AIHub為大家整理了OpenAI Sora技術報告最后提到的參考論文合集，總共32篇。
如果你想了解Sora更多信息，點擊下面訪問：
- Unsupervised Learning of Video Representations using LSTMs
- Recurrent Environment Simulators
- World Models
- Generating Videos with Scene Dynamics
- MoCoGAN: Decomposing Motion and Content for Video Generation
- Adversarial Video Generation on Complex Datasets
- Generating Long Videos of Dynamic Scenes
- VideoGPT: Video Generation using VQ-VAE and Transformers
- NüWA: Visual Synthesis Pre-training for Neural visUal World creAtion
- Imagen Video: High Definition Video Generation with Diffusion Models
- Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
- Photorealistic Video Generation with Diffusion Models
- Attention Is All You Need
- Language Models are Few-Shot Learner
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- ViViT: A Video Vision Transformer
- Masked Autoencoders Are Scalable Vision Learners
- Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
- High-Resolution Image Synthesis with Latent Diffusion Models
- Auto-Encoding Variational Bayes
- Deep Unsupervised Learning using Nonequilibrium Thermodynamics
- Denoising Diffusion Probabilistic Models
- Improved Denoising Diffusion Probabilistic Models
- Diffusion Models Beat GANs on Image Synthesis
- Elucidating the Design Space of Diffusion-Based Generative Models
- Scalable Diffusion Models with Transformers
- openai/imagegpt-large ：https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf
- Zero-Shot Text-to-Image Generation
- Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
- dataautogpt3： https://cdn.openai.com/papers/dall-e-3.pdf
- Hierarchical Text-Conditional Image Generation with CLIP Latents
- SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
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