make style

src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py

@@ -290,7 +290,9 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
     # corresponds to doing no classifier free guidance.
     @property
     def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
+        if isinstance(self.guidance_scale, (int, float)):
+            return self.guidance_scale
+        return self.guidance_scale.max() > 1
 
     @property
     def num_timesteps(self):
@@ -415,10 +417,10 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = max_guidance_scale > 1.0
+        self._guidance_scale = max_guidance_scale
 
         # 3. Encode input image
-        image_embeddings = self._encode_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
+        image_embeddings = self._encode_image(image, device, num_videos_per_prompt, self.do_classifier_free_guidance)
 
         # NOTE: Stable Diffusion Video was conditioned on fps - 1, which
         # is why it is reduced here.
@@ -434,7 +436,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         if needs_upcasting:
             self.vae.to(dtype=torch.float32)
 
-        image_latents = self._encode_vae_image(image, device, num_videos_per_prompt, do_classifier_free_guidance)
+        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)
 
         # cast back to fp16 if needed
@@ -453,7 +455,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
             image_embeddings.dtype,
             batch_size,
             num_videos_per_prompt,
-            do_classifier_free_guidance,
+            self.do_classifier_free_guidance,
         )
         added_time_ids = added_time_ids.to(device)
 
@@ -489,7 +491,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                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)
 
                 # Concatenate image_latents over channels dimention
@@ -505,7 +507,7 @@ class StableVideoDiffusionPipeline(DiffusionPipeline):
                 )[0]
 
                 # perform guidance
-                if do_classifier_free_guidance:
+                if self.do_classifier_free_guidance:
                     noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_cond - noise_pred_uncond)