from __future__ import annotations import math import os import numpy as np from PIL import Image import torch import tqdm from typing import Callable, List, OrderedDict, Tuple from functools import partial from dataclasses import dataclass from modules import processing, shared, images, devices, sd_models from modules.shared import opts import modules.gfpgan_model from modules.ui import plaintext_to_html import modules.codeformer_model import piexif import piexif.helper import gradio as gr class LruCache(OrderedDict): @dataclass(frozen=True) class Key: image_hash: int info_hash: int args_hash: int @dataclass class Value: image: Image.Image info: str def __init__(self, max_size:int = 5, *args, **kwargs): super().__init__(*args, **kwargs) self._max_size = max_size def get(self, key: LruCache.Key) -> LruCache.Value: ret = super().get(key) if ret is not None: self.move_to_end(key) # Move to end of eviction list return ret def put(self, key: LruCache.Key, value: LruCache.Value) -> None: self[key] = value while len(self) > self._max_size: self.popitem(last=False) cached_images: LruCache = LruCache(max_size = 5) def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool ): devices.torch_gc() 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 = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)] 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]: restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8)) res = Image.fromarray(restored_img) if gfpgan_visibility < 1.0: res = Image.blend(image, res, gfpgan_visibility) info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n" return (res, info) def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]: restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight) res = Image.fromarray(restored_img) if codeformer_visibility < 1.0: res = Image.blend(image, res, codeformer_visibility) info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n" return (res, info) def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): upscaler = shared.sd_upscalers[scaler_index] res = upscaler.scaler.upscale(image, resize, upscaler.data_path) if mode == 1 and crop: cropped = Image.new("RGB", (resize_w, resize_h)) cropped.paste(res, box=(resize_w // 2 - res.width // 2, resize_h // 2 - res.height // 2)) res = cropped return res def run_prepare_crop(image: Image.Image, info: str) -> Tuple[Image.Image, str]: # Actual crop happens in run_upscalers_blend, this just sets upscaling_resize and adds info text nonlocal upscaling_resize if resize_mode == 1: upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height) crop_info = " (crop)" if upscaling_crop else "" info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n" return (image, info) @dataclass class UpscaleParams: upscaler_idx: int blend_alpha: float def run_upscalers_blend( params: List[UpscaleParams], image: Image.Image, info: str) -> Tuple[Image.Image, str]: blended_result: Image.Image = None for upscaler in params: upscale_args = (upscaler.upscaler_idx, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) cache_key = LruCache.Key( image_hash = hash(np.array(image.getdata()).tobytes()), info_hash = hash(info), args_hash = hash(upscale_args + (upscaler.blend_alpha,)) ) cached_entry = cached_images.get(cache_key) if cached_entry is None: res = upscale(image, *upscale_args) info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {upscaler.blend_alpha}, model:{shared.sd_upscalers[upscaler.upscaler_idx].name}\n" cached_images.put(cache_key, LruCache.Value(image=res, info=info)) else: res, info = cached_entry.image, cached_entry.info if blended_result is None: blended_result = res else: blended_result = Image.blend(blended_result, res, upscaler.blend_alpha) return (blended_result, info) # Build a list of operations to run facefix_ops: List[Callable] = [] facefix_ops += [run_gfpgan] if gfpgan_visibility > 0 else [] facefix_ops += [run_codeformer] if codeformer_visibility > 0 else [] upscale_ops: List[Callable] = [] upscale_ops += [run_prepare_crop] if resize_mode == 1 else [] if upscaling_resize != 0: step_params: List[UpscaleParams] = [] step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_1, blend_alpha=1.0 )) if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: step_params.append( UpscaleParams( upscaler_idx=extras_upscaler_2, blend_alpha=extras_upscaler_2_visibility ) ) upscale_ops.append( partial(run_upscalers_blend, step_params) ) extras_ops: List[Callable] = (upscale_ops + facefix_ops) if upscale_first else (facefix_ops + upscale_ops) for image, image_name in zip(imageArr, imageNameArr): if image is None: return outputs, "Please select an input image.", '' existing_pnginfo = image.info or {} image = image.convert("RGB") info = "" # Run each operation on each image for op in extras_ops: image, info = op(image, info) if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: basename = '' 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) if opts.enable_pnginfo: image.info = existing_pnginfo image.info["extras"] = info if extras_mode != 2 or show_extras_results : outputs.append(image) devices.torch_gc() return outputs, plaintext_to_html(info), '' def clear_cache(): cached_images.clear() def run_pnginfo(image): if image is None: return '', '', '' items = image.info geninfo = '' if "exif" in image.info: exif = piexif.load(image.info["exif"]) exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'') try: exif_comment = piexif.helper.UserComment.load(exif_comment) except ValueError: exif_comment = exif_comment.decode('utf8', errors="ignore") items['exif comment'] = exif_comment geninfo = exif_comment for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif', 'loop', 'background', 'timestamp', 'duration']: items.pop(field, None) geninfo = items.get('parameters', geninfo) info = '' for key, text in items.items(): info += f"""

{plaintext_to_html(str(key))}

{plaintext_to_html(str(text))}

""".strip()+"\n" if len(info) == 0: message = "Nothing found in the image." info = f"

{message}

" return '', geninfo, info def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name): def weighted_sum(theta0, theta1, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) def get_difference(theta1, theta2): return theta1 - theta2 def add_difference(theta0, theta1_2_diff, alpha): return theta0 + (alpha * theta1_2_diff) primary_model_info = sd_models.checkpoints_list[primary_model_name] secondary_model_info = sd_models.checkpoints_list[secondary_model_name] teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None) print(f"Loading {primary_model_info.filename}...") primary_model = torch.load(primary_model_info.filename, map_location='cpu') theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) print(f"Loading {secondary_model_info.filename}...") secondary_model = torch.load(secondary_model_info.filename, map_location='cpu') theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model) if teritary_model_info is not None: print(f"Loading {teritary_model_info.filename}...") teritary_model = torch.load(teritary_model_info.filename, map_location='cpu') theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model) else: teritary_model = None theta_2 = None theta_funcs = { "Weighted sum": (None, weighted_sum), "Add difference": (get_difference, add_difference), } theta_func1, theta_func2 = theta_funcs[interp_method] print(f"Merging...") if theta_func1: for key in tqdm.tqdm(theta_1.keys()): if 'model' in key: if key in theta_2: t2 = theta_2.get(key, torch.zeros_like(theta_1[key])) theta_1[key] = theta_func1(theta_1[key], t2) else: theta_1[key] = torch.zeros_like(theta_1[key]) del theta_2, teritary_model for key in tqdm.tqdm(theta_0.keys()): if 'model' in key and key in theta_1: theta_0[key] = theta_func2(theta_0[key], theta_1[key], multiplier) if save_as_half: theta_0[key] = theta_0[key].half() # I believe this part should be discarded, but I'll leave it for now until I am sure for key in theta_1.keys(): if 'model' in key and key not in theta_0: theta_0[key] = theta_1[key] if save_as_half: theta_0[key] = theta_0[key].half() ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt' filename = filename if custom_name == '' else (custom_name + '.ckpt') output_modelname = os.path.join(ckpt_dir, filename) print(f"Saving to {output_modelname}...") torch.save(primary_model, output_modelname) sd_models.list_models() print(f"Checkpoint saved.") return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]