import os import torch import glob import json from lightx2v.common.ops.attn import MaskMap 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.models.networks.wan.infer.pre_infer import WanPreInfer from lightx2v.models.networks.wan.infer.post_infer import WanPostInfer from lightx2v.models.networks.wan.infer.transformer_infer import ( WanTransformerInfer, ) from lightx2v.models.networks.wan.infer.feature_caching.transformer_infer import ( WanTransformerInferTeaCaching, WanTransformerInferTaylorCaching, WanTransformerInferAdaCaching, WanTransformerInferCustomCaching, WanTransformerInferFirstBlock, WanTransformerInferDualBlock, WanTransformerInferDynamicBlock, ) from lightx2v.models.networks.wan.infer.dist_infer.transformer_infer import WanTransformerDistInfer from safetensors import safe_open from lightx2v.utils.envs import * from lightx2v.utils.utils import * from loguru import logger class WanModel: pre_weight_class = WanPreWeights post_weight_class = WanPostWeights transformer_weight_class = WanTransformerWeights def __init__(self, model_path, config, device): self.model_path = model_path self.config = config self.clean_cuda_cache = self.config.get("clean_cuda_cache", False) self.dit_quantized = self.config.mm_config.get("mm_type", "Default") != "Default" if self.dit_quantized: dit_quant_scheme = self.config.mm_config.get("mm_type").split("-")[1] self.dit_quantized_ckpt = find_hf_model_path(config, "dit_quantized_ckpt", subdir=dit_quant_scheme) else: self.dit_quantized_ckpt = None assert not self.config.get("lazy_load", False) self.config.dit_quantized_ckpt = self.dit_quantized_ckpt quant_config_path = os.path.join(self.config.dit_quantized_ckpt, "config.json") if os.path.exists(quant_config_path): with open(quant_config_path, "r") as f: quant_model_config = json.load(f) self.config.update(quant_model_config) self.weight_auto_quant = self.config.mm_config.get("weight_auto_quant", False) if self.dit_quantized: assert self.weight_auto_quant or self.dit_quantized_ckpt is not None self.device = device self._init_infer_class() self._init_weights() self._init_infer() def _init_infer_class(self): self.pre_infer_class = WanPreInfer self.post_infer_class = WanPostInfer if self.config.get("parallel_attn_type", None): self.transformer_infer_class = WanTransformerDistInfer else: if self.config["feature_caching"] == "NoCaching": self.transformer_infer_class = WanTransformerInfer elif self.config["feature_caching"] == "Tea": self.transformer_infer_class = WanTransformerInferTeaCaching elif self.config["feature_caching"] == "TaylorSeer": self.transformer_infer_class = WanTransformerInferTaylorCaching elif self.config["feature_caching"] == "Ada": self.transformer_infer_class = WanTransformerInferAdaCaching elif self.config["feature_caching"] == "Custom": self.transformer_infer_class = WanTransformerInferCustomCaching elif self.config["feature_caching"] == "FirstBlock": self.transformer_infer_class = WanTransformerInferFirstBlock elif self.config["feature_caching"] == "DualBlock": self.transformer_infer_class = WanTransformerInferDualBlock elif self.config["feature_caching"] == "DynamicBlock": self.transformer_infer_class = WanTransformerInferDynamicBlock else: raise NotImplementedError(f"Unsupported feature_caching type: {self.config['feature_caching']}") def _load_safetensor_to_dict(self, file_path, use_bf16, skip_bf16): with safe_open(file_path, framework="pt") as f: return {key: (f.get_tensor(key).to(torch.bfloat16) if use_bf16 or all(s not in key for s in skip_bf16) else f.get_tensor(key)).pin_memory().to(self.device) for key in f.keys()} def _load_ckpt(self, use_bf16, skip_bf16): safetensors_path = find_hf_model_path(self.config, "dit_original_ckpt", subdir="original") safetensors_files = glob.glob(os.path.join(safetensors_path, "*.safetensors")) weight_dict = {} for file_path in safetensors_files: file_weights = self._load_safetensor_to_dict(file_path, use_bf16, skip_bf16) weight_dict.update(file_weights) return weight_dict def _load_quant_ckpt(self, use_bf16, skip_bf16): ckpt_path = self.dit_quantized_ckpt logger.info(f"Loading quant dit model from {ckpt_path}") index_files = [f for f in os.listdir(ckpt_path) if f.endswith(".index.json")] if not index_files: raise FileNotFoundError(f"No *.index.json found in {ckpt_path}") index_path = os.path.join(ckpt_path, index_files[0]) logger.info(f" Using