nodes/kijai-ComfyUI-WanVideoWrapper
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init: support lite model

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kijai
2025-09-27 00:49:02 +03:00
parent 37365817e8
commit ab31158673
9 changed files with 712 additions and 25 deletions
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nodes_model_loading.py
+32 -18
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@@ -1141,6 +1141,15 @@ class WanVideoModelLoader:
is_humo = "audio_proj.audio_proj_glob_1.layer.weight" in sd
is_wananimate = "pose_patch_embedding.weight" in sd
#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:
n_registers = sd["blocks.0.cross_attn.ip_adapter.registers"].shape[1]
lynx_layers = "full"
elif "blocks.0.cross_attn.ip_adapter.to_v_ip.weight" in sd:
n_registers = 0
lynx_layers = "lite"
model_type = "t2v"
if "audio_injector.injector.0.k.weight" in sd:
model_type = "s2v"
@@ -1259,6 +1268,7 @@ class WanVideoModelLoader:
"humo_audio": is_humo,
"is_wananimate": is_wananimate,
"rms_norm_function": rms_norm_function,
"lynx_layers": lynx_layers,
}
@@ -1399,27 +1409,31 @@ class WanVideoModelLoader:
del unianimate_sd
if not gguf:
if merge_loras and lora is not None:
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 lora is not None:
if merge_loras:
if not lora_low_mem_load:
load_weights(transformer, sd, weight_dtype, base_dtype, transformer_load_device)
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()
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)
else:
load_weights(transformer, sd, weight_dtype, base_dtype, transformer_load_device)
transformer.patched_linear = False
sd = None
else:
from .custom_linear import _replace_linear
transformer = _replace_linear(transformer, base_dtype, sd, scale_weights=scale_weights)
transformer.patched_linear = True
if "fast" in quantization:
if lora is not None and not merge_loras: