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, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility): devices.torch_gc() imageArr = [] # Also keep track of original file names imageNameArr = [] 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]) else: imageArr.append(image) imageNameArr.append(None) outpath = opts.outdir_samples or opts.outdir_extras_samples outputs = [] 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 upscaling_resize != 1.0: def upscale(image, scaler_index, resize): 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) + 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) 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) if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0: res2 = upscale(image, extras_upscaler_2, upscaling_resize) 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()))] images.save_image(image, path=outpath, 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=image_name if opts.use_original_name_batch else None) if opts.enable_pnginfo: image.info = existing_pnginfo image.info["extras"] = info 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, interp_method, interp_amount, save_as_half, custom_name): # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation) def weighted_sum(theta0, theta1, alpha): return ((1 - alpha) * theta0) + (alpha * theta1) # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) def sigmoid(theta0, theta1, alpha): alpha = alpha * alpha * (3 - (2 * alpha)) return theta0 + ((theta1 - theta0) * alpha) # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep) def inv_sigmoid(theta0, theta1, alpha): import math alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0) return theta0 + ((theta1 - theta0) * alpha) primary_model_info = sd_models.checkpoints_list[primary_model_name] secondary_model_info = sd_models.checkpoints_list[secondary_model_name] print(f"Loading {primary_model_info.filename}...") primary_model = torch.load(primary_model_info.filename, map_location='cpu') print(f"Loading {secondary_model_info.filename}...") secondary_model = torch.load(secondary_model_info.filename, map_location='cpu') theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model) theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model) theta_funcs = { "Weighted Sum": weighted_sum, "Sigmoid": sigmoid, "Inverse Sigmoid": inv_sigmoid, } theta_func = theta_funcs[interp_method] print(f"Merging...") for key in tqdm.tqdm(theta_0.keys()): if 'model' in key and key in theta_1: theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint if save_as_half: theta_0[key] = theta_0[key].half() 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(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 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(3)]