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Make Convolution AutoTuning opt-in for now
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@ -143,6 +143,7 @@ class PerformanceFeature(enum.Enum):
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Fp16Accumulation = "fp16_accumulation"
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Fp16Accumulation = "fp16_accumulation"
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Fp8MatrixMultiplication = "fp8_matrix_mult"
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Fp8MatrixMultiplication = "fp8_matrix_mult"
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CublasOps = "cublas_ops"
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CublasOps = "cublas_ops"
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AutoTune = "autotune"
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parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
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parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
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@ -52,7 +52,7 @@ except (ModuleNotFoundError, TypeError):
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cast_to = comfy.model_management.cast_to #TODO: remove once no more references
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cast_to = comfy.model_management.cast_to #TODO: remove once no more references
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if torch.cuda.is_available() and torch.backends.cudnn.is_available():
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if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.benchmark = True
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def cast_to_input(weight, input, non_blocking=False, copy=True):
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def cast_to_input(weight, input, non_blocking=False, copy=True):
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