pipeline = DiffusionPipeline.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    custom_pipeline="pipeline_flux_with_cfg"
)

import torch

pipeline = CogVideoXPipelineForDDIMInversion.from_pretrained(
    "THUDM/CogVideoX1.5-5B",
    torch_dtype=torch.bfloat16,
    custom_pipeline="cogvideox_ddim_inversion"
).to("cuda")

output = pipeline_for_inversion(
    prompt="prompt that describes the edited video",
    video_path="path/to/input.mp4",
    guidance_scale=6.0,
    num_inference_steps=50,
    skip_frames_start=0,
    skip_frames_end=0,
    frame_sample_step=None,
    max_num_frames=81,
    width=720,
    height=480,
    seed=42,
)
pipeline.export_latents_to_video(output.inverse_latents[-1], "path/to/inverse_video.mp4", fps=8)
pipeline.export_latents_to_video(output.recon_latents[-1], "path/to/recon_video.mp4", fps=8)