More consistent euler_a

1 files changed, 6 insertions, 11 deletions

diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index 4c793a8..a8ecedf 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -185,6 +185,8 @@ class VlpnStableDiffusion(DiffusionPipeline):
             latent_dist = self.vae.encode(latents.to(self.device)).latent_dist
             latents = latent_dist.sample(generator=generator)
             latents = 0.18215 * latents
+
+            # expand init_latents for batch_size
             latents = torch.cat([latents] * batch_size)
 
             # get the original timestep using init_timestep
@@ -195,9 +197,6 @@ class VlpnStableDiffusion(DiffusionPipeline):
                 timesteps = torch.tensor(
                     [num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
                 )
-            elif isinstance(self.scheduler, EulerAScheduler):
-                timesteps = self.scheduler.timesteps[-init_timestep]
-                timesteps = torch.tensor([timesteps] * batch_size, device=self.device)
             else:
                 timesteps = self.scheduler.timesteps[-init_timestep]
                 timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)
@@ -273,8 +272,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
         if isinstance(self.scheduler, LMSDiscreteScheduler):
             latents = latents * self.scheduler.sigmas[0]
         elif isinstance(self.scheduler, EulerAScheduler):
-            sigma = self.scheduler.timesteps[0]
-            latents = latents * sigma
+            latents = latents * self.scheduler.sigmas[0]
 
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
@@ -301,12 +299,10 @@ class VlpnStableDiffusion(DiffusionPipeline):
 
             noise_pred = None
             if isinstance(self.scheduler, EulerAScheduler):
-                sigma = t.reshape(1)
+                sigma = self.scheduler.sigmas[t].reshape(1)
                 sigma_in = torch.cat([sigma] * latent_model_input.shape[0])
-                # noise_pred = model(latent_model_input,sigma_in,uncond_embeddings, text_embeddings,guidance_scale)
                 noise_pred = CFGDenoiserForward(self.unet, latent_model_input, sigma_in,
                                                 text_embeddings, guidance_scale, quantize=True, DSsigmas=self.scheduler.DSsigmas)
-                # noise_pred = self.unet(latent_model_input, sigma_in, encoder_hidden_states=text_embeddings).sample
             else:
                 # predict the noise residual
                 noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
@@ -320,9 +316,8 @@ class VlpnStableDiffusion(DiffusionPipeline):
             if isinstance(self.scheduler, LMSDiscreteScheduler):
                 latents = self.scheduler.step(noise_pred, t_index, latents, **extra_step_kwargs).prev_sample
             elif isinstance(self.scheduler, EulerAScheduler):
-                if t_index < self.scheduler.timesteps.shape[0] - 1:  # avoid out of bound error
-                    t_prev = self.scheduler.timesteps[t_index+1]
-                    latents = self.scheduler.step(noise_pred, t, t_prev, latents, **extra_step_kwargs).prev_sample
+                latents = self.scheduler.step(noise_pred, t_index, t_index + 1,
+                                              latents, **extra_step_kwargs).prev_sample
             else:
                 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample