stable-diffusion-webui
218 строк · 9.0 Кб
1from collections import namedtuple2
3import numpy as np4from tqdm import trange5
6import modules.scripts as scripts7import gradio as gr8
9from modules import processing, shared, sd_samplers, sd_samplers_common10
11import torch12import k_diffusion as K13
14def find_noise_for_image(p, cond, uncond, cfg_scale, steps):15x = p.init_latent16
17s_in = x.new_ones([x.shape[0]])18if shared.sd_model.parameterization == "v":19dnw = K.external.CompVisVDenoiser(shared.sd_model)20skip = 121else:22dnw = K.external.CompVisDenoiser(shared.sd_model)23skip = 024sigmas = dnw.get_sigmas(steps).flip(0)25
26shared.state.sampling_steps = steps27
28for i in trange(1, len(sigmas)):29shared.state.sampling_step += 130
31x_in = torch.cat([x] * 2)32sigma_in = torch.cat([sigmas[i] * s_in] * 2)33cond_in = torch.cat([uncond, cond])34
35image_conditioning = torch.cat([p.image_conditioning] * 2)36cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}37
38c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]39t = dnw.sigma_to_t(sigma_in)40
41eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)42denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)43
44denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale45
46d = (x - denoised) / sigmas[i]47dt = sigmas[i] - sigmas[i - 1]48
49x = x + d * dt50
51sd_samplers_common.store_latent(x)52
53# This shouldn't be necessary, but solved some VRAM issues54del x_in, sigma_in, cond_in, c_out, c_in, t,55del eps, denoised_uncond, denoised_cond, denoised, d, dt56
57shared.state.nextjob()58
59return x / x.std()60
61
62Cached = namedtuple("Cached", ["noise", "cfg_scale", "steps", "latent", "original_prompt", "original_negative_prompt", "sigma_adjustment"])63
64
65# Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
66def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):67x = p.init_latent68
69s_in = x.new_ones([x.shape[0]])70if shared.sd_model.parameterization == "v":71dnw = K.external.CompVisVDenoiser(shared.sd_model)72skip = 173else:74dnw = K.external.CompVisDenoiser(shared.sd_model)75skip = 076sigmas = dnw.get_sigmas(steps).flip(0)77
78shared.state.sampling_steps = steps79
80for i in trange(1, len(sigmas)):81shared.state.sampling_step += 182
83x_in = torch.cat([x] * 2)84sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)85cond_in = torch.cat([uncond, cond])86
87image_conditioning = torch.cat([p.image_conditioning] * 2)88cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}89
90c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]91
92if i == 1:93t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))94else:95t = dnw.sigma_to_t(sigma_in)96
97eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)98denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)99
100denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale101
102if i == 1:103d = (x - denoised) / (2 * sigmas[i])104else:105d = (x - denoised) / sigmas[i - 1]106
107dt = sigmas[i] - sigmas[i - 1]108x = x + d * dt109
110sd_samplers_common.store_latent(x)111
112# This shouldn't be necessary, but solved some VRAM issues113del x_in, sigma_in, cond_in, c_out, c_in, t,114del eps, denoised_uncond, denoised_cond, denoised, d, dt115
116shared.state.nextjob()117
118return x / sigmas[-1]119
120
121class Script(scripts.Script):122def __init__(self):123self.cache = None124
125def title(self):126return "img2img alternative test"127
128def show(self, is_img2img):129return is_img2img130
131def ui(self, is_img2img):132info = gr.Markdown('''133* `CFG Scale` should be 2 or lower.
134''')135
136override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True, elem_id=self.elem_id("override_sampler"))137
138override_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"))139original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt"))140original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt"))141
142override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps"))143st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st"))144
145override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength"))146
147cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg"))148randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness"))149sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))150
151return [152info,153override_sampler,154override_prompt, original_prompt, original_negative_prompt,155override_steps, st,156override_strength,157cfg, randomness, sigma_adjustment,158]159
160def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):161# Override162if override_sampler:163p.sampler_name = "Euler"164if override_prompt:165p.prompt = original_prompt166p.negative_prompt = original_negative_prompt167if override_steps:168p.steps = st169if override_strength:170p.denoising_strength = 1.0171
172def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):173lat = (p.init_latent.cpu().numpy() * 10).astype(int)174
175same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \176and self.cache.original_prompt == original_prompt \177and self.cache.original_negative_prompt == original_negative_prompt \178and self.cache.sigma_adjustment == sigma_adjustment179same_everything = same_params and self.cache.latent.shape == lat.shape and np.abs(self.cache.latent-lat).sum() < 100180
181if same_everything:182rec_noise = self.cache.noise183else:184shared.state.job_count += 1185cond = p.sd_model.get_learned_conditioning(p.batch_size * [original_prompt])186uncond = p.sd_model.get_learned_conditioning(p.batch_size * [original_negative_prompt])187if sigma_adjustment:188rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)189else:190rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)191self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)192
193rand_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)194
195combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)196
197sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)198
199sigmas = sampler.model_wrap.get_sigmas(p.steps)200
201noise_dt = combined_noise - (p.init_latent / sigmas[0])202
203p.seed = p.seed + 1204
205return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)206
207p.sample = sample_extra208
209p.extra_generation_params["Decode prompt"] = original_prompt210p.extra_generation_params["Decode negative prompt"] = original_negative_prompt211p.extra_generation_params["Decode CFG scale"] = cfg212p.extra_generation_params["Decode steps"] = st213p.extra_generation_params["Randomness"] = randomness214p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment215
216processed = processing.process_images(p)217
218return processed219