[Tests] reduce the model size in the audioldm2 fast test (#7846)

* chore: initial model size reduction

* chore: fixing expected values for failing tests

* requested edits

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Author: ariG23498

tests/pipelines/audioldm2/test_audioldm2.py

@@ -73,14 +73,15 @@ class AudioLDM2PipelineFastTests(PipelineTesterMixin, unittest.TestCase):
     def get_dummy_components(self):
         torch.manual_seed(0)
         unet = AudioLDM2UNet2DConditionModel(
-            block_out_channels=(32, 64),
-            layers_per_block=2,
+            block_out_channels=(8, 16),
+            layers_per_block=1,
+            norm_num_groups=8,
             sample_size=32,
             in_channels=4,
             out_channels=4,
             down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
             up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=([None, 16, 32], [None, 16, 32]),
+            cross_attention_dim=(8, 16),
         )
         scheduler = DDIMScheduler(
             beta_start=0.00085,
@@ -91,9 +92,10 @@ def get_dummy_components(self):
         )
         torch.manual_seed(0)
         vae = AutoencoderKL(
-            block_out_channels=[32, 64],
+            block_out_channels=[8, 16],
             in_channels=1,
             out_channels=1,
+            norm_num_groups=8,
             down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
             up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
             latent_channels=4,
@@ -102,32 +104,34 @@ def get_dummy_components(self):
         text_branch_config = ClapTextConfig(
             bos_token_id=0,
             eos_token_id=2,
-            hidden_size=16,
+            hidden_size=8,
             intermediate_size=37,
             layer_norm_eps=1e-05,
-            num_attention_heads=2,
-            num_hidden_layers=2,
+            num_attention_heads=1,
+            num_hidden_layers=1,
             pad_token_id=1,
             vocab_size=1000,
-            projection_dim=16,
+            projection_dim=8,
         )
         audio_branch_config = ClapAudioConfig(
-            spec_size=64,
+            spec_size=8,
             window_size=4,
-            num_mel_bins=64,
+            num_mel_bins=8,
             intermediate_size=37,
             layer_norm_eps=1e-05,
-            depths=[2, 2],
-            num_attention_heads=[2, 2],
-            num_hidden_layers=2,
+            depths=[1, 1],
+            num_attention_heads=[1, 1],
+            num_hidden_layers=1,
             hidden_size=192,
-            projection_dim=16,
+            projection_dim=8,
             patch_size=2,
             patch_stride=2,
             patch_embed_input_channels=4,
         )
         text_encoder_config = ClapConfig.from_text_audio_configs(
-            text_config=text_branch_config, audio_config=audio_branch_config, projection_dim=16
+            text_config=text_branch_config,
+            audio_config=audio_branch_config,
+            projection_dim=16,
         )
         text_encoder = ClapModel(text_encoder_config)
         tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
@@ -141,17 +145,17 @@ def get_dummy_components(self):
             d_model=32,
             d_ff=37,
             d_kv=8,
-            num_heads=2,
-            num_layers=2,
+            num_heads=1,
+            num_layers=1,
         )
         text_encoder_2 = T5EncoderModel(text_encoder_2_config)
         tokenizer_2 = T5Tokenizer.from_pretrained("hf-internal-testing/tiny-random-T5Model", model_max_length=77)
 
         torch.manual_seed(0)
         language_model_config = GPT2Config(
             n_embd=16,
-            n_head=2,
-            n_layer=2,
+            n_head=1,
+            n_layer=1,
             vocab_size=1000,
             n_ctx=99,
             n_positions=99,
@@ -160,7 +164,11 @@ def get_dummy_components(self):
         language_model.config.max_new_tokens = 8
 
         torch.manual_seed(0)
-        projection_model = AudioLDM2ProjectionModel(text_encoder_dim=16, text_encoder_1_dim=32, langauge_model_dim=16)
+        projection_model = AudioLDM2ProjectionModel(
+            text_encoder_dim=16,
+            text_encoder_1_dim=32,
+            langauge_model_dim=16,
+        )
 
         vocoder_config = SpeechT5HifiGanConfig(
             model_in_dim=8,
@@ -220,7 +228,18 @@ def test_audioldm2_ddim(self):
 
         audio_slice = audio[:10]
         expected_slice = np.array(
-            [0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]
+            [
+                2.602e-03,
+                1.729e-03,
+                1.863e-03,
+                -2.219e-03,
+                -2.656e-03,
+                -2.017e-03,
+                -2.648e-03,
+                -2.115e-03,
+                -2.502e-03,
+                -2.081e-03,
+            ]
         )
 
         assert np.abs(audio_slice - expected_slice).max() < 1e-4
@@ -361,7 +380,7 @@ def test_audioldm2_negative_prompt(self):
 
         audio_slice = audio[:10]
         expected_slice = np.array(
-            [0.0025, 0.0018, 0.0018, -0.0023, -0.0026, -0.0020, -0.0026, -0.0021, -0.0027, -0.0020]
+            [0.0026, 0.0017, 0.0018, -0.0022, -0.0026, -0.002, -0.0026, -0.0021, -0.0025, -0.0021]
         )
 
         assert np.abs(audio_slice - expected_slice).max() < 1e-4
@@ -388,7 +407,7 @@ def test_audioldm2_num_waveforms_per_prompt(self):
         assert audios.shape == (batch_size, 256)
 
         # test num_waveforms_per_prompt for single prompt
-        num_waveforms_per_prompt = 2
+        num_waveforms_per_prompt = 1
         audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
 
         assert audios.shape == (num_waveforms_per_prompt, 256)