105 lines
3.4 KiB
Python
105 lines
3.4 KiB
Python
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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
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from modules.processing import Processed
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from modules.sd_samplers import samplers
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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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cache = [None, None, None, None, None]
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class Script(scripts.Script):
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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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cfg = gr.Slider(label="Decode CFG scale", minimum=0.1, maximum=3.0, 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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return [original_prompt, cfg, st]
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def run(self, p, original_prompt, cfg, st):
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p.batch_size = 1
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p.batch_count = 1
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def sample_extra(x, conditioning, unconditional_conditioning):
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lat = tuple([int(x*10) for x in p.init_latent.cpu().numpy().flatten().tolist()])
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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:
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noise = cache[0]
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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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noise = find_noise_for_image(p, cond, unconditional_conditioning, cfg, st)
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cache[0] = noise
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cache[1] = cfg
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cache[2] = st
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cache[3] = lat
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cache[4] = original_prompt
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sampler = samplers[p.sampler_index].constructor(p.sd_model)
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samples_ddim = sampler.sample(p, noise, conditioning, unconditional_conditioning)
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return samples_ddim
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p.sample = sample_extra
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processed = processing.process_images(p)
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return processed
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