safetensors index: {index_path}") with open(index_path, "r") as f: index_data = json.load(f) weight_dict = {} for filename in set(index_data["weight_map"].values()): safetensor_path = os.path.join(ckpt_path, filename) with safe_open(safetensor_path, framework="pt") as f: logger.info(f"Loading weights from {safetensor_path}") for k in f.keys(): if f.get_tensor(k).dtype == torch.float: if use_bf16 or all(s not in k for s in skip_bf16): weight_dict[k] = f.get_tensor(k).pin_memory().to(torch.bfloat16).to(self.device) else: weight_dict[k] = f.get_tensor(k).pin_memory().to(self.device) else: weight_dict[k] = f.get_tensor(k).pin_memory().to(self.device) return weight_dict def _load_quant_split_ckpt(self, use_bf16, skip_bf16): lazy_load_model_path = self.dit_quantized_ckpt logger.info(f"Loading splited quant model from {lazy_load_model_path}") pre_post_weight_dict = {} safetensor_path = os.path.join(lazy_load_model_path, "non_block.safetensors") with safe_open(safetensor_path, framework="pt", device="cpu") as f: for k in f.keys(): if f.get_tensor(k).dtype == torch.float: if use_bf16 or all(s not in k for s in skip_bf16): pre_post_weight_dict[k] = f.get_tensor(k).pin_memory().to(torch.bfloat16).to(self.device) else: pre_post_weight_dict[k] = f.get_tensor(k).pin_memory().to(self.device) else: pre_post_weight_dict[k] = f.get_tensor(k).pin_memory().to(self.device) return pre_post_weight_dict def _init_weights(self, weight_dict=None): use_bf16 = GET_DTYPE() == "BF16" # Some layers run with float32 to achieve high accuracy skip_bf16 = { "norm", "embedding", "modulation", "time", "img_emb.proj.0", "img_emb.proj.4", } if weight_dict is None: if not self.dit_quantized or self.weight_auto_quant: self.original_weight_dict = self._load_ckpt(use_bf16, skip_bf16) else: if not self.config.get("lazy_load", False): self.original_weight_dict = self._load_quant_ckpt(use_bf16, skip_bf16) else: self.original_weight_dict = self._load_quant_split_ckpt(use_bf16, skip_bf16) else: self.original_weight_dict = weight_dict # init weights self.pre_weight = self.pre_weight_class(self.config) self.post_weight = self.post_weight_class(self.config) self.transformer_weights = self.transformer_weight_class(self.config) # load weights self.pre_weight.load(self.original_weight_dict) self.post_weight.load(self.original_weight_dict) self.transformer_weights.load(self.original_weight_dict) def _init_infer(self): self.pre_infer = self.pre_infer_class(self.config) self.post_infer = self.post_infer_class(self.config) self.transformer_infer = self.transformer_infer_class(self.config) def set_scheduler(self, scheduler): self.scheduler = scheduler self.pre_infer.set_scheduler(scheduler) self.post_infer.set_scheduler(scheduler) self.transformer_infer.set_scheduler(scheduler) def to_cpu(self): self.pre_weight.to_cpu() self.post_weight.to_cpu() self.transformer_weights.to_cpu() def to_cuda(self): self.pre_weight.to_cuda() self.post_weight.to_cuda() self.transformer_weights.to_cuda() @torch.no_grad() def infer(self, inputs): if self.transformer_infer.mask_map is None: _, c, h, w = self.scheduler.latents.shape video_token_num = c * (h // 2) * (w // 2) self.transformer_infer.mask_map = MaskMap(video_token_num, c) if self.config.get("cpu_offload", False): self.pre_weight.to_cuda() self.post_weight.to_cuda() embed, grid_sizes, pre_infer_out = self.pre_infer.infer(self.pre_weight, inputs, positive=True) x = self.transformer_infer.infer(self.transformer_weights, grid_sizes, embed, *pre_infer_out) noise_pred_cond = self.post_infer.infer(self.post_weight, x, embed, grid_sizes)[0] self.scheduler.noise_pred = noise_pred_cond if self.clean_cuda_cache: del x, embed, pre_infer_out, noise_pred_cond, grid_sizes torch.cuda.empty_cache() if self.config["enable_cfg"]: embed, grid_sizes, pre_infer_out = self.pre_infer.infer(self.pre_weight, inputs, positive=False) x = self.transformer_infer.infer(self.transformer_weights, grid_sizes, embed, *pre_infer_out) noise_pred_uncond = self.post_infer.infer(self.post_weight, x, embed, grid_sizes)[0] self.scheduler.noise_pred = noise_pred_uncond + self.config.sample_guide_scale * (self.scheduler.noise_pred - noise_pred_uncond) if self.config.get("cpu_offload", False): self.pre_weight.to_cpu() self.post_weight.to_cpu() if self.clean_cuda_cache: del x, embed, pre_infer_out, noise_pred_uncond, grid_sizes torch.cuda.empty_cache()