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[Tests] Optimize test configurations for faster execution #5535

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Oct 26, 2023
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[Tests] Optimize test configurations for faster execution #5535

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21 changes: 14 additions & 7 deletions tests/pipelines/controlnet/test_controlnet_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,23 +72,25 @@ class ControlNetImg2ImgPipelineFastTests(
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
block_out_channels=(4, 8),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
norm_num_groups=1,
)
torch.manual_seed(0)
controlnet = ControlNetModel(
block_out_channels=(32, 64),
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
torch.manual_seed(0)
scheduler = DDIMScheduler(
Expand All @@ -100,12 +102,13 @@ def get_dummy_components(self):
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
Expand Down Expand Up @@ -186,14 +189,15 @@ class StableDiffusionMultiControlNetPipelineFastTests(
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
block_out_channels=(4, 8),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
norm_num_groups=1,
)
torch.manual_seed(0)

Expand All @@ -203,23 +207,25 @@ def init_weights(m):
m.bias.data.fill_(1.0)

controlnet1 = ControlNetModel(
block_out_channels=(32, 64),
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
controlnet1.controlnet_down_blocks.apply(init_weights)

torch.manual_seed(0)
controlnet2 = ControlNetModel(
block_out_channels=(32, 64),
block_out_channels=(4, 8),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
norm_num_groups=1,
)
controlnet2.controlnet_down_blocks.apply(init_weights)

Expand All @@ -233,12 +239,13 @@ def init_weights(m):
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
block_out_channels=[4, 8],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
norm_num_groups=2,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
Expand Down