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import re
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import shutil
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import numpy as np
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from PIL import Image
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import torch
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import tqdm
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from typing import Callable, List, OrderedDict, Tuple
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from functools import partial
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from dataclasses import dataclass
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from modules import processing, shared, images, devices, sd_models, sd_samplers, sd_vae
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from modules.shared import opts
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import modules.gfpgan_model
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from modules.ui import plaintext_to_html
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import modules.codeformer_model
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from modules import shared, images, sd_models, sd_vae
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from modules.ui_common import plaintext_to_html
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import gradio as gr
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import safetensors.torch
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class LruCache(OrderedDict):
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@dataclass(frozen=True)
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class Key:
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image_hash: int
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info_hash: int
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args_hash: int
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@dataclass
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class Value:
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image: Image.Image
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info: str
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def __init__(self, max_size: int = 5, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._max_size = max_size
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def get(self, key: LruCache.Key) -> LruCache.Value:
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ret = super().get(key)
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if ret is not None:
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self.move_to_end(key) # Move to end of eviction list
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return ret
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def put(self, key: LruCache.Key, value: LruCache.Value) -> None:
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self[key] = value
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while len(self) > self._max_size:
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self.popitem(last=False)
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cached_images: LruCache = LruCache(max_size=5)
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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, save_output: bool = True):
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devices.torch_gc()
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shared.state.begin()
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shared.state.job = 'extras'
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imageArr = []
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# Also keep track of original file names
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imageNameArr = []
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outputs = []
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if extras_mode == 1:
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#convert file to pillow image
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for img in image_folder:
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image = Image.open(img)
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imageArr.append(image)
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imageNameArr.append(os.path.splitext(img.orig_name)[0])
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elif extras_mode == 2:
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assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
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if input_dir == '':
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return outputs, "Please select an input directory.", ''
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image_list = shared.listfiles(input_dir)
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for img in image_list:
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try:
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image = Image.open(img)
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except Exception:
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continue
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imageArr.append(image)
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imageNameArr.append(img)
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else:
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imageArr.append(image)
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imageNameArr.append(None)
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if extras_mode == 2 and output_dir != '':
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outpath = output_dir
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else:
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outpath = opts.outdir_samples or opts.outdir_extras_samples
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# Extra operation definitions
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def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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shared.state.job = 'extras-gfpgan'
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restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
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res = Image.fromarray(restored_img)
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if gfpgan_visibility < 1.0:
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res = Image.blend(image, res, gfpgan_visibility)
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info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n"
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return (res, info)
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def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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shared.state.job = 'extras-codeformer'
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restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
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res = Image.fromarray(restored_img)
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if codeformer_visibility < 1.0:
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res = Image.blend(image, res, codeformer_visibility)
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info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
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return (res, info)
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def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
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shared.state.job = 'extras-upscale'
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upscaler = shared.sd_upscalers[scaler_index]
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res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
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if mode == 1 and crop:
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cropped = Image.new("RGB", (resize_w, resize_h))
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cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2))
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res = cropped
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return res
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def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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# Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text
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nonlocal upscaling_resize
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if resize_mode == 1:
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upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
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crop_info = " (crop)" if upscaling_crop else ""
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info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
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return (image, info)
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@dataclass
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class UpscaleParams:
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upscaler_idx: int
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blend_alpha: float
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def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]:
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blended_result: Image.Image = None
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image_hash: str = hash(np.array(image.getdata()).tobytes())
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for upscaler in params:
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upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode,
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upscaling_resize_w, upscaling_resize_h, upscaling_crop)
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cache_key = LruCache.Key(image_hash=image_hash,
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info_hash=hash(info),
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args_hash=hash(upscale_args))
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cached_entry = cached_images.get(cache_key)
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if cached_entry is None:
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res = upscale(image, *upscale_args)
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info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n"
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cached_images.put(cache_key, LruCache.Value(image=res, info=info))
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else:
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res, info = cached_entry.image, cached_entry.info
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if blended_result is None:
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blended_result = res
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else:
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blended_result = Image.blend(blended_result, res, upscaler.blend_alpha)
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return (blended_result, info)
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# Build a list of operations to run
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facefix_ops: List[Callable] = []
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facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else []
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facefix_ops += [run_codeformer] if codeformer_visibility > 0 else []
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upscale_ops: List[Callable] = []
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upscale_ops += [run_prepare_crop] if resize_mode == 1 else []
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if upscaling_resize != 0:
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step_params: List[UpscaleParams] = []
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step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0))
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if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
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step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility))
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upscale_ops.append(partial(run_upscalers_blend, step_params))
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extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops)
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for image, image_name in zip(imageArr, imageNameArr):
|
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if image is None:
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return outputs, "Please select an input image.", ''
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shared.state.textinfo = f'Processing image {image_name}'
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|
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existing_pnginfo = image.info or {}
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image = image.convert("RGB")
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info = ""
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# Run each operation on each image
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for op in extras_ops:
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image, info = op(image, info)
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if opts.use_original_name_batch and image_name is not None:
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basename = os.path.splitext(os.path.basename(image_name))[0]
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else:
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basename = ''
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|
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if opts.enable_pnginfo: # append info before save
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image.info = existing_pnginfo
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image.info["extras"] = info
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if save_output:
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# Add upscaler name as a suffix.
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suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
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# Add second upscaler if applicable.
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if suffix and extras_upscaler_2 and extras_upscaler_2_visibility:
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suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}"
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|
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images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
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no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
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|
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if extras_mode != 2 or show_extras_results :
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outputs.append(image)
|
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|
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devices.torch_gc()
|
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|
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return outputs, plaintext_to_html(info), ''
|
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|
||||
def clear_cache():
|
||||
cached_images.clear()
|
||||
|
||||
|
||||
def run_pnginfo(image):
|
||||
if image is None:
|
||||
|
|
@ -285,7 +71,7 @@ def to_half(tensor, enable):
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return tensor
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|
||||
|
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def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae):
|
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def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights):
|
||||
shared.state.begin()
|
||||
shared.state.job = 'model-merge'
|
||||
|
||||
|
|
@ -430,6 +216,12 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
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for key in theta_0.keys():
|
||||
theta_0[key] = to_half(theta_0[key], save_as_half)
|
||||
|
||||
if discard_weights:
|
||||
regex = re.compile(discard_weights)
|
||||
for key in list(theta_0):
|
||||
if re.search(regex, key):
|
||||
theta_0.pop(key, None)
|
||||
|
||||
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
|
||||
|
||||
filename = filename_generator() if custom_name == '' else custom_name
|
||||
|
|
|
|||
|
|
@ -715,6 +715,8 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
|
|||
do_not_save_samples=True,
|
||||
)
|
||||
|
||||
p.disable_extra_networks = True
|
||||
|
||||
if preview_from_txt2img:
|
||||
p.prompt = preview_prompt
|
||||
p.negative_prompt = preview_negative_prompt
|
||||
|
|
|
|||
|
|
@ -2,6 +2,7 @@ import os
|
|||
import sys
|
||||
import traceback
|
||||
from collections import namedtuple
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
import torch
|
||||
|
|
@ -20,19 +21,20 @@ Category = namedtuple("Category", ["name", "topn", "items"])
|
|||
|
||||
re_topn = re.compile(r"\.top(\d+)\.")
