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ops: tolerate already force casted dynamic weight

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Some custom nodes .to weights completely out of load context which
can wreak havoc if its for a model that is not active. Detect this
condition and just let it fall-through to the non-dynamic loader
straight up.
This commit is contained in:
Rattus 2026-06-11 12:41:01 +10:00
parent 431a1888d3
commit c2f9b1ba37
1 changed files with 8 additions and 8 deletions
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16
comfy/ops.py
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@ -299,21 +299,21 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
non_blocking = comfy.model_management.device_supports_non_blocking(device) non_blocking = comfy.model_management.device_supports_non_blocking(device)
if hasattr(s, "_v"): if hasattr(s, "_v") and comfy.model_management.is_device_cpu(device):
#vbar doesn't support CPU weights, but some custom nodes have weird paths #vbar doesn't support CPU weights, but some custom nodes have weird paths
#that might switch the layer to the CPU and expect it to work. We have to take #that might switch the layer to the CPU and expect it to work. We have to take
#a clone conservatively as we are mmapped and some SFT files are packed misaligned #a clone conservatively as we are mmapped and some SFT files are packed misaligned
#If you are a custom node author reading this, please move your layer to the GPU #If you are a custom node author reading this, please move your layer to the GPU
#or declare your ModelPatcher as CPU in the first place. #or declare your ModelPatcher as CPU in the first place.
if comfy.model_management.is_device_cpu(device): materialize_meta_param(s, ["weight", "bias"])
materialize_meta_param(s, ["weight", "bias"]) weight = s.weight.to(dtype=dtype, copy=True)
weight = s.weight.to(dtype=dtype, copy=True) if isinstance(weight, QuantizedTensor):
if isinstance(weight, QuantizedTensor): weight = weight.dequantize()
weight = weight.dequantize() bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None
bias = s.bias.to(dtype=bias_dtype, copy=True) if s.bias is not None else None return format_return((weight, bias, (None, None, None)), offloadable)
return format_return((weight, bias, (None, None, None)), offloadable)
elif hasattr(s, "_v") and s.weight.device != device:
prefetched = hasattr(s, "_prefetch") prefetched = hasattr(s, "_prefetch")
offload_stream = None offload_stream = None
offload_device = None offload_device = None