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The Mini Sora open-source community is positioned as a community-driven initiative (free of charge and devoid of any exploitation) organized spontaneously by community members. The Mini Sora project aims to explore the implementation path and future development direction of Sora.
- Regular roundtable discussions will be held with the Sora team and the community to explore possibilities.
- We will delve into existing technological pathways for video generation.
- State of the Art on Diffusion Models for Visual Computing
- CSUR 24 Paper: Diffusion Models: A Comprehensive Survey of Methods and Applications
- Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
- Generate Impressive Videos with Text Instructions: A Review of OpenAI Sora, Stable Diffusion, Lumiere and Comparable
- OpenDiT: An Easy, Fast and Memory-Efficient System for DiT Training and Inference
- GPU-Friendly: Ideally, it should have low requirements for GPU memory size and the number of GPUs, such as being trainable and inferable with compute power like 8 A100 80G cards, 8 A6000 48G cards, or RTX4090 24G.
- Training-Efficiency: It should achieve good results without requiring extensive training time.
- Inference-Efficiency: When generating videos during inference, there is no need for high length or resolution; acceptable parameters include 3-10 seconds in length and 480p resolution.
Sora Night Talk on Video Diffusion Overview
Zhihu Notes: A Survey on Generative Diffusion Model: An Overview of Generative Diffusion Models
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Technical Report: Video generation models as world simulators
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Latte: Latte: Latent Diffusion Transformer for Video Generation
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Stable Cascade (ICLR 24 Paper): Würstchen: An efficient architecture for large-scale text-to-image diffusion models
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Updating...
| Paper | Link |
| 1) Guided-Diffusion: Diffusion Models Beat GANs on Image Synthesis | NeurIPS 21 Paper, Github |
| 2) Latent Diffusion: High-Resolution Image Synthesis with Latent Diffusion Models | CVPR 22 Paper, Github |
| 3) EDM: Elucidating the Design Space of Diffusion-Based Generative Models | NeurIPS 22 Paper, Github |
| 4) DDPM: Denoising Diffusion Probabilistic Models | NeurIPS 20 Paper, Github |
| 5) DDIM: Denoising Diffusion Implicit Models | ICLR 21 Paper, Github |
| 6) Score-Based Diffusion: Score-Based Generative Modeling through Stochastic Differential Equations | ICLR 21 Paper, Github, Blog |
| 7) Stable Cascade: Würstchen: An efficient architecture for large-scale text-to-image diffusion models | ICLR 24 Paper, Github, Blog |
| 8) Diffusion Models in Vision: A Survey | TPAMI 23 Paper, Github |
| Paper | Link |
| 1) UViT: All are Worth Words: A ViT Backbone for Diffusion Models | CVPR 23 Paper, Github, ModelScope |
| 2) DiT: Scalable Diffusion Models with Transformers | ICCV 23 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 |
| 5) k-diffusion: Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers | Paper, Github |
| 6) OpenDiT: An Easy, Fast and Memory-Efficient System for DiT Training and Inference | Github |
| 7) Large-DiT: Large Diffusion Transformer | Github |
| Paper | Link |
| 1) Animatediff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning | ICLR 24 Paper, Github, ModelScope |
| 2) I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models | Paper, Github, ModelScope |
| 3) Imagen Video: High Definition Video Generation with Diffusion Models | Paper |
| 4) MoCoGAN: Decomposing Motion and Content for Video Generation | CVPR 18 Paper |
| 5) Adversarial Video Generation on Complex Datasets | Paper |
| 6) W.A.L.T: Photorealistic Video Generation with Diffusion Models | Paper Project |
| 7) VideoGPT: Video Generation using VQ-VAE and Transformers | Paper, Github |
| 8) Video Diffusion Models | Paper, Github, Project |
| 9) MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation | NeurIPS 22 Paper, Github, Project, Blog |
| 10) VideoPoet: A Large Language Model for Zero-Shot Video Generation | Paper |
| 11) MAGVIT: Masked Generative Video Transformer | CVPR 23 Paper, Github, Project, Colab |
| 12) EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions | Paper, Github, Project |
| 13) SimDA: Simple Diffusion Adapter for Efficient Video Generation | Paper, Github, Project |
| 14) StableVideo: Text-driven Consistency-aware Diffusion Video Editing | ICCV 23 Paper, Github, Project |
| 15) SVD: Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets | Paper, Github |
| 16) ADD: Adversarial Diffusion Distillation | Paper, Github |
| 17) GenTron: Diffusion Transformers for Image and Video Generation | CVPR 24 Paper, Project |
| Paper | Link |
| 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 | ICLR 24 Paper, Github |
| 5) The What, Why, and How of Context Length Extension Techniques in Large Language Models – A Detailed Survey | Paper |
| 6) MovieChat: From Dense Token to Sparse Memory for Long Video Understanding | CVPR 24 Paper, Github, Project |
| Paper | Link |
| 1) ViViT: A Video Vision Transformer | ICCV 21 Paper, Github |
| 2) VideoLDM: Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models | CVPR 23 Paper |
