minisora 开源社区定位为由社区同学自发组织的开源社区（免费不收取任何费用、不割韭菜），minisora 计划探索实现 Sora 的实现路径可发展可能
- 将定期举办Sora的圆桌和社区一起探讨可能性
- 视频生成的现有技术路径探讨
- Sora 技术报告: Video generation models as world simulators
- DiT: Scalable Diffusion Models with Transformers
- Latte: Latte: Latent Diffusion Transformer for Video Generation
- Coming soon...
| Paper | Links |
|---|---|
| 1) Diffusion Models Beat GANs on Image Synthesis | Paper, Github |
| 2) High-Resolution Image Synthesis with Latent Diffusion Models | Paper, Github |
| 3) Elucidating the Design Space of Diffusion-Based Generative Models | Paper, Github |
| 4) Denoising Diffusion Probabilistic Models | Paper, Github |
| 5) Score-Based Generative Modeling through Stochastic Differential Equations | Paper, Github |
| Paper | Links |
|---|---|
| 1) UViT: All are Worth Words: A ViT Backbone for Diffusion Models | Paper, Github ModelScope |
| 2) DiT: Scalable Diffusion Models with Transformers | Paper, Github ModelScope |
| 3) SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers | Paper, Github ModelScope |
| 4) FiT: Flexible Vision Transformer for Diffusion Model | Paper, Github |
| Paper | Links |
|---|---|
| 1) Animatediff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning | Paper, Github ModelScope |
| 2) I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models | Paper, Github ModelScope |
| 4) Imagen Video: High Definition Video Generation with Diffusion Models | Paper |
| 5) MoCoGAN: Decomposing Motion and Content for Video Generation | Paper |
| 6) Adversarial Video Generation on Complex Datasets | Paper |
| 7) Photorealistic Video Generation with Diffusion Models | Paper |
| 8) VideoGPT: Video Generation using VQ-VAE and Transformers | Paper, Github |
| 9) Video Diffusion Models | Paper, Github, Project |
| 10) MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation | Paper, Github, Project, Blog |
| Paper | Links |
|---|---|
| 1) World Model on Million-Length Video And Language With RingAttention | Paper, Github |
| 2) Ring Attention with Blockwise Transformers for Near-Infinite Context | Paper, Github |
| 3) Extending LLMs' Context Window with 100 Samples | Paper, Github |
| 4) Efficient Streaming Language Models with Attention Sinks | Paper, Github |
