import numpy as np from tqdm import trange import modules.scripts as scripts import gradio as gr from modules import processing, shared, sd_samplers 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() cache = [None, None, None, None, None] class Script(scripts.Script): 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) cfg = gr.Slider(label="Decode CFG scale", minimum=0.1, maximum=3.0, step=0.1, value=1.0) st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50) return [original_prompt, cfg, st] def run(self, p, original_prompt, cfg, st): p.batch_size = 1 p.batch_count = 1 def sample_extra(x, conditioning, unconditional_conditioning): lat = tuple([int(x*10) for x in p.init_latent.cpu().numpy().flatten().tolist()]) if cache[0] is not None and cache[1] == cfg and cache[2] == st and len(cache[3]) == len(lat) and sum(np.array(cache[3])-np.array(lat)) < 100 and cache[4] == original_prompt: noise = cache[0] else: shared.state.job_count += 1 cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt]) noise = find_noise_for_image(p, cond, unconditional_conditioning, cfg, st) cache[0] = noise cache[1] = cfg cache[2] = st cache[3] = lat cache[4] = original_prompt sampler = samplers[p.sampler_index].constructor(p.sd_model) samples_ddim = sampler.sample(p, noise, conditioning, unconditional_conditioning) return samples_ddim p.sample = sample_extra processed = processing.process_images(p) return processed