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