删除t2v和i2v的区分，统一接口

examples/vae_trt/convert_vae_trt_engine.py

@@ -5,8 +5,8 @@ import argparse
 import torch
 from loguru import logger
 
-from lightx2v.text2v.models.video_encoders.hf.autoencoder_kl_causal_3d.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
-from lightx2v.text2v.models.video_encoders.trt.autoencoder_kl_causal_3d.trt_vae_infer import HyVaeTrtModelInfer
+from lightx2v.models.video_encoders.hf.autoencoder_kl_causal_3d.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
+from lightx2v.models.video_encoders.trt.autoencoder_kl_causal_3d.trt_vae_infer import HyVaeTrtModelInfer
 
 
 def parse_args():

lightx2v/__main__.py

@@ -10,25 +10,25 @@ import torchvision.transforms.functional as TF
 import numpy as np
 from contextlib import contextmanager
 from PIL import Image
-from lightx2v.text2v.models.text_encoders.hf.llama.model import TextEncoderHFLlamaModel
-from lightx2v.text2v.models.text_encoders.hf.clip.model import TextEncoderHFClipModel
-from lightx2v.text2v.models.text_encoders.hf.t5.model import T5EncoderModel
-from lightx2v.text2v.models.text_encoders.hf.llava.model import TextEncoderHFLlavaModel
-
-from lightx2v.text2v.models.schedulers.hunyuan.scheduler import HunyuanScheduler
-from lightx2v.text2v.models.schedulers.hunyuan.feature_caching.scheduler import HunyuanSchedulerTaylorCaching, HunyuanSchedulerTeaCaching
-from lightx2v.text2v.models.schedulers.wan.scheduler import WanScheduler
-from lightx2v.text2v.models.schedulers.wan.feature_caching.scheduler import WanSchedulerTeaCaching
-
-from lightx2v.text2v.models.networks.hunyuan.model import HunyuanModel
-from lightx2v.text2v.models.networks.wan.model import WanModel
-from lightx2v.text2v.models.networks.wan.lora_adapter import WanLoraWrapper
-
-from lightx2v.text2v.models.video_encoders.hf.autoencoder_kl_causal_3d.model import VideoEncoderKLCausal3DModel
-from lightx2v.text2v.models.video_encoders.hf.wan.vae import WanVAE
+from lightx2v.models.input_encoders.hf.llama.model import TextEncoderHFLlamaModel
+from lightx2v.models.input_encoders.hf.clip.model import TextEncoderHFClipModel
+from lightx2v.models.input_encoders.hf.t5.model import T5EncoderModel
+from lightx2v.models.input_encoders.hf.llava.model import TextEncoderHFLlavaModel
+from lightx2v.models.input_encoders.hf.xlm_roberta.model import CLIPModel
+
+from lightx2v.models.schedulers.hunyuan.scheduler import HunyuanScheduler
+from lightx2v.models.schedulers.hunyuan.feature_caching.scheduler import HunyuanSchedulerTaylorCaching, HunyuanSchedulerTeaCaching
+from lightx2v.models.schedulers.wan.scheduler import WanScheduler
+from lightx2v.models.schedulers.wan.feature_caching.scheduler import WanSchedulerTeaCaching
+
+from lightx2v.models.networks.hunyuan.model import HunyuanModel
+from lightx2v.models.networks.wan.model import WanModel
+from lightx2v.models.networks.wan.lora_adapter import WanLoraWrapper
+
+from lightx2v.models.video_encoders.hf.autoencoder_kl_causal_3d.model import VideoEncoderKLCausal3DModel
+from lightx2v.models.video_encoders.hf.wan.vae import WanVAE
 from lightx2v.utils.utils import save_videos_grid, seed_all, cache_video
 from lightx2v.common.ops import *
-from lightx2v.image2v.models.wan.model import CLIPModel
 from lightx2v.utils.set_config import set_config
 
 

lightx2v/image2v/models/wan/model.py → lightx2v/models/input_encoders/hf/xlm_roberta/model.py

@@ -9,7 +9,7 @@ import torch.nn.functional as F
 import torchvision.transforms as T
 
 from lightx2v.attentions import attention
-from lightx2v.text2v.models.text_encoders.hf.t5.tokenizer import HuggingfaceTokenizer
+from lightx2v.models.input_encoders.hf.t5.tokenizer import HuggingfaceTokenizer
 
 from .xlm_roberta import XLMRoberta