import math from os.path import exists from tqdm import trange from modules import scripts, shared, processing, sd_samplers, script_callbacks, rng from modules import devices, prompt_parser, sd_models, extra_networks import modules.images as images import k_diffusion import gradio as gr import numpy as np from PIL import Image, ImageEnhance import torch import importlib def safe_import(import_name, pkg_name = None): try: __import__(import_name) except Exception: pkg_name = pkg_name or import_name import pip if hasattr(pip, 'main'): pip.main(['install', pkg_name]) else: pip._internal.main(['install', pkg_name]) __import__(import_name) safe_import('kornia') safe_import('omegaconf') safe_import('pathlib') from omegaconf import DictConfig, OmegaConf from pathlib import Path import kornia from skimage import exposure config_path = Path(__file__).parent.resolve() / '../config.yaml' class CustomHiresFix(scripts.Script): def __init__(self): super().__init__() if not exists(config_path): open(config_path, 'w').close() self.config: DictConfig = OmegaConf.load(config_path) self.callback_set = False self.orig_clip_skip = None self.orig_cfg = None self.p: processing.StableDiffusionProcessing = None self.pp = None self.sampler = None self.cond = None self.uncond = None self.step = None self.tv = None self.width = None self.height = None self.use_cn = False self.external_code = None self.cn_image = None self.cn_units = [] def title(self): return "Custom Hires Fix" def show(self, is_img2img): return scripts.AlwaysVisible def ui(self, is_img2img): with gr.Accordion(label='Custom hires fix', open=False): enable = gr.Checkbox(label='Enable extension', value=self.config.get('enable', False)) with gr.Row(): width = gr.Slider(minimum=512, maximum=2048, step=8, label="Upscale width to", value=self.config.get('width', 1024), allow_flagging='never', show_progress=False) height = gr.Slider(minimum=512, maximum=2048, step=8, label="Upscale height to", value=self.config.get('height', 0), allow_flagging='never', show_progress=False) steps = gr.Slider(minimum=8, maximum=25, step=1, label="Steps", value=self.config.get('steps', 15)) with gr.Row(): prompt = gr.Textbox(label='Prompt for upscale (added to generation prompt)', placeholder='Leave empty for using generation prompt', value=self.config.get('prompt', '')) with gr.Row(): negative_prompt = gr.Textbox(label='Negative prompt for upscale (replaces generation prompt)', placeholder='Leave empty for using generation negative prompt', value=self.config.get('negative_prompt', '')) with gr.Row(): first_upscaler = gr.Dropdown([*[x.name for x in shared.sd_upscalers if x.name not in ['None', 'Nearest', 'LDSR']]], label='First upscaler', value=self.config.get('first_upscaler', 'R-ESRGAN 4x+')) second_upscaler = gr.Dropdown([*[x.name for x in shared.sd_upscalers if x.name not in ['None', 'Nearest', 'LDSR']]], label='Second upscaler', value=self.config.get('second_upscaler', 'R-ESRGAN 4x+')) with gr.Row(): first_latent = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Latent upscale ratio (1)", value=self.config.get('first_latent', 0.3)) second_latent = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Latent upscale ratio (2)", value=self.config.get('second_latent', 0.1)) with gr.Row(): filter = gr.Dropdown(['Noise sync (sharp)', 'Morphological (smooth)', 'Combined (balanced)'], label='Filter mode', value=self.config.get('filter', 'Noise sync (sharp)')) strength = gr.Slider(minimum=1.0, maximum=3.5, step=0.1, label="Generation strength", value=self.config.get('strength', 2.0)) denoise_offset = gr.Slider(minimum=-0.05, maximum=0.15, step=0.01, label="Denoise offset", value=self.config.get('denoise_offset', 0.05)) with gr.Accordion(label='Extra', open=False): with gr.Row(): filter_offset = gr.Slider(minimum=-1.0, maximum=1.0, step=0.1, label="Filter offset (higher - smoother)", value=self.config.get('filter_offset', 0.0)) clip_skip = gr.Slider(minimum=0, maximum=5, step=1, label="Clip skip for upscale (0 - not change)", value=self.config.get('clip_skip', 0)) with gr.Row(): start_control_at = gr.Slider(minimum=0.0, maximum=0.7, step=0.01, label="CN start for enabled units", value=self.config.get('start_control_at', 0.0)) cn_ref = gr.Checkbox(label='Use last image for reference', value=self.config.get('cn_ref', False)) with gr.Row(): sampler = gr.Dropdown(['Restart', 'DPM++ 2M', 'DPM++ 2M Karras', 'DPM++ 2M SDE', 'DPM++ 2M SDE Karras', 'DPM++ 2M SDE Heun', 'DPM++ 2M SDE Heun Karras', 'DPM++ 3M SDE', 'DPM++ 3M SDE Karras', 'Restart + DPM++ 3M SDE'], label='Sampler', value=self.config.get('sampler', 'DPM++ 2M Karras')) if is_img2img: width.change(fn=lambda x: gr.update(value=0), inputs=width, outputs=height) height.change(fn=lambda x: gr.update(value=0), inputs=height, outputs=width) else: width.change(fn=lambda x: gr.update(value=0), inputs=width, outputs=height) height.change(fn=lambda x: gr.update(value=0), inputs=height, outputs=width) ui = [enable, width, height, steps, first_upscaler, second_upscaler, first_latent, second_latent, prompt, negative_prompt, strength, filter, filter_offset, denoise_offset, clip_skip, sampler, cn_ref, start_control_at] for elem in ui: setattr(elem, "do_not_save_to_config", True) return ui def process(self, p, *args, **kwargs): self.p = p self.cn_units = [] try: self.external_code = importlib.import_module('extensions.sd-webui-controlnet.scripts.external_code', 'external_code') cn_units = self.external_code.get_all_units_in_processing(p) for unit in cn_units: self.cn_units += [unit] self.use_cn = len(self.cn_units) > 0 except ImportError: self.use_cn = False def postprocess_image(self, p, pp: scripts.PostprocessImageArgs, enable, width, height, steps, first_upscaler, second_upscaler, first_latent, second_latent, prompt, negative_prompt, strength, filter, filter_offset, denoise_offset, clip_skip, sampler, cn_ref, start_control_at ): if not enable: return self.step = 0 self.pp = pp self.config.width = width self.config.height = height self.config.prompt = prompt.strip() self.config.negative_prompt = negative_prompt.strip() self.config.steps = steps self.config.first_upscaler = first_upscaler self.config.second_upscaler = second_upscaler self.config.first_latent = first_latent self.config.second_latent = second_latent self.config.strength = strength self.config.filter = filter self.config.filter_offset = filter_offset self.config.denoise_offset = denoise_offset self.config.clip_skip = clip_skip self.config.sampler = sampler self.config.cn_ref = cn_ref self.config.start_control_at = start_control_at self.orig_clip_skip = shared.opts.CLIP_stop_at_last_layers self.orig_cfg = p.cfg_scale if clip_skip > 0: shared.opts.CLIP_stop_at_last_layers = clip_skip if 'Restart' in self.config.sampler: self.sampler = sd_samplers.create_sampler('Restart', p.sd_model) else: self.sampler = sd_samplers.create_sampler(sampler, p.sd_model) def denoise_callback(params: script_callbacks.CFGDenoiserParams): if params.sampling_step > 0: p.cfg_scale = self.orig_cfg if self.step == 1 and self.config.strength != 1.0: params.sigma[-1] = params.sigma[0] * (1 - (1 - self.config.strength) / 100) elif self.step == 2 and self.config.filter == 'Noise sync (sharp)': params.sigma[-1] = params.sigma[0] * (1 - (self.tv - 1 + self.config.filter_offset - (self.config.denoise_offset * 5)) / 50) elif self.step == 2 and self.config.filter == 'Combined (balanced)': params.sigma[-1] = params.sigma[0] * (1 - (self.tv - 1 + self.config.filter_offset - (self.config.denoise_offset * 5)) / 100) if self.callback_set is False: script_callbacks.on_cfg_denoiser(denoise_callback) self.callback_set = True _, loras_act = extra_networks.parse_prompt(prompt) extra_networks.activate(p, loras_act) _, loras_deact = extra_networks.parse_prompt(negative_prompt) extra_networks.deactivate(p, loras_deact) self.cn_image = pp.image with devices.autocast(): shared.state.nextjob() x = self.gen(pp.image) shared.state.nextjob() x = self.filter(x) shared.opts.CLIP_stop_at_last_layers = self.orig_clip_skip sd_models.apply_token_merging(p.sd_model, p.get_token_merging_ratio()) pp.image = x extra_networks.deactivate(p, loras_act) OmegaConf.save(self.config, config_path) def enable_cn(self, image: np.ndarray): for unit in self.cn_units: if unit.model != 'None': unit.guidance_start = self.config.start_control_at if unit.enabled else unit.guidance_start unit.processor_res = min(image.shape[0], image.shape[0]) unit.enabled = True if