|
||||
|
||||
def category_types():
|
||||
return [f.stem for f in Path(shared.interrogator.content_dir).glob('*.txt')]
|
||||
|
||||
|
||||
def download_default_clip_interrogate_categories(content_dir):
|
||||
print("Downloading CLIP categories...")
|
||||
|
||||
tmpdir = content_dir + "_tmp"
|
||||
category_types = ["artists", "flavors", "mediums", "movements"]
|
||||
|
||||
try:
|
||||
os.makedirs(tmpdir)
|
||||
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/artists.txt", os.path.join(tmpdir, "artists.txt"))
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/flavors.txt", os.path.join(tmpdir, "flavors.top3.txt"))
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/mediums.txt", os.path.join(tmpdir, "mediums.txt"))
|
||||
torch.hub.download_url_to_file("https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/movements.txt", os.path.join(tmpdir, "movements.txt"))
|
||||
|
||||
for category_type in category_types:
|
||||
torch.hub.download_url_to_file(f"https://raw.githubusercontent.com/pharmapsychotic/clip-interrogator/main/clip_interrogator/data/{category_type}.txt", os.path.join(tmpdir, f"{category_type}.txt"))
|
||||
os.rename(tmpdir, content_dir)
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -51,32 +53,38 @@ class InterrogateModels:
|
|||
|
||||
def __init__(self, content_dir):
|
||||
self.loaded_categories = None
|
||||
self.skip_categories = []
|
||||
self.content_dir = content_dir
|
||||
self.running_on_cpu = devices.device_interrogate == torch.device("cpu")
|
||||
|
||||
def categories(self):
|
||||
if self.loaded_categories is not None:
|
||||
return self.loaded_categories
|
||||
|
||||
self.loaded_categories = []
|
||||
|
||||
if not os.path.exists(self.content_dir):
|
||||
download_default_clip_interrogate_categories(self.content_dir)
|
||||
|
||||
if os.path.exists(self.content_dir):
|
||||
for filename in os.listdir(self.content_dir):
|
||||
m = re_topn.search(filename)
|
||||
topn = 1 if m is None else int(m.group(1))
|
||||
if self.loaded_categories is not None and self.skip_categories == shared.opts.interrogate_clip_skip_categories:
|
||||
return self.loaded_categories
|
||||
|
||||
with open(os.path.join(self.content_dir, filename), "r", encoding="utf8") as file:
|
||||
self.loaded_categories = []
|
||||
|
||||
if os.path.exists(self.content_dir):
|
||||
self.skip_categories = shared.opts.interrogate_clip_skip_categories
|
||||
category_types = []
|
||||
for filename in Path(self.content_dir).glob('*.txt'):
|
||||
category_types.append(filename.stem)
|
||||
if filename.stem in self.skip_categories:
|
||||
continue
|
||||
m = re_topn.search(filename.stem)
|
||||
topn = 1 if m is None else int(m.group(1))
|
||||
with open(filename, "r", encoding="utf8") as file:
|
||||
lines = [x.strip() for x in file.readlines()]
|
||||
|
||||
self.loaded_categories.append(Category(name=filename, topn=topn, items=lines))
|
||||
self.loaded_categories.append(Category(name=filename.stem, topn=topn, items=lines))
|
||||
|
||||
return self.loaded_categories
|
||||
|
||||
def load_blip_model(self):
|
||||
import models.blip
|
||||
with paths.Prioritize("BLIP"):
|
||||
import models.blip
|
||||
|
||||
files = modelloader.load_models(
|
||||
model_path=os.path.join(paths.models_path, "BLIP"),
|
||||
|
|
@ -139,6 +147,8 @@ class InterrogateModels:
|
|||
def rank(self, image_features, text_array, top_count=1):
|
||||
import clip
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
if shared.opts.interrogate_clip_dict_limit != 0:
|
||||
text_array = text_array[0:int(shared.opts.interrogate_clip_dict_limit)]
|
||||
|
||||
|
|
|
|||
|
|
@ -38,3 +38,17 @@ for d, must_exist, what, options in path_dirs:
|
|||
else:
|
||||
sys.path.append(d)
|
||||
paths[what] = d
|
||||
|
||||
|
||||
class Prioritize:
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
self.path = None
|
||||
|
||||
def __enter__(self):
|
||||
self.path = sys.path.copy()
|
||||
sys.path = [paths[self.name]] + sys.path
|
||||
|
||||
def __exit__(self, exc_type, exc_val, exc_tb):
|
||||
sys.path = self.path
|
||||
self.path = None
|
||||
|
|
|
|||
103
modules/postprocessing.py
Normal file
103
modules/postprocessing.py
Normal file
|
|
@ -0,0 +1,103 @@
|
|||
import os
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
|
||||
from modules.shared import opts
|
||||
|
||||
|
||||
def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output: bool = True):
|
||||
devices.torch_gc()
|
||||
|
||||
shared.state.begin()
|
||||
shared.state.job = 'extras'
|
||||
|
||||
image_data = []
|
||||
image_names = []
|
||||
outputs = []
|
||||
|
||||
if extras_mode == 1:
|
||||
for img in image_folder:
|
||||
image = Image.open(img)
|
||||
image_data.append(image)
|
||||
image_names.append(os.path.splitext(img.orig_name)[0])
|
||||
elif extras_mode == 2:
|
||||
assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
|
||||
assert input_dir, 'input directory not selected'
|
||||
|
||||
image_list = shared.listfiles(input_dir)
|
||||
for filename in image_list:
|
||||
try:
|
||||
image = Image.open(filename)
|
||||
except Exception:
|
||||
continue
|
||||
image_data.append(image)
|
||||
image_names.append(filename)
|
||||
else:
|
||||
assert image, 'image not selected'
|
||||
|
||||
image_data.append(image)
|
||||
image_names.append(None)
|
||||
|
||||
if extras_mode == 2 and output_dir != '':
|
||||
outpath = output_dir
|
||||
else:
|
||||
outpath = opts.outdir_samples or opts.outdir_extras_samples
|
||||
|
||||
infotext = ''
|
||||
|
||||
for image, name in zip(image_data, image_names):
|
||||
shared.state.textinfo = name
|
||||
|
||||
existing_pnginfo = image.info or {}
|
||||
|
||||
pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
|
||||
|
||||
scripts.scripts_postproc.run(pp, args)
|
||||
|
||||
if opts.use_original_name_batch and name is not None:
|
||||
basename = os.path.splitext(os.path.basename(name))[0]
|
||||
else:
|
||||
basename = ''
|
||||
|
||||
infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
|
||||
|
||||
if opts.enable_pnginfo:
|
||||
pp.image.info = existing_pnginfo
|
||||
pp.image.info["postprocessing"] = infotext
|
||||
|
||||
if save_output:
|
||||
images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=infotext, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
