642 lines
21 KiB
Python
642 lines
21 KiB
Python
import os
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import torch
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import gc
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from typing import cast, no_type_check, Any
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import logging
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import json
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import numpy as np
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import comfy.model_management as mm
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from comfy.utils import ProgressBar
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import folder_paths
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from tqdm import tqdm
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from easydict import EasyDict
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# Import LightX2V modules
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from .lightx2v.lightx2v.utils.profiler import ProfilingContext
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from .lightx2v.lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
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from .lightx2v.lightx2v.models.input_encoders.hf.xlm_roberta.model import (
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CLIPModel as ClipVisionModel,
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)
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from .lightx2v.lightx2v.utils.utils import seed_all
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from .lightx2v.lightx2v.models.video_encoders.hf.wan.vae import WanVAE
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from .lightx2v.lightx2v.models.networks.wan.model import WanModel
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from .lightx2v.lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
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from .lightx2v.lightx2v.models.schedulers.wan.scheduler import WanScheduler
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from .lightx2v.lightx2v.models.schedulers.wan.feature_caching.scheduler import (
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WanSchedulerTeaCaching,
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)
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class Lightx2vWanVideoT5EncoderLoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"t5_model_path": (
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"STRING",
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{"default": "models/t5/models_t5_umt5-xxl-enc-bf16.pth"},
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),
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"tokenizer_path": ("STRING", {"default": "models/t5/google/umt5-xxl"}),
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"text_len": (
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"INT",
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{"default": 512, "min": 64, "max": 2048, "step": 1},
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),
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"precision": (["bf16", "fp16", "fp32"], {"default": "bf16"}),
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"device": (["cuda", "cpu"], {"default": "cuda"}),
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"additional_param": ("STRING", {"default": "default_value"}),
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}
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}
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RETURN_TYPES = ("LIGHT_T5_ENCODER",)
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RETURN_NAMES = ("t5_encoder",)
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FUNCTION = "load_t5_encoder"
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CATEGORY = "LightX2V"
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def load_t5_encoder(
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self,
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t5_model_path,
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tokenizer_path,
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text_len,
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precision,
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device,
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additional_param,
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):
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# Map precision to torch dtype
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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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dtype = dtype_map[precision]
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# Resolve device
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if device == "cuda":
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device = mm.get_torch_device()
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else:
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device = torch.device("cpu")
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# Load the T5 encoder
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t5_encoder = T5EncoderModel(
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text_len=text_len,
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dtype=dtype,
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device=device, # type:ignore
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checkpoint_path=t5_model_path,
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tokenizer_path=tokenizer_path,
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shard_fn=None,
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)
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return (t5_encoder,)
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class Lightx2vWanVideoT5Encoder:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"t5_encoder": ("LIGHT_T5_ENCODER",),
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"prompt": (
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"STRING",
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{
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"multiline": True,
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"default": "A beautiful landscape with mountains and a lake",
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},
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),
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"negative_prompt": ("STRING", {"multiline": True, "default": ""}),
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}
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}
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RETURN_TYPES = ("LIGHT_TEXT_EMBEDDINGS",)
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RETURN_NAMES = ("text_embeddings",)
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FUNCTION = "encode_text"
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CATEGORY = "LightX2V"
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def encode_text(self, t5_encoder, prompt, negative_prompt):
