import os
import torch
import gc
from typing import cast, Any
import logging
import json
import numpy as np
import comfy.model_management as mm
from comfy.utils import ProgressBar

# import folder_paths
from tqdm import tqdm
from easydict import EasyDict


# Import LightX2V modules
from .lightx2v.lightx2v.utils.profiler import ProfilingContext
from .lightx2v.lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
from .lightx2v.lightx2v.models.input_encoders.hf.xlm_roberta.model import (
    CLIPModel as ClipVisionModel,
)
from .lightx2v.lightx2v.models.video_encoders.hf.wan.vae import WanVAE
from .lightx2v.lightx2v.models.networks.wan.model import WanModel
from .lightx2v.lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
from .lightx2v.lightx2v.models.schedulers.wan.scheduler import WanScheduler
from .lightx2v.lightx2v.models.schedulers.wan.feature_caching.scheduler import (
    WanSchedulerTeaCaching,
)


class WanVideoTeaCache:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "rel_l1_thresh": (
                    "FLOAT",
                    {
                        "default": 0.3,
                        "min": 0.0,
                        "max": 1.0,
                        "step": 0.001,
                        "tooltip": "Higher values will make TeaCache more aggressive, faster, but may cause artifacts. Good value range for 1.3B: 0.05 - 0.08, for other models 0.15-0.30",
                    },
                ),
                "start_step": (
                    "INT",
                    {
                        "default": 1,
                        "min": 0,
                        "max": 9999,
                        "step": 1,
                        "tooltip": "Start percentage of the steps to apply TeaCache",
                    },
                ),
                "end_step": (
                    "INT",
                    {
                        "default": -1,
                        "min": -1,
                        "max": 9999,
                        "step": 1,
                        "tooltip": "End steps to apply TeaCache",
                    },
                ),
                "cache_device": (
                    ["main_device", "offload_device"],
                    {"default": "offload_device", "tooltip": "Device to cache to"},
                ),
                "use_coefficients": (
                    "BOOLEAN",
                    {
                        "default": True,
                        "tooltip": "Use calculated coefficients for more accuracy. When enabled therel_l1_thresh should be about 10 times higher than without",
                    },
                ),
            },
            "optional": {
                "mode": (
                    ["e", "e0"],
                    {
                        "default": "e",
                        "tooltip": "Choice between using e (time embeds, default) or e0 (modulated time embeds)",
                    },
                ),
            },
        }

    RETURN_TYPES = ("LIGHT_TEACACHEARGS",)
    RETURN_NAMES = ("teacache_args",)
    FUNCTION = "process"
    CATEGORY = "LightX2V"

    EXPERIMENTAL = True

    def process(
        self,
        rel_l1_thresh,
        start_step,
        end_step,
        cache_device,
        use_coefficients,
        mode="e",
    ):
        if cache_device == "main_device":
            teacache_device = mm.get_torch_device()
        else:
            teacache_device = mm.unet_offload_device()
        teacache_args = {
            "rel_l1_thresh": rel_l1_thresh,
            "start_step": start_step,
            "end_step": end_step,
            "cache_device": teacache_device,
            "use_coefficients": use_coefficients,
            "mode": mode,
        }
        return (teacache_args,)


class Lightx2vWanVideoT5EncoderLoader:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "t5_model_path": (
                    "STRING",
                    {
                        "default": "/mnt/aigc/shared_data/cache/huggingface/hub/Wan2.1-I2V-14B-480P/models_t5_umt5-xxl-enc-bf16.pth"
                    },
                ),
                "tokenizer_path": (
                    "STRING",
                    {
                        "default": "/mnt/aigc/shared_data/cache/huggingface/hub/Wan2.1-I2V-14B-480P/google/umt5-xxl"
                    },
                ),
                "text_len": (
                    "INT",
                    {"default": 512, "min": 64, "max": 2048, "step": 1},
                ),
                "precision": (["bf16", "fp16", "fp32"], {"default": "bf16"}),
                "device": (["cuda", "cpu"], {"default": "cuda"}),
                "additional_param": ("STRING", {"default": "default_value"}),
            }
        }

    RETURN_TYPES = ("LIGHT_T5_ENCODER",)
    RETURN_NAMES = ("t5_encoder",)
    FUNCTION = "load_t5_encoder"
    CATEGORY = "LightX2V"

    def load_t5_encoder(
        self,
        t5_model_path,
        tokenizer_path,
        text_len,
        precision,
        device,
        additional_param,
    ):
        # Map precision to torch dtype
        dtype_map = {
            "bf16": torch.bfloat16,
            "fp16": torch.float16,
            "fp32": torch.float32,
        }
        dtype = dtype_map[precision]

        # Resolve device
        if device == "cuda":
            device = mm.get_torch_device()
        else:
            device = torch.device("cpu")

        # Load the T5 encoder
        t5_encoder = T5EncoderModel(
            text_len=text_len,
            dtype=dtype,
            device=device,  # type:ignore
            checkpoint_path=t5_model_path,
            tokenizer_path=tokenizer_path,
            shard_fn=None,
        )

        return (t5_encoder,)


class Lightx2vWanVideoT5Encoder:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "t5_encoder": ("LIGHT_T5_ENCODER",),
                "prompt": (
                    "STRING",
                    {
                        "multiline": True,
                        "default": "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.",
                    },
                ),
                "negative_prompt": (
                    "STRING",
                    {
                        "multiline": True,
                        "default": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
                    },
                ),
            }
        }

    RETURN_TYPES = ("LIGHT_TEXT_EMBEDDINGS",)
    RETURN_NAMES = ("text_embeddings",)
    FUNCTION = "encode_text"
    CATEGORY = "LightX2V"

    def encode_text(self, t5_encoder, prompt, negative_prompt):
        # Create a config object with required attributes
        class Config:
            def __init__(self, cpu_offload=False):
                self.cpu_offload = cpu_offload

        # NOTE(xxx): adapt the config if cpu_offload is set, t5 model must be on cuda device
        config = Config(cpu_offload=False)

        # Encode the text
        context = t5_encoder.infer([prompt], config)
        context_null = t5_encoder.infer(
            [negative_prompt if negative_prompt else ""], config
        )

        # Create text embeddings dictionary
        text_embeddings = {"context": context, "context_null": context_null}

        print(f"Text Encoder Output Shape: {context[0].shape}")

        return (text_embeddings,)


class Lightx2vWanVideoVaeLoader:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "vae_model_path": (
                    "STRING",
                    {
                        "default": "/mnt/aigc/shared_data/cache/huggingface/hub/Wan2.1-I2V-14B-480P/Wan2.1_VAE.pth"
                    },
                ),
                "precision": (["bf16", "fp16", "fp32"], {"default": "fp16"}),
                "device": (["cuda", "cpu"], {"default": "cuda"}),
                "parallel": ("BOOLEAN", {"default": False}),
            }
        }

    RETURN_TYPES = ("LIGHT_WAN_VAE",)
    RETURN_NAMES = ("wan_vae",)
    FUNCTION = "load_vae"
    CATEGORY = "LightX2V"

    def load_vae(self, vae_model_path, precision, device, parallel):
        # Map precision to torch dtype
        dtype_map = {
            "bf16": torch.bfloat16,
            "fp16": torch.float16,
            "fp32": torch.float32,
        }
        dtype = dtype_map[precision]

        # Resolve device
        if device == "cuda":
            device = mm.get_torch_device()
        else:
            device = torch.device("cpu")

        # Load the VAE
        vae = WanVAE(
            z_dim=16,
            vae_pth=vae_model_path,
            dtype=dtype,
            device=device,  # type:ignore
            parallel=parallel,
        )

        return (vae,)


class Lightx2vWanVideoVaeDecoder:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "wan_vae": ("LIGHT_WAN_VAE",),
                "latent": ("LIGHT_LATENT",),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "decode_latent"
    CATEGORY = "LightX2V"

    def decode_latent(self, wan_vae, latent):
        config = EasyDict({"cpu_offload": False})

        # 获取潜在表示和生成器
        latents = latent["samples"]
        generator = latent["generator"]

        # 使用VAE解码潜在表示
        with torch.no_grad():
            # 解码得到视频帧
            decoded_images = wan_vae.decode(latents, generator=generator, config=config)

            # 将像素值从 [-1, 1] 归一化到 [0, 1]
            images = (decoded_images + 1) / 2

            # 重新排列维度为ComfyUI标准的图像格式 [T, H, W, C]
            # 从 [1, C, T, H, W] 转换为 [T, H, W, C]
            images = images.squeeze(0).permute(1, 2, 3, 0).cpu()

