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Update VAE Decode endpoints #10939

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Merged
merged 1 commit into from
Mar 2, 2025
Merged

Update VAE Decode endpoints #10939

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206 changes: 103 additions & 103 deletions tests/remote/test_remote_decode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -344,7 +344,7 @@ class RemoteAutoencoderKLSDv1Tests(
512,
512,
)
endpoint = "https://bz0b3zkoojf30bhx.us-east-1.aws.endpoints.huggingface.cloud/"
endpoint = "https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/"
dtype = torch.float16
scaling_factor = 0.18215
shift_factor = None
Expand All @@ -354,105 +354,105 @@ class RemoteAutoencoderKLSDv1Tests(
return_pt_slice = torch.tensor([-0.2177, 0.0217, -0.2258, 0.0412, -0.1687, -0.1232, -0.2416, -0.2130, -0.0543])


# class RemoteAutoencoderKLSDXLTests(
# RemoteAutoencoderKLMixin,
# unittest.TestCase,
# ):
# shape = (
# 1,
# 4,
# 128,
# 128,
# )
# out_hw = (
# 1024,
# 1024,
# )
# endpoint = "https://fagf07t3bwf0615i.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.float16
# scaling_factor = 0.13025
# shift_factor = None
# processor_cls = VaeImageProcessor
# output_pt_slice = torch.tensor([104, 52, 23, 114, 61, 35, 108, 87, 38], dtype=torch.uint8)
# partial_postprocess_return_pt_slice = torch.tensor([77, 86, 89, 49, 60, 75, 52, 65, 78], dtype=torch.uint8)
# return_pt_slice = torch.tensor([-0.3945, -0.3289, -0.2993, -0.6177, -0.5259, -0.4119, -0.5898, -0.4863, -0.3845])


# class RemoteAutoencoderKLFluxTests(
# RemoteAutoencoderKLMixin,
# unittest.TestCase,
# ):
# shape = (
# 1,
# 16,
# 128,
# 128,
# )
# out_hw = (
# 1024,
# 1024,
# )
# endpoint = "https://fnohtuwsskxgxsnn.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.bfloat16
# scaling_factor = 0.3611
# shift_factor = 0.1159
# processor_cls = VaeImageProcessor
# output_pt_slice = torch.tensor([110, 72, 91, 62, 35, 52, 69, 55, 69], dtype=torch.uint8)
# partial_postprocess_return_pt_slice = torch.tensor(
# [202, 203, 203, 197, 195, 193, 189, 188, 178], dtype=torch.uint8
# )
# return_pt_slice = torch.tensor([0.5820, 0.5962, 0.5898, 0.5439, 0.5327, 0.5112, 0.4797, 0.4773, 0.3984])


# class RemoteAutoencoderKLFluxPackedTests(
# RemoteAutoencoderKLMixin,
# unittest.TestCase,
# ):
# shape = (
# 1,
# 4096,
# 64,
# )
# out_hw = (
# 1024,
# 1024,
# )
# height = 1024
# width = 1024
# endpoint = "https://fnohtuwsskxgxsnn.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.bfloat16
# scaling_factor = 0.3611
# shift_factor = 0.1159
# processor_cls = VaeImageProcessor
# # slices are different due to randn on different shape. we can pack the latent instead if we want the same
# output_pt_slice = torch.tensor([96, 116, 157, 45, 67, 104, 34, 56, 89], dtype=torch.uint8)
# partial_postprocess_return_pt_slice = torch.tensor(
# [168, 212, 202, 155, 191, 185, 150, 180, 168], dtype=torch.uint8
# )
# return_pt_slice = torch.tensor([0.3198, 0.6631, 0.5864, 0.2131, 0.4944, 0.4482, 0.1776, 0.4153, 0.3176])


