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[Tests] reduce the model size in the amused fast test (#7804)

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* chore: reducing model sizes

* chore: shrinks further

* chore: shrinks further

* chore: shrinking model for img2img pipeline

* chore: reducing size of model for inpaint pipeline

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
This commit is contained in:
Aritra Roy Gosthipaty
2024-04-30 08:11:26 +05:30
committed by GitHub
parent 21f023ec1a
commit b02e2113ff
3 changed files with 54 additions and 54 deletions
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36
tests/pipelines/amused/test_amused.py
View File

@@ -38,17 +38,17 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self):
torch.manual_seed(0)
transformer = UVit2DModel(
hidden_size=32,
hidden_size=8,
use_bias=False,
hidden_dropout=0.0,
cond_embed_dim=32,
cond_embed_dim=8,
micro_cond_encode_dim=2,
micro_cond_embed_dim=10,
encoder_hidden_size=32,
encoder_hidden_size=8,
vocab_size=32,
codebook_size=32,
in_channels=32,
block_out_channels=32,
codebook_size=8,
in_channels=8,
block_out_channels=8,
num_res_blocks=1,
downsample=True,
upsample=True,
@@ -56,7 +56,7 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
num_hidden_layers=1,
num_attention_heads=1,
attention_dropout=0.0,
intermediate_size=32,
intermediate_size=8,
layer_norm_eps=1e-06,
ln_elementwise_affine=True,
)
@@ -64,17 +64,17 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
torch.manual_seed(0)
vqvae = VQModel(
act_fn="silu",
block_out_channels=[32],
block_out_channels=[8],
down_block_types=[
"DownEncoderBlock2D",
],
in_channels=3,
latent_channels=32,
layers_per_block=2,
norm_num_groups=32,
num_vq_embeddings=32,
latent_channels=8,
layers_per_block=1,
norm_num_groups=8,
num_vq_embeddings=8,
out_channels=3,
sample_size=32,
sample_size=8,
up_block_types=[
"UpDecoderBlock2D",
],
@@ -85,14 +85,14 @@ class AmusedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=64,
hidden_size=8,
intermediate_size=8,
layer_norm_eps=1e-05,
num_attention_heads=8,
num_hidden_layers=3,
num_attention_heads=1,
num_hidden_layers=1,
pad_token_id=1,
vocab_size=1000,
projection_dim=32,
projection_dim=8,
)
text_encoder = CLIPTextModelWithProjection(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

36
tests/pipelines/amused/test_amused_img2img.py
View File

@@ -42,17 +42,17 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self):
torch.manual_seed(0)
transformer = UVit2DModel(
hidden_size=32,
hidden_size=8,
use_bias=False,
hidden_dropout=0.0,
cond_embed_dim=32,
cond_embed_dim=8,
micro_cond_encode_dim=2,
micro_cond_embed_dim=10,
encoder_hidden_size=32,
encoder_hidden_size=8,
vocab_size=32,
codebook_size=32,
in_channels=32,
block_out_channels=32,
codebook_size=8,
in_channels=8,
block_out_channels=8,
num_res_blocks=1,
downsample=True,
upsample=True,
@@ -60,7 +60,7 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
num_hidden_layers=1,
num_attention_heads=1,
attention_dropout=0.0,
intermediate_size=32,
intermediate_size=8,
layer_norm_eps=1e-06,
ln_elementwise_affine=True,
)
@@ -68,17 +68,17 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
torch.manual_seed(0)
vqvae = VQModel(
act_fn="silu",
block_out_channels=[32],
block_out_channels=[8],
down_block_types=[
"DownEncoderBlock2D",
],
in_channels=3,
latent_channels=32,
layers_per_block=2,
norm_num_groups=32,
num_vq_embeddings=32,
latent_channels=8,
layers_per_block=1,
norm_num_groups=8,
num_vq_embeddings=32, # reducing this to 16 or 8 -> RuntimeError: "cdist_cuda" not implemented for 'Half'
out_channels=3,
sample_size=32,
sample_size=8,
up_block_types=[
"UpDecoderBlock2D",
],
@@ -89,14 +89,14 @@ class AmusedImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=64,
hidden_size=8,
intermediate_size=8,
layer_norm_eps=1e-05,
num_attention_heads=8,
num_hidden_layers=3,
num_attention_heads=1,
num_hidden_layers=1,
pad_token_id=1,
vocab_size=1000,
projection_dim=32,
projection_dim=8,
)
text_encoder = CLIPTextModelWithProjection(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

36
tests/pipelines/amused/test_amused_inpaint.py
View File

@@ -42,17 +42,17 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self):
torch.manual_seed(0)
transformer = UVit2DModel(
hidden_size=32,
hidden_size=8,
use_bias=False,
hidden_dropout=0.0,
cond_embed_dim=32,
cond_embed_dim=8,
micro_cond_encode_dim=2,
micro_cond_embed_dim=10,
encoder_hidden_size=32,
encoder_hidden_size=8,
vocab_size=32,
codebook_size=32,
in_channels=32,
block_out_channels=32,
codebook_size=32, # codebook size needs to be consistent with num_vq_embeddings for inpaint tests
in_channels=8,
block_out_channels=8,
num_res_blocks=1,
downsample=True,
upsample=True,
@@ -60,7 +60,7 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
num_hidden_layers=1,
num_attention_heads=1,
attention_dropout=0.0,
intermediate_size=32,
intermediate_size=8,
layer_norm_eps=1e-06,
ln_elementwise_affine=True,
)
@@ -68,17 +68,17 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
torch.manual_seed(0)
vqvae = VQModel(
act_fn="silu",
block_out_channels=[32],
block_out_channels=[8],
down_block_types=[
"DownEncoderBlock2D",
],
in_channels=3,
latent_channels=32,
layers_per_block=2,
norm_num_groups=32,
num_vq_embeddings=32,
latent_channels=8,
layers_per_block=1,
norm_num_groups=8,
num_vq_embeddings=32, # reducing this to 16 or 8 -> RuntimeError: "cdist_cuda" not implemented for 'Half'
out_channels=3,
sample_size=32,
sample_size=8,
up_block_types=[
"UpDecoderBlock2D",
],
@@ -89,14 +89,14 @@ class AmusedInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=64,
hidden_size=8,
intermediate_size=8,
layer_norm_eps=1e-05,
num_attention_heads=8,
num_hidden_layers=3,
num_attention_heads=1,
num_hidden_layers=1,
pad_token_id=1,
vocab_size=1000,
projection_dim=32,
projection_dim=8,
)
text_encoder = CLIPTextModelWithProjection(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")