AILSJ为大家整理了OpenAI Sora技术报告最后提到的参考论文合集，总共32篇。
如果你想了解Sora更多信息，点击下面访问：
- 点此查看Sora介绍：功能、案例和使用
- 点此查看Sora视频生成提示和案例大全
- 点此查看Sora技术报告中文版
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 16×16 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
