模型介绍
Self Forcing trains autoregressive video diffusion models by **simulating the inference process during training**, performing autoregressive rollout with KV caching. It resolves the train-test distribution mismatch and enables **real-time, streaming video generation on a single RTX 4090** while matching the quality of state-of-the-art diffusion models. We tested this repo on the following setup: * Nvidia GPU with at least 24 GB memory (RTX 4090, A100, and H100 are tested). * Linux operating system. * 64 GB RAM. Note: * **Our model works better with long, detailed prompts** since it's trained with such prompts. We will integrate prompt extension into the codebase (similar to Wan2.1) in the future. For now, it is recommended to use third-party LLMs (such as GPT-4o) to extend your prompt before providing to the model. * You may want to adjust FPS so it plays smoothly on your device. * The speed can be improved by enabling `torch.compile`, TAEHV-VAE, or using FP8 Linear layers, although the latter two options may sacrifice quality. It is recommended to use `torch.compile` if possible and enable TAEHV-VAE if further speedup is needed.
主要特点
特点摘要待依据来源补齐，不凭模型名称推断能力。
局限与注意事项
暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。
来源报价与费用分析
同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。
| 来源 / 渠道 | 输入 | 输出 | 计费单位 | 采集时间 |
|---|---|---|---|---|
| 暂未取得可核验报价，请查阅来源渠道。 | ||||
按月估算基础 Token 费用
预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。
公开评测与运行配置
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
查看完整已收录榜单 →常见问题
开放权重是否代表可以免费商用？
不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。
这些价格是实时结算价格吗？
不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。
资料来源与可追溯记录
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。