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# Create a config object with required attributes
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class Config:
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def __init__(self, cpu_offload=False):
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self.cpu_offload = cpu_offload
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# NOTE(xxx): adapt the config if cpu_offload is set, t5 model must be on cuda device
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config = Config(cpu_offload=False)
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# Encode the text
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context = t5_encoder.infer([prompt], config)
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context_null = t5_encoder.infer(
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[negative_prompt if negative_prompt else ""], config
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)
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# Create text embeddings dictionary
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text_embeddings = {"context": context, "context_null": context_null}
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return (text_embeddings,)
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class Lightx2vWanVideoClipVisionEncoderLoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"clip_model_path": (
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"STRING",
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{
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"default": "models/clip/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"
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},
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),
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"tokenizer_path": (
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"STRING",
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{"default": "models/clip/xlm-roberta-large"},
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),
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"precision": (["fp16", "fp32"], {"default": "fp16"}),
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"device": (["cuda", "cpu"], {"default": "cuda"}),
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}
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}
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RETURN_TYPES = ("LIGHT_CLIP_VISION_ENCODER",)
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RETURN_NAMES = ("clip_vision_encoder",)
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FUNCTION = "load_clip_vision_encoder"
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CATEGORY = "LightX2V"
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def load_clip_vision_encoder(
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self, clip_model_path, tokenizer_path, precision, device
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):
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# Map precision to torch dtype
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dtype_map = {"fp16": torch.float16, "fp32": torch.float32}
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dtype = dtype_map[precision]
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# Resolve device
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if device == "cuda":
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device = mm.get_torch_device()
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else:
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device = torch.device("cpu")
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# Load the CLIP vision encoder
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clip_vision_encoder = ClipVisionModel(
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dtype=dtype,
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device=device,
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checkpoint_path=clip_model_path,
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tokenizer_path=tokenizer_path,
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)
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return (clip_vision_encoder,)
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class Lightx2vWanVideoVaeLoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"vae_model_path": ("STRING", {"default": "models/vae/Wan2.1_VAE.pth"}),
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"precision": (["bf16", "fp16", "fp32"], {"default": "fp16"}),
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"device": (["cuda", "cpu"], {"default": "cuda"}),
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"parallel": ("BOOLEAN", {"default": False}),
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}
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}
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RETURN_TYPES = ("LIGHT_WAN_VAE",)
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RETURN_NAMES = ("wan_vae",)
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FUNCTION = "load_vae"
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CATEGORY = "LightX2V"
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def load_vae(self, vae_model_path, precision, device, parallel):
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# Map precision to torch dtype
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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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dtype = dtype_map[precision]
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# Resolve device
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if device == "cuda":
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device = mm.get_torch_device()
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else:
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device = torch.device("cpu")
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# Load the VAE
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vae = WanVAE(
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z_dim=16,
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vae_pth=vae_model_path,
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dtype=dtype,
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device=device, # type:ignore
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parallel=parallel,
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)
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return (vae,)
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class Lightx2vWanVideoVaeDecoder:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"vae": ("LIGHT_WAN_VAE",),
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"latent": ("LIGHT_LATENT",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "decode_latent"
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CATEGORY = "LightX2V"
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def decode_latent(self, vae, latent):
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# 创建一个带有必要属性的配置对象
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class Config:
