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update ldm

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patil-suraj 2022-06-10 14:58:33 +02:00
parent abbbc27e88
commit 7bb3dcd18e
1 changed files with 8 additions and 33 deletions
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41
models/vision/latent_diffusion/modeling_latent_diffusion.py
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@ -924,42 +924,17 @@ class LatentDiffusion(DiffusionPipeline):
pred_noise_t_uncond, pred_noise_t = pred_noise_t.chunk(2)
pred_noise_t = pred_noise_t_uncond + guidance_scale * (pred_noise_t - pred_noise_t_uncond)
# 2. 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
# 2. predict previous mean of image x_t-1
pred_prev_image = self.noise_scheduler.compute_prev_image_step(pred_noise_t, image, t, num_inference_steps, eta)
# 3. compute alphas, betas
alpha_prod_t = self.noise_scheduler.get_alpha_prod(train_step)
alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(prev_train_step)
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
# 4. Compute predicted previous image from predicted noise
# First: compute predicted original image from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_original_image = (image - beta_prod_t.sqrt() * pred_noise_t) / alpha_prod_t.sqrt()
# Second: Compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
std_dev_t = (beta_prod_t_prev / beta_prod_t).sqrt() * (1 - alpha_prod_t / alpha_prod_t_prev).sqrt()
std_dev_t = eta * std_dev_t
# Third: Compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_image_direction = (1 - alpha_prod_t_prev - std_dev_t**2).sqrt() * pred_noise_t
# Forth: Compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_prev_image = alpha_prod_t_prev.sqrt() * pred_original_image + pred_image_direction
# 5. Sample x_t-1 image optionally if η > 0.0 by adding noise to pred_prev_image
# Note: eta = 1.0 essentially corresponds to DDPM
if eta > 0.0:
# 3. optionally sample variance
variance = 0
if eta > 0:
noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
prev_image = pred_prev_image + std_dev_t * noise
else:
prev_image = pred_prev_image
variance = self.noise_scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
# 6. Set current image to prev_image: x_t -> x_t-1
image = prev_image
# 4. set current image to prev_image: x_t -> x_t-1
image = pred_prev_image + variance
# scale and decode image with vae
image = 1 / 0.18215 * image