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Merge f8af8dadaf into 35c1470935

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liminfei-amd 2026-07-03 09:38:33 +08:00 committed by GitHub
commit f5f68e41c4
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1 changed files with 7 additions and 7 deletions
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14
comfy/ops.py
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@ -1235,7 +1235,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
run_every_op()
input_shape = input.shape
reshaped_3d = False
reshaped_nd = False
#If cast needs to apply lora, it should be done in the compute dtype
compute_dtype = input.dtype
@ -1272,12 +1272,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
# Inference path (unchanged)
if _use_quantized and quantize_input:
# Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
# Reshape >=3D tensors to 2D for quantization (needed for NVFP4 and others)
input_reshaped = input.reshape(-1, input_shape[-1]) if input.ndim >= 3 else input
# Fall back to non-quantized for non-2D tensors
if input_reshaped.ndim == 2:
reshaped_3d = input.ndim == 3
reshaped_nd = input.ndim >= 3
# dtype is now implicit in the layout class
scale = getattr(self, 'input_scale', None)
if scale is not None:
@ -1292,9 +1292,9 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
weight_only_quant=weight_only_quant,
)
# Reshape output back to 3D if input was 3D
if reshaped_3d:
output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))
# Reshape output back to original rank if input was >2D
if reshaped_nd:
output = output.reshape((*input_shape[:-1], self.weight.shape[0]))
return output