rework extras tab to use script system
This commit is contained in:
AUTOMATIC
parent
68303c96e5
commit
b5230197a6
13 changed files with 670 additions and 459 deletions
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@ -104,11 +104,6 @@ function create_tab_index_args(tabId, args){
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return res
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}
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function get_extras_tab_index(){
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const [,,...args] = [...arguments]
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return [get_tab_index('mode_extras'), get_tab_index('extras_resize_mode'), ...args]
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}
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function get_img2img_tab_index() {
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let res = args_to_array(arguments)
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res.splice(-2)
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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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@ -45,10 +44,8 @@ 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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@ -244,7 +241,7 @@ 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 +257,7 @@ 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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@ -1,219 +1,103 @@
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from __future__ import annotations
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import os
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import numpy as np
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from PIL import Image
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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 shared, images, devices, ui_components
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from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
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from modules.shared import opts
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import modules.gfpgan_model
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import modules.codeformer_model
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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_postprocessing(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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def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, 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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image_data = []
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image_names = []
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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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image_data.append(image)
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image_names.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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assert input_dir, 'input directory not selected'
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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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for filename in image_list:
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try:
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image = Image.open(img)
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image = Image.open(filename)
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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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image_data.append(image)
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image_names.append(filename)
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else:
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imageArr.append(image)
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imageNameArr.append(None)
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assert image, 'image not selected'
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image_data.append(image)
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image_names.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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infotext = ''
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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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for image, name in zip(image_data, image_names):
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shared.state.textinfo = name
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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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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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pp = scripts_postprocessing.PostprocessedImage(image.convert("RGB"))
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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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scripts.scripts_postproc.run(pp, args)
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if opts.use_original_name_batch and name is not None:
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basename = os.path.splitext(os.path.basename(name))[0]
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else:
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basename = ''
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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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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])
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if opts.enable_pnginfo:
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pp.image.info = existing_pnginfo
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pp.image.info["postprocessing"] = infotext
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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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images.save_image(pp.image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=pp.info, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None)
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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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if extras_mode != 2 or show_extras_results :
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outputs.append(image)
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if extras_mode != 2 or show_extras_results:
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outputs.append(pp.image)
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devices.torch_gc()
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return outputs, ui_components.plaintext_to_html(info), ''
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return outputs, ui_common.plaintext_to_html(infotext), ''
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def clear_cache():
