> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-zh/shi-pin-sheng-cheng/opensora.md).

# OpenSora

使用 OpenSora 生成视频，开源的 Sora 替代方案

{% hint style="info" %}
**有更新的替代方案可用！** [**FramePack**](/guides/guides_v2-zh/shi-pin-sheng-cheng/framepack.md) 仅用 6GB 显存即可生成视频， [**Wan2.1**](/guides/guides_v2-zh/shi-pin-sheng-cheng/wan-video.md) 提供更优质的效果，且 [**LTX-2**](/guides/guides_v2-zh/shi-pin-sheng-cheng/ltx-video-2.md) 还支持原生音频生成。
{% endhint %}

在 CLORE.AI GPU 上使用开源 Sora 替代方案 OpenSora 生成视频。

{% hint style="success" %}
所有示例都可以在通过以下方式租用的 GPU 服务器上运行 [CLORE.AI 市场](https://clore.ai/marketplace).
{% endhint %}

## 为什么选择 OpenSora？

* **开源** - 完整的 Apache 2.0 许可证
* **受 Sora 启发** - 类似 OpenAI Sora 的 DiT 架构
* **可扩展** - 多种模型尺寸和分辨率
* **长视频** - 最长可生成 16 秒
* **持续开发中** - 定期更新和改进

## 模型变体

| 模型             | 分辨率  | 时长  | 显存   | 质量 |
| -------------- | ---- | --- | ---- | -- |
| OpenSora 1.2   | 720p | 16秒 | 24GB | 优秀 |
| OpenSora 1.1   | 480p | 8秒  | 16GB | 好  |
| OpenSora 1.0   | 256p | 4秒  | 8GB  | 基础 |
| Open-Sora-Plan | 512p | 10秒 | 20GB | 很高 |

## 在 CLORE.AI 上快速部署

**Docker 镜像：**

```
pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel
```

**端口：**

```
22/tcp
7860/http
```

**命令：**

```bash
git clone https://github.com/hpcaitech/Open-Sora && \
cd Open-Sora && \
pip install -e . && \
pip install gradio && \
python scripts/inference.py \
    --prompt "一只玩毛线球的猫" \
    --num-frames 51 \
    --resolution 480p \
    --save-dir ./outputs
```

## 访问你的服务

部署后，找到你的 `http_pub` URL 在 **我的订单**:

1. 前往 **我的订单** 页面
2. 点击你的订单
3. 找到 `http_pub` URL（例如， `abc123.clorecloud.net`)

使用 `https://YOUR_HTTP_PUB_URL` 替代 `localhost` 在下面的示例中。

## 硬件要求

| 模型版本         | 最低 GPU        | 推荐            | 最佳        |
| ------------ | ------------- | ------------- | --------- |
| OpenSora 1.0 | RTX 3070 8GB  | RTX 3090 24GB | RTX 4090  |
| OpenSora 1.1 | RTX 3090 16GB | RTX 4090 24GB | A100 40GB |
| OpenSora 1.2 | RTX 4090 24GB | A100 40GB     | A100 80GB |

## 安装

### 从源码安装

```bash
git clone https://github.com/hpcaitech/Open-Sora
cd Open-Sora

# 安装依赖
pip install -e .

# 下载模型权重
python scripts/download_weights.py --version 1.2
```

### 使用 pip

```bash
pip install opensora
```

## 基础用法

### 命令行

```bash
# 简单生成
python scripts/inference.py \
    --prompt "海面上壮丽的日落，电影感" \
    --num-frames 51 \
    --resolution 480p \
    --save-dir ./outputs

# 高质量
python scripts/inference.py \
    --prompt "一只雄伟的鹰翱翔于云间" \
    --num-frames 102 \
    --resolution 720p \
    --num-sampling-steps 100 \
    --save-dir ./outputs
```

### Python API

```python
import torch
from opensora.models import OpenSoraModel
from opensora.utils import export_to_video

# 加载模型
model = OpenSoraModel.from_pretrained("hpcaitech/OpenSora-v1.2")
model.to("cuda")

# 生成视频
prompt = "一枚火箭发射升空，戏剧性光照，电影感"

video = model.generate(
    prompt=prompt,
    num_frames=51,
    height=480,
    width=854,
    num_inference_steps=50,
    guidance_scale=7.0
)

# 保存
export_to_video(video, "rocket.mp4", fps=24)
```

