distill_model.py

import os
import sys
import torch
import glob
import json
from lightx2v.models.networks.wan.model import WanModel
from lightx2v.models.networks.wan.weights.pre_weights import WanPreWeights
from lightx2v.models.networks.wan.weights.post_weights import WanPostWeights
from lightx2v.models.networks.wan.weights.transformer_weights import (
    WanTransformerWeights,
)
from lightx2v.utils.envs import *


class WanDistillModel(WanModel):
    pre_weight_class = WanPreWeights
    post_weight_class = WanPostWeights
    transformer_weight_class = WanTransformerWeights

    def __init__(self, model_path, config, device):
        super().__init__(model_path, config, device)

    def _load_ckpt(self, use_bf16, skip_bf16):
        ckpt_path = os.path.join(self.model_path, "distill_model.pt")
        if not os.path.exists(ckpt_path):
            return super()._load_ckpt(use_bf16, skip_bf16)

        weight_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
        weight_dict = {key: (weight_dict[key].to(torch.bfloat16) if use_bf16 or all(s not in key for s in skip_bf16) else weight_dict[key]).pin_memory().to(self.device) for key in weight_dict.keys()}

        return weight_dict