Open-Sora - An open-source video generation model and reproduction solution based on a Sora-like architecture.
Open-Sora is an open-source video generation model developed by the Colossal-AI team, designed to replicate OpenAI's Sora video generation product. Open-Sora is also based on the DiT architecture and undergoes training in three stages: large-scale image pre-training, large-scale...
What is Open-Sora?
Open-Sora is an open-source video generation model developed by the Colossal-AI team, designed to replicate OpenAI's Sora video generation product. Open-Sora is also based on the DiT architecture and employs a three-stage training process: large-scale image pre-training, large-scale video pre-training, and fine-tuning with high-quality video data to generate video content that matches text descriptions. This open-source solution covers the entire training process of the video generation model, including data processing, all training details, and model checkpoints, and is freely available for anyone interested in text-based video generation models to learn and use.
Open-Sora official website entrance
- Official project homepage:https://hpcaitech.github.io/Open-Sora/
- GitHub repository:https://github.com/hpcaitech/Open-Sora
Open-Sora model architecture
The Open-Sora model adopts the currently popular Diffusion Transformer (DiT) architecture, uses Huawei's open-source PixArt-α high-quality text-to-image generation model, and extends it to generate videos by adding a temporal attention layer. The specific design is as follows:
Core components
- Pre-trained VAE (Variational Autoencoder)A Video Encoder (VAE) is a component used for data compression that maps input video data to a low-dimensional representation of a latent space. In Open-Sora, the encoder portion of the VAE is used to compress video data during the training phase, while during the inference phase, it samples Gaussian noise from the latent space and generates the video.
- Text encoderThis component is responsible for converting text prompts (such as sentences describing video content) into text embeddings, which are then combined with video data to ensure that the generated video matches the text description.
- STDiT (Spatial Temporal Diffusion Transformer)This is the core component of Open-Sora, a DiT model that utilizes a spatial-temporal attention mechanism. STDiT models temporal relationships in video data by sequentially superimposing one-dimensional temporal attention modules onto two-dimensional spatial attention modules. Furthermore, cross-attention modules are used to align semantic information in the text.
Architecture Design
- Spatial-temporal attention mechanismsEach layer of the STDiT model contains a spatial attention module and a temporal attention module. The spatial attention module processes the two-dimensional spatial features of video frames, while the temporal attention module processes the temporal relationships between frames. This design enables the model to effectively handle the spatial and temporal dimensions of video data.
- Cross attentionFollowing the temporal attention module, the cross-attention module is used to fuse text embeddings with video features, ensuring that the generated video content matches the text description.
- Training and reasoning processDuring the training phase, the VAE's encoder compresses the video data and then uses it along with text embeddings to train the STDiT model. During the inference phase, noise is sampled from the VAE's latent space and fed into the STDiT model along with text prompts to generate denoised features. Finally, the VAE's decoder decodes the data to obtain the final video.
Reproduction solution for Open-Sora
Open-Sora's training and reproduction scheme references the work of Stable Video Diffusion (SVD) and consists of three stages: large-scale image pre-training, large-scale video pre-training, and fine-tuning with high-quality video data. Through these three stages, the Open-Sora model can gradually improve its video generation capabilities, from basic image understanding to complex video content generation, ultimately achieving high-quality video generation results.
Phase 1: Large-scale image pre-training
In the first stage, the model is pre-trained on a large-scale image dataset to establish a basic understanding of image content. The purpose of this stage is to leverage existing high-quality image generation models (such as Stable Diffusion) as a foundation to initialize the weights of the video generation model. In this way, the model can learn rich visual features from the image data, laying a solid foundation for subsequent video pre-training.
Phase Two: Large-Scale Video Pre-training
The second stage focuses on pre-training on large-scale video data to enhance the model's understanding of video time series. In this stage, the model is trained with a large amount of video data to learn the temporal relationships and dynamic changes within the videos. To improve the model's generalization ability, it's necessary to ensure the diversity of video subjects. Furthermore, a temporal attention module is added to the model in this stage to better handle time series data. Training in this stage builds upon the first stage, using the weights from the previous stage as a starting point.
Phase 3: Fine-tuning of high-quality video data
The final stage involves fine-tuning the model using high-quality video data to further enhance the quality and realism of the generated videos. While this stage may use less video data than the second stage, the video length, resolution, and quality are all higher. This fine-tuning process helps the model capture more detailed and realistic video content, resulting in videos that better meet user expectations.