nodes/kijai-ComfyUI-WanVideoWrapper
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Update nodes_model_loading.py

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kijai
2025-07-22 13:58:43 +03:00
parent 3988accdf3
commit 98fa84bb17
1 changed files with 20 additions and 13 deletions
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nodes_model_loading.py
+20 -13
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@@ -670,6 +670,9 @@ class WanVideoSetLoRAs:
log.info(f"Loading LoRA: {l['name']} with strength: {l['strength']}")
lora_path = l["path"]
lora_strength = l["strength"]
if lora_strength == 0:
log.warning(f"LoRA {lora_path} has strength 0, skipping...")
continue
lora_sd = load_torch_file(lora_path, safe_load=True)
if "dwpose_embedding.0.weight" in lora_sd: #unianimate
raise NotImplementedError("Unianimate LoRA patching is not implemented in this node.")
@@ -701,7 +704,7 @@ class WanVideoModelLoader:
"model": (folder_paths.get_filename_list("unet_gguf") + folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
"base_precision": (["fp32", "bf16", "fp16", "fp16_fast"], {"default": "bf16"}),
"quantization": (["disabled", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp8_e4m3fn_fast_no_ffn", "fp8_e4m3fn_scaled"], {"default": "disabled", "tooltip": "optional quantization method"}),
"quantization": (["disabled", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2", "fp8_e4m3fn_fast_no_ffn", "fp8_e4m3fn_scaled", "fp8_e5m2_scaled"], {"default": "disabled", "tooltip": "optional quantization method"}),
"load_device": (["main_device", "offload_device"], {"default": "main_device", "tooltip": "Initial device to load the model to, NOT recommended with the larger models unless you have 48GB+ VRAM"}),
},
"optional": {
@@ -785,17 +788,18 @@ class WanVideoModelLoader:
sd = load_gguf_checkpoint(model_path)
if quantization == "disabled":
if "scaled_fp8" in sd:
quantization = "fp8_e4m3fn_scaled"
else:
for k, v in sd.items():
if isinstance(v, torch.Tensor):
if v.dtype == torch.float8_e4m3fn:
quantization = "fp8_e4m3fn"
break
elif v.dtype == torch.float8_e5m2:
quantization = "fp8_e5m2"
break
for k, v in sd.items():
if isinstance(v, torch.Tensor):
if v.dtype == torch.float8_e4m3fn:
quantization = "fp8_e4m3fn"
if "scaled_fp8" in sd:
quantization = "fp8_e4m3fn_scaled"
break
elif v.dtype == torch.float8_e5m2:
quantization = "fp8_e5m2"
if "scaled_fp8" in sd:
quantization = "fp8_e5m2_scaled"
break
if "scaled_fp8" in sd and quantization != "fp8_e4m3fn_scaled":
raise ValueError("The model is a scaled fp8 model, please set quantization to 'fp8_e4m3fn_scaled'")
@@ -1004,7 +1008,7 @@ class WanVideoModelLoader:
if not gguf:
if "fp8_e4m3fn" in quantization:
dtype = torch.float8_e4m3fn
elif quantization == "fp8_e5m2":
elif "fp8_e5m2" in quantization:
dtype = torch.float8_e5m2
else:
dtype = base_dtype
@@ -1040,6 +1044,9 @@ class WanVideoModelLoader:
log.info(f"Loading LoRA: {l['name']} with strength: {l['strength']}")
lora_path = l["path"]
lora_strength = l["strength"]
if lora_strength == 0:
log.warning(f"LoRA {lora_path} has strength 0, skipping...")
continue
lora_sd = load_torch_file(lora_path, safe_load=True)
if "dwpose_embedding.0.weight" in lora_sd: #unianimate
from .unianimate.nodes import update_transformer