123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218 |
- 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, sd_samplers_common
- import torch
- import k_diffusion as K
- def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
- x = p.init_latent
- s_in = x.new_ones([x.shape[0]])
- if shared.sd_model.parameterization == "v":
- dnw = K.external.CompVisVDenoiser(shared.sd_model)
- skip = 1
- else:
- dnw = K.external.CompVisDenoiser(shared.sd_model)
- skip = 0
- 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])
- image_conditioning = torch.cat([p.image_conditioning] * 2)
- cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
- c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
- 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_common.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", "sigma_adjustment"])
- # Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
- def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
- x = p.init_latent
- s_in = x.new_ones([x.shape[0]])
- if shared.sd_model.parameterization == "v":
- dnw = K.external.CompVisVDenoiser(shared.sd_model)
- skip = 1
- else:
- dnw = K.external.CompVisDenoiser(shared.sd_model)
- skip = 0
- 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 - 1] * s_in] * 2)
- cond_in = torch.cat([uncond, cond])
- image_conditioning = torch.cat([p.image_conditioning] * 2)
- cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
- c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
- if i == 1:
- t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2))
- else:
- 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
- if i == 1:
- d = (x - denoised) / (2 * sigmas[i])
- else:
- d = (x - denoised) / sigmas[i - 1]
- dt = sigmas[i] - sigmas[i - 1]
- x = x + d * dt
- sd_samplers_common.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 / sigmas[-1]
- 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):
- info = gr.Markdown('''
- * `CFG Scale` should be 2 or lower.
- ''')
- 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"))
- 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"))
- original_prompt = gr.Textbox(label="Original prompt", lines=1, elem_id=self.elem_id("original_prompt"))
- original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1, elem_id=self.elem_id("original_negative_prompt"))
- override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True, elem_id=self.elem_id("override_steps"))
- st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50, elem_id=self.elem_id("st"))
- override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True, elem_id=self.elem_id("override_strength"))
- 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"))
- randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0, elem_id=self.elem_id("randomness"))
- sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))
- return [
- info,
- override_sampler,
- override_prompt, original_prompt, original_negative_prompt,
- override_steps, st,
- override_strength,
- cfg, randomness, sigma_adjustment,
- ]
- def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
- # Override
- if override_sampler:
- p.sampler_name = "Euler"
- if override_prompt:
- p.prompt = original_prompt
- p.negative_prompt = original_negative_prompt
- if override_steps:
- p.steps = st
- if override_strength:
- p.denoising_strength = 1.0
- def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
- 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 \
- and self.cache.sigma_adjustment == sigma_adjustment
- 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])
- if sigma_adjustment:
- rec_noise = find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg, st)
- else:
- rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
- self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
- 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)
-
- combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
-
- sampler = sd_samplers.create_sampler(p.sampler_name, 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, image_conditioning=p.image_conditioning)
- p.sample = sample_extra
- p.extra_generation_params["Decode prompt"] = original_prompt
- p.extra_generation_params["Decode negative prompt"] = original_negative_prompt
- p.extra_generation_params["Decode CFG scale"] = cfg
- p.extra_generation_params["Decode steps"] = st
- p.extra_generation_params["Randomness"] = randomness
- p.extra_generation_params["Sigma Adjustment"] = sigma_adjustment
- processed = processing.process_images(p)
- return processed
|