unit.image is None: unit.image = image self.p.width = image.shape[1] self.p.height = image.shape[0] self.external_code.update_cn_script_in_processing(self.p, self.cn_units) for script in self.p.scripts.alwayson_scripts: if script.title().lower() == 'controlnet': script.controlnet_hack(self.p) def process_prompt(self): prompt = self.p.prompt.strip().split('AND', 1)[0] if self.config.prompt != '': prompt = f'{prompt} {self.config.prompt}' if self.config.negative_prompt != '': negative_prompt = self.config.negative_prompt else: negative_prompt = self.p.negative_prompt.strip() with devices.autocast(): if self.width is not None and self.height is not None and hasattr(prompt_parser, 'SdConditioning'): c = prompt_parser.SdConditioning([prompt], False, self.width, self.height) uc = prompt_parser.SdConditioning([negative_prompt], False, self.width, self.height) else: c = [prompt] uc = [negative_prompt] self.cond = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, c, self.config.steps) self.uncond = prompt_parser.get_learned_conditioning(shared.sd_model, uc, self.config.steps) def gen(self, x): self.step = 1 ratio = x.width / x.height self.width = self.config.width if self.config.width > 0 else int(self.config.height * ratio) self.height = self.config.height if self.config.height > 0 else int(self.config.width / ratio) self.width = int((self.width - x.width) // 2 + x.width) self.height = int((self.height - x.height) // 2 + x.height) sd_models.apply_token_merging(self.p.sd_model, self.p.get_token_merging_ratio(for_hr=True) / 2) if self.use_cn: self.enable_cn(np.array(self.cn_image.resize((self.width, self.height)))) with devices.autocast(), torch.inference_mode(): self.process_prompt() x_big = None if self.config.first_latent > 0: image = np.array(x).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) decoded_sample = torch.from_numpy(image) decoded_sample = decoded_sample.to(shared.device).to(devices.dtype_vae) decoded_sample = 2.0 * decoded_sample - 1.0 encoded_sample = shared.sd_model.encode_first_stage(decoded_sample.unsqueeze(0).to(devices.dtype_vae)) sample = shared.sd_model.get_first_stage_encoding(encoded_sample) x_big = torch.nn.functional.interpolate(sample, (self.height // 8, self.width // 8), mode='nearest') if self.config.first_latent < 1: x = images.resize_image(0, x, self.width, self.height, upscaler_name=self.config.first_upscaler) image = np.array(x).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) decoded_sample = torch.from_numpy(image) decoded_sample = decoded_sample.to(shared.device).to(devices.dtype_vae) decoded_sample = 2.0 * decoded_sample - 1.0 encoded_sample = shared.sd_model.encode_first_stage(decoded_sample.unsqueeze(0).to(devices.dtype_vae)) sample = shared.sd_model.get_first_stage_encoding(encoded_sample) else: sample = x_big if x_big is not None and self.config.first_latent != 1: sample = (sample * (1 - self.config.first_latent)) + (x_big * self.config.first_latent) image_conditioning = self.p.img2img_image_conditioning(decoded_sample, sample) noise = torch.zeros_like(sample) noise = kornia.augmentation.RandomGaussianNoise(mean=0.0, std=1.0, p=1.0)(noise) steps = int(max(((self.p.steps - self.config.steps) / 2) + self.config.steps, self.config.steps)) self.p.denoising_strength = 0.45 + self.config.denoise_offset * 0.2 self.p.cfg_scale = self.orig_cfg + 3 def denoiser_override(n): sigmas = k_diffusion.sampling.get_sigmas_polyexponential(n, 0.01, 15, 0.5, devices.device) return sigmas self.p.rng = rng.ImageRNG(sample.shape[1:], self.p.seeds, subseeds=self.p.subseeds, subseed_strength=self.p.subseed_strength, seed_resize_from_h=self.p.seed_resize_from_h, seed_resize_from_w=self.p.seed_resize_from_w) self.p.sampler_noise_scheduler_override = denoiser_override self.p.batch_size = 1 sample = self.sampler.sample_img2img(self.p, sample.to(devices.dtype), noise, self.cond, self.uncond, steps=steps, image_conditioning=image_conditioning).to(devices.dtype_vae) b, c, w, h = sample.size() self.tv = kornia.losses.TotalVariation()(sample).mean() / (w * h) devices.torch_gc() decoded_sample = processing.decode_first_stage(shared.sd_model, sample) if math.isnan(decoded_sample.min()): devices.torch_gc() sample = torch.clamp(sample, -3, 