AudioLDM2 Fixes (#11244)

src/diffusers/pipelines/audioldm2/pipeline_audioldm2.py

@@ -20,7 +20,7 @@ import torch
 from transformers import (
     ClapFeatureExtractor,
     ClapModel,
-    GPT2Model,
+    GPT2LMHeadModel,
     RobertaTokenizer,
     RobertaTokenizerFast,
     SpeechT5HifiGan,
@@ -196,7 +196,7 @@ class AudioLDM2Pipeline(DiffusionPipeline):
         text_encoder: ClapModel,
         text_encoder_2: Union[T5EncoderModel, VitsModel],
         projection_model: AudioLDM2ProjectionModel,
-        language_model: GPT2Model,
+        language_model: GPT2LMHeadModel,
         tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
         tokenizer_2: Union[T5Tokenizer, T5TokenizerFast, VitsTokenizer],
         feature_extractor: ClapFeatureExtractor,
@@ -259,7 +259,10 @@ class AudioLDM2Pipeline(DiffusionPipeline):
             )
 
         device_type = torch_device.type
-        device = torch.device(f"{device_type}:{gpu_id or torch_device.index}")
+        device_str = device_type
+        if gpu_id or torch_device.index:
+            device_str = f"{device_str}:{gpu_id or torch_device.index}"
+        device = torch.device(device_str)
 
         if self.device.type != "cpu":
             self.to("cpu", silence_dtype_warnings=True)
@@ -316,9 +319,9 @@ class AudioLDM2Pipeline(DiffusionPipeline):
             model_inputs = prepare_inputs_for_generation(inputs_embeds, **model_kwargs)
 
             # forward pass to get next hidden states
-            output = self.language_model(**model_inputs, return_dict=True)
+            output = self.language_model(**model_inputs, output_hidden_states=True, return_dict=True)
 
-            next_hidden_states = output.last_hidden_state
+            next_hidden_states = output.hidden_states[-1]
 
             # Update the model input
             inputs_embeds = torch.cat([inputs_embeds, next_hidden_states[:, -1:, :]], dim=1)

tests/pipelines/audioldm2/test_audioldm2.py

@@ -26,7 +26,7 @@ from transformers import (
     ClapModel,
     ClapTextConfig,
     GPT2Config,
-    GPT2Model,
+    GPT2LMHeadModel,
     RobertaTokenizer,
     SpeechT5HifiGan,
     SpeechT5HifiGanConfig,
@@ -162,7 +162,7 @@ class AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             n_ctx=99,
             n_positions=99,
         )
-        language_model = GPT2Model(language_model_config)
+        language_model = GPT2LMHeadModel(language_model_config)
         language_model.config.max_new_tokens = 8
 
         torch.manual_seed(0)
@@ -516,6 +516,18 @@ class AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def test_encode_prompt_works_in_isolation(self):
         pass
 
+    @unittest.skip("Not supported yet due to CLAPModel.")
+    def test_sequential_offload_forward_pass_twice(self):
+        pass
+
+    @unittest.skip("Not supported yet, the second forward has mixed devices and `vocoder` is not offloaded.")
+    def test_cpu_offload_forward_pass_twice(self):
+        pass
+
+    @unittest.skip("Not supported yet. `vocoder` is not offloaded.")
+    def test_model_cpu_offload_forward_pass(self):
+        pass
+
 
 @nightly
 class AudioLDM2PipelineSlowTests(unittest.TestCase):