nodes/kijai-ComfyUI-WanVideoWrapper
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Fix comfy org scaled models giving black output

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kijai
2025-07-29 08:05:48 +03:00
parent de11d52348
commit 234cffbf24
1 changed files with 4 additions and 19 deletions
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nodes_model_loading.py
+4 -19
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@@ -1030,7 +1030,7 @@ class WanVideoModelLoader:
dtype = torch.float8_e5m2
else:
dtype = base_dtype
params_to_keep = {"norm", "head", "bias", "time_in", "vector_in", "patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter", "add"}
params_to_keep = {"norm", "head", "bias", "time_in", "patch_embedding", "time_", "img_emb", "modulation", "text_embedding", "adapter", "add"}
#if lora is not None:
# transformer_load_device = device
if not lora_low_mem_load:
@@ -1042,6 +1042,8 @@ class WanVideoModelLoader:
total=param_count,
leave=True):
dtype_to_use = base_dtype if any(keyword in name for keyword in params_to_keep) else dtype
if "scaled" in quantization:
dtype_to_use = sd[name].dtype
if "patch_embedding" in name:
dtype_to_use = torch.float32
set_module_tensor_to_device(transformer, name, device=transformer_load_device, dtype=dtype_to_use, value=sd[name])
@@ -1163,7 +1165,7 @@ class WanVideoModelLoader:
if "scaled" in quantization and not merge_loras:
log.info("Using FP8 scaled linear quantization")
convert_linear_with_lora_and_scale(patcher.model.diffusion_model, scale_weights, params_to_keep=params_to_keep, patches=patcher.patches)
convert_linear_with_lora_and_scale(patcher.model.diffusion_model, scale_weights, patches=patcher.patches)
elif lora is not None and not merge_loras and not gguf:
log.info("LoRAs will be applied at runtime")
convert_linear_with_lora_and_scale(patcher.model.diffusion_model, patches=patcher.patches)
@@ -1216,23 +1218,6 @@ class WanVideoModelLoader:
compile_args = compile_args,
)
# #compile
# if compile_args is not None and vram_management_args is None:
# torch._dynamo.config.cache_size_limit = compile_args["dynamo_cache_size_limit"]
# try:
# if hasattr(torch, '_dynamo') and hasattr(torch._dynamo, 'config'):
# torch._dynamo.config.recompile_limit = compile_args["dynamo_recompile_limit"]
# except Exception as e:
# log.warning(f"Could not set recompile_limit: {e}")
# if compile_args["compile_transformer_blocks_only"]:
# for i, block in enumerate(patcher.model.diffusion_model.blocks):
# patcher.model.diffusion_model.blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
# if vace_layers is not None:
# for i, block in enumerate(patcher.model.diffusion_model.vace_blocks):
# patcher.model.diffusion_model.vace_blocks[i] = torch.compile(block, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
# else:
# patcher.model.diffusion_model = torch.compile(patcher.model.diffusion_model, fullgraph=compile_args["fullgraph"], dynamic=compile_args["dynamic"], backend=compile_args["backend"], mode=compile_args["mode"])
if load_device == "offload_device" and patcher.model.diffusion_model.device != offload_device:
log.info(f"Moving diffusion model from {patcher.model.diffusion_model.device} to {offload_device}")
patcher.model.diffusion_model.to(offload_device)