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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, sd_samplers_common |
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import torch |
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import k_diffusion as K |
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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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if shared.sd_model.parameterization == "v": |
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dnw = K.external.CompVisVDenoiser(shared.sd_model) |
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skip = 1 |
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else: |
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dnw = K.external.CompVisDenoiser(shared.sd_model) |
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skip = 0 |
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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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image_conditioning = torch.cat([p.image_conditioning] * 2) |
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cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} |
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c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]] |
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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_common.store_latent(x) |
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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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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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if shared.sd_model.parameterization == "v": |
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dnw = K.external.CompVisVDenoiser(shared.sd_model) |
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skip = 1 |
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else: |
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dnw = K.external.CompVisDenoiser(shared.sd_model) |
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skip = 0 |
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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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image_conditioning = torch.cat([p.image_conditioning] * 2) |
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cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} |
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c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]] |
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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_common.store_latent(x) |
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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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info = gr.Markdown(''' |
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* `CFG Scale` should be 2 or lower. |
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''') |
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override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True, elem_id=self.elem_id("override_sampler")) |
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override_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")) |
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original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt")) |
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original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt")) |
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override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps")) |
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st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st")) |
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override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength")) |
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cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0, elem_id=self.elem_id("cfg")) |
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randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness")) |
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sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment")) |
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return [ |
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info, |
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override_sampler, |
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override_prompt, original_prompt, original_negative_prompt, |
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override_steps, st, |
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override_strength, |
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cfg, randomness, sigma_adjustment, |
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] |
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def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment): |
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if override_sampler: |
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p.sampler_name = "Euler" |
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if override_prompt: |
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p.prompt = original_prompt |
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p.negative_prompt = original_negative_prompt |
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if override_steps: |
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p.steps = st |
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if override_strength: |
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p.denoising_strength = 1.0 |
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def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): |
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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:], 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) |
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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(p.sampler_name, 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, image_conditioning=p.image_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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