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[Tests] reduce the model size in the blipdiffusion fast test (#7849)

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reducing model size
This commit is contained in:
Aritra Roy Gosthipaty
2024-05-03 07:46:48 +05:30
committed by GitHub
parent 0d7c479023
commit fa489eaed6
1 changed files with 18 additions and 16 deletions
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34
tests/pipelines/blipdiffusion/test_blipdiffusion.py
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@@ -64,9 +64,9 @@ class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
vocab_size=1000,
hidden_size=16,
intermediate_size=16,
projection_dim=16,
hidden_size=8,
intermediate_size=8,
projection_dim=8,
num_hidden_layers=1,
num_attention_heads=1,
max_position_embeddings=77,
@@ -78,17 +78,17 @@ class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
out_channels=4,
down_block_types=("DownEncoderBlock2D",),
up_block_types=("UpDecoderBlock2D",),
block_out_channels=(32,),
block_out_channels=(8,),
norm_num_groups=8,
layers_per_block=1,
act_fn="silu",
latent_channels=4,
norm_num_groups=16,
sample_size=16,
sample_size=8,
)
blip_vision_config = {
"hidden_size": 16,
"intermediate_size": 16,
"hidden_size": 8,
"intermediate_size": 8,
"num_hidden_layers": 1,
"num_attention_heads": 1,
"image_size": 224,
@@ -98,32 +98,32 @@ class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
blip_qformer_config = {
"vocab_size": 1000,
"hidden_size": 16,
"hidden_size": 8,
"num_hidden_layers": 1,
"num_attention_heads": 1,
"intermediate_size": 16,
"intermediate_size": 8,
"max_position_embeddings": 512,
"cross_attention_frequency": 1,
"encoder_hidden_size": 16,
"encoder_hidden_size": 8,
}
qformer_config = Blip2Config(
vision_config=blip_vision_config,
qformer_config=blip_qformer_config,
num_query_tokens=16,
num_query_tokens=8,
tokenizer="hf-internal-testing/tiny-random-bert",
)
qformer = Blip2QFormerModel(qformer_config)
unet = UNet2DConditionModel(
block_out_channels=(16, 32),
norm_num_groups=16,
block_out_channels=(8, 16),
norm_num_groups=8,
layers_per_block=1,
sample_size=16,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=16,
cross_attention_dim=8,
)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
@@ -189,7 +189,9 @@ class BlipDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
assert image.shape == (1, 16, 16, 4)
expected_slice = np.array([0.7096, 0.5900, 0.6703, 0.4032, 0.7766, 0.3629, 0.5447, 0.4149, 0.8172])
expected_slice = np.array(
[0.5329548, 0.8372512, 0.33269387, 0.82096875, 0.43657133, 0.3783, 0.5953028, 0.51934963, 0.42142007]
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2