Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import os
 import torch
-from diffusers import CogVideoXPipeline, CogVideoXDPMScheduler, CogVideoXImageToVideoPipeline, CogVideoXTransformer3DModel
+from diffusers import CogVideoXImageToVideoPipeline
 from diffusers.utils import export_to_video, load_image
 from datetime import datetime
 
@@ -22,20 +22,9 @@ hf_hub_download(
     local_dir="checkpoints"
 )
 
-pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16)
-pipe.scheduler = CogVideoXDPMScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
-
-pipe_image = CogVideoXImageToVideoPipeline.from_pretrained(
-    "THUDM/CogVideoX-5b-I2V",
-    transformer=CogVideoXTransformer3DModel.from_pretrained(
-        "THUDM/CogVideoX-5b-I2V", subfolder="transformer", torch_dtype=torch.bfloat16
-    ),
-    vae=pipe.vae,
-    scheduler=pipe.scheduler,
-    tokenizer=pipe.tokenizer,
-    text_encoder=pipe.text_encoder,
-    torch_dtype=torch.bfloat16,
-)
+pipe = CogVideoXImageToVideoPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V", torch_dtype=torch.bfloat16)
+lora_path = "your lora path"
+lora_rank = 256
 def infer(prompt, image_path, orbit_type, progress=gr.Progress(track_tqdm=True)):
     lora_path = "checkpoints/"
     if orbit_type == "Left":