|
||||
|
||||
if extras_mode != 2 or show_extras_results:
|
||||
outputs.append(pp.image)
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
return outputs, ui_common.plaintext_to_html(infotext), ''
|
||||
|
||||
|
||||
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, save_output: bool = True):
|
||||
"""old handler for API"""
|
||||
|
||||
args = scripts.scripts_postproc.create_args_for_run({
|
||||
"Upscale": {
|
||||
"upscale_mode": resize_mode,
|
||||
"upscale_by": upscaling_resize,
|
||||
"upscale_to_width": upscaling_resize_w,
|
||||
"upscale_to_height": upscaling_resize_h,
|
||||
"upscale_crop": upscaling_crop,
|
||||
"upscaler_1_name": extras_upscaler_1,
|
||||
"upscaler_2_name": extras_upscaler_2,
|
||||
"upscaler_2_visibility": extras_upscaler_2_visibility,
|
||||
},
|
||||
"GFPGAN": {
|
||||
"gfpgan_visibility": gfpgan_visibility,
|
||||
},
|
||||
"CodeFormer": {
|
||||
"codeformer_visibility": codeformer_visibility,
|
||||
"codeformer_weight": codeformer_weight,
|
||||
},
|
||||
})
|
||||
|
||||
return run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output=save_output)
|
||||
|
|
@ -140,6 +140,7 @@ class StableDiffusionProcessing:
|
|||
self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
|
||||
self.override_settings_restore_afterwards = override_settings_restore_afterwards
|
||||
self.is_using_inpainting_conditioning = False
|
||||
self.disable_extra_networks = False
|
||||
|
||||
if not seed_enable_extras:
|
||||
self.subseed = -1
|
||||
|
|
@ -579,7 +580,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
with devices.autocast():
|
||||
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
|
||||
|
||||
extra_networks.activate(p, extra_network_data)
|
||||
if not p.disable_extra_networks:
|
||||
extra_networks.activate(p, extra_network_data)
|
||||
|
||||
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
|
||||
processed = Processed(p, [], p.seed, "")
|
||||
|
|
@ -723,7 +725,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|||
if opts.grid_save:
|
||||
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
|
||||
|
||||
extra_networks.deactivate(p, extra_network_data)
|
||||
if not p.disable_extra_networks:
|
||||
extra_networks.deactivate(p, extra_network_data)
|
||||
|
||||
devices.torch_gc()
|
||||
|
||||
res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from collections import namedtuple
|
|||
import gradio as gr
|
||||
|
||||
from modules.processing import StableDiffusionProcessing
|
||||
from modules import shared, paths, script_callbacks, extensions, script_loading
|
||||
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
|
||||
|
||||
AlwaysVisible = object()
|
||||
|
||||
|
|
@ -150,8 +150,10 @@ def basedir():
|
|||
return current_basedir
|
||||
|
||||
|
||||
scripts_data = []
|
||||
ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
|
||||
|
||||
scripts_data = []
|
||||
postprocessing_scripts_data = []
|
||||
ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
|
||||
|
||||
|
||||
|
|
@ -190,23 +192,31 @@ def list_files_with_name(filename):
|
|||
def load_scripts():
|
||||
global current_basedir
|
||||
scripts_data.clear()
|
||||
postprocessing_scripts_data.clear()
|
||||
script_callbacks.clear_callbacks()
|
||||
|
||||
scripts_list = list_scripts("scripts", ".py")
|
||||
|
||||
syspath = sys.path
|
||||
|
||||
def register_scripts_from_module(module):
|
||||
for key, script_class in module.__dict__.items():
|
||||
if type(script_class) != type:
|
||||
continue
|
||||
|
||||
if issubclass(script_class, Script):
|
||||
scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
|
||||
elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
|
||||
postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
|
||||
|
||||
for scriptfile in sorted(scripts_list):
|
||||
try:
|
||||
if scriptfile.basedir != paths.script_path:
|
||||
sys.path = [scriptfile.basedir] + sys.path
|
||||
current_basedir = scriptfile.basedir
|
||||
|
||||
module = script_loading.load_module(scriptfile.path)
|
||||
|
||||
for key, script_class in module.__dict__.items():
|
||||
if type(script_class) == type and issubclass(script_class, Script):
|
||||
scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
|
||||
script_module = script_loading.load_module(scriptfile.path)
|
||||
register_scripts_from_module(script_module)
|
||||
|
||||
except Exception:
|
||||
print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
|
||||
|
|
@ -413,6 +423,7 @@ class ScriptRunner:
|
|||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
|
||||
scripts_current: ScriptRunner = None
|
||||
|
||||
|
||||
|
|
@ -423,12 +434,13 @@ def reload_script_body_only():
|
|||
|
||||
|
||||
def reload_scripts():
|
||||
global scripts_txt2img, scripts_img2img
|
||||
global scripts_txt2img, scripts_img2img, scripts_postproc
|
||||
|
||||
load_scripts()
|
||||
|
||||
scripts_txt2img = ScriptRunner()
|
||||
scripts_img2img = ScriptRunner()
|
||||
scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
|
||||
|
||||
|
||||
def IOComponent_init(self, *args, **kwargs):
|
||||
|
|
|
|||
147
modules/scripts_postprocessing.py
Normal file
147
modules/scripts_postprocessing.py
Normal file
|
|
@ -0,0 +1,147 @@
|
|||
import os
|
||||
import gradio as gr
|
||||
|
||||
from modules import errors, shared
|
||||
|
||||
|
||||
class PostprocessedImage:
|
||||
def __init__(self, image):
|
||||
self.image = image
|
||||
self.info = {}
|
||||
|
||||
|
||||
class ScriptPostprocessing:
|
||||
filename = None
|
||||
controls = None
|
||||
args_from = None
|
||||
args_to = None
|
||||
|
||||
order = 1000
|
||||
"""scripts will be ordred by this value in postprocessing UI"""
|
||||
|
||||
name = None
|
||||
"""this function should return the title of the script."""
|
||||
|
||||
group = None
|
||||
"""A gr.Group component that has all script's UI inside it"""
|
||||
|
||||
def ui(self):
|
||||
"""
|
||||
This function should create gradio UI elements. See https://gradio.app/docs/#components
|
||||
The return value should be a dictionary that maps parameter names to components used in processing.
|
||||
Values of those components will be passed to process() function.
