remove guidance_scale

src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py

@@ -422,7 +422,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
 
         # 3. Encode input image
         image_embeddings = self._encode_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance)
-
+        print(f"image embeddings: {image_embeddings.shape}")
         # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
         # is why it is reduced here.
         # See: https://github.com/Stability-AI/generative-models/blob/ed0997173f98eaf8f4edf7ba5fe8f15c6b877fd3/scripts/sampling/simple_video_sample.py#L188
@@ -439,6 +439,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
 
         image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance)
         image_latents = image_latents.to(image_embeddings.dtype)
+        print(f"Image latents: {image_latents.shape}")
 
         # cast back to fp16 if needed
         if needs_upcasting:
@@ -447,6 +448,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         # Repeat the image latents for each frame so we can concatenate them with the noise
         # image_latents [batch, channels, height, width] ->[batch, num_frames, channels, height, width]
         image_latents = image_latents.unsqueeze(1).repeat(1, num_frames, 1, 1, 1)
+        print(f"Image latents after reshape: {image_latents.shape}")
 
         # 5. Get Added Time IDs
         added_time_ids = self._get_add_time_ids(
@@ -459,6 +461,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
             self.do_classifier_free_guidance,
         )
         added_time_ids = added_time_ids.to(device)
+        print(f"added_time_ids: {added_time_ids.shape}")
 
         # 4. Prepare timesteps
         self.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -477,6 +480,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
             generator,
             latents,
         )
+        print(f"latents: {latents.shape}")
 
         # 7. Prepare guidance scale
         guidance_scale = torch.linspace(min_guidance_scale, max_guidance_scale, num_frames).unsqueeze(0)
@@ -494,9 +498,11 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
                 # expand the latents if we are doing classifier free guidance
                 latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                print(f"initial latent_model_input: {latent_model_input.shape}")
 
                 # Concatenate image_latents over channels dimention
                 latent_model_input = torch.cat([latent_model_input, image_latents], dim=2)
+                print(f"concatenated latent_model_input: {latent_model_input.shape}")
 
                 # predict the noise residual
                 noise_pred = self.unet(
@@ -506,6 +512,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
                     added_time_ids=added_time_ids,
                     return_dict=False,
                 )[0]
+                print(f"noise_pred: {noise_pred.shape}")
 
                 # perform guidance
                 if self.do_classifier_free_guidance: