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Update nodes_model_loading.py

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kijai
2025-08-21 19:07:11 +03:00
parent b4d2290859
commit 2dfaeb0093
1 changed files with 15 additions and 7 deletions
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nodes_model_loading.py
+15 -7
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@@ -780,10 +780,17 @@ def load_weights_gguf(transformer, reader, sd, base_dtype, transformer_load_devi
from .gguf.gguf import _replace_with_gguf_linear, GGUFParameter
import gguf
log.info("Using GGUF to load and assign model weights to device...")
param_count = sum(1 for _ in transformer.named_parameters())
sd = {}
for tensor in reader.tensors:
# Combine tensors from all readers
all_tensors = []
for r in reader:
all_tensors.extend(r.tensors)
for tensor in all_tensors:
# if the tensor is a torch supported dtype do not use GGUFParameter
is_gguf_quant = tensor.tensor_type not in [gguf.GGMLQuantizationType.F32, gguf.GGMLQuantizationType.F16]
weights = torch.from_numpy(tensor.data.copy()).to(transformer_load_device)
@@ -987,10 +994,10 @@ class WanVideoModelLoader:
if not gguf:
sd = load_torch_file(model_path, device=transformer_load_device, safe_load=True)
else:
#from diffusers.models.model_loading_utils import load_gguf_checkpoint
#sd = load_gguf_checkpoint(model_path)
gguf_reader=[]
from .gguf.gguf import load_gguf
sd, gguf_reader = load_gguf(model_path)
sd, reader = load_gguf(model_path)
gguf_reader.append(reader)
if quantization == "disabled":
for k, v in sd.items():
@@ -1021,9 +1028,10 @@ class WanVideoModelLoader:
if extra_model is not None:
if gguf:
if not vace_model["path"].endswith(".gguf"):
raise ValueError("With GGUF main model the VACE module must also be a GGUF quantized, if the main model already has VACE included, you can disconnect the VACE module loader")
vace_sd = load_gguf_checkpoint(vace_model["path"])
if not extra_model["path"].endswith(".gguf"):
raise ValueError("With GGUF main model the extra model must also be a GGUF quantized, if the main model already has extra included, you can disconnect the extra module loader")
extra_sd, extra_reader = load_gguf(extra_model["path"])
gguf_reader.append(extra_reader)
else:
extra_sd = load_torch_file(extra_model["path"], device=transformer_load_device, safe_load=True)
sd.update(extra_sd)