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def __init__(self, cpu_offload=False):
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self.cpu_offload = cpu_offload
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config = Config(cpu_offload=False)
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# 获取潜在表示和生成器
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latents = latent["samples"]
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generator = latent["generator"]
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# 使用VAE解码潜在表示
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with torch.no_grad():
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# 解码得到视频帧
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decoded_images = vae.decode(latents, generator=generator, config=config)
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# 将像素值从 [-1, 1] 归一化到 [0, 1]
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images = (decoded_images + 1) / 2
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# 重新排列维度为ComfyUI标准的图像格式 [T, H, W, C]
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# 从 [1, C, T, H, W] 转换为 [T, H, W, C]
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images = images.squeeze(0).permute(1, 2, 3, 0).cpu()
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# 确保像素值在有效范围内
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images = torch.clamp(images, 0, 1)
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# 清理缓存以释放GPU内存
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torch.cuda.empty_cache()
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gc.collect()
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return (images,)
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class Lightx2vWanVideoImageEncoder:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"vae": ("LIGHT_WAN_VAE",),
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"image": ("IMAGE",),
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"clip_vision_encoder": ("LIGHT_CLIP_VISION_ENCODER",),
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"target_height": (
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"INT",
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{"default": 576, "min": 256, "max": 1024, "step": 8},
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),
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"target_width": (
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"INT",
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{"default": 1024, "min": 256, "max": 1024, "step": 8},
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),
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"vae_stride": ("INT", {"default": 8, "min": 1, "max": 32, "step": 1}),
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"patch_size": ("INT", {"default": 2, "min": 1, "max": 16, "step": 1}),
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}
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}
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RETURN_TYPES = ("LIGHT_IMAGE_EMBEDDINGS",)
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RETURN_NAMES = ("image_embeddings",)
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FUNCTION = "encode_image"
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CATEGORY = "LightX2V"
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def encode_image(
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self,
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vae,
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image,
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clip_vision_encoder,
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target_height,
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target_width,
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vae_stride,
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patch_size,
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):
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# 创建配置对象
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class Config:
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def __init__(
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self,
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target_height,
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target_width,
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vae_stride,
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patch_size,
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cpu_offload=False,
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):
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self.target_height = target_height
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self.target_width = target_width
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self.vae_stride = [1, vae_stride, vae_stride]
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self.patch_size = [1, patch_size, patch_size]
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self.cpu_offload = cpu_offload
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config = Config(target_height, target_width, vae_stride, patch_size)
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# 将图像转换为期望的张量格式
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device = mm.get_torch_device()
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img = image[0].permute(2, 0, 1).to(device) # [C, H, W]
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img = img.sub_(0.5).div_(0.5) # 归一化到 [-1, 1]
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# 使用CLIP视觉编码器编码图像
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clip_encoder_out = clip_vision_encoder.visual(
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[img[:, None, :, :]], config
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).squeeze(0)
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# 计算宽高比和尺寸
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h, w = img.shape[1:]
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aspect_ratio = h / w
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max_area = config.target_height * config.target_width
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lat_h = round(
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np.sqrt(max_area * aspect_ratio)
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// config.vae_stride[1]
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// config.patch_size[1]
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* config.patch_size[1]
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)
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lat_w = round(
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np.sqrt(max_area / aspect_ratio)
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// config.vae_stride[2]
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// config.patch_size[2]
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* config.patch_size[2]
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)
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h = lat_h * config.vae_stride[1]
