from __future__ import annotations import os import numpy as np from PIL import Image from typing import Callable, List, OrderedDict, Tuple from functools import partial from dataclasses import dataclass from modules import shared, images, devices, ui_components from modules.shared import opts import modules.gfpgan_model import modules.codeformer_model class LruCache(OrderedDict): @dataclass(frozen=True) class Key: image_hash: int info_hash: int args_hash: int @dataclass class Value: image: Image.Image info: str def __init__(self, max_size: int = 5, *args, **kwargs): super().__init__(*args, **kwargs) self._max_size = max_size def get(self, key: LruCache.Key) -> LruCache.Value: ret = super().get(key) if ret is not None: self.move_to_end(key) # Move to end of eviction list return ret def put(self, key: LruCache.Key, value: LruCache.Value) -> None: self[key] = value while len(self) > self._max_size: self.popitem(last=False) cached_images: LruCache = LruCache(max_size=5) def run_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): devices.torch_gc() shared.state.begin() shared.state.job = 'extras' imageArr = [] # Also keep track of original file names imageNameArr = [] outputs = [] if extras_mode == 1: #convert file to pillow image for img in image_folder: image = Image.open(img) imageArr.append(image) imageNameArr.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' if input_dir == '': return outputs, "Please select an input directory.", '' image_list = shared.listfiles(input_dir) for img in image_list: try: image = Image.open(img) except Exception: continue imageArr.append(image) imageNameArr.append(img) else: imageArr.append(image) imageNameArr.append(None) if extras_mode == 2 and output_dir != '': outpath = output_dir else: outpath = opts.outdir_samples or opts.outdir_extras_samples # Extra operation definitions def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]: shared.state.job = 'extras-gfpgan' restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8)) res = Image.fromarray(restored_img) if gfpgan_visibility < 1.0: res = Image.blend(image, res, gfpgan_visibility) info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n" return (res, info) def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]: shared.state.job = 'extras-codeformer' restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight) res = Image.fromarray(restored_img) if codeformer_visibility < 1.0: res = Image.blend(image, res, codeformer_visibility) info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n" return (res, info) def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): shared.state.job = 'extras-upscale' upscaler = shared.sd_upscalers[scaler_index] res = upscaler.scaler.upscale(image, resize, upscaler.data_path) if mode == 1 and crop: cropped = Image.new("RGB", (resize_w, resize_h)) cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2)) res = cropped return res def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]: # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text nonlocal upscaling_resize if resize_mode == 1: upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height) crop_info = " (crop)" if upscaling_crop else "" info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n" return (image, info) @dataclass class UpscaleParams: upscaler_idx: int blend_alpha: float def run_upscalers_blend(params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]: blended_result: Image.Image = None image_hash: str = hash(np.array(image.getdata()).tobytes()) for upscaler in params: upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) cache_key = LruCache.Key(image_hash=image_hash, info_hash=hash(info), args_hash=hash(upscale_args)) cached_entry = cached_images.get(cache_key) if cached_entry is None: res = upscale(image, *upscale_args) info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n" cached_images.put(cache_key, LruCache.Value(image=res, info=info)) else: res, info = cached_entry.image, cached_entry.info if blended_result is None: blended_result = res else: blended_result = Image.blend(blended_result, res, upscaler.blend_alpha) return (blended_result, info) # Build a list of operations to run facefix_ops: List[Callable] = [] facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else [] facefix_ops += [run_codeformer] if codeformer_visibility > 0 else [] upscale_ops: List[Callable] = [] upscale_ops += [run_prepare_crop] if resize_mode == 1 else [] if upscaling_resize != 0: step_params: List[UpscaleParams] = [] step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_1, blend_alpha=1.0)) if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: step_params.append(UpscaleParams(upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility)) upscale_ops.append(partial(run_upscalers_blend, step_params)) extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops) for image, image_name in zip(imageArr, imageNameArr): if image is None: return outputs, "Please select an input image.", '' shared.state.textinfo = f'Processing image {image_name}' existing_pnginfo = image.info or {} image = image.convert("RGB") info = "" # Run each operation on each image for op in extras_ops: image, info = op(image, info) if opts.use_original_name_batch and image_name is not None: basename = os.path.splitext(os.path.basename(image_name))[0] else: basename = '' if opts.enable_pnginfo: # append info before save image.info = existing_pnginfo image.info["extras"] = info if save_output: # Add upscaler name as a suffix. suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else "" # Add second upscaler if applicable. if suffix and extras_upscaler_2 and extras_upscaler_2_visibility: suffix += f"-{shared.sd_upscalers[extras_upscaler_2].name}" images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix) if extras_mode != 2 or show_extras_results : outputs.append(image) devices.torch_gc() return outputs, ui_components.plaintext_to_html(info), '' def clear_cache(): cached_images.clear()