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| from collections import namedtuple | |
| import numpy as np | |
| from tqdm import trange | |
| import modules.scripts as scripts | |
| import gradio as gr | |
| from modules import processing, shared, sd_samplers, prompt_parser, sd_samplers_common | |
| from modules.processing import Processed | |
| from modules.shared import opts, cmd_opts, state | |
| import torch | |
| import k_diffusion as K | |
| from PIL import Image | |
| from torch import autocast | |
| from einops import rearrange, repeat | |
| def find_noise_for_image(p, cond, uncond, cfg_scale, steps): | |
| x = p.init_latent | |
| s_in = x.new_ones([x.shape[0]]) | |
| dnw = K.external.CompVisDenoiser(shared.sd_model) | |
| sigmas = dnw.get_sigmas(steps).flip(0) | |
| shared.state.sampling_steps = steps | |
| for i in trange(1, len(sigmas)): | |
| shared.state.sampling_step += 1 | |
| x_in = torch.cat([x] * 2) | |
| sigma_in = torch.cat([sigmas[i] * s_in] * 2) | |
| cond_in = torch.cat([uncond, cond]) | |
| image_conditioning = torch.cat([p.image_conditioning] * 2) | |
| cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} | |
| c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] | |
| t = dnw.sigma_to_t(sigma_in) | |
| eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) | |
| denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2) | |
| denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale | |
| d = (x - denoised) / sigmas[i] | |
| dt = sigmas[i] - sigmas[i - 1] | |
| x = x + d * dt | |
| sd_samplers_common.store_latent(x) | |
| # This shouldn't be necessary, but solved some VRAM issues | |
| del x_in, sigma_in, cond_in, c_out, c_in, t, | |
| del eps, denoised_uncond, denoised_cond, denoised, d, dt | |
| shared.state.nextjob() | |
| return x / x.std() | |
| Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"]) | |
| # Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736 | |
| def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): | |
| x = p.init_latent | |
| s_in = x.new_ones([x.shape[0]]) | |
| dnw = K.external.CompVisDenoiser(shared.sd_model) | |
| sigmas = dnw.get_sigmas(steps).flip(0) | |
| shared.state.sampling_steps = steps | |
| for i in trange(1, len(sigmas)): | |
| shared.state.sampling_step += 1 | |
| x_in = torch.cat([x] * 2) | |
| sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2) | |
| cond_in = torch.cat([uncond, cond]) | |
| image_conditioning = torch.cat([p.image_conditioning] * 2) | |
| cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} | |
| c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] | |
| if i == 1: | |
| t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2)) | |
| else: | |
| t = dnw.sigma_to_t(sigma_in) | |
| eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in) | |
| denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2) | |
| denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale | |
| if i == 1: | |
| d = (x - denoised) / (2 * sigmas[i]) | |
| else: | |
| d = (x - denoised) / sigmas[i - 1] | |
| dt = sigmas[i] - sigmas[i - 1] | |
| x = x + d * dt | |
| sd_samplers_common.store_latent(x) | |
| # This shouldn't be necessary, but solved some VRAM issues | |
| del x_in, sigma_in, cond_in, c_out, c_in, t, | |
| del eps, denoised_uncond, denoised_cond, denoised, d, dt | |
| shared.state.nextjob() | |
| return x / sigmas[-1] | |
| class Script(scripts.Script): | |
| def __init__(self): | |
| self.cache = None | |
| def title(self): | |
| return "img2img alternative test" | |
| def show(self, is_img2img): | |
| return is_img2img | |
| def ui(self, is_img2img): | |
| info = gr.Markdown(''' | |
| * `CFG Scale` should be 2 or lower. | |
| ''') | |
| override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True, elem_id=self.elem_id("override_sampler")) | |
| override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True, elem_id=self.elem_id("override_prompt")) | |
| original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt")) | |
| original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt")) | |
| override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps")) | |
| st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st")) | |
| override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength")) | |
| cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg")) | |
| randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness")) | |
| sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment")) | |
| return [ | |
| info, | |
| override_sampler, | |
| override_prompt, original_prompt, original_negative_prompt, | |
| override_steps, st, | |
| override_strength, | |
| cfg, randomness, sigma_adjustment, | |
| ] | |
| def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment): | |
| # Override | |
| if override_sampler: | |
| p.sampler_name = "Euler" | |
| if override_prompt: | |
| p.prompt = original_prompt | |
| p.negative_prompt = original_negative_prompt | |
| if override_steps: | |
| p.steps = st | |
| if override_strength: | |
| p.denoising_strength = 1.0 | |
| def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): | |
| lat = (p.init_latent.cpu().numpy() * 10).astype(int) | |
| same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \ | |
| and self.cache.original_prompt == original_prompt \ | |
| and self.cache.original_negative_prompt == original_negative_prompt \ | |
| and self.cache.sigma_adjustment == sigma_adjustment | |
| same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100 | |
| if same_everything: | |
| rec_noise = self.cache.noise | |
| else: | |
| shared.state.job_count += 1 | |
| cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt]) | |
| uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt]) | |
| if sigma_adjustment: | |
| rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st) | |
| else: | |
| rec_noise = find_noise_for_image(p, cond, uncond, cfg, st) | |
| self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment) | |
| rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p) | |
| combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5) | |
| sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model) | |
| sigmas = sampler.model_wrap.get_sigmas(p.steps) | |
| noise_dt = combined_noise - (p.init_latent / sigmas[0]) | |
| p.seed = p.seed + 1 | |
| return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning) | |
| p.sample = sample_extra | |
| p.extra_generation_params["Decode prompt"] = original_prompt | |
| p.extra_generation_params["Decode negative prompt"] = original_negative_prompt | |
| p.extra_generation_params["Decode CFG scale"] = cfg | |
| p.extra_generation_params["Decode steps"] = st | |
| p.extra_generation_params["Randomness"] = randomness | |
| p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment | |
| processed = processing.process_images(p) | |
| return processed | |