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Unverified Commit 54043c3e authored by hlky's avatar hlky Committed by GitHub
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Update VAE Decode endpoints (#10939)

parent fc4229a0
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tests/remote/test_remote_decode.py
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...@@ -344,7 +344,7 @@ class RemoteAutoencoderKLSDv1Tests( ...@@ -344,7 +344,7 @@ class RemoteAutoencoderKLSDv1Tests(
512, 512,
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 dtype = torch.float16
scaling_factor = 0.18215 scaling_factor = 0.18215
shift_factor = None shift_factor = None
...@@ -354,105 +354,105 @@ class RemoteAutoencoderKLSDv1Tests( ...@@ -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]) 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( class RemoteAutoencoderKLSDXLTests(
# RemoteAutoencoderKLMixin, RemoteAutoencoderKLMixin,
# unittest.TestCase, unittest.TestCase,
# ): ):
# shape = ( shape = (
# 1, 1,
# 4, 4,
# 128, 128,
# 128, 128,
# ) )
# out_hw = ( out_hw = (
# 1024, 1024,
# 1024, 1024,
# ) )
# endpoint = "https://fagf07t3bwf0615i.us-east-1.aws.endpoints.huggingface.cloud/" endpoint = "https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.float16 dtype = torch.float16
# scaling_factor = 0.13025 scaling_factor = 0.13025
# shift_factor = None shift_factor = None
# processor_cls = VaeImageProcessor processor_cls = VaeImageProcessor
# output_pt_slice = torch.tensor([104, 52, 23, 114, 61, 35, 108, 87, 38], dtype=torch.uint8) 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) 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]) 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( class RemoteAutoencoderKLFluxTests(
# RemoteAutoencoderKLMixin, RemoteAutoencoderKLMixin,
# unittest.TestCase, unittest.TestCase,
# ): ):
# shape = ( shape = (
# 1, 1,
# 16, 16,
# 128, 128,
# 128, 128,
# ) )
# out_hw = ( out_hw = (
# 1024, 1024,
# 1024, 1024,
# ) )
# endpoint = "https://fnohtuwsskxgxsnn.us-east-1.aws.endpoints.huggingface.cloud/" endpoint = "https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.bfloat16 dtype = torch.bfloat16
# scaling_factor = 0.3611 scaling_factor = 0.3611
# shift_factor = 0.1159 shift_factor = 0.1159
# processor_cls = VaeImageProcessor processor_cls = VaeImageProcessor
# output_pt_slice = torch.tensor([110, 72, 91, 62, 35, 52, 69, 55, 69], dtype=torch.uint8) output_pt_slice = torch.tensor([110, 72, 91, 62, 35, 52, 69, 55, 69], dtype=torch.uint8)
# partial_postprocess_return_pt_slice = torch.tensor( partial_postprocess_return_pt_slice = torch.tensor(
# [202, 203, 203, 197, 195, 193, 189, 188, 178], dtype=torch.uint8 [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]) 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( class RemoteAutoencoderKLFluxPackedTests(
# RemoteAutoencoderKLMixin, RemoteAutoencoderKLMixin,
# unittest.TestCase, unittest.TestCase,
# ): ):
# shape = ( shape = (
# 1, 1,
# 4096, 4096,
# 64, 64,
# ) )
# out_hw = ( out_hw = (
# 1024, 1024,
# 1024, 1024,
# ) )
# height = 1024 height = 1024
# width = 1024 width = 1024
# endpoint = "https://fnohtuwsskxgxsnn.us-east-1.aws.endpoints.huggingface.cloud/" endpoint = "https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.bfloat16 dtype = torch.bfloat16
# scaling_factor = 0.3611 scaling_factor = 0.3611
# shift_factor = 0.1159 shift_factor = 0.1159
# processor_cls = VaeImageProcessor processor_cls = VaeImageProcessor
# # slices are different due to randn on different shape. we can pack the latent instead if we want the same # 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) output_pt_slice = torch.tensor([96, 116, 157, 45, 67, 104, 34, 56, 89], dtype=torch.uint8)
# partial_postprocess_return_pt_slice = torch.tensor( partial_postprocess_return_pt_slice = torch.tensor(
# [168, 212, 202, 155, 191, 185, 150, 180, 168], dtype=torch.uint8 [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]) 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( class RemoteAutoencoderKLHunyuanVideoTests(
# RemoteAutoencoderKLHunyuanVideoMixin, RemoteAutoencoderKLHunyuanVideoMixin,
# unittest.TestCase, unittest.TestCase,
# ): ):
# shape = ( shape = (
# 1, 1,
# 16, 16,
# 3, 3,
# 40, 40,
# 64, 64,
# ) )
# out_hw = ( out_hw = (
# 320, 320,
# 512, 512,
# ) )
# endpoint = "https://lsx2injm3ts8wbvv.us-east-1.aws.endpoints.huggingface.cloud/" endpoint = "https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud/"
# dtype = torch.float16 dtype = torch.float16
# scaling_factor = 0.476986 scaling_factor = 0.476986
# processor_cls = VideoProcessor processor_cls = VideoProcessor
# output_pt_slice = torch.tensor([112, 92, 85, 112, 93, 85, 112, 94, 85], dtype=torch.uint8) output_pt_slice = torch.tensor([112, 92, 85, 112, 93, 85, 112, 94, 85], dtype=torch.uint8)
# partial_postprocess_return_pt_slice = torch.tensor( partial_postprocess_return_pt_slice = torch.tensor(
# [149, 161, 168, 136, 150, 156, 129, 143, 149], dtype=torch.uint8 [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]) return_pt_slice = torch.tensor([0.1656, 0.2661, 0.3157, 0.0693, 0.1755, 0.2252, 0.0127, 0.1221, 0.1708])
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