Mirrors
/
diffusers
mirror of https://github.com/ShivamShrirao/diffusers.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

save clean-up

This commit is contained in:
Patrick von Platen 2022-06-08 10:52:07 +00:00
parent e8977e957c
commit ef044a7231
1 changed files with 26 additions and 31 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

57
models/vision/ddim/modeling_ddim.py
View File

@ -40,39 +40,34 @@ class DDIM(DiffusionPipeline):
inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps) inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
self.unet.to(torch_device) self.unet.to(torch_device)
x = self.noise_scheduler.sample_noise((batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator) image = self.noise_scheduler.sample_noise((batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator)
b = self.noise_scheduler.betas.to(torch_device) for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
# get actual t and t-1
train_step = inference_step_times[t]
prev_train_step = inference_step_times[t - 1] if t > 0 else - 1
seq = inference_step_times # compute alphas
seq_next = [-1] + list(seq[:-1]) alpha_prod_t = self.noise_scheduler.get_alpha_prod(train_step)
# for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps): alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(prev_train_step)
# train_step = inference_step_times[t] alpha_prod_t_rsqrt = 1 / alpha_prod_t.sqrt()
for i, j in zip(reversed(seq), reversed(seq_next)): alpha_prod_t_prev_rsqrt = 1 / alpha_prod_t_prev.sqrt()
beta_prod_t_sqrt = (1 - alpha_prod_t).sqrt()
beta_prod_t_prev_sqrt = (1 - alpha_prod_t_prev).sqrt()
n = batch_size # compute relevant coefficients
x0_preds = [] coeff_1 = (alpha_prod_t_prev - alpha_prod_t).sqrt() * alpha_prod_t_prev_rsqrt * beta_prod_t_prev_sqrt / beta_prod_t_sqrt * eta
xs = [x] coeff_2 = ((1 - alpha_prod_t_prev) - coeff_1 ** 2).sqrt()
# i = train_step with torch.no_grad():
# j = inference_step_times[t-1] if t > 0 else -1 noise_residual = self.unet(image, train_step)
if True:
print(i)
t = (torch.ones(n) * i).to(x.device)
next_t = (torch.ones(n) * j).to(x.device)
at = compute_alpha(b, t.long())
at_next = compute_alpha(b, next_t.long())
xt = xs[-1].to('cuda')
with torch.no_grad():
et = self.unet(xt, t)
x0_t = (xt - et * (1 - at).sqrt()) / at.sqrt()
x0_preds.append(x0_t.to('cpu'))
# eta
c1 = (
eta * ((1 - at / at_next) * (1 - at_next) / (1 - at)).sqrt()
)
c2 = ((1 - at_next) - c1 ** 2).sqrt()
xt_next = at_next.sqrt() * x0_t + c1 * torch.randn_like(x) + c2 * et
xs.append(xt_next.to('cpu'))
return xt_next print(train_step)
pred_mean = (image - noise_residual * beta_prod_t_sqrt) * alpha_prod_t_rsqrt
xt_next = alpha_prod_t_prev.sqrt() * pred_mean + coeff_1 * torch.randn_like(image) + coeff_2 * noise_residual
# xt_next = 1 / alpha_prod_t_rsqrt * pred_mean + coeff_1 * torch.randn_like(image) + coeff_2 * noise_residual
# eta
image = xt_next
return image