Upload custom_hires_fix.py

custom_hires_fix.py

@@ -0,0 +1,416 @@
+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