| 3) LVDM: Latent Video Diffusion Models for High-Fidelity Long Video Generation | Paper, Github |
| 4) LFDM: Conditional Image-to-Video Generation with Latent Flow Diffusion Models | CVPR 23 Paper, Github |
| 5) MotionDirector: Motion Customization of Text-to-Video Diffusion Models | Paper, Github |
| 6) TGAN-ODE: Latent Neural Differential Equations for Video Generation | Paper, Github |
| 7) Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators | Paper, Github |
| 8) VideoCrafter1: Open Diffusion Models for High-Quality Video Generation | Paper, Github |
| 9) VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models | Paper, Github |
| Paper | Link |
| 1) Stable Audio: Fast Timing-Conditioned Latent Audio Diffusion | Link |
| 2) MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation | CVPR 23 Paper, Github |
| 3) Pengi: An Audio Language Model for Audio Tasks | NeurIPS 23 Paper, Github |
| 4) Vast: A vision-audio-subtitle-text omni-modality foundation model and dataset | NeurlPS 23 Paper, Github |
| Paper | Link |
| 1) Layered Neural Atlases for Consistent Video Editing | TOG 21 Paper, Github, Project, |
| 2) StableVideo: Text-driven Consistency-aware Diffusion Video Editing | ICCV 23 Paper, Github, Project |
| 3) CoDeF: Content Deformation Fields for Temporally Consistent Video Processing | Paper, Github, Project |
| 4) Consistency Models | ICML 23 Paper, Github |
| Paper | Link |
| 1) RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models | Paper, Github, Project |
| 2) Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs | Paper, Github |
| 3) LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models | TMLR 23 Paper, Github |
| 4) LLM BLUEPRINT: ENABLING TEXT-TO-IMAGE GEN-ERATION WITH COMPLEX AND DETAILED PROMPTS | ICLR 24 Paper, Github |
| 5) Progressive Text-to-Image Diffusion with Soft Latent Direction | Paper |
| 6) Self-correcting LLM-controlled Diffusion Models | CVPR 24 Paper, Github |
| 7) LayoutLLM-T2I: Eliciting Layout Guidance from LLM for Text-to-Image Generation | MM 23 Paper |
| 8) LayoutGPT: Compositional Visual Planning and Generation with Large Language Models | NeurIPS 23 Paper, Github |
| 9) Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition | Paper, Github |
| 10) InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions | Paper, Github |
| 11) Controllable Text-to-Image Generation with GPT-4 | Paper |
| 12) LLM-grounded Video Diffusion Models | ICLR 24 Paper |
| 13) VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning | Paper |
| 14) FlowZero: Zero-Shot Text-to-Video Synthesis with LLM-Driven Dynamic Scene Syntax | Paper |
| 15) VideoDrafter: Content-Consistent Multi-Scene Video Generation with LLM | Paper |
| 16) VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning | Paper |
| 17) Free-Bloom: Zero-Shot Text-to-Video Generator with LLM Director and LDM Animator | NeurIPS 23 Paper |
| 18) Empowering Dynamics-aware Text-to-Video Diffusion with Large Language Models | Paper |
| 19) MotionZero: Exploiting Motion Priors for Zero-shot Text-to-Video Generation | Paper |
| 20) GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning | Paper |
| Paper | Link |
| Paper | Link |
| 1) NExT-GPT: Any-to-Any Multimodal LLM | Paper, Github |
| Dataset Name | Link |
| 1) HD-VILA-100M: Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions | Paper,Github, |
| 2) HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips | Paper, Github, Project, |
| 3) YT-Temporal-180M: A dataset for learning multimodal script knowledge derived from 6 million public YouTube videos | Paper, Github, Project, |
| 4) Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers | Paper, Github, Project, |
| 5) UCF101: Action Recognition Data Set | Paper, Project, |
| 6) video2dataset: A Simple Tool For Large Video Dataset Curation | Tool, Github, |
| Resources | Link |
| 1) Datawhale - AI视频生成学习 | Feishu doc |
| 2) A Survey on Generative Diffusion Model | TKDE 24 Paper, Github |
| 3) Awesome-Video-Diffusion-Models: A Survey on Video Diffusion Models | Paper, Github |
| 4) Awesome-Text-To-Video：A Survey on Text-to-Video Generation/Synthesis | Github |
| 5) video-generation-survey: A reading list of video generation | Github |
| 6) Awesome-Video-Diffusion | Github |
| 7) Video Generation Task in Papers With Code | Task |
| 8) Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models | Paper, Github |
| 9) Open-Sora-Plan (PKU-YuanGroup) | Github |
| 10) State of the Art on Diffusion Models for Visual Computing | Paper |
| 11) Diffusion Models: A Comprehensive Survey of Methods and Applications | CSUR 24 Paper, Github |
| 12) Generate Impressive Videos with Text Instructions: A Review of OpenAI Sora, Stable Diffusion, Lumiere and Comparable | Paper |
| 13) On the Design Fundamentals of Diffusion Models: A Survey | Paper |
| 14) Efficient Diffusion Models for Vision: A Survey | Paper |
| 15) Text-to-Image Diffusion Models in Generative AI: A Survey | Paper |
| 16) Awesome-Diffusion-Transformers: | GitHub, Page |
We greatly appreciate your contributions to the Mini Sora open-source community and helping us make it even better than it is now!
For more details, please refer to the Contribution Guidelines
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