Ethanfel
97954cd06e
Aliased tensors (patch_embedding / expanded_patch_embedding / original_patch_embedding) share memory, causing safetensors save_file to warn about duplicates. Clone each tensor to break sharing while keeping all keys for loader compatibility. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
117 lines
4.2 KiB
Python
117 lines
4.2 KiB
Python
import os
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import json
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import logging
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import torch
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import folder_paths
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from safetensors.torch import save_file
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from comfy.utils import ProgressBar
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log = logging.getLogger("ComfyUI-WanVideoSaveMerged")
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class WanVideoSaveMergedModel:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"model": ("WANVIDEOMODEL", {"tooltip": "WanVideo model with merged LoRA from the WanVideo Model Loader"}),
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"filename_prefix": ("STRING", {"default": "merged_wanvideo", "tooltip": "Filename prefix for the saved model"}),
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},
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"optional": {
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"save_dtype": (["same", "bf16", "fp16", "fp32"], {
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"default": "same",
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"tooltip": "Cast weights to this dtype before saving. 'same' keeps the current dtype of each tensor. Recommended to set explicitly if model was loaded in fp8."
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}),
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"custom_path": ("STRING", {
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"default": "",
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"tooltip": "Absolute path to save directory. Leave empty to save in ComfyUI/models/diffusion_models/"
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}),
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},
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}
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RETURN_TYPES = ()
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FUNCTION = "save_model"
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CATEGORY = "WanVideoWrapper"
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OUTPUT_NODE = True
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DESCRIPTION = "Saves the WanVideo diffusion model (including merged LoRAs) as a safetensors file"
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def save_model(self, model, filename_prefix, save_dtype="same", custom_path=""):
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dtype_map = {
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"bf16": torch.bfloat16,
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"fp16": torch.float16,
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"fp32": torch.float32,
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}
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# Build output directory
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if custom_path and os.path.isabs(custom_path):
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output_dir = custom_path
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else:
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output_dir = os.path.join(folder_paths.models_dir, "diffusion_models")
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os.makedirs(output_dir, exist_ok=True)
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# Build filename, avoid overwriting
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filename = f"{filename_prefix}.safetensors"
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output_path = os.path.join(output_dir, filename)
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counter = 1
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while os.path.exists(output_path):
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filename = f"{filename_prefix}_{counter}.safetensors"
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output_path = os.path.join(output_dir, filename)
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counter += 1
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# Gather metadata about the merge for traceability
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metadata = {}
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model_name = model.model.pipeline.get("model_name", "unknown")
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metadata["source_model"] = str(model_name)
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lora_info = model.model.pipeline.get("lora")
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if lora_info is not None:
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lora_entries = []
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for l in lora_info:
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lora_entries.append({
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"name": l.get("name", "unknown"),
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"strength": l.get("strength", 1.0),
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})
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metadata["merged_loras"] = json.dumps(lora_entries)
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metadata["save_dtype"] = save_dtype
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# Extract state dict from the diffusion model (keys are already bare,
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# e.g. "blocks.0.self_attn.k.weight" — matching original checkpoint format)
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diffusion_model = model.model.diffusion_model
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state_dict = diffusion_model.state_dict()
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target_dtype = dtype_map.get(save_dtype)
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pbar = ProgressBar(len(state_dict))
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clean_sd = {}
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for k, v in state_dict.items():
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tensor = v.cpu()
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if target_dtype is not None:
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tensor = tensor.to(target_dtype)
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# Clone to break shared memory between aliased tensors
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# (e.g. patch_embedding / expanded_patch_embedding / original_patch_embedding)
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# safetensors save_file doesn't handle shared tensors, and save_model
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# deduplicates keys which breaks compatibility with ComfyUI's load_file
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clean_sd[k] = tensor.clone()
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pbar.update(1)
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log.info(f"Saving merged WanVideo model to: {output_path}")
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log.info(f"Number of tensors: {len(clean_sd)}")
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save_file(clean_sd, output_path, metadata=metadata)
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log.info(f"Model saved successfully: {filename}")
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del clean_sd
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return ()
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NODE_CLASS_MAPPINGS = {
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"WanVideoSaveMergedModel": WanVideoSaveMergedModel,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"WanVideoSaveMergedModel": "WanVideo Save Merged Model",
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}
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