[Text-to-video] Add `torch.compile()` compatibility (#3949)

* use sample directly instead of the dataclass.

* more usage of directly samples instead of dataclasses

* more usage of directly samples instead of dataclasses

* use direct sample in the pipeline.

* direct usage of sample in the img2img case.

src/diffusers/models/unet_3d_blocks.py

@@ -250,10 +250,11 @@ class UNetMidBlock3DCrossAttn(nn.Module):
                 hidden_states,
                 encoder_hidden_states=encoder_hidden_states,
                 cross_attention_kwargs=cross_attention_kwargs,
-            ).sample
+                return_dict=False,
+            )[0]
             hidden_states = temp_attn(
-                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs
-            ).sample
+                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False
+            )[0]
             hidden_states = resnet(hidden_states, temb)
             hidden_states = temp_conv(hidden_states, num_frames=num_frames)
 
@@ -377,10 +378,11 @@ class CrossAttnDownBlock3D(nn.Module):
                 hidden_states,
                 encoder_hidden_states=encoder_hidden_states,
                 cross_attention_kwargs=cross_attention_kwargs,
-            ).sample
+                return_dict=False,
+            )[0]
             hidden_states = temp_attn(
-                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs
-            ).sample
+                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False
+            )[0]
 
             output_states += (hidden_states,)
 
@@ -590,10 +592,11 @@ class CrossAttnUpBlock3D(nn.Module):
                 hidden_states,
                 encoder_hidden_states=encoder_hidden_states,
                 cross_attention_kwargs=cross_attention_kwargs,
-            ).sample
+                return_dict=False,
+            )[0]
             hidden_states = temp_attn(
-                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs
-            ).sample
+                hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False
+            )[0]
 
         if self.upsamplers is not None:
             for upsampler in self.upsamplers:

src/diffusers/models/unet_3d_condition.py

@@ -526,8 +526,11 @@ class UNet3DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
         sample = self.conv_in(sample)
 
         sample = self.transformer_in(
-            sample, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs
-        ).sample
+            sample,
+            num_frames=num_frames,
+            cross_attention_kwargs=cross_attention_kwargs,
+            return_dict=False,
+        )[0]
 
         # 3. down
         down_block_res_samples = (sample,)

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py

@@ -648,7 +648,8 @@ class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lora
                     t,
                     encoder_hidden_states=prompt_embeds,
                     cross_attention_kwargs=cross_attention_kwargs,
-                ).sample
+                    return_dict=False,
+                )[0]
 
                 # perform guidance
                 if do_classifier_free_guidance:

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py

@@ -723,7 +723,8 @@ class VideoToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lor
                     t,
                     encoder_hidden_states=prompt_embeds,
                     cross_attention_kwargs=cross_attention_kwargs,
-                ).sample
+                    return_dict=False,
+                )[0]
 
                 # perform guidance
                 if do_classifier_free_guidance: