A "reverse" version of the k_euler sampler for Stable Diffusion, which finds the noise that will reconstruct the supplied image
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import torch | |
import numpy as np | |
import k_diffusion as K | |
from PIL import Image | |
from torch import autocast | |
from einops import rearrange, repeat | |
def pil_img_to_torch(pil_img, half=False): | |
image = np.array(pil_img).astype(np.float32) / 255.0 | |
image = rearrange(torch.from_numpy(image), 'h w c -> c h w') | |
if half: | |
image = image.half() | |
return (2.0 * image - 1.0).unsqueeze(0) | |
def pil_img_to_latent(model, img, batch_size=1, device='cuda', half=True): | |
init_image = pil_img_to_torch(img, half=half).to(device) | |
init_image = repeat(init_image, '1 ... -> b ...', b=batch_size) | |
if half: | |
return model.get_first_stage_encoding(model.encode_first_stage(init_image.half())) | |
return model.get_first_stage_encoding(model.encode_first_stage(init_image)) | |
def find_noise_for_image(model, img, prompt, steps=200, cond_scale=0.0, verbose=False, normalize=True): | |
x = pil_img_to_latent(img, batch_size=1, device='cuda', half=True) | |
with torch.no_grad(): | |
with autocast('cuda'): | |
uncond = model.get_learned_conditioning(['']) | |
cond = model.get_learned_conditioning([prompt]) | |
s_in = x.new_ones([x.shape[0]]) | |
dnw = K.external.CompVisDenoiser(model) | |
sigmas = dnw.get_sigmas(steps).flip(0) | |
if verbose: | |
print(sigmas) | |
with torch.no_grad(): | |
with autocast('cuda'): | |
for i in trange(1, len(sigmas)): | |
x_in = torch.cat([x] * 2) | |
sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2) | |
cond_in = torch.cat([uncond, cond]) | |
c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] | |
if i == 1: | |
t = dnw.sigma_to_t(torch.cat([sigmas[i] * s_in] * 2)) | |
else: | |
t = dnw.sigma_to_t(sigma_in) | |
eps = 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) * cond_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 | |
if normalize: | |
return (x / x.std()) * sigmas[-1] | |
else: | |
return x |
What is the k_diffusion library you are using for this? Can I get a github or pypi?
It's an alternative sampler
What is the k_diffusion library you are using for this? Can I get a github or pypi?
It's an alternative sampler
Thanks a lot.
is there a diffusers compatible version?
Got it working. Thank you for this! Getting amazing results now!
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What is the k_diffusion library you are using for this? Can I get a github or pypi?