import math import os import numpy as np from PIL import Image import torch import tqdm 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 cached_images = {} 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): 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 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 = "" if gfpgan_visibility > 0: restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8)) res = Image.fromarray(restored_img) if gfpgan_visibility < 1.0: res = Image.blend(image, res, gfpgan_visibility) info += f"GFPGAN visibility:{round(gfpgan_visibility, 2)}\n" image = res if codeformer_visibility > 0: restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight) res = Image.fromarray(restored_img) if codeformer_visibility < 1.0: res = Image.blend(image, res, codeformer_visibility) info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n" image = res 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" if upscaling_resize != 1.0: def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop): small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10)) pixels = tuple(np.array(small).flatten().tolist()) key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight, resize_mode, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop) + pixels c = cached_images.get(key) if c is None: upscaler = shared.sd_upscalers[scaler_index] c = upscaler.scaler.upscale(image, resize, upscaler.data_path) if mode == 1 and crop: cropped = Image.new("RGB", (resize_w, resize_h)) cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2)) c = cropped cached_images[key] = c return c info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n" res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop) info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n" res = Image.blend(res, res2, extras_upscaler_2_visibility) image = res while len(cached_images) > 2: del cached_images[next(iter(cached_images.keys()))] if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: 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 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)]