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w = lat_w * config.vae_stride[2]
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# 创建掩码
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msk = torch.ones(1, 81, lat_h, lat_w, device=device)
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msk[:, 1:] = 0
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msk = torch.concat(
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[torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1
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)
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msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
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msk = msk.transpose(1, 2)[0]
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# 使用VAE编码图像
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resized_img = torch.nn.functional.interpolate(
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img[None].cpu(), size=(h, w), mode="bicubic"
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).transpose(0, 1)
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padding = torch.zeros(3, 80, h, w, device=device)
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concat_img = torch.concat([resized_img.to(device), padding], dim=1)
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vae_encode_out = vae.encode([concat_img], config)[0]
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# TODO(xxx): hard code
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vae_encode_out = torch.concat([msk, vae_encode_out]).to(torch.bfloat16)
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# 创建图像嵌入字典
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image_embeddings = {
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"clip_encoder_out": clip_encoder_out,
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"vae_encode_out": vae_encode_out,
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"lat_h": lat_h,
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"lat_w": lat_w,
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}
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return (image_embeddings,)
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class Lightx2vWanVideoModelLoader:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model_path": ("STRING", {"default": "models/wan"}),
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"model_type": (["t2v", "i2v"], {"default": "t2v"}),
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"precision": (["bf16", "fp16", "fp32"], {"default": "bf16"}),
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"device": (["cuda", "cpu"], {"default": "cuda"}),
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"feature_caching": (["NoCaching", "Tea"], {"default": "Tea"}),
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"teacache_thresh": (
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"FLOAT",
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{
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"default": 0.26,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Only used with Tea feature caching",
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},
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),
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"attention_mode": (
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["sdpa", "flash_attn_2", "flash_attn_3"],
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{"default": "flash_attn_3"},
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),
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"cpu_offload": ("BOOLEAN", {"default": False}),
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"mm_type": ("STRING", {"default": "Default"}),
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},
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"optional": {
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"lora_path": ("STRING", {"default": None}),
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"lora_strength": (
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"FLOAT",
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{"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01},
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),
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},
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}
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RETURN_TYPES = ("LIGHT_WAN_MODEL",)
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RETURN_NAMES = ("wan_model",)
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FUNCTION = "load_model"
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CATEGORY = "LightX2V"
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def load_model(
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self,
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model_path,
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model_type,
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precision,
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device,
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feature_caching,
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teacache_thresh,
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attention_mode,
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mm_type,
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lora_path=None,
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lora_strength=1.0,
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):
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# 映射精度到torch dtype
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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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dtype = dtype_map[precision]
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# 解析设备
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if device == "cuda":
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device = mm.get_torch_device()
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else:
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device = torch.device("cpu")
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# 首先在模型路径中查找config.json
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model_path_dir = os.path.dirname(model_path)
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config_json_path = os.path.join(model_path_dir, "config.json")
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config_json = {}
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if os.path.exists(config_json_path):
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with open(config_json_path, "r") as f:
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config_json = json.load(f)
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else:
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logging.error(f"Config file not found at {config_json_path}")
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raise FileNotFoundError(f"Config file not found at {config_json_path}")
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# 创建配置字典
