🚀 非官方Diffusers格式的LTX-Video权重
本项目提供了https://huggingface.co/Lightricks/LTX-Video (版本0.9.1)的非官方Diffusers格式权重。该项目支持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
环境准备
确保你已经安装了torch
和diffusers
库,并且拥有支持CUDA的GPU设备。
文本到视频
以下是一个使用文本生成视频的示例代码:
import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video
pipe = LTXPipeline.from_pretrained("a-r-r-o-w/LTX-Video-0.9.1-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,
decode_timestep=0.03,
decode_noise_scale=0.025,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
图像到视频
以下是一个使用图像生成视频的示例代码:
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-0.9.1-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,
decode_timestep=0.03,
decode_noise_scale=0.025,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
✨ 主要特性
- 文本到视频转换:通过输入文本描述,生成相应的视频内容。
- 图像到视频转换:以图像为基础,结合文本提示,生成相关视频。
- 支持CUDA加速:利用GPU进行快速推理,提高视频生成效率。
📦 安装指南
确保你已经安装了Python环境,并且可以使用pip
进行包管理。可以使用以下命令安装所需的库:
pip install torch diffusers
💻 使用示例
基础用法
上述的文本到视频和图像到视频的示例代码展示了该项目的基础用法。你可以根据自己的需求修改提示词、负提示词、视频尺寸、帧数等参数。
高级用法
在实际应用中,你可以尝试不同的提示词组合、调整推理步数、解码时间步长和解码噪声比例等参数,以获得更好的视频生成效果。同时,你还可以将生成的视频用于其他应用场景,如社交媒体分享、视频编辑等。