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
def process(self, pp: PostprocessedImage, **args):
|
||||
"""
|
||||
This function is called to postprocess the image.
|
||||
args contains a dictionary with all values returned by components from ui()
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
def image_changed(self):
|
||||
pass
|
||||
|
||||
|
||||
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
|
||||
try:
|
||||
res = func(*args, **kwargs)
|
||||
return res
|
||||
except Exception as e:
|
||||
errors.display(e, f"calling {filename}/{funcname}")
|
||||
|
||||
return default
|
||||
|
||||
|
||||
class ScriptPostprocessingRunner:
|
||||
def __init__(self):
|
||||
self.scripts = None
|
||||
self.ui_created = False
|
||||
|
||||
def initialize_scripts(self, scripts_data):
|
||||
self.scripts = []
|
||||
|
||||
for script_class, path, basedir, script_module in scripts_data:
|
||||
script: ScriptPostprocessing = script_class()
|
||||
script.filename = path
|
||||
|
||||
self.scripts.append(script)
|
||||
|
||||
def create_script_ui(self, script, inputs):
|
||||
script.args_from = len(inputs)
|
||||
script.args_to = len(inputs)
|
||||
|
||||
script.controls = wrap_call(script.ui, script.filename, "ui")
|
||||
|
||||
for control in script.controls.values():
|
||||
control.custom_script_source = os.path.basename(script.filename)
|
||||
|
||||
inputs += list(script.controls.values())
|
||||
script.args_to = len(inputs)
|
||||
|
||||
def scripts_in_preferred_order(self):
|
||||
if self.scripts is None:
|
||||
import modules.scripts
|
||||
self.initialize_scripts(modules.scripts.postprocessing_scripts_data)
|
||||
|
||||
scripts_order = [x.lower().strip() for x in shared.opts.postprocessing_scipts_order.split(",")]
|
||||
|
||||
def script_score(name):
|
||||
name = name.lower()
|
||||
for i, possible_match in enumerate(scripts_order):
|
||||
if possible_match in name:
|
||||
return i
|
||||
|
||||
return len(self.scripts)
|
||||
|
||||
script_scores = {script.name: (script_score(script.name), script.order, script.name, original_index) for original_index, script in enumerate(self.scripts)}
|
||||
|
||||
return sorted(self.scripts, key=lambda x: script_scores[x.name])
|
||||
|
||||
def setup_ui(self):
|
||||
inputs = []
|
||||
|
||||
for script in self.scripts_in_preferred_order():
|
||||
with gr.Box() as group:
|
||||
self.create_script_ui(script, inputs)
|
||||
|
||||
script.group = group
|
||||
|
||||
self.ui_created = True
|
||||
return inputs
|
||||
|
||||
def run(self, pp: PostprocessedImage, args):
|
||||
for script in self.scripts_in_preferred_order():
|
||||
shared.state.job = script.name
|
||||
|
||||
script_args = args[script.args_from:script.args_to]
|
||||
|
||||
process_args = {}
|
||||
for (name, component), value in zip(script.controls.items(), script_args):
|
||||
process_args[name] = value
|
||||
|
||||
script.process(pp, **process_args)
|
||||
|
||||
def create_args_for_run(self, scripts_args):
|
||||
if not self.ui_created:
|
||||
with gr.Blocks(analytics_enabled=False):
|
||||
self.setup_ui()
|
||||
|
||||
scripts = self.scripts_in_preferred_order()
|
||||
args = [None] * max([x.args_to for x in scripts])
|
||||
|
||||
for script in scripts:
|
||||
script_args_dict = scripts_args.get(script.name, None)
|
||||
if script_args_dict is not None:
|
||||
|
||||
for i, name in enumerate(script.controls):
|
||||
args[script.args_from + i] = script_args_dict.get(name, None)
|
||||
|
||||
return args
|
||||
|
||||
def image_changed(self):
|
||||
for script in self.scripts_in_preferred_order():
|
||||
script.image_changed()
|
||||
|
|
@ -41,7 +41,9 @@ class DisableInitialization:
|
|||
return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
|
||||
|
||||
def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
|
||||
return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
|
||||
res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
|
||||
res.name_or_path = pretrained_model_name_or_path
|
||||
return res
|
||||
|
||||
def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
|
||||
args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from torch import einsum
|
|||
from ldm.util import default
|
||||
from einops import rearrange
|
||||
|
||||
from modules import shared
|
||||
from modules import shared, errors
|
||||
from modules.hypernetworks import hypernetwork
|
||||
|
||||
from .sub_quadratic_attention import efficient_dot_product_attention
|
||||
|
|
@ -279,6 +279,21 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
|
|||
)
|
||||
|
||||
|
||||
def get_xformers_flash_attention_op(q, k, v):
|
||||
if not shared.cmd_opts.xformers_flash_attention:
|
||||
return None
|
||||
|
||||
try:
|
||||
flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
|
||||
fw, bw = flash_attention_op
|
||||
if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
|
||||
return flash_attention_op
|
||||
except Exception as e:
|
||||
errors.display_once(e, "enabling flash attention")
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def xformers_attention_forward(self, x, context=None, mask=None):
|
||||
h = self.heads
|
||||
q_in = self.to_q(x)
|
||||
|
|
@ -290,7 +305,8 @@ def xformers_attention_forward(self, x, context=None, mask=None):
|
|||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
|
||||
del q_in, k_in, v_in
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
|
||||
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
|
||||
|
||||
out = rearrange(out, 'b n h d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
|
|
@ -365,7 +381,7 @@ def xformers_attnblock_forward(self, x):
|
|||
q = q.contiguous()
|
||||
k = k.contiguous()
|
||||
v = v.contiguous()
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v)
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, op=get_xformers_flash_attention_op(q, k, v))
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h)
|
||||
out = self.proj_out(out)
|
||||
return x + out
|
||||
|
|
|
|||
|
|
@ -57,6 +57,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
|
|||
parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
|
||||
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
|
||||
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
|
||||
parser.add_argument("--xformers-flash-attention", action='store_true', help="enable xformers with Flash Attention to improve reproducibility (supported for SD2.x or variant only)")
|
||||
parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
|
||||
parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
|
||||
parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
|
||||
|
|
@ -398,7 +399,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": ["Automatic", "None"] + list(sd_vae.vae_dict)}, refresh=sd_vae.refresh_vae_list),
|
||||
"sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
|
||||
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01 }),
|
||||
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
|
||||
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
|
||||
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
|
||||
