| 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 = [] |
| 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 + 0 |
|
|
| 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 |