## 高级生成

### 使用负面提示词

```python
video = model.generate(
    prompt="野外老虎的专业摄影",
    negative_prompt="模糊、低质量、失真、伪影",
    num_frames=51,
    num_inference_steps=75,
    guidance_scale=7.5
)
```

### 长视频

```python
# 以 24fps 生成 16 秒视频
video = model.generate(
    prompt="花园中花朵绽放的延时摄影",
    num_frames=384,  # 24fps 下 16 秒
    height=480,
    width=854,
    num_inference_steps=100
)

export_to_video(video, "timelapse.mp4", fps=24)
```

### 高分辨率

```python
# 720p 生成（需要更多显存）
video = model.generate(
    prompt="夜晚灯火通明的城市俯视图",
    num_frames=51,
    height=720,
    width=1280,
    num_inference_steps=75
)
```

## 提示词示例

### 电影感

```python
prompts = [
    "武士拔刀的电影镜头，戏剧性光照，4K",
    "暴风雨中悬崖上的城堡的史诗广角镜头",
    "水滴慢动作落入静止池塘",
    "夜晚穿过霓虹灯照亮的赛博朋克小巷的跟拍镜头"
]
```

### 自然

```python
prompts = [
    "极光在雪山上舞动，延时摄影",
    "蝴蝶破茧而出的微距镜头",
    "日落时分，海浪拍击火山岩",
    "黎明时薄雾在古老森林中流动"
]
```

### 抽象

```python
prompts = [
    "彩色颜料滴入水中，慢动作",
    "分形不断演化和变形，迷幻色彩",
    "液态金属变形为不同形状"
]
```

## 配置选项

### 分辨率预设

```python
resolutions = {
    "256p": (256, 455),
    "360p": (360, 640),
    "480p": (480, 854),
    "720p": (720, 1280),
    "1080p": (1080, 1920)  # 需要高显存
}
```

### 质量设置

```python
# 快速预览
config_fast = {
    "num_frames": 25,
    "num_inference_steps": 25,
    "guidance_scale": 5.0
}

# 平衡
config_balanced = {
    "num_frames": 51,
    "num_inference_steps": 50,
    "guidance_scale": 7.0
}

# 最高画质
config_quality = {
    "num_frames": 102,
    "num_inference_steps": 100,
    "guidance_scale": 7.5
}
```

## Gradio 界面

```python
import gradio as gr
import torch
from opensora.models import OpenSoraModel
from opensora.utils import export_to_video
import tempfile

model = OpenSoraModel.from_pretrained("hpcaitech/OpenSora-v1.2")
model.to("cuda")

def generate_video(prompt, negative_prompt, frames, steps, guidance, resolution, seed):
    res_map = {"480p": (480, 854), "720p": (720, 1280)}
    height, width = res_map.get(resolution, (480, 854))

    generator = torch.Generator("cuda").manual_seed(seed) if seed > 0 else None

    video = model.generate(
        prompt=prompt,
        negative_prompt=negative_prompt,
        num_frames=frames,
        height=height,
        width=width,
        num_inference_steps=steps,
        guidance_scale=guidance,
        generator=generator
    )

    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f:
        export_to_video(video, f.name, fps=24)
        return f.name

demo = gr.Interface(
    fn=generate_video,
    inputs=[
        gr.Textbox(label="提示词", lines=2),
        gr.Textbox(label="负面提示词", value="模糊、低质量"),
        gr.Slider(25, 200, value=51, step=1, label="帧数"),
        gr.Slider(20, 150, value=50, step=5, label="步数"),
        gr.Slider(3, 15, value=7, step=0.5, label="引导"),
        gr.Dropdown(["480p", "720p"], value="480p", label="分辨率"),
        gr.Number(value=-1, label="种子")
    ],
    outputs=gr.Video(label="生成的视频"),
    title="OpenSora - 文本转视频",
    description="使用 OpenSora 生成视频。在 CLORE.AI 上运行。"
)

demo.launch(server_name="0.0.0.0", server_port=7860)
```

## 内存优化

```python
# 启用内存优化
model.enable_model_cpu_offload()
model.enable_vae_tiling()

# 适用于超低显存
model.enable_sequential_cpu_offload()

# 使用更低精度
model = OpenSoraModel.from_pretrained(
    "hpcaitech/OpenSora-v1.2",
    torch_dtype=torch.float16
)
```