"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", ui_components.FormColorPicker, {}),
|
||||
|
|
@ -406,7 +407,8 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
|
|||
"enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
|
||||
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
|
||||
"comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
|
||||
'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
|
||||
"extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('compatibility', "Compatibility"), {
|
||||
|
|
@ -422,6 +424,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
|||
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
|
||||
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
|
||||
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
|
||||
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
|
||||
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
|
||||
"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
|
||||
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
|
||||
|
|
@ -429,6 +432,10 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
|
|||
"deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
|
||||
"extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, { "choices": ["cards", "thumbs"] }),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "User interface"), {
|
||||
"return_grid": OptionInfo(True, "Show grid in results for web"),
|
||||
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
|
||||
|
|
@ -443,9 +450,12 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
|
||||
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row"),
|
||||
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
|
||||
"quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"),
|
||||
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
|
||||
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
|
||||
"localization": OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('ui', "Live previews"), {
|
||||
|
|
@ -470,6 +480,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
|
|||
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
|
||||
'postprocessing_scipts_order': OptionInfo("upscale, gfpgan, codeformer", "Postprocessing operation order"),
|
||||
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section((None, "Hidden options"), {
|
||||
"disabled_extensions": OptionInfo([], "Disable those extensions"),
|
||||
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
|
||||
|
|
|
|||
313
modules/ui.py
313
modules/ui.py
|
|
@ -5,7 +5,6 @@ import mimetypes
|
|||
import os
|
||||
import platform
|
||||
import random
|
||||
import subprocess as sp
|
||||
import sys
|
||||
import tempfile
|
||||
import time
|
||||
|
|
@ -20,7 +19,7 @@ import numpy as np
|
|||
from PIL import Image, PngImagePlugin
|
||||
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
|
||||
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks
|
||||
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, postprocessing, ui_components, ui_common, ui_postprocessing
|
||||
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
|
||||
from modules.paths import script_path
|
||||
|
||||
|
|
@ -41,6 +40,7 @@ from modules.sd_samplers import samplers, samplers_for_img2img
|
|||
from modules.textual_inversion import textual_inversion
|
||||
import modules.hypernetworks.ui
|
||||
from modules.generation_parameters_copypaste import image_from_url_text
|
||||
import modules.extras
|
||||
|
||||
warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
|
||||
|
||||
|
|
@ -75,6 +75,7 @@ css_hide_progressbar = """
|
|||
.wrap .m-12::before { content:"Loading..." }
|
||||
.wrap .z-20 svg { display:none!important; }
|
||||
.wrap .z-20::before { content:"Loading..." }
|
||||
.wrap.cover-bg .z-20::before { content:"" }
|
||||
.progress-bar { display:none!important; }
|
||||
.meta-text { display:none!important; }
|
||||
.meta-text-center { display:none!important; }
|
||||
|
|
@ -85,7 +86,6 @@ css_hide_progressbar = """
|
|||
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
|
||||
reuse_symbol = '\u267b\ufe0f' # ♻️
|
||||
paste_symbol = '\u2199\ufe0f' # ↙
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
refresh_symbol = '\U0001f504' # 🔄
|
||||
save_style_symbol = '\U0001f4be' # 💾
|
||||
apply_style_symbol = '\U0001f4cb' # 📋
|
||||
|
|
@ -94,78 +94,14 @@ extra_networks_symbol = '\U0001F3B4' # 🎴
|
|||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
return text
|
||||
return ui_common.plaintext_to_html(text)
|
||||
|
||||
|
||||
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 = []
|
||||
fullfns = []
|
||||
|
||||
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
|
||||
class MyObject:
|
||||
def __init__(self, d=None):
|
||||
if d is not None:
|
||||
for key, value in d.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
data = json.loads(js_data)
|
||||
|
||||
p = MyObject(data)
|
||||
path = opts.outdir_save
|
||||
save_to_dirs = opts.use_save_to_dirs_for_ui
|
||||
extension: str = opts.samples_format
|
||||
start_index = 0
|
||||
|
||||
if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
||||
|
||||
images = [images[index]]
|
||||
start_index = index
|
||||
|
||||
os.makedirs(opts.outdir_save, exist_ok=True)
|
||||
|
||||
with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
|
||||
at_start = file.tell() == 0
|
||||
writer = csv.writer(file)
|
||||
if at_start:
|
||||
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
||||
|
||||
for image_index, filedata in enumerate(images, start_index):
|
||||
image = image_from_url_text(filedata)
|
||||
|
||||
is_grid = image_index < p.index_of_first_image
|
||||
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
||||
|
||||
fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
|
||||
filename = os.path.relpath(fullfn, path)
|
||||
filenames.append(filename)
|
||||
fullfns.append(fullfn)
|
||||
if txt_fullfn:
|
||||
filenames.append(os.path.basename(txt_fullfn))
|
||||
fullfns.append(txt_fullfn)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
||||
# Make Zip
|
||||
if do_make_zip:
|
||||
zip_filepath = os.path.join(path, "images.zip")
|
||||
|
||||
from zipfile import ZipFile
|
||||
with ZipFile(zip_filepath, "w") as zip_file:
|
||||
for i in range(len(fullfns)):
|
||||
with open(fullfns[i], mode="rb") as f:
|
||||
zip_file.writestr(filenames[i], f.read())
|
||||
fullfns.insert(0, zip_filepath)
|
||||
|
||||
return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
|
||||
|
||||
def visit(x, func, path=""):
|
||||
if hasattr(x, 'children'):
|
||||
for c in x.children:
|
||||
|
|
@ -443,19 +379,6 @@ def apply_setting(key, value):
|
|||
opts.save(shared.config_filename)
|
||||
return getattr(opts, key)
|
||||
|
||||
|
||||
def update_generation_info(generation_info, html_info, img_index):
|
||||
try:
|
||||
generation_info = json.loads(generation_info)
|
||||
if img_index < 0 or img_index >= len(generation_info["infotexts"]):
|
||||
return html_info, gr.update()
|
||||
return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
|
||||
except Exception:
|
||||
pass
|
||||
# if the json parse or anything else fails, just return the old html_info
|
