LTX Video Diffusers

🚀 非官方Diffusers格式的LTX-Video权重

这是https://huggingface.co/Lightricks/LTX-Video (版本0.9.0) 的非官方Diffusers格式权重。该项目可实现文本到视频以及图像到视频的转换功能，为视频生成提供了便捷的解决方案。

🚀 快速开始

本项目提供了文本到视频和图像到视频两种功能的使用示例，以下是详细的代码示例。

💻 使用示例

基础用法 - 文本到视频

import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video

pipe = LTXPipeline.from_pretrained("a-r-r-o-w/LTX-Video-diffusers", torch_dtype=torch.bfloat16)
pipe.to("cuda")

prompt = "A woman with long brown hair and light skin smiles at another woman with long blonde hair. The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek. The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage"
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"

video = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=704,
    height=480,
    num_frames=161,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)

基础用法 - 图像到视频

import torch
from diffusers import LTXImageToVideoPipeline
from diffusers.utils import export_to_video, load_image

pipe = LTXImageToVideoPipeline.from_pretrained("a-r-r-o-w/LTX-Video-diffusers", torch_dtype=torch.bfloat16)
pipe.to("cuda")

image = load_image(
    "https://huggingface.co/datasets/a-r-r-o-w/tiny-meme-dataset-captioned/resolve/main/images/8.png"
)
prompt = "A young girl stands calmly in the foreground, looking directly at the camera, as a house fire rages in the background. Flames engulf the structure, with smoke billowing into the air. Firefighters in protective gear rush to the scene, a fire truck labeled '38' visible behind them. The girl's neutral expression contrasts sharply with the chaos of the fire, creating a poignant and emotionally charged scene."
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"

video = pipe(
    image=image,
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=704,
    height=480,
    num_frames=161,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)