            # 确保像素值在有效范围内
            images = torch.clamp(images, 0, 1)

        # 清理缓存以释放GPU内存
        torch.cuda.empty_cache()
        gc.collect()

        return (images,)


class Lightx2vWanVideoClipVisionEncoderLoader:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "clip_model_path": (
                    "STRING",
                    {
                        "default": "/mnt/aigc/shared_data/cache/huggingface/hub/Wan2.1-I2V-14B-480P/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"
                    },
                ),
                "tokenizer_path": (
                    "STRING",
                    {
                        "default": "/mnt/aigc/shared_data/cache/huggingface/hub/Wan2.1-I2V-14B-480P/xlm-roberta-large"
                    },
                ),
                "precision": (["fp16", "fp32"], {"default": "fp16"}),
                "device": (["cuda", "cpu"], {"default": "cuda"}),
            }
        }

    RETURN_TYPES = ("LIGHT_CLIP_VISION_ENCODER",)
    RETURN_NAMES = ("clip_vision_encoder",)
    FUNCTION = "load_clip_vision_encoder"
    CATEGORY = "LightX2V"

    def load_clip_vision_encoder(
        self, clip_model_path, tokenizer_path, precision, device
    ):
        # Map precision to torch dtype
        dtype_map = {"fp16": torch.float16, "fp32": torch.float32}
        dtype = dtype_map[precision]

        # Resolve device
        if device == "cuda":
            device = mm.get_torch_device()
        else:
            device = torch.device("cpu")

        # Load the CLIP vision encoder
        clip_vision_encoder = ClipVisionModel(
            dtype=dtype,
            device=device,
            checkpoint_path=clip_model_path,
            tokenizer_path=tokenizer_path,
        )

        return (clip_vision_encoder,)


class Lightx2vWanVideoImageEncoder:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "vae": ("LIGHT_WAN_VAE",),
                "clip_vision_encoder": ("LIGHT_CLIP_VISION_ENCODER",),
                "image": ("IMAGE",),
                "width": (
                    "INT",
                    {
                        "default": 832,
                        "min": 64,
                        "max": 2048,
                        "step": 8,
                        "tooltip": "Width of the image to encode",
                    },
                ),
                "height": (
                    "INT",
                    {
                        "default": 480,
                        "min": 64,
                        "max": 29048,
                        "step": 8,
                        "tooltip": "Height of the image to encode",
                    },
                ),
                "num_frames": (
                    "INT",
                    {
                        "default": 81,
                        "min": 1,
                        "max": 10000,
                        "step": 4,
                        "tooltip": "Number of frames to encode",
                    },
                ),
            }
        }

    RETURN_TYPES = ("LIGHT_IMAGE_EMBEDDINGS",)
    RETURN_NAMES = ("image_embeddings",)
    FUNCTION = "encode_image"
    CATEGORY = "LightX2V"

    def encode_image(
        self,
        vae,
        image,
        clip_vision_encoder,
        height,
        width,
        num_frames,
    ):
        # 创建配置对象

        config = EasyDict(
            {
                "cpu_offload": False,
                "target_height": height,
                "target_width": width,
                "target_video_length": num_frames,
                "vae_stride": (4, 8, 8),
                "patch_size": (1, 2, 2),
            }
        )
        # skip lint
        config = cast(Any, config)

        # 将图像转换为期望的张量格式
        device = mm.get_torch_device()
        img = image[0].permute(2, 0, 1).to(device)  # [C, H, W]
        img = img.sub_(0.5).div_(0.5)  # 归一化到 [-1, 1]

        # 使用CLIP视觉编码器编码图像
        clip_encoder_out = (
            clip_vision_encoder.visual([img[:, None, :, :]], config)
            .squeeze(0)
            .to(torch.bfloat16)
        )

        # 计算宽高比和尺寸
        h, w = img.shape[1:]
        aspect_ratio = h / w
        max_area = config.target_height * config.target_width
        lat_h = round(
            np.sqrt(max_area * aspect_ratio)
            // config.vae_stride[1]
            // config.patch_size[1]
            * config.patch_size[1]
        )
        lat_w = round(
            np.sqrt(max_area / aspect_ratio)
            // config.vae_stride[2]
            // config.patch_size[2]
            * config.patch_size[2]
        )

        # XXX: trick
        config.lat_h = lat_h
        config.lat_w = lat_w

        h = lat_h * config.vae_stride[1]
        w = lat_w * config.vae_stride[2]