||||
return html_info, gr.update()
|
||||
|
||||
|
||||
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
|
||||
def refresh():
|
||||
refresh_method()
|
||||
|
|
@ -476,107 +399,7 @@ def create_refresh_button(refresh_component, refresh_method, refreshed_args, ele
|
|||
|
||||
|
||||
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])
|
||||
elif "microsoft-standard-WSL2" in platform.uname().release:
|
||||
sp.Popen(["wsl-open", path])
|
||||
else:
|
||||
sp.Popen(["xdg-open", path])
|
||||
|
||||
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
|
||||
with gr.Group(elem_id=f"{tabname}_gallery_container"):
|
||||
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(elem_id=f"image_buttons_{tabname}"):
|
||||
open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
|
||||
|
||||
if tabname != "extras":
|
||||
save = gr.Button('Save', elem_id=f'save_{tabname}')
|
||||
save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
|
||||
|
||||
buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
|
||||
|
||||
open_folder_button.click(
|
||||
fn=lambda: open_folder(opts.outdir_samples or outdir),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
if tabname != "extras":
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
|
||||
if tabname == 'txt2img' or tabname == 'img2img':
|
||||
generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
|
||||
generation_info_button.click(
|
||||
fn=update_generation_info,
|
||||
_js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
|
||||
inputs=[generation_info, html_info, html_info],
|
||||
outputs=[html_info, html_info],
|
||||
)
|
||||
|
||||
save.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
save_zip.click(
|
||||
fn=wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
]
|
||||
)
|
||||
|
||||
else:
|
||||
html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
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, html_log
|
||||
return ui_common.create_output_panel(tabname, outdir)
|
||||
|
||||
|
||||
def create_sampler_and_steps_selection(choices, tabname):
|
||||
|
|
@ -935,7 +758,7 @@ def create_ui():
|
|||
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
|
||||
|
||||
elif category == "checkboxes":
|
||||
with FormRow(elem_id="img2img_checkboxes"):
|
||||
with FormRow(elem_id="img2img_checkboxes", variant="compact"):
|
||||
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
|
||||
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
|
||||
|
||||
|
|
@ -1122,86 +945,7 @@ def create_ui():
|
|||
modules.scripts.scripts_current = None
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as extras_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='compact'):
|
||||
with gr.Tabs(elem_id="mode_extras"):
|
||||
with gr.TabItem('Single Image', elem_id="extras_single_tab"):
|
||||
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
|
||||
|
||||
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab"):
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
|
||||
|
||||
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab"):
|
||||
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
|
||||
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
|
||||
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
|
||||
|
||||
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
||||
|
||||
with gr.Tabs(elem_id="extras_resize_mode"):
|
||||
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab"):
|
||||
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
|
||||
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab"):
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
|
||||
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
|
||||
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
|
||||
|
||||
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")
|
||||
|
||||
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")
|
||||
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1, elem_id="extras_upscaler_2_visibility")
|
||||
|
||||
with gr.Group():
|
||||
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan, elem_id="extras_gfpgan_visibility")
|
||||
|
||||
with gr.Group():
|
||||
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer, elem_id="extras_codeformer_visibility")
|
||||
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, elem_id="extras_codeformer_weight")
|
||||
|
||||
with gr.Group():
|
||||
upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False, elem_id="extras_upscale_before_face_fix")
|
||||
|
||||
result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
|
||||
|
||||
submit.click(
|
||||
fn=wrap_gradio_gpu_call(modules.extras.run_extras, extra_outputs=[None, '']),
|
||||
_js="get_extras_tab_index",
|
||||
inputs=[
|
||||
dummy_component,
|
||||
dummy_component,
|
||||
extras_image,
|
||||
image_batch,
|
||||
extras_batch_input_dir,
|
||||
extras_batch_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_before_face_fix,
|
||||
],
|
||||
outputs=[
|
||||
result_images,
|
||||
html_info_x,
|
||||
html_info,
|
||||
]
|
||||
)
|
||||
parameters_copypaste.add_paste_fields("extras", extras_image, None)
|
||||
|
||||
extras_image.change(
|
||||
fn=modules.extras.clear_cache,
|
||||
inputs=[], outputs=[]
|
||||
)
|
||||
ui_postprocessing.create_ui()
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
|
|
@ -1222,10 +966,19 @@ def create_ui():
|
|||
outputs=[html, generation_info, html2],
|
||||
)
|
||||
|
||||
def update_interp_description(value):
|
||||
interp_description_css = "<p style='margin-bottom: 2.5em'>{}</p>"
|
||||
interp_descriptions = {
|
||||
"No interpolation": interp_description_css.format("No interpolation will be used. Requires one model; A. Allows for format conversion and VAE baking."),
|
||||
"Weighted sum": interp_description_css.format("A weighted sum will be used for interpolation. Requires two models; A and B. The result is calculated as A * (1 - M) + B * M"),
|
||||
"Add difference": interp_description_css.format("The difference between the last two models will be added to the first. Requires three models; A, B and C. The result is calculated as A + (B - C) * M")
|
||||
}
|
||||
return interp_descriptions[value]
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Column(variant='compact'):
|
||||
gr.HTML(value="<p style='margin-bottom: 2.5em'>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
|
||||
interp_description = gr.HTML(value=update_interp_description("Weighted sum"), elem_id="modelmerger_interp_description")
|
||||
|
||||
with FormRow(elem_id="modelmerger_models"):
|
||||
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
|
||||
|
|
@ -1240,6 +993,7 @@ def create_ui():
|
|||
custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
|
||||
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
|
||||
interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
|
||||
interp_method.change(fn=update_interp_description, inputs=[interp_method], outputs=[interp_description])
|
||||
|
||||
with FormRow():
|
||||
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
|
||||
|
|
@ -1254,6 +1008,9 @@ def create_ui():
|
|||
bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
|
||||
create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
|
||||
|
||||
with FormRow():
|
||||
discard_weights = gr.Textbox(value="", label="Discard weights with matching name", elem_id="modelmerger_discard_weights")
|
||||
|
||||
with gr.Row():
|
||||
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
|
||||
|
||||
|
|
@ -1265,7 +1022,7 @@ def create_ui():
|
|||
with gr.Row().style(equal_height=False):
|
||||
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
|
||||
|
||||
with gr.Row().style(equal_height=False):
|
||||
with gr.Row(variant="compact").style(equal_height=False):
|
||||
with gr.Tabs(elem_id="train_tabs"):
|
||||
|
||||
with gr.Tab(label="Create embedding"):
|
||||
|
|
@ -1844,6 +1601,7 @@ def create_ui():
|
|||
checkpoint_format,
|
||||
config_source,
|
||||
bake_in_vae,
|
||||
discard_weights,
|
||||
],
|
||||
outputs=[
|
||||
primary_model_name,
|
||||
|
|
@ -1931,28 +1689,27 @@ def create_ui():
|
|||
with open(ui_config_file, "w", encoding="utf8") as file:
|
||||
json.dump(ui_settings, file, indent=4)
|
||||
|
||||
# Required as a workaround for change() event not triggering when loading values from ui-config.json
|
||||
interp_description.value = update_interp_description(interp_method.value)
|
||||
|
||||
return demo
|
||||
|
||||
|
||||
def reload_javascript():
|
||||
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
|
||||
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>"
|
||||
head = f'<script type="text/javascript" src="file={os.path.abspath("script.js")}"></script>\n'
|
||||
|
||||
inline = f"{localization.localization_js(shared.opts.localization)};"
|
||||
if cmd_opts.theme is not None:
|
||||
javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
|
||||
inline += f"set_theme('{cmd_opts.theme}');"
|
||||
|
||||
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
|
||||
for script in modules.scripts.list_scripts("javascript", ".js"):
|
||||
head += f'<script type="text/javascript" src="file={script.path}"></script>\n'
|
||||
|
||||
head += f'<script type="text/javascript">{inline}</script>\n'
|
||||
|
||||
def template_response(*args, **kwargs):
|
||||
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
|
||||
res.body = res.body.replace(
|
||||
b'</head>', f'{javascript}</head>'.encode("utf8"))
|
||||
res.body = res.body.replace(b'</head>', f'{head}</head>'.encode("utf8"))
|
||||
res.init_headers()
|
||||
return res
|
||||
|
||||
|
|
|
|||
202
modules/ui_common.py
Normal file
202
modules/ui_common.py
Normal file
|
|
@ -0,0 +1,202 @@
|
|||
import json
|
||||
import html
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
|
||||
import gradio as gr
|
||||
import subprocess as sp
|
||||
|
||||
from modules import call_queue, shared
|
||||
from modules.generation_parameters_copypaste import image_from_url_text
|
||||
import modules.images
|
||||
|
||||
folder_symbol = '\U0001f4c2' # 📂
|
||||
|
||||
|
||||
def update_generation_info(generation_info, html_info, img_index):
|
||||
try:
|
||||
generation_info = json.loads(generation_info)
|
||||
if img_index < 0 or img_index >= len(generation_info["infotexts"]):
|
||||
return html_info, gr.update()
|
||||
return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
|
||||
except Exception:
|
||||
pass
|
||||
# if the json parse or anything else fails, just return the old html_info
|
||||
return html_info, gr.update()
|
||||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
return text
|
||||
|
||||
|
||||
def save_files(js_data, images, do_make_zip, index):
|
||||
import csv
|
||||
filenames = []
|
||||
fullfns = []
|
||||
|
||||
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
|
||||
class MyObject:
|
||||
def __init__(self, d=None):
|
||||
if d is not None:
|
||||
for key, value in d.items():
|
||||
setattr(self, key, value)
|
||||
|
||||
data = json.loads(js_data)
|
||||
|
||||
p = MyObject(data)
|
||||
path = shared.opts.outdir_save
|
||||
save_to_dirs = shared.opts.use_save_to_dirs_for_ui
|
||||
extension: str = shared.opts.samples_format
|
||||
start_index = 0
|
||||
|
||||
if index > -1 and shared.opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
||||
|
||||
images = [images[index]]
|
||||
start_index = index
|
||||
|
||||
os.makedirs(shared.opts.outdir_save, exist_ok=True)
|
||||
|
||||
with open(os.path.join(shared.opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
|
||||
at_start = file.tell() == 0
|
||||
writer = csv.writer(file)
|
||||
if at_start:
|
||||
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
||||
|
||||
for image_index, filedata in enumerate(images, start_index):
|
||||
image = image_from_url_text(filedata)
|
||||
|
||||
is_grid = image_index < p.index_of_first_image
|
||||
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
||||
|
||||
fullfn, txt_fullfn = modules.images.save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
||||
|
||||
filename = os.path.relpath(fullfn, path)
|
||||
filenames.append(filename)
|
||||
fullfns.append(fullfn)
|
||||
if txt_fullfn:
|
||||
filenames.append(os.path.basename(txt_fullfn))
|
||||
fullfns.append(txt_fullfn)
|
||||
|
||||
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
||||
|
||||
# Make Zip
|
||||
if do_make_zip:
|
||||
zip_filepath = os.path.join(path, "images.zip")
|
||||
|
||||
from zipfile import ZipFile
|
||||
with ZipFile(zip_filepath, "w") as zip_file:
|
||||
for i in range(len(fullfns)):
|
||||
with open(fullfns[i], mode="rb") as f:
|
||||
zip_file.writestr(filenames[i], f.read())
|
||||
fullfns.insert(0, zip_filepath)
|
||||
|
||||
return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
|
||||
|
||||
|
||||
def create_output_panel(tabname, outdir):
|
||||
from modules import shared
|
||||
import modules.generation_parameters_copypaste as parameters_copypaste
|
||||
|
||||
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])
|
||||
elif "microsoft-standard-WSL2" in platform.uname().release:
|
||||
sp.Popen(["wsl-open", path])
|
||||
else:
|
||||
sp.Popen(["xdg-open", path])
|
||||
|
||||
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
|
||||
with gr.Group(elem_id=f"{tabname}_gallery_container"):
|
||||
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(elem_id=f"image_buttons_{tabname}"):
|
||||
open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
|
||||
|
||||
if tabname != "extras":
|
||||
save = gr.Button('Save', elem_id=f'save_{tabname}')
|
||||
save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
|
||||
|
||||
buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
|
||||
|
||||
open_folder_button.click(
|
||||
fn=lambda: open_folder(shared.opts.outdir_samples or outdir),
|
||||
inputs=[],
|
||||
outputs=[],
|
||||
)
|
||||
|
||||
if tabname != "extras":
|
||||
with gr.Row():
|
||||
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
|
||||
|
||||
with gr.Group():
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
|
||||
if tabname == 'txt2img' or tabname == 'img2img':
|
||||
generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
|
||||
generation_info_button.click(
|
||||
fn=update_generation_info,
|
||||