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config = {
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"do_mm_calib": False,
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"cpu_offload": False,
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"parallel_attn_type": None, # [None, "ulysses", "ring"]
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"parallel_vae": False,
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"max_area": False,
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"vae_stride": (4, 8, 8),
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"patch_size": (1, 2, 2),
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"feature_caching": feature_caching, # ["NoCaching", "TaylorSeer", "Tea"]
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"teacache_thresh": teacache_thresh,
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"use_ret_steps": False,
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"use_bfloat16": dtype == torch.bfloat16,
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"mm_config": {
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"mm_type": mm_type,
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"weight_auto_quant": False if mm_type == "Default" else True,
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},
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"model_path": model_path,
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"task": model_type,
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"device": device,
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"attention_type": attention_mode,
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"lora_path": lora_path if lora_path and lora_path.strip() else None,
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"strength_model": lora_strength,
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}
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# merge model dir config.json and config dict
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config.update(**config_json)
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# NOTE(xxx): adapt to Lightx2v
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config = EasyDict(config)
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logging.info(
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"Loaded config:\n" + json.dumps(config, indent=4, ensure_ascii=False)
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)
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logging.info(f"Loading WanModel from {model_path} with type {model_type}")
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model = WanModel(model_path, config, device)
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# 如果指定了LoRA路径，应用LoRA
|
|
if lora_path and os.path.exists(lora_path):
|
|
logging.info(
|
|
f"Applying LoRA from {lora_path} with strength {lora_strength}"
|
|
)
|
|
lora_wrapper = WanLoraWrapper(model)
|
|
lora_name = lora_wrapper.load_lora(lora_path)
|
|
lora_wrapper.apply_lora(lora_name, lora_strength)
|
|
logging.info(f"LoRA {lora_name} applied successfully")
|
|
|
|
wan_model = {"wan_model": model, "config": config}
|
|
|
|
return (wan_model,)
|
|
|
|
|
|
class Lightx2vWanVideoSampler:
|
|
@classmethod
|
|
def INPUT_TYPES(cls):
|
|
return {
|
|
"required": {
|
|
"model": ("LIGHT_WAN_MODEL",),
|
|
"text_embeddings": ("LIGHT_TEXT_EMBEDDINGS",),
|
|
"steps": ("INT", {"default": 20, "min": 1, "max": 100, "step": 1}),
|
|
"cfg_scale": (
|
|
"FLOAT",
|
|
{"default": 7.5, "min": 0.0, "max": 20.0, "step": 0.1},
|
|
),
|
|
"seed": ("INT", {"default": 42}),
|
|
},
|
|
"optional": {
|
|
"image_embeddings": ("LIGHT_IMAGE_EMBEDDINGS",),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("LIGHT_LATENT",)
|
|
RETURN_NAMES = ("latent",)
|
|
FUNCTION = "sample"
|
|
CATEGORY = "LightX2V"
|
|
|
|
def sample(
|
|
self,
|
|
model,
|
|
text_embeddings,
|
|
steps,
|
|
cfg_scale,
|
|
seed,
|
|
image_embeddings=None,
|
|
):
|
|
# Update model config
|
|
|
|
config = cast(Any, model.get("config"))
|
|
model = cast(WanModel, model.get("wan_model"))
|
|
|
|
seed_all(seed)
|
|
|
|
# wan_runner.init_scheduler
|
|
if config.feature_caching == "NoCaching": # type:ignore
|
|
scheduler = WanScheduler(config)
|
|
elif config.feature_caching == "Tea": # type:ignore
|
|
scheduler = WanSchedulerTeaCaching(config)
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Unsupported feature_caching type: {config.feature_caching}" # type:ignore
|
|
)
|
|
|
|
# setup scheduler
|
|
model.set_scheduler(scheduler)
|
|
|
|
# wan_runner.set_target_shape
|
|
if config.task == "i2v":
|
|
if image_embeddings is None:
|
|
raise ValueError("image_embeddings must be provided for i2v task")
|
|
config.lat_h = image_embeddings["lat_h"]
|
|
config.lat_w = image_embeddings["lat_w"]
|
|
config.target_shape = (16, 21, config.lat_h, config.lat_w)
|
|
elif config.task == "t2v":
|
|
config.target_shape = (
|
|
16,
|
|
(config.target_video_length - 1) // 4 + 1,
|
|
int(config.target_height) // config.vae_stride[1],
|
|
int(config.target_width) // config.vae_stride[2],
|
|
)
|
|
|
|
# Set up inputs
|
|
inputs = {
|
|
"text_encoder_output": text_embeddings,
|
|
"image_encoder_output": image_embeddings,
|
|
}
|
|
|
|
# Prepare for sampling
|
|
scheduler.prepare(inputs.get("image_encoder_output"))
|
|
|
|
# Run sampling
|
|
progress = ProgressBar(steps)
|
|
for step_index in tqdm(
|
|
range(scheduler.infer_steps), desc="inference", unit="step"
|
|
):
|
|
with ProfilingContext("scheduler.step_pre"):
|
|
scheduler.step_pre(step_index=step_index)
|
|
with ProfilingContext("model.infer"):
|
|
model.infer(inputs)
|
|
with ProfilingContext("scheduler.step_post"):
|
|
scheduler.step_post()
|
|
|
|
progress.update(1)
|
|
|
|
scheduler.clear()
|
|
|
|
del inputs, model, text_embeddings, image_embeddings
|
|
torch.cuda.empty_cache()
|
|
|
|
return ({"samples": scheduler.latents, "generator": scheduler.generator},)
|
|
|
|
|
|
# Register the nodes
|
|
NODE_CLASS_MAPPINGS = {
|
|
"Lightx2vWanVideoT5EncoderLoader": Lightx2vWanVideoT5EncoderLoader,
|
|
"Lightx2vWanVideoT5Encoder": Lightx2vWanVideoT5Encoder,
|
|
"Lightx2vWanVideoClipVisionEncoderLoader": Lightx2vWanVideoClipVisionEncoderLoader,
|
|
"Lightx2vWanVideoVaeLoader": Lightx2vWanVideoVaeLoader,
|
|
"Lightx2vWanVideoImageEncoder": Lightx2vWanVideoImageEncoder,
|
|
"Lightx2vWanVideoVaeDecoder": Lightx2vWanVideoVaeDecoder,
|
|
"Lightx2vWanVideoModelLoader": Lightx2vWanVideoModelLoader,
|
|
"Lightx2vWanVideoSampler": Lightx2vWanVideoSampler,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"Lightx2vWanVideoT5EncoderLoader": "LightX2V WAN T5 Encoder Loader",
|
|
"Lightx2vWanVideoT5Encoder": "LightX2V WAN T5 Encoder",
|
|
"Lightx2vWanVideoClipVisionEncoderLoader": "LightX2V WAN CLIP Vision Encoder Loader",
|
|
"Lightx2vWanVideoClipVisionEncoder": "LightX2V WAN CLIP Vision Encoder",
|
|
"Lightx2vWanVideoVaeLoader": "LightX2V WAN VAE Loader",
|
|
"Lightx2vWanVideoImageEncoder": "LightX2V WAN Image Encoder",
|
|
"Lightx2vWanVideoVaeDecoder": "LightX2V WAN VAE Decoder",
|
|
"Lightx2vWanVideoModelLoader": "LightX2V WAN Model Loader",
|
|
"Lightx2vWanVideoSampler": "LightX2V WAN Video Sampler",
|
|
}
|