3) decoded_sample = processing.decode_first_stage(shared.sd_model, sample) decoded_sample = torch.clamp((decoded_sample + 1.0) / 2.0, min=0.0, max=1.0).squeeze() x_sample = 255. * np.moveaxis(decoded_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) image = Image.fromarray(x_sample) return image def filter(self, x): if 'Restart' == self.config.sampler: self.sampler = sd_samplers.create_sampler('Restart', shared.sd_model) elif 'Restart + DPM++ 3M SDE' == self.config.sampler: self.sampler = sd_samplers.create_sampler('DPM++ 3M SDE', shared.sd_model) self.step = 2 ratio = x.width / x.height self.width = self.config.width if self.config.width > 0 else int(self.config.height * ratio) self.height = self.config.height if self.config.height > 0 else int(self.config.width / ratio) sd_models.apply_token_merging(self.p.sd_model, self.p.get_token_merging_ratio(for_hr=True)) if self.use_cn: self.cn_image = x if self.config.cn_ref else self.cn_image self.enable_cn(np.array(self.cn_image.resize((self.width, self.height)))) with devices.autocast(), torch.inference_mode(): self.process_prompt() x_big = None if self.config.second_latent > 0: image = np.array(x).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) decoded_sample = torch.from_numpy(image) decoded_sample = decoded_sample.to(shared.device).to(devices.dtype_vae) decoded_sample = 2.0 * decoded_sample - 1.0 encoded_sample = shared.sd_model.encode_first_stage(decoded_sample.unsqueeze(0).to(devices.dtype_vae)) sample = shared.sd_model.get_first_stage_encoding(encoded_sample) x_big = torch.nn.functional.interpolate(sample, (self.height // 8, self.width // 8), mode='nearest') if self.config.second_latent < 1: x = images.resize_image(0, x, self.width, self.height, upscaler_name=self.config.second_upscaler) image = np.array(x).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) decoded_sample = torch.from_numpy(image) decoded_sample = decoded_sample.to(shared.device).to(devices.dtype_vae) decoded_sample = 2.0 * decoded_sample - 1.0 encoded_sample = shared.sd_model.encode_first_stage(decoded_sample.unsqueeze(0).to(devices.dtype_vae)) sample = shared.sd_model.get_first_stage_encoding(encoded_sample) else: sample = x_big if x_big is not None and self.config.second_latent != 1: sample = (sample * (1 - self.config.second_latent)) + (x_big * self.config.second_latent) image_conditioning = self.p.img2img_image_conditioning(decoded_sample, sample) noise = torch.zeros_like(sample) noise = kornia.augmentation.RandomGaussianNoise(mean=0.0, std=1.0, p=1.0)(noise) self.p.denoising_strength = 0.45 + self.config.denoise_offset self.p.cfg_scale = self.orig_cfg + 3 if self.config.filter == 'Morphological (smooth)': noise_mask = kornia.morphology.gradient(sample, torch.ones(5, 5).to(devices.device)) noise_mask = kornia.filters.median_blur(noise_mask, (3, 3)) noise_mask = (0.1 + noise_mask / noise_mask.max()) * (max( (1.75 - (self.tv - 1) * 4), 1.75) - self.config.filter_offset) noise = noise * noise_mask elif self.config.filter == 'Combined (balanced)': noise_mask = kornia.morphology.gradient(sample, torch.ones(5, 5).to(devices.device)) noise_mask = kornia.filters.median_blur(noise_mask, (3, 3)) noise_mask = (0.1 + noise_mask / noise_mask.max()) * (max( (1.75 - (self.tv - 1) / 2), 1.75) - self.config.filter_offset) noise = noise * noise_mask def denoiser_override(n): return k_diffusion.sampling.get_sigmas_polyexponential(n, 0.01, 7, 0.5, devices.device) self.p.sampler_noise_scheduler_override = denoiser_override self.p.batch_size = 1 samples = self.sampler.sample_img2img(self.p, sample.to(devices.dtype), noise, self.cond, self.uncond, steps=self.config.steps, image_conditioning=image_conditioning ).to(devices.dtype_vae) devices.torch_gc() self.p.iteration += 1 decoded_sample = processing.decode_first_stage(shared.sd_model, samples) if math.isnan(decoded_sample.min()): devices.torch_gc() samples = torch.clamp(samples, -3, 3) decoded_sample = processing.decode_first_stage(shared.sd_model, samples) decoded_sample = torch.clamp((decoded_sample + 1.0) / 2.0, min=0.0, max=1.0).squeeze() x_sample = 255. * np.moveaxis(decoded_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) image = Image.fromarray(x_sample) return image