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Update VAE Decode endpoints (#10939)
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hlky
GitHub
@@ -344,7 +344,7 @@ class RemoteAutoencoderKLSDv1Tests(
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512,
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512,
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)
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endpoint = "https://bz0b3zkoojf30bhx.us-east-1.aws.endpoints.huggingface.cloud/"
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endpoint = "https://q1bj3bpq6kzilnsu.us-east-1.aws.endpoints.huggingface.cloud/"
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dtype = torch.float16
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scaling_factor = 0.18215
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shift_factor = None
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@@ -354,105 +354,105 @@ class RemoteAutoencoderKLSDv1Tests(
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return_pt_slice = torch.tensor([-0.2177, 0.0217, -0.2258, 0.0412, -0.1687, -0.1232, -0.2416, -0.2130, -0.0543])
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# class RemoteAutoencoderKLSDXLTests(
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# RemoteAutoencoderKLMixin,
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# unittest.TestCase,
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# ):
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# shape = (
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# 1,
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# 4,
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# 128,
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# 128,
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# )
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# out_hw = (
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# 1024,
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# 1024,
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# )
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# endpoint = "https://fagf07t3bwf0615i.us-east-1.aws.endpoints.huggingface.cloud/"
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# dtype = torch.float16
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# scaling_factor = 0.13025
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# shift_factor = None
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# processor_cls = VaeImageProcessor
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# output_pt_slice = torch.tensor([104, 52, 23, 114, 61, 35, 108, 87, 38], dtype=torch.uint8)
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# partial_postprocess_return_pt_slice = torch.tensor([77, 86, 89, 49, 60, 75, 52, 65, 78], dtype=torch.uint8)
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# return_pt_slice = torch.tensor([-0.3945, -0.3289, -0.2993, -0.6177, -0.5259, -0.4119, -0.5898, -0.4863, -0.3845])
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class RemoteAutoencoderKLSDXLTests(
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RemoteAutoencoderKLMixin,
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unittest.TestCase,
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):
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shape = (
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1,
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4,
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128,
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128,
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)
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out_hw = (
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1024,
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1024,
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)
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endpoint = "https://x2dmsqunjd6k9prw.us-east-1.aws.endpoints.huggingface.cloud/"
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dtype = torch.float16
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scaling_factor = 0.13025
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shift_factor = None
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processor_cls = VaeImageProcessor
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output_pt_slice = torch.tensor([104, 52, 23, 114, 61, 35, 108, 87, 38], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor([77, 86, 89, 49, 60, 75, 52, 65, 78], dtype=torch.uint8)
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return_pt_slice = torch.tensor([-0.3945, -0.3289, -0.2993, -0.6177, -0.5259, -0.4119, -0.5898, -0.4863, -0.3845])
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# class RemoteAutoencoderKLFluxTests(
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# RemoteAutoencoderKLMixin,
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# unittest.TestCase,
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# ):
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# shape = (
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# 1,
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# 16,
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# 128,
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# 128,
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# )
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# out_hw = (
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# 1024,
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# 1024,
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# )
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# endpoint = "https://fnohtuwsskxgxsnn.us-east-1.aws.endpoints.huggingface.cloud/"
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# dtype = torch.bfloat16
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# scaling_factor = 0.3611
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# shift_factor = 0.1159
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# processor_cls = VaeImageProcessor
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# output_pt_slice = torch.tensor([110, 72, 91, 62, 35, 52, 69, 55, 69], dtype=torch.uint8)
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# partial_postprocess_return_pt_slice = torch.tensor(
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# [202, 203, 203, 197, 195, 193, 189, 188, 178], dtype=torch.uint8
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# )
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# return_pt_slice = torch.tensor([0.5820, 0.5962, 0.5898, 0.5439, 0.5327, 0.5112, 0.4797, 0.4773, 0.3984])
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class RemoteAutoencoderKLFluxTests(
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RemoteAutoencoderKLMixin,
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unittest.TestCase,
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):
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shape = (
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1,
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16,
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128,
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128,
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)
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out_hw = (
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1024,
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1024,
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)
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endpoint = "https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/"
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dtype = torch.bfloat16
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scaling_factor = 0.3611
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shift_factor = 0.1159
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processor_cls = VaeImageProcessor
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output_pt_slice = torch.tensor([110, 72, 91, 62, 35, 52, 69, 55, 69], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor(
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[202, 203, 203, 197, 195, 193, 189, 188, 178], dtype=torch.uint8
