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.sd_samplers import samplers 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]) 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"]) 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): original_prompt = gr.Textbox(label="Original prompt", lines=1) original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1) cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0) st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50) randomness = gr.Slider(label="randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0) return [original_prompt, original_negative_prompt, cfg, st, randomness] def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness): p.batch_size = 1 p.batch_count = 1 def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): 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 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]) rec_noise = find_noise_for_image(p, cond, uncond, cfg, st) self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt) rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])]) combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5) sampler = samplers[p.sampler_index].constructor(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) p.sample = sample_extra p.extra_generation_params = { "Decode prompt": original_prompt, "Decode CFG scale": cfg, "Decode steps": st, } processed = processing.process_images(p) return processed