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Fix quant_state None on AMD GPUs by caching quant_state_dict at load time #3

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Merged
mengqin merged 1 commit into from
May 20, 2026
Merged

Fix quant_state None on AMD GPUs by caching quant_state_dict at load time #3

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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ git clone https://github.com/mengqin/ComfyUI-UnetBnbModelLoader ComfyUI/custom_n
.\python_embeded\python.exe -s -m pip install -r .\ComfyUI\custom_nodes\ComfyUI-UnetBnbModelLoader\requirements.txt
```

Because this plugin relies on bitsandbytes, we are unable to support macOS and AMD GPUs.
Because this plugin relies on bitsandbytes, we are unable to support macOS.

## Usage

Expand Down
17 changes: 10 additions & 7 deletions ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,8 @@ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
data=weight_data, quantized_stats=quant_state_dict, device=device
)
self.weight = bnb_param

self._bnb_quant_state_dict = quant_state_dict

for k in bnb_state_dict.keys():
state_dict.pop(k)
if k in unexpected_keys: unexpected_keys.remove(k)
Expand Down Expand Up @@ -94,9 +95,10 @@ def forward(self, x):
if getattr(self, "is_bnb_quantized", lambda : False)():
if not patches_for_this_layer:
bias = self.bias.to(device=x.device, dtype=x.dtype) if self.bias is not None else None
return bnb.matmul_4bit(
x, self.weight.t(), bias=bias, quant_state=getattr(self.weight, "quant_state", None)
).to(x.dtype)
qs = getattr(self.weight, "quant_state", None)
if qs is None and hasattr(self, "_bnb_quant_state_dict"):
qs = bnb.functional.QuantState.from_dict(self._bnb_quant_state_dict, device=x.device)
return bnb.matmul_4bit(x, self.weight.t(), bias=bias, quant_state=qs).to(x.dtype)

try:
base_w = self.weight.to(x.device)
Expand All @@ -113,9 +115,10 @@ def forward(self, x):

if weight_final_fp32 is None:
bias = self.bias.to(device=x.device, dtype=x.dtype) if self.bias is not None else None
return bnb.matmul_4bit(
x, self.weight.t(), bias=bias, quant_state=getattr(self.weight, "quant_state", None)
).to(x.dtype)
qs = getattr(self.weight, "quant_state", None)
if qs is None and hasattr(self, "_bnb_quant_state_dict"):
qs = bnb.functional.QuantState.from_dict(self._bnb_quant_state_dict, device=x.device)
return bnb.matmul_4bit(x, self.weight.t(), bias=bias, quant_state=qs).to(x.dtype)

weight_final = comfy.float.stochastic_rounding(weight_final_fp32, x.dtype)
bias = self.bias.to(device=x.device, dtype=x.dtype) if self.bias is not None else None
Expand Down