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Nov 15, 2023
Merged

Make Video Tests faster #5787

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717cc12
update test
DN6 Nov 14, 2023
39714ae
update
DN6 Nov 14, 2023
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10 changes: 5 additions & 5 deletions src/diffusers/models/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -985,30 +985,30 @@ class TemporalConvLayer(nn.Module):
dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
"""

def __init__(self, in_dim: int, out_dim: Optional[int] = None, dropout: float = 0.0):
def __init__(self, in_dim: int, out_dim: Optional[int] = None, dropout: float = 0.0, norm_num_groups: int = 32):
super().__init__()
out_dim = out_dim or in_dim
self.in_dim = in_dim
self.out_dim = out_dim

# conv layers
self.conv1 = nn.Sequential(
nn.GroupNorm(32, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0))
nn.GroupNorm(norm_num_groups, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0))
)
self.conv2 = nn.Sequential(
nn.GroupNorm(32, out_dim),
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv3 = nn.Sequential(
nn.GroupNorm(32, out_dim),
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
)
self.conv4 = nn.Sequential(
nn.GroupNorm(32, out_dim),
nn.GroupNorm(norm_num_groups, out_dim),
nn.SiLU(),
nn.Dropout(dropout),
nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding=(1, 0, 0)),
Expand Down
6 changes: 6 additions & 0 deletions src/diffusers/models/unet_3d_blocks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -269,6 +269,7 @@ def __init__(
in_channels,
in_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
]
attentions = []
Expand Down Expand Up @@ -316,6 +317,7 @@ def __init__(
in_channels,
in_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)

Expand Down Expand Up @@ -406,6 +408,7 @@ def __init__(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)
attentions.append(
Expand Down Expand Up @@ -529,6 +532,7 @@ def __init__(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)

Expand Down Expand Up @@ -622,6 +626,7 @@ def __init__(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)
attentions.append(
Expand Down Expand Up @@ -764,6 +769,7 @@ def __init__(
out_channels,
out_channels,
dropout=0.1,
norm_num_groups=resnet_groups,
)
)

Expand Down
1 change: 1 addition & 0 deletions src/diffusers/models/unet_3d_condition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,6 +173,7 @@ def __init__(
attention_head_dim=attention_head_dim,
in_channels=block_out_channels[0],
num_layers=1,
norm_num_groups=norm_num_groups,
)

# class embedding
Expand Down
11 changes: 7 additions & 4 deletions tests/pipelines/text_to_video_synthesis/test_text_to_video.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,15 +62,16 @@ class TextToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet3DConditionModel(
block_out_channels=(32, 32),
layers_per_block=2,
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock3D", "DownBlock3D"),
up_block_types=("UpBlock3D", "CrossAttnUpBlock3D"),
cross_attention_dim=4,
attention_head_dim=4,
norm_num_groups=2,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
Expand All @@ -81,13 +82,14 @@ def get_dummy_components(self):
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=(32,),
block_out_channels=(8,),
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D"],
latent_channels=4,
sample_size=32,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
Expand Down Expand Up @@ -142,10 +144,11 @@ def test_text_to_video_default_case(self):
image_slice = frames[0][-3:, -3:, -1]

assert frames[0].shape == (32, 32, 3)
expected_slice = np.array([91.0, 152.0, 66.0, 192.0, 94.0, 126.0, 101.0, 123.0, 152.0])
expected_slice = np.array([192.0, 44.0, 157.0, 140.0, 108.0, 104.0, 123.0, 144.0, 129.0])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

@unittest.skipIf(torch_device != "cuda", reason="Feature isn't heavily used. Test in CUDA environment only.")
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False, expected_max_diff=3e-3)

Expand Down
24 changes: 15 additions & 9 deletions tests/pipelines/text_to_video_synthesis/test_video_to_video.py
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Expand Up @@ -70,15 +70,16 @@ class VideoToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet3DConditionModel(
block_out_channels=(32, 64, 64, 64),
layers_per_block=2,
block_out_channels=(4, 8),
layers_per_block=1,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D"),
up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"),
down_block_types=("CrossAttnDownBlock3D", "DownBlock3D"),
up_block_types=("UpBlock3D", "CrossAttnUpBlock3D"),
cross_attention_dim=32,
attention_head_dim=4,
norm_num_groups=2,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
Expand All @@ -89,13 +90,18 @@ def get_dummy_components(self):
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
block_out_channels=[
8,
],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
down_block_types=[
"DownEncoderBlock2D",
],
up_block_types=["UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
sample_size=32,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
Expand Down Expand Up @@ -154,7 +160,7 @@ def test_text_to_video_default_case(self):
image_slice = frames[0][-3:, -3:, -1]

assert frames[0].shape == (32, 32, 3)
expected_slice = np.array([106, 117, 113, 174, 137, 112, 148, 151, 131])
expected_slice = np.array([162.0, 136.0, 132.0, 140.0, 139.0, 137.0, 169.0, 134.0, 132.0])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

Expand Down