Add condtional generation to AudioDiffusionPipeline (#1826)

* Add condtional generation

* add fast test for conditional audio generation

src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py

@@ -89,9 +89,11 @@ class AudioDiffusionPipeline(DiffusionPipeline):
         step_generator: torch.Generator = None,
         eta: float = 0,
         noise: torch.Tensor = None,
+        encoding: torch.Tensor = None,
         return_dict=True,
     ) -> Union[
-        Union[AudioPipelineOutput, ImagePipelineOutput], Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]]
+        Union[AudioPipelineOutput, ImagePipelineOutput],
+        Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
     ]:
         """Generate random mel spectrogram from audio input and convert to audio.
 
@@ -108,6 +110,7 @@ class AudioDiffusionPipeline(DiffusionPipeline):
             step_generator (`torch.Generator`): random number generator used to de-noise or None
             eta (`float`): parameter between 0 and 1 used with DDIM scheduler
             noise (`torch.Tensor`): noise tensor of shape (batch_size, 1, height, width) or None
+            encoding (`torch.Tensor`): for UNet2DConditionModel shape (batch_size, seq_length, cross_attention_dim)
             return_dict (`bool`): if True return AudioPipelineOutput, ImagePipelineOutput else Tuple
 
         Returns:
@@ -124,7 +127,12 @@ class AudioDiffusionPipeline(DiffusionPipeline):
         self.mel.set_resolution(x_res=input_dims[1], y_res=input_dims[0])
         if noise is None:
             noise = torch.randn(
-                (batch_size, self.unet.in_channels, self.unet.sample_size[0], self.unet.sample_size[1]),
+                (
+                    batch_size,
+                    self.unet.in_channels,
+                    self.unet.sample_size[0],
+                    self.unet.sample_size[1],
+                ),
                 generator=generator,
                 device=self.device,
             )
@@ -157,15 +165,25 @@ class AudioDiffusionPipeline(DiffusionPipeline):
             mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))
 
         for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
-            model_output = self.unet(images, t)["sample"]
+            if isinstance(self.unet, UNet2DConditionModel):
+                model_output = self.unet(images, t, encoding)["sample"]
+            else:
+                model_output = self.unet(images, t)["sample"]
 
             if isinstance(self.scheduler, DDIMScheduler):
                 images = self.scheduler.step(
-                    model_output=model_output, timestep=t, sample=images, eta=eta, generator=step_generator
+                    model_output=model_output,
+                    timestep=t,
+                    sample=images,
+                    eta=eta,
+                    generator=step_generator,
                 )["prev_sample"]
             else:
                 images = self.scheduler.step(
-                    model_output=model_output, timestep=t, sample=images, generator=step_generator
+                    model_output=model_output,
+                    timestep=t,
+                    sample=images,
+                    generator=step_generator,
                 )["prev_sample"]
 
             if mask is not None:

tests/pipelines/audio_diffusion/test_audio_diffusion.py

@@ -26,6 +26,7 @@ from diffusers import (
     DDPMScheduler,
     DiffusionPipeline,
     Mel,
+    UNet2DConditionModel,
     UNet2DModel,
 )
 from diffusers.utils import slow, torch_device
@@ -56,6 +57,21 @@ class PipelineFastTests(unittest.TestCase):
         )
         return model
 
+    @property
+    def dummy_unet_condition(self):
+        torch.manual_seed(0)
+        model = UNet2DConditionModel(
+            sample_size=(64, 32),
+            in_channels=1,
+            out_channels=1,
+            layers_per_block=2,
+            block_out_channels=(128, 128),
+            down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
+            up_block_types=("UpBlock2D", "CrossAttnUpBlock2D"),
+            cross_attention_dim=10,
+        )
+        return model
+
     @property
     def dummy_vqvae_and_unet(self):
         torch.manual_seed(0)
@@ -128,6 +144,19 @@ class PipelineFastTests(unittest.TestCase):
         expected_slice = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121])
         assert np.abs(image_slice.flatten() - expected_slice).max() == 0
 
+        dummy_unet_condition = self.dummy_unet_condition
+        pipe = AudioDiffusionPipeline(
+            vqvae=self.dummy_vqvae_and_unet[0], unet=dummy_unet_condition, mel=mel, scheduler=scheduler
+        )
+
+        np.random.seed(0)
+        encoding = torch.rand((1, 1, 10))
+        output = pipe(generator=generator, encoding=encoding)
+        image = output.images[0]
+        image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
+        expected_slice = np.array([120, 139, 147, 123, 124, 96, 115, 121, 126, 144])
+        assert np.abs(image_slice.flatten() - expected_slice).max() == 0
+
 
 @slow
 @require_torch_gpu