_js="function(x, y, z){ return [x, y, selected_gallery_index()] }",
|
||||
inputs=[generation_info, html_info, html_info],
|
||||
outputs=[html_info, html_info],
|
||||
)
|
||||
|
||||
save.click(
|
||||
fn=call_queue.wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
],
|
||||
show_progress=False,
|
||||
)
|
||||
|
||||
save_zip.click(
|
||||
fn=call_queue.wrap_gradio_call(save_files),
|
||||
_js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
|
||||
inputs=[
|
||||
generation_info,
|
||||
result_gallery,
|
||||
html_info,
|
||||
html_info,
|
||||
],
|
||||
outputs=[
|
||||
download_files,
|
||||
html_log,
|
||||
]
|
||||
)
|
||||
|
||||
else:
|
||||
html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
|
||||
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
||||
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
||||
|
||||
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, html_log
|
||||
|
|
@ -47,3 +47,4 @@ class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
|
|||
|
||||
def get_block_name(self):
|
||||
return "colorpicker"
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import os.path
|
|||
from modules import shared
|
||||
import gradio as gr
|
||||
import json
|
||||
import html
|
||||
|
||||
from modules.generation_parameters_copypaste import image_from_url_text
|
||||
|
||||
|
|
@ -26,6 +27,7 @@ class ExtraNetworksPage:
|
|||
pass
|
||||
|
||||
def create_html(self, tabname):
|
||||
view = shared.opts.extra_networks_default_view
|
||||
items_html = ''
|
||||
|
||||
for item in self.list_items():
|
||||
|
|
@ -36,7 +38,7 @@ class ExtraNetworksPage:
|
|||
items_html = shared.html("extra-networks-no-cards.html").format(dirs=dirs)
|
||||
|
||||
res = f"""
|
||||
<div id='{tabname}_{self.name}_cards' class='extra-network-cards'>
|
||||
<div id='{tabname}_{self.name}_cards' class='extra-network-{view}'>
|
||||
{items_html}
|
||||
</div>
|
||||
"""
|
||||
|
|
@ -53,12 +55,13 @@ class ExtraNetworksPage:
|
|||
preview = item.get("preview", None)
|
||||
|
||||
args = {
|
||||
"preview_html": "style='background-image: url(" + json.dumps(preview) + ")'" if preview else '',
|
||||
"prompt": json.dumps(item["prompt"]),
|
||||
"preview_html": "style='background-image: url(\"" + html.escape(preview) + "\")'" if preview else '',
|
||||
"prompt": item["prompt"],
|
||||
"tabname": json.dumps(tabname),
|
||||
"local_preview": json.dumps(item["local_preview"]),
|
||||
"name": item["name"],
|
||||
"allow_negative_prompt": "true" if self.allow_negative_prompt else "false",
|
||||
"card_clicked": '"' + html.escape(f"""return cardClicked({json.dumps(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"',
|
||||
"save_card_preview": '"' + html.escape(f"""return saveCardPreview(event, {json.dumps(tabname)}, {json.dumps(item["local_preview"])})""") + '"',
|
||||
}
|
||||
|
||||
return self.card_page.format(**args)
|
||||
|
|
@ -79,10 +82,26 @@ class ExtraNetworksUi:
|
|||
self.tabname = None
|
||||
|
||||
|
||||
def pages_in_preferred_order(pages):
|
||||
tab_order = [x.lower().strip() for x in shared.opts.ui_extra_networks_tab_reorder.split(",")]
|
||||
|
||||
def tab_name_score(name):
|
||||
name = name.lower()
|
||||
for i, possible_match in enumerate(tab_order):
|
||||
if possible_match in name:
|
||||
return i
|
||||
|
||||
return len(pages)
|
||||
|
||||
tab_scores = {page.name: (tab_name_score(page.name), original_index) for original_index, page in enumerate(pages)}
|
||||
|
||||
return sorted(pages, key=lambda x: tab_scores[x.name])
|
||||
|
||||
|
||||
def create_ui(container, button, tabname):
|
||||
ui = ExtraNetworksUi()
|
||||
ui.pages = []
|
||||
ui.stored_extra_pages = extra_pages.copy()
|
||||
ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
|
||||
ui.tabname = tabname
|
||||
|
||||
with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import json
|
||||
import os
|
||||
|
||||
from modules import shared, ui_extra_networks
|
||||
|
|
@ -25,7 +26,7 @@ class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage):
|
|||
"name": name,
|
||||
"filename": path,
|
||||
"preview": preview,
|
||||
"prompt": f"<hypernet:{name}:1.0>",
|
||||
"prompt": json.dumps(f"<hypernet:{name}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
|
||||
"local_preview": path + ".png",
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,3 +1,4 @@
|
|||
import json
|
||||
import os
|
||||
|
||||
from modules import ui_extra_networks, sd_hijack
|
||||
|
|
@ -24,7 +25,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
|
|||
"name": embedding.name,
|
||||
"filename": embedding.filename,
|
||||
"preview": preview,
|
||||
"prompt": embedding.name,
|
||||
"prompt": json.dumps(embedding.name),
|
||||
"local_preview": path + ".preview.png",
|
||||
}
|
||||
|
||||
|
|
|
|||
57
modules/ui_postprocessing.py
Normal file
57
modules/ui_postprocessing.py
Normal file
|
|
@ -0,0 +1,57 @@
|
|||
import gradio as gr
|
||||
from modules import scripts_postprocessing, scripts, shared, gfpgan_model, codeformer_model, ui_common, postprocessing, call_queue
|
||||
import modules.generation_parameters_copypaste as parameters_copypaste
|
||||
|
||||
|
||||
def create_ui():
|
||||
tab_index = gr.State(value=0)
|
||||
|
||||
with gr.Row().style(equal_height=False, variant='compact'):
|
||||
with gr.Column(variant='compact'):
|
||||
with gr.Tabs(elem_id="mode_extras"):
|
||||
with gr.TabItem('Single Image', elem_id="extras_single_tab") as tab_single:
|
||||
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
|
||||
|
||||
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab") as tab_batch:
|
||||
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
|
||||
|
||||
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab") as tab_batch_dir:
|
||||
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
|
||||
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
|
||||
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
|
||||
|
||||
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
||||
|
||||
script_inputs = scripts.scripts_postproc.setup_ui()
|
||||
|
||||
with gr.Column():
|
||||
result_images, html_info_x, html_info, html_log = ui_common.create_output_panel("extras", shared.opts.outdir_extras_samples)
|
||||
|
||||
tab_single.select(fn=lambda: 0, inputs=[], outputs=[tab_index])
|
||||
tab_batch.select(fn=lambda: 1, inputs=[], outputs=[tab_index])
|
||||
tab_batch_dir.select(fn=lambda: 2, inputs=[], outputs=[tab_index])
|
||||
|
||||
submit.click(
|
||||
fn=call_queue.wrap_gradio_gpu_call(postprocessing.run_postprocessing, extra_outputs=[None, '']),
|
||||
inputs=[
|
||||
tab_index,
|
||||
extras_image,
|
||||
image_batch,
|
||||
extras_batch_input_dir,
|
||||
extras_batch_output_dir,
|
||||
show_extras_results,
|
||||
*script_inputs
|
||||
],
|
||||
outputs=[
|
||||
result_images,
|
||||
html_info_x,
|
||||
html_info,
|
||||
]
|
||||
)
|
||||
|
||||
parameters_copypaste.add_paste_fields("extras", extras_image, None)
|
||||
|
||||
extras_image.change(
|
||||
fn=scripts.scripts_postproc.image_changed,
|
||||
inputs=[], outputs=[]
|
||||
)
|
||||
Loading…
Add table
Add a link
Reference in a new issue