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 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.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.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): elem_prefix = ('i2i' if is_img2img else 't2i') + '_script_i2i_alternative_test_' 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=elem_prefix + "override_sampler") override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True, elem_id=elem_prefix + "override_prompt") original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=elem_prefix + "original_prompt") original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=elem_prefix + "original_negative_prompt") override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=elem_prefix + "override_steps") st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=elem_prefix + "st") override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=elem_prefix + "override_strength") cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=elem_prefix + "cfg") randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=elem_prefix + "randomness") sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=elem_prefix + "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