        # TODO(xxx)：81？
        msk = torch.ones(1, 81, lat_h, lat_w, device=device)
        msk[:, 1:] = 0
        msk = torch.concat(
            [torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:]], dim=1
        )
        msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w)
        msk = msk.transpose(1, 2)[0]

        resized_img = torch.nn.functional.interpolate(
            img[None].cpu(), size=(h, w), mode="bicubic"
        ).transpose(0, 1)
        padding = torch.zeros(3, 80, h, w, device=device)
        concat_img = torch.concat([resized_img.to(device), padding], dim=1)

        vae_encode_out = vae.encode([concat_img], config)[0]

        # TODO(xxx): hard code
        vae_encode_out = torch.concat([msk, vae_encode_out]).to(torch.bfloat16)

        image_embeddings = {
            "clip_encoder_out": clip_encoder_out,
            "vae_encode_out": vae_encode_out,
            "config": config,
        }

        print(f"Image Encoder Output Shape: {clip_encoder_out.shape}")
        print(f"VAE Encoder Output Shape: {vae_encode_out.shape}")
        print(f"Latent Height: {lat_h}, Latent Width: {lat_w}")
        print(f"Image Shape: {img.shape}")
        print(f"Configuration: {config}")

        return (image_embeddings,)


class Lightx2vWanVideoEmptyEmbeds:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "width": (
                    "INT",
                    {
                        "default": 832,
                        "min": 64,
                        "max": 2048,
                        "step": 8,
                        "tooltip": "Width of the image to encode",
                    },
                ),
                "height": (
                    "INT",
                    {
                        "default": 480,
                        "min": 64,
                        "max": 29048,
                        "step": 8,
                        "tooltip": "Height of the image to encode",
                    },
                ),
                "num_frames": (
                    "INT",
                    {
                        "default": 81,
                        "min": 1,
                        "max": 10000,
                        "step": 4,
                        "tooltip": "Number of frames to encode",
                    },
                ),
            }
        }

    RETURN_TYPES = ("LIGHT_IMAGE_EMBEDDINGS",)
    RETURN_NAMES = ("image_embeddings",)
    FUNCTION = "process"
    CATEGORY = "LightX2V"

    def process(self, num_frames, width, height, control_embeds=None):
        config = EasyDict(
            {
                "target_height": height,
                "target_width": width,
                "target_video_length": num_frames,
                "vae_stride": (4, 8, 8),
                "patch_size": (1, 2, 2),
            }
        )

        return ({"config": config},)


class Lightx2vWanVideoModelLoader:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "model_path": (
                    "STRING",
                    {
                        "default": "/mnt/aigc/shared_data/cache/huggingface/hub/Wan2.1-I2V-14B-480P"
                    },
                ),
                "model_type": (["t2v", "i2v"], {"default": "i2v"}),
                "precision": (["bf16", "fp16", "fp32"], {"default": "bf16"}),
                "device": (["cuda", "cpu"], {"default": "cuda"}),
                "attention_type": (
                    ["sdpa", "flash_attn2", "flash_attn3"],
                    {"default": "flash_attn3"},
                ),
                "cpu_offload": ("BOOLEAN", {"default": False}),
                "mm_type": ("STRING", {"default": "Default"}),
            },
            "optional": {
                "teacache_args": ("LIGHT_TEACACHEARGS", {"default": None}),
                "lora_path": ("STRING", {"default": None}),
                "lora_strength": (
                    "FLOAT",
                    {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.01},
                ),
            },
        }

    RETURN_TYPES = ("LIGHT_WAN_MODEL",)
    RETURN_NAMES = ("wan_model",)
    FUNCTION = "load_model"
    CATEGORY = "LightX2V"

    def load_model(
        self,
        model_path,
        model_type,
        precision,
        device,
        attention_type,
        mm_type,
        lora_path=None,
        lora_strength=1.0,
        cpu_offload=False,
        teacache_args=None,
    ):
        # 映射精度到torch dtype
        dtype_map = {
            "bf16": torch.bfloat16,
            "fp16": torch.float16,
            "fp32": torch.float32,
        }
        dtype = dtype_map[precision]

        # 解析设备
        if device == "cuda":
            device = mm.get_torch_device()
        else:
            device = torch.device("cpu")

        # 首先在模型路径中查找config.json
        model_path_dir = os.path.dirname(model_path)
        config_json_path = os.path.join(model_path_dir, "config.json")

        config_json = {}
        if os.path.exists(config_json_path):
            with open(config_json_path, "r") as f:
                config_json = json.load(f)
        else:
            logging.error(f"Config file not found at {config_json_path}")
            raise FileNotFoundError(f"Config file not found at {config_json_path}")

        feature_caching = "Tea" if teacache_args is not None else "NoCaching"
        if teacache_args:
            teacache_thresh = teacache_args["rel_l1_thresh"]
        else:
            teacache_thresh = 0.26

