🚀 适用于Wan2.1 14B T2V的废弃场所LoRA
本项目基于Wan2.1 14B T2V模型训练了一个LoRA,能够生成废弃场所相关的视频,为文本到视频的转换提供了新的可能性。
🚀 快速开始
模型文件下载
推荐设置
- LoRA强度:1.0
- 嵌入式引导比例:6.0
- 流偏移:5.0
触发词
关键触发短语为:abandoned places
ComfyUI工作流
此LoRA可与 Kijai的Wan视频包装器工作流 的修改版本配合使用。主要修改是添加了一个连接到基础模型的Wan LoRA节点。
修改后的工作流见上方下载部分。
✨ 主要特性
- 基于Wan2.1 14B T2V基础模型进行训练。
- 在不同的对象和场景类型中能保持一致的生成效果。
- 提供简单且易于调整的提示示例。
📦 安装指南
暂未提供明确的安装步骤,可参考上述下载和设置信息进行操作。
💻 使用示例
基础用法
以下是一些示例提示及其对应的输出视频:
- 提示文本:abandoned places A steady zoom-out tall abandoned building covered in vines and trees stands in the middle of an abandoned city. The sky is overcast and the air is thick with fog. The city is mostly obscured by the fog, but you can see some other buildings in the distance. There is a sense of decay and abandonment in the image.
输出视频:example_videos/1.mp4
- 提示文本:abandoned places A steady zoom-in to An old, rusty car, partially obscured by vegetation, sits in the middle of a dense forest. The car's body is a faded light blue color, with a rusted hood and roof. The car's door is open and the interior is dark and dusty. The windows are all broken and the seats are torn. The car is surrounded by tall trees and thick bushes. The ground is covered in leaves and debris. The overall atmosphere is one of decay and neglect.
输出视频:example_videos/2.mp4
- 提示文本:abandoned places A grand, abandoned mansion, with peeling paint and broken windows, stands in a grove of overgrown trees. The front steps are crumbling and the driveway is cracked. The sky is overcast.
输出视频:example_videos/3.mp4
- 提示文本:abandoned places A steady zoom-out from the center of an abandoned industrial complex, where a single rusted smokestack stands tall against the cloudy sky. The camera moves back, revealing a network of crumbling buildings and broken glass windows. The sound of distant thunder rumbles as the wind kicks up dust and loose debris.
输出视频:example_videos/4.mp4
高级用法
对于提示,可参考上述示例提示,这种提示方式似乎效果很好。
📚 详细文档
模型信息
模型权重以Safetensors格式提供,详见上方下载部分。
训练详情
属性 |
详情 |
基础模型 |
Wan2.1 14B T2V |
训练数据 |
基于6分钟的视频进行训练,该视频由98个废弃场所的短视频片段组成(每个片段单独标注) |
训练轮数 |
50 |
额外信息
训练使用了 Diffusion Pipe for Training。
致谢
特别感谢Kijai提供的ComfyUI Wan视频包装器,以及tdrussell提供的训练脚本!
📄 许可证
本项目采用Apache-2.0许可证。