# class RemoteAutoencoderKLHunyuanVideoTests(
# RemoteAutoencoderKLHunyuanVideoMixin,
# unittest.TestCase,
# ):
# shape = (
# 1,
# 16,
# 3,
# 40,
# 64,
# )
# out_hw = (
# 320,
# 512,
# )
# endpoint = "https://lsx2injm3ts8wbvv.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.float16
# scaling_factor = 0.476986
# processor_cls = VideoProcessor
# output_pt_slice = torch.tensor([112, 92, 85, 112, 93, 85, 112, 94, 85], dtype=torch.uint8)
# partial_postprocess_return_pt_slice = torch.tensor(
# [149, 161, 168, 136, 150, 156, 129, 143, 149], dtype=torch.uint8
# )
# return_pt_slice = torch.tensor([0.1656, 0.2661, 0.3157, 0.0693, 0.1755, 0.2252, 0.0127, 0.1221, 0.1708])
class RemoteAutoencoderKLSDXLTests(
RemoteAutoencoderKLMixin,
unittest.TestCase,
):
shape = (
1,
4,
128,
128,
)
out_hw = (
1024,
1024,
)
endpoint = "https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud/"
dtype = torch.float16
scaling_factor = 0.13025
shift_factor = None
processor_cls = VaeImageProcessor
output_pt_slice = torch.tensor([104, 52, 23, 114, 61, 35, 108, 87, 38], dtype=torch.uint8)
partial_postprocess_return_pt_slice = torch.tensor([77, 86, 89, 49, 60, 75, 52, 65, 78], dtype=torch.uint8)
return_pt_slice = torch.tensor([-0.3945, -0.3289, -0.2993, -0.6177, -0.5259, -0.4119, -0.5898, -0.4863, -0.3845])


class RemoteAutoencoderKLFluxTests(
RemoteAutoencoderKLMixin,
unittest.TestCase,
):
shape = (
1,
16,
128,
128,
)
out_hw = (
1024,
1024,
)
endpoint = "https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/"
dtype = torch.bfloat16
scaling_factor = 0.3611
shift_factor = 0.1159
processor_cls = VaeImageProcessor
output_pt_slice = torch.tensor([110, 72, 91, 62, 35, 52, 69, 55, 69], dtype=torch.uint8)
partial_postprocess_return_pt_slice = torch.tensor(
[202, 203, 203, 197, 195, 193, 189, 188, 178], dtype=torch.uint8
)
return_pt_slice = torch.tensor([0.5820, 0.5962, 0.5898, 0.5439, 0.5327, 0.5112, 0.4797, 0.4773, 0.3984])


class RemoteAutoencoderKLFluxPackedTests(
RemoteAutoencoderKLMixin,
unittest.TestCase,
):
shape = (
1,
4096,
64,
)
out_hw = (
1024,
1024,
)
height = 1024
width = 1024
endpoint = "https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/"
dtype = torch.bfloat16
scaling_factor = 0.3611
shift_factor = 0.1159
processor_cls = VaeImageProcessor
# slices are different due to randn on different shape. we can pack the latent instead if we want the same
output_pt_slice = torch.tensor([96, 116, 157, 45, 67, 104, 34, 56, 89], dtype=torch.uint8)
partial_postprocess_return_pt_slice = torch.tensor(
[168, 212, 202, 155, 191, 185, 150, 180, 168], dtype=torch.uint8
)
return_pt_slice = torch.tensor([0.3198, 0.6631, 0.5864, 0.2131, 0.4944, 0.4482, 0.1776, 0.4153, 0.3176])


class RemoteAutoencoderKLHunyuanVideoTests(
RemoteAutoencoderKLHunyuanVideoMixin,
unittest.TestCase,
):
shape = (
1,
16,
3,
40,
64,
)
out_hw = (
320,
512,
)
endpoint = "https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud/"
dtype = torch.float16
scaling_factor = 0.476986
processor_cls = VideoProcessor
output_pt_slice = torch.tensor([112, 92, 85, 112, 93, 85, 112, 94, 85], dtype=torch.uint8)
partial_postprocess_return_pt_slice = torch.tensor(
[149, 161, 168, 136, 150, 156, 129, 143, 149], dtype=torch.uint8
)
return_pt_slice = torch.tensor([0.1656, 0.2661, 0.3157, 0.0693, 0.1755, 0.2252, 0.0127, 0.1221, 0.1708])