        # 创建配置字典
        config = {
            "do_mm_calib": False,
            "cpu_offload": cpu_offload,
            "parallel_attn_type": None,  # [None, "ulysses", "ring"]
            "parallel_vae": False,
            "max_area": False,
            "vae_stride": (4, 8, 8),
            "patch_size": (1, 2, 2),
            "feature_caching": feature_caching,  # ["NoCaching", "TaylorSeer", "Tea"]
            "teacache_thresh": teacache_thresh,
            "use_ret_steps": False,
            "use_bfloat16": dtype == torch.bfloat16,
            "mm_config": {
                "mm_type": mm_type,
                "weight_auto_quant": False if mm_type == "Default" else True,
            },
            "model_path": model_path,
            "task": model_type,
            "model_cls": "wan2.1",
            "device": device,
            "attention_type": attention_type,
            "lora_path": lora_path if lora_path and lora_path.strip() else None,
            "strength_model": lora_strength,
        }
        # merge model dir config.json and config dict
        config.update(**config_json)
        # NOTE(xxx): adapt to Lightx2v
        config = EasyDict(config)
        logging.info(f"Loaded config:\n {config}")
        logging.info(f"Loading WanModel from {model_path} with type {model_type}")
        model = WanModel(model_path, config, device)

        # 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",),
                "image_embeddings": ("LIGHT_IMAGE_EMBEDDINGS",),
                "steps": ("INT", {"default": 20, "min": 1, "max": 100, "step": 1}),
                "shift": ("FLOAT", {"default": 5.0}),
                "cfg_scale": (
                    "FLOAT",
                    {"default": 5, "min": 0.0, "max": 20.0, "step": 0.1},
                ),
                "seed": ("INT", {"default": 42}),
            }
        }

    RETURN_TYPES = ("LIGHT_LATENT",)
    RETURN_NAMES = ("latent",)
    FUNCTION = "sample"
    CATEGORY = "LightX2V"

    def sample(
        self,
        model,
        text_embeddings,
        steps,
        shift,
        cfg_scale,
        seed,
        image_embeddings,
    ):
        # Update model config

        model_config = model.get("config")
        image_config = image_embeddings.get("config")
        model_config.update(image_config)
        model_config = cast(Any, model_config)

        logging.info(f"Loaded config:\n {model_config}")

        # wan model
        wan_model = cast(WanModel, model.get("wan_model"))
        # clip vision result
        clip_encoder_out = image_embeddings.get("clip_encoder_out", None)
        # text result
        vae_encode_out = image_embeddings.get("vae_encode_out", None)

        if model_config.task == "i2v" and (
            clip_encoder_out is None or vae_encode_out is None
        ):
            raise ValueError("clip_encoder_out must be provided for i2v task")

        model_config.infer_steps = steps
        model_config.sample_shift = shift
        model_config.sample_guide_scale = cfg_scale
        model_config.seed = seed

        # wan_runner.set_target_shape
        if model_config.task == "i2v":
            model_config.target_shape = (16, 21, model_config.lat_h, model_config.lat_w)
        elif model_config.task == "t2v":
            model_config.target_shape = (
                16,
                (model_config.target_video_length - 1) // 4 + 1,
                int(model_config.target_height) // model_config.vae_stride[1],
                int(model_config.target_width) // model_config.vae_stride[2],
            )

        # wan_runner.init_scheduler
        if model_config.feature_caching == "NoCaching":
            scheduler = WanScheduler(model_config)
        elif model_config.feature_caching == "Tea":
            scheduler = WanSchedulerTeaCaching(model_config)
        else:
            raise NotImplementedError(
                f"Unsupported feature_caching type: {model_config.feature_caching}"  # type:ignore
            )

        # setup scheduler
        wan_model.set_scheduler(scheduler)

        # 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"):
                wan_model.infer(inputs)
            with ProfilingContext("scheduler.step_post"):
                scheduler.step_post()

            progress.update(1)

        scheduler.clear()

        del inputs, wan_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,
    "Lightx2vTeaCache": WanVideoTeaCache,
    "Lightx2vWanVideoEmptyEmbeds": Lightx2vWanVideoEmptyEmbeds,
    "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",
    "Lightx2vTeaCache": "LightX2V WAN Tea Cache",
    "Lightx2vWanVideoEmptyEmbeds": "LightX2V WAN Video Empty Embeds",
}