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cached_images.clear()
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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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"""old handler for API"""
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args = scripts.scripts_postproc.create_args_for_run({
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"Upscale": {
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"upscale_mode": resize_mode,
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"upscale_by": upscaling_resize,
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"upscale_to_width": upscaling_resize_w,
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"upscale_to_height": upscaling_resize_h,
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"upscale_crop": upscaling_crop,
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"upscaler_1_name": extras_upscaler_1,
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"upscaler_2_name": extras_upscaler_2,
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"upscaler_2_visibility": extras_upscaler_2_visibility,
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},
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"GFPGAN": {
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"gfpgan_visibility": gfpgan_visibility,
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},
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"CodeFormer": {
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"codeformer_visibility": codeformer_visibility,
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"codeformer_weight": codeformer_weight,
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},
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})
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return run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir, show_extras_results, *args, save_output=save_output)
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@ -7,7 +7,7 @@ from collections import namedtuple
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import gradio as gr
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from modules.processing import StableDiffusionProcessing
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from modules import shared, paths, script_callbacks, extensions, script_loading
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from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
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AlwaysVisible = object()
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@ -150,8 +150,10 @@ def basedir():
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return current_basedir
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scripts_data = []
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ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"])
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scripts_data = []
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postprocessing_scripts_data = []
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ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir", "module"])
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@ -190,23 +192,31 @@ def list_files_with_name(filename):
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def load_scripts():
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global current_basedir
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scripts_data.clear()
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postprocessing_scripts_data.clear()
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script_callbacks.clear_callbacks()
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scripts_list = list_scripts("scripts", ".py")
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syspath = sys.path
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def register_scripts_from_module(module):
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for key, script_class in module.__dict__.items():
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if type(script_class) != type:
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continue
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if issubclass(script_class, Script):
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scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
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elif issubclass(script_class, scripts_postprocessing.ScriptPostprocessing):
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postprocessing_scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
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for scriptfile in sorted(scripts_list):
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try:
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if scriptfile.basedir != paths.script_path:
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sys.path = [scriptfile.basedir] + sys.path
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current_basedir = scriptfile.basedir
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module = script_loading.load_module(scriptfile.path)
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for key, script_class in module.__dict__.items():
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if type(script_class) == type and issubclass(script_class, Script):
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scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir, module))
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script_module = script_loading.load_module(scriptfile.path)
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register_scripts_from_module(script_module)
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except Exception:
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print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
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@ -413,6 +423,7 @@ class ScriptRunner:
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scripts_txt2img = ScriptRunner()
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scripts_img2img = ScriptRunner()
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scripts_postproc = scripts_postprocessing.ScriptPostprocessingRunner()
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scripts_current: ScriptRunner = None
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@ -423,12 +434,13 @@ def reload_script_body_only():
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def reload_scripts():
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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()
|
||||
|
|
@ -474,6 +474,11 @@ 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"),
|
||||
|
|
|
|||
265
modules/ui.py
265
modules/ui.py
|
|
@ -20,7 +20,7 @@ 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, postprocessing, ui_components
|
||||
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
|
||||
|
||||
|
|
@ -86,7 +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' # 📋
|
||||
|
|
@ -95,7 +94,7 @@ extra_networks_symbol = '\U0001F3B4' # 🎴
|
|||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