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)
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return_pt_slice = torch.tensor([0.5820, 0.5962, 0.5898, 0.5439, 0.5327, 0.5112, 0.4797, 0.4773, 0.3984])
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# class RemoteAutoencoderKLFluxPackedTests(
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# RemoteAutoencoderKLMixin,
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# unittest.TestCase,
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# ):
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# shape = (
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# 1,
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# 4096,
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# 64,
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# )
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# out_hw = (
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# 1024,
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# 1024,
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# )
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# height = 1024
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# width = 1024
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# endpoint = "https://fnohtuwsskxgxsnn.us-east-1.aws.endpoints.huggingface.cloud/"
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# dtype = torch.bfloat16
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# scaling_factor = 0.3611
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# shift_factor = 0.1159
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# processor_cls = VaeImageProcessor
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# # slices are different due to randn on different shape. we can pack the latent instead if we want the same
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# output_pt_slice = torch.tensor([96, 116, 157, 45, 67, 104, 34, 56, 89], dtype=torch.uint8)
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# partial_postprocess_return_pt_slice = torch.tensor(
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# [168, 212, 202, 155, 191, 185, 150, 180, 168], dtype=torch.uint8
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# )
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# return_pt_slice = torch.tensor([0.3198, 0.6631, 0.5864, 0.2131, 0.4944, 0.4482, 0.1776, 0.4153, 0.3176])
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class RemoteAutoencoderKLFluxPackedTests(
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RemoteAutoencoderKLMixin,
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unittest.TestCase,
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):
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shape = (
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1,
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4096,
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64,
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)
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out_hw = (
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1024,
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1024,
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)
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height = 1024
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width = 1024
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endpoint = "https://whhx50ex1aryqvw6.us-east-1.aws.endpoints.huggingface.cloud/"
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dtype = torch.bfloat16
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scaling_factor = 0.3611
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shift_factor = 0.1159
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processor_cls = VaeImageProcessor
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# slices are different due to randn on different shape. we can pack the latent instead if we want the same
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output_pt_slice = torch.tensor([96, 116, 157, 45, 67, 104, 34, 56, 89], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor(
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[168, 212, 202, 155, 191, 185, 150, 180, 168], dtype=torch.uint8
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)
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return_pt_slice = torch.tensor([0.3198, 0.6631, 0.5864, 0.2131, 0.4944, 0.4482, 0.1776, 0.4153, 0.3176])
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# class RemoteAutoencoderKLHunyuanVideoTests(
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# RemoteAutoencoderKLHunyuanVideoMixin,
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# unittest.TestCase,
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# ):
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# shape = (
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# 1,
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# 16,
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# 3,
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# 40,
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# 64,
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# )
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# out_hw = (
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# 320,
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# 512,
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# )
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# endpoint = "https://lsx2injm3ts8wbvv.us-east-1.aws.endpoints.huggingface.cloud/"
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# dtype = torch.float16
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# scaling_factor = 0.476986
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# processor_cls = VideoProcessor
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# output_pt_slice = torch.tensor([112, 92, 85, 112, 93, 85, 112, 94, 85], dtype=torch.uint8)
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# partial_postprocess_return_pt_slice = torch.tensor(
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# [149, 161, 168, 136, 150, 156, 129, 143, 149], dtype=torch.uint8
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# )
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# 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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class RemoteAutoencoderKLHunyuanVideoTests(
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RemoteAutoencoderKLHunyuanVideoMixin,
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unittest.TestCase,
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):
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shape = (
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1,
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16,
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3,
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40,
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64,
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)
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out_hw = (
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320,
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512,
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)
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endpoint = "https://o7ywnmrahorts457.us-east-1.aws.endpoints.huggingface.cloud/"
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dtype = torch.float16
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scaling_factor = 0.476986
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processor_cls = VideoProcessor
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output_pt_slice = torch.tensor([112, 92, 85, 112, 93, 85, 112, 94, 85], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor(
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[149, 161, 168, 136, 150, 156, 129, 143, 149], dtype=torch.uint8
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)
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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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