## 批量生成

```python
import os

prompts = [
    "从火焰中重生的凤凰",
    "夜晚雨水落在城市街道上",
    "花朵绽放的延时摄影",
    "山脉上空的北极光"
]

output_dir = "./videos"
os.makedirs(output_dir, exist_ok=True)

for i, prompt in enumerate(prompts):
    print(f"正在生成 {i+1}/{len(prompts)}：{prompt[:40]}...")

    video = model.generate(
        prompt=prompt,
        num_frames=51,
        num_inference_steps=50
    )

    export_to_video(video, f"{output_dir}/video_{i:03d}.mp4", fps=24)

    # 在每次生成之间清理内存
    torch.cuda.empty_cache()
```

## 性能

| 分辨率  | 帧数  | 步数  | GPU       | 时间      |
| ---- | --- | --- | --------- | ------- |
| 480p | 51  | 50  | RTX 4090  | \~3 分钟  |
| 480p | 51  | 50  | A100 40GB | 约 2 分钟  |
| 720p | 51  | 50  | A100 40GB | \~5 分钟  |
| 720p | 102 | 100 | A100 80GB | \~15 分钟 |

## 成本估算

CLORE.AI 市场的典型费率：

| GPU           | 小时费率    | \~每小时 51 个 480p 视频 |
| ------------- | ------- | ------------------ |
| RTX 4090 24GB | \~$0.10 | \~15-20            |
| A100 40GB     | \~$0.17 | \~25-30            |
| A100 80GB     | \~$0.25 | \~35（可支持 720p）     |

*价格会变化。请查看* [*CLORE.AI 市场*](https://clore.ai/marketplace) *以获取当前费率。*

## 故障排查

### 内存不足

```bash
# 使用更小的分辨率
python scripts/inference.py --resolution 360p --num-frames 25

# 启用 CPU 卸载
python scripts/inference.py --cpu-offload

# 减小批次大小
python scripts/inference.py --batch-size 1
```

### 生成缓慢

* 减少 `推理步数` （30-50 通常就足够）
* 预览使用更低分辨率
* 确保 GPU 正在被使用（检查 `nvidia-smi`)

### 质量较差

* 将步数提高到 75-100
* 使用更具描述性的提示词
* 为伪影添加负面提示词
* 尝试不同的引导强度（5-10）

### 视频伪影

* 降低引导强度
* 增加推理步数
* 使用时间平滑
* 使用视频稳定化进行后期处理

## OpenSora 与其他方案对比

| 功能    | OpenSora 1.2 | Hunyuan | Wan2.1     | SVD   |
| ----- | ------------ | ------- | ---------- | ----- |
| 架构    | DiT          | DiT     | DiT        | U-Net |
| 最大时长  | 16秒          | 5秒      | 5秒         | 4秒    |
| 最大分辨率 | 720p         | 720p    | 720p       | 576p  |
| 质量    | 很高           | 优秀      | 优秀         | 好     |
| 速度    | 中等           | 慢       | 快          | 快     |
| 许可证   | Apache 2.0   | 打开      | Apache 2.0 | 打开    |

**在以下情况下使用 OpenSora：**

* 需要更长的视频生成
* 希望使用完整的 Apache 2.0 许可证
* 对类似 Sora 的架构感兴趣
* 需要活跃的社区支持

## Open-Sora-Plan 替代方案

另一个开源选项：

```bash
git clone https://github.com/PKU-YuanGroup/Open-Sora-Plan
cd Open-Sora-Plan
pip install -e .

python scripts/inference.py \
    --prompt "你的提示词" \
    --output video.mp4
```

## 下一步

* [Hunyuan Video](/guides/guides_v2-zh/shi-pin-sheng-cheng/hunyuan-video.md) - 高质量文本生成视频
* [Wan2.1 视频](/guides/guides_v2-zh/shi-pin-sheng-cheng/wan-video.md) - 快速生成
* [Stable Video Diffusion](/guides/guides_v2-zh/shi-pin-sheng-cheng/stable-video-diffusion.md) - 图像动画
* [RIFE 插帧](/guides/guides_v2-zh/shi-pin-chu-li/rife-interpolation.md) - 帧插值


---

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