return ui_components.plaintext_to_html(text)
|
||||
return ui_common.plaintext_to_html(text)
|
||||
|
||||
|
||||
def send_gradio_gallery_to_image(x):
|
||||
|
|
@ -103,70 +102,6 @@ def send_gradio_gallery_to_image(x):
|
|||
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:
|
||||
|
|
@ -444,19 +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()
|
||||
|
|
@ -477,107 +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):
|
||||
|
|
@ -1106,86 +928,7 @@ 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(postprocessing.run_postprocessing, 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=postprocessing.clear_cache,
|
||||
inputs=[], outputs=[]
|
||||
)
|
||||
ui_postprocessing.create_ui()
|
||||
|
||||
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
|
||||
with gr.Row().style(equal_height=False):
|
||||
|
|
|
|||
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 scipy 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
|
||||
|
|
@ -1,5 +1,3 @@
|
|||
import html
|
||||
|
||||
import gradio as gr
|
||||
|
||||
|
||||
|
|
@ -50,7 +48,3 @@ class FormColorPicker(gr.ColorPicker, gr.components.FormComponent):
|
|||
def get_block_name(self):
|
||||
return "colorpicker"
|
||||
|
||||
|
||||
def plaintext_to_html(text):
|
||||
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
||||
return text
|
||||
|
|
|
|||
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=[]
|
||||
)
|
||||
36
scripts/postprocessing_codeformer.py
Normal file
36
scripts/postprocessing_codeformer.py
Normal file
|
|
@ -0,0 +1,36 @@
|
|||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, codeformer_model
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
class ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "CodeFormer"
|
||||
order = 3000
|
||||
|
||||
def ui(self):
|
||||
with FormRow():
|
||||
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, 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, elem_id="extras_codeformer_weight")
|
||||
|
||||
return {
|
||||
"codeformer_visibility": codeformer_visibility,
|
||||
"codeformer_weight": codeformer_weight,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):
|
||||
if codeformer_visibility == 0:
|
||||
return
|
||||
|
||||
restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)
|
||||
res = Image.fromarray(restored_img)
|
||||
|
||||
if codeformer_visibility < 1.0:
|
||||
res = Image.blend(pp.image, res, codeformer_visibility)
|
||||
|
||||
pp.image = res
|
||||
pp.info["CodeFormer visibility"] = round(codeformer_visibility, 3)
|
||||
pp.info["CodeFormer weight"] = round(codeformer_weight, 3)
|
||||
33
scripts/postprocessing_gfpgan.py
Normal file
33
scripts/postprocessing_gfpgan.py
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, gfpgan_model
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
class ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "GFPGAN"
|
||||
order = 2000
|
||||
|
||||
def ui(self):
|
||||
with FormRow():
|
||||
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, elem_id="extras_gfpgan_visibility")
|
||||
|
||||
return {
|
||||
"gfpgan_visibility": gfpgan_visibility,
|
||||
}
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):
|
||||
if gfpgan_visibility == 0:
|
||||
return
|
||||
|
||||
restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))
|
||||
res = Image.fromarray(restored_img)
|
||||
|
||||
if gfpgan_visibility < 1.0:
|
||||
res = Image.blend(pp.image, res, gfpgan_visibility)
|
||||
|
||||
pp.image = res
|
||||
pp.info["GFPGAN visibility"] = round(gfpgan_visibility, 3)
|
||||
106
scripts/postprocessing_upscale.py
Normal file
106
scripts/postprocessing_upscale.py
Normal file
|
|
@ -0,0 +1,106 @@
|
|||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
from modules import scripts_postprocessing, shared
|
||||
import gradio as gr
|
||||
|
||||
from modules.ui_components import FormRow
|
||||
|
||||
|
||||
upscale_cache = {}
|
||||
|
||||
|
||||
class ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):
|
||||
name = "Upscale"
|
||||
order = 1000
|
||||
|
||||
def ui(self):
|
||||
selected_tab = gr.State(value=0)
|
||||
|
||||
with gr.Tabs(elem_id="extras_resize_mode"):
|
||||
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab") as tab_scale_by:
|
||||
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") as tab_scale_to:
|
||||
with FormRow():
|
||||
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 FormRow():
|
||||
extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
|
||||
|
||||
with FormRow():
|
||||
extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)
|
||||
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=0.0, elem_id="extras_upscaler_2_visibility")
|
||||
|
||||
tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])
|
||||
tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])
|
||||
|
||||
return {
|
||||
"upscale_mode": selected_tab,
|
||||
"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,
|
||||
}
|
||||
|
||||
def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop):
|
||||
if upscale_mode == 1:
|
||||
upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)
|
||||
info["Postprocess upscale to"] = f"{upscale_to_width}x{upscale_to_height}"
|
||||
else:
|
||||
info["Postprocess upscale by"] = upscale_by
|
||||
|
||||
cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
cached_image = upscale_cache.pop(cache_key, None)
|
||||
|
||||
if cached_image is not None:
|
||||
image = cached_image
|
||||
else:
|
||||
image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path)
|
||||
|
||||
upscale_cache[cache_key] = image
|
||||
if len(upscale_cache) > shared.opts.upscaling_max_images_in_cache:
|
||||
upscale_cache.pop(next(iter(upscale_cache), None), None)
|
||||
|
||||
if upscale_mode == 1 and upscale_crop:
|
||||
cropped = Image.new("RGB", (upscale_to_width, upscale_to_height))
|
||||
cropped.paste(image, box=(upscale_to_width // 2 - image.width // 2, upscale_to_height // 2 - image.height // 2))
|
||||
image = cropped
|
||||
info["Postprocess crop to"] = f"{image.width}x{image.height}"
|
||||
|
||||
return image
|
||||
|
||||
def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):
|
||||
if upscaler_1_name == "None":
|
||||
upscaler_1_name = None
|
||||
|
||||
upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_1_name]), None)
|
||||
assert upscaler1 or (upscaler_1_name is None), f'could not find upscaler named {upscaler_1_name}'
|
||||
|
||||
if not upscaler1:
|
||||
return
|
||||
|
||||
if upscaler_2_name == "None":
|
||||
upscaler_2_name = None
|
||||
|
||||
upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != "None"]), None)
|
||||
assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
|
||||
|
||||
upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
pp.info[f"Postprocess upscaler"] = upscaler1.name
|
||||
|
||||
if upscaler2 and upscaler_2_visibility > 0:
|
||||
second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
|
||||
upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
|
||||
|
||||
pp.info[f"Postprocess upscaler 2"] = upscaler2.name
|
||||
|
||||
pp.image = upscaled_image
|
||||
|
||||
def image_changed(self):
|
||||
upscale_cache.clear()
|
||||
Loading…
Reference in a new issue