nodes/kijai-ComfyUI-WanVideoWrapper
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Update nodes_model_loading.py

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kijai
2025-09-27 16:11:07 +03:00
parent 358baa2c2a
commit 4ba6c88ad8
1 changed files with 21 additions and 21 deletions
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nodes_model_loading.py
+21 -21
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@@ -10,6 +10,7 @@ from .wanvideo.modules.model import WanModel, LoRALinearLayer
from .wanvideo.modules.t5 import T5EncoderModel
from .wanvideo.modules.clip import CLIPModel
from .wanvideo.wan_video_vae import WanVideoVAE, WanVideoVAE38
from .custom_linear import _replace_linear
from accelerate import init_empty_weights
from .utils import set_module_tensor_to_device
@@ -1150,7 +1151,7 @@ class WanVideoModelLoader:
#lynx
lynx_layers = "none"
if "blocks.0.cross_attn.ip_adapter.to_v_ip.weight" in sd and "blocks.0.ref_adapter.to_k_ref.weight" in sd:
if "blocks.0.cross_attn.ip_adapter.to_v_ip.weight" in sd and "blocks.0.self_attn.ref_adapter.to_k_ref.weight" in sd:
log.info("Lynx full model detected")
n_registers = sd["blocks.0.cross_attn.ip_adapter.registers"].shape[1]
lynx_layers = "full"
@@ -1418,27 +1419,26 @@ class WanVideoModelLoader:
del unianimate_sd
if not gguf:
if lora is not None:
if merge_loras:
if not lora_low_mem_load:
load_weights(transformer, sd, weight_dtype, base_dtype, transformer_load_device)
if lora is not None and merge_loras:
if not lora_low_mem_load:
load_weights(transformer, sd, weight_dtype, base_dtype, transformer_load_device)
if control_lora:
patch_control_lora(patcher.model.diffusion_model, device)
patcher.model.is_patched = True
if control_lora:
patch_control_lora(patcher.model.diffusion_model, device)
patcher.model.is_patched = True
log.info("Merging LoRA to the model...")
patcher = apply_lora(
patcher, device, transformer_load_device, params_to_keep=params_to_keep, dtype=weight_dtype, base_dtype=base_dtype, state_dict=sd,
low_mem_load=lora_low_mem_load, control_lora=control_lora, scale_weights=scale_weights)
if not control_lora:
scale_weights.clear()
patcher.patches.clear()
transformer.patched_linear = False
sd = None
else:
from .custom_linear import _replace_linear
transformer = _replace_linear(transformer, base_dtype, sd, scale_weights=scale_weights)
log.info("Merging LoRA to the model...")
patcher = apply_lora(
patcher, device, transformer_load_device, params_to_keep=params_to_keep, dtype=weight_dtype, base_dtype=base_dtype, state_dict=sd,
low_mem_load=lora_low_mem_load, control_lora=control_lora, scale_weights=scale_weights)
if not control_lora:
scale_weights.clear()
patcher.patches.clear()
transformer.patched_linear = False
sd = None
elif "scaled" in quantization:
transformer = _replace_linear(transformer, base_dtype, sd, scale_weights=scale_weights)
transformer.patched_linear = True
else:
load_weights(transformer, sd, weight_dtype, base_dtype, transformer_load_device)
transformer.patched_linear = False