config fixes (#3060)

examples/community/sd_text2img_k_diffusion.py

@@ -105,7 +105,7 @@ class StableDiffusionPipeline(DiffusionPipeline):
         )
 
         model = ModelWrapper(unet, scheduler.alphas_cumprod)
-        if scheduler.prediction_type == "v_prediction":
+        if scheduler.config.prediction_type == "v_prediction":
             self.k_diffusion_model = CompVisVDenoiser(model)
         else:
             self.k_diffusion_model = CompVisDenoiser(model)

src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py

@@ -60,9 +60,9 @@ class AudioDiffusionPipeline(DiffusionPipeline):
         input_module = self.vqvae if self.vqvae is not None else self.unet
         # For backwards compatibility
         sample_size = (
-            (input_module.sample_size, input_module.sample_size)
-            if type(input_module.sample_size) == int
-            else input_module.sample_size
+            (input_module.config.sample_size, input_module.config.sample_size)
+            if type(input_module.config.sample_size) == int
+            else input_module.config.sample_size
         )
         return sample_size
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py

@@ -113,7 +113,7 @@ class StableDiffusionKDiffusionPipeline(DiffusionPipeline, TextualInversionLoade
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
 
         model = ModelWrapper(unet, scheduler.alphas_cumprod)
-        if scheduler.prediction_type == "v_prediction":
+        if scheduler.config.prediction_type == "v_prediction":
             self.k_diffusion_model = CompVisVDenoiser(model)
         else:
             self.k_diffusion_model = CompVisDenoiser(model)

tests/pipelines/audio_diffusion/test_audio_diffusion.py

@@ -115,8 +115,11 @@ class PipelineFastTests(unittest.TestCase):
         output = pipe(generator=generator, steps=4, return_dict=False)
         image_from_tuple = output[0][0]
 
-        assert audio.shape == (1, (self.dummy_unet.sample_size[1] - 1) * mel.hop_length)
-        assert image.height == self.dummy_unet.sample_size[0] and image.width == self.dummy_unet.sample_size[1]
+        assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
+        assert (
+            image.height == self.dummy_unet.config.sample_size[0]
+            and image.width == self.dummy_unet.config.sample_size[1]
+        )
         image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
         image_from_tuple_slice = np.frombuffer(image_from_tuple.tobytes(), dtype="uint8")[:10]
         expected_slice = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127])
@@ -133,14 +136,14 @@ class PipelineFastTests(unittest.TestCase):
         pipe.set_progress_bar_config(disable=None)
 
         np.random.seed(0)
-        raw_audio = np.random.uniform(-1, 1, ((dummy_vqvae_and_unet[0].sample_size[1] - 1) * mel.hop_length,))
+        raw_audio = np.random.uniform(-1, 1, ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,))
         generator = torch.Generator(device=device).manual_seed(42)
         output = pipe(raw_audio=raw_audio, generator=generator, start_step=5, steps=10)
         image = output.images[0]
 
         assert (
-            image.height == self.dummy_vqvae_and_unet[0].sample_size[0]
-            and image.width == self.dummy_vqvae_and_unet[0].sample_size[1]
+            image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
+            and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
         )
         image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
         expected_slice = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121])
@@ -183,8 +186,8 @@ class PipelineIntegrationTests(unittest.TestCase):
         audio = output.audios[0]
         image = output.images[0]
 
-        assert audio.shape == (1, (pipe.unet.sample_size[1] - 1) * pipe.mel.hop_length)
-        assert image.height == pipe.unet.sample_size[0] and image.width == pipe.unet.sample_size[1]
+        assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
+        assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
         image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
         expected_slice = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26])