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