nodes/kijai-ComfyUI-WanVideoWrapper
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Better errors when trying to load models that don't exist

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kijai
2025-11-13 16:19:05 +02:00
parent ff26836cab
commit 4e31081262
1 changed files with 9 additions and 9 deletions
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nodes_model_loading.py
+9 -9
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@@ -395,7 +395,7 @@ class WanVideoLoraSelect:
return (loras_list,) return (loras_list,)
try: try:
lora_path = folder_paths.get_full_path("loras", lora) lora_path = folder_paths.get_full_path_or_raise("loras", lora)
except: except:
lora_path = lora lora_path = lora
@@ -532,7 +532,7 @@ class WanVideoLoraSelectMulti:
if not lora_name or lora_name == "none" or s == 0.0: if not lora_name or lora_name == "none" or s == 0.0:
continue continue
loras_list.append({ loras_list.append({
"path": folder_paths.get_full_path("loras", lora_name), "path": folder_paths.get_full_path_or_raise("loras", lora_name),
"strength": s, "strength": s,
"name": os.path.splitext(lora_name)[0], "name": os.path.splitext(lora_name)[0],
"blocks": blocks.get("selected_blocks", {}), "blocks": blocks.get("selected_blocks", {}),
@@ -560,7 +560,7 @@ class WanVideoVACEModelSelect:
DESCRIPTION = "VACE model to use when not using model that has it included, loaded from 'ComfyUI/models/diffusion_models'" DESCRIPTION = "VACE model to use when not using model that has it included, loaded from 'ComfyUI/models/diffusion_models'"
def getvacepath(self, vace_model): def getvacepath(self, vace_model):
vace_model = [{"path": folder_paths.get_full_path("diffusion_models", vace_model)}] vace_model = [{"path": folder_paths.get_full_path_or_raise("diffusion_models", vace_model)}]
return (vace_model,) return (vace_model,)
class WanVideoExtraModelSelect: class WanVideoExtraModelSelect:
@@ -582,7 +582,7 @@ class WanVideoExtraModelSelect:
DESCRIPTION = "Extra model to load and add to the main model, ie. VACE or MTV Crafter 'ComfyUI/models/diffusion_models'" DESCRIPTION = "Extra model to load and add to the main model, ie. VACE or MTV Crafter 'ComfyUI/models/diffusion_models'"
def getmodelpath(self, extra_model, prev_model=None): def getmodelpath(self, extra_model, prev_model=None):
extra_model = {"path": folder_paths.get_full_path("diffusion_models", extra_model)} extra_model = {"path": folder_paths.get_full_path_or_raise("diffusion_models", extra_model)}
if prev_model is not None and isinstance(prev_model, list): if prev_model is not None and isinstance(prev_model, list):
extra_model_list = prev_model + [extra_model] extra_model_list = prev_model + [extra_model]
else: else:
@@ -1683,7 +1683,7 @@ class WanVideoVAELoader:
def loadmodel(self, model_name, precision, compile_args=None): def loadmodel(self, model_name, precision, compile_args=None):
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
model_path = folder_paths.get_full_path("vae", model_name) model_path = folder_paths.get_full_path_or_raise("vae", model_name)
vae_sd = load_torch_file(model_path, safe_load=True) vae_sd = load_torch_file(model_path, safe_load=True)
has_model_prefix = any(k.startswith("model.") for k in vae_sd.keys()) has_model_prefix = any(k.startswith("model.") for k in vae_sd.keys())
@@ -1734,7 +1734,7 @@ class WanVideoTinyVAELoader:
from .taehv import TAEHV from .taehv import TAEHV
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
model_path = folder_paths.get_full_path("vae_approx", model_name) model_path = folder_paths.get_full_path_or_raise("vae_approx", model_name)
vae_sd = load_torch_file(model_path, safe_load=True) vae_sd = load_torch_file(model_path, safe_load=True)
vae = TAEHV(vae_sd, parallel=parallel, dtype=dtype) vae = TAEHV(vae_sd, parallel=parallel, dtype=dtype)
@@ -1772,7 +1772,7 @@ class LoadWanVideoT5TextEncoder:
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
model_path = folder_paths.get_full_path("text_encoders", model_name) model_path = folder_paths.get_full_path_or_raise("text_encoders", model_name)
sd = load_torch_file(model_path, safe_load=True) sd = load_torch_file(model_path, safe_load=True)
if quantization == "disabled": if quantization == "disabled":
@@ -1882,10 +1882,10 @@ class LoadWanVideoClipTextEncoder:
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
model_path = folder_paths.get_full_path("clip_vision", model_name) model_path = folder_paths.get_full_path_or_raise("clip_vision", model_name)
# We also support legacy setups where the model is in the text_encoders folder # We also support legacy setups where the model is in the text_encoders folder
if model_path is None: if model_path is None:
model_path = folder_paths.get_full_path("text_encoders", model_name) model_path = folder_paths.get_full_path_or_raise("text_encoders", model_name)
sd = load_torch_file(model_path, safe_load=True) sd = load_torch_file(model_path, safe_load=True)
if "log_scale" not in sd: if "log_scale" not in sd:
raise ValueError("Invalid CLIP model, this node expectes the 'open-clip-xlm-roberta-large-vit-huge-14' model") raise ValueError("Invalid CLIP model, this node expectes the 'open-clip-xlm-roberta-large-vit-huge-14' model")