OneDiffusion - An open-source diffusion model that seamlessly supports bidirectional image synthesis and understanding.
OneDiffusion, a multi-functional large-scale diffusion model launched by AI2, seamlessly supports bidirectional image synthesis and understanding, covering various tasks such as text-to-image generation, conditional image generation, and image understanding. It is based on combining all conditions and objectives...
What is OneDiffusion?
OneDiffusion, a multi-functional large-scale diffusion model from AI2, seamlessly supports bidirectional image synthesis and understanding, covering various tasks such as text-to-image generation, conditional image generation, and image understanding. It is trained by modeling all conditional and target images as a sequence of "views," enabling the use of any frame as a conditional image during inference. OneDiffusion provides a general visual model solution with its unified training framework, scalability, and multi-task support.
Main functions of OneDiffusion
- Text-to-image synthesisGenerate high-quality, realistic images from text prompts.
- Conditional image generationGenerate a new image based on the input image (such as a depth map or pose map).
- Image understandingIt performs tasks such as depth estimation, pose estimation, and image segmentation.
- Multi-view generationGenerate a consistent view from multiple perspectives from a single image.
- Instant personalizationPersonalized generation using sequence image input.
- ID customizationImage customization and generation based on personal identification information.
- Zero-shot high-resolution generationIt can generate high-resolution images even if high resolution is not encountered during training.
OneDiffusion's technical principles
- Stream matching framework: Train a continuous-time generative model using the flow matching framework to learn the time-dependent vector field transformation probability distribution.
- Sequence modelingThe entire condition and target image are modeled as a series of "views" and processed as a sequence during training, with each view having a different noise level.
- Flexible frameworkDuring inference, any view can be used as a conditional input or set as noise to generate an output image.
- Unified training frameworkBased on a unified training framework, it eliminates the need for specific architectures, supports scalable multi-task training, and can adapt to any resolution.
- Noise DispatchDuring training, the temporal variables and Gaussian noise of each view are sampled independently to achieve views with different noise levels.
OneDiffusion project address
- GitHub repository:https://github.com/lehduong/OneDiffusion/
- arXiv technical paper:https://arxiv.org/pdf/2411.16318
Application scenarios of OneDiffusion
- Artistic Creation and DesignWith text-to-image synthesis, artists and designers can generate visual content from creative descriptions, accelerating the creative process.
- Advertising and MarketingConditional image generation: Customizes images based on specific brand styles or market trends for use in advertising and marketing materials.
- Game developmentIn game design, rapid prototyping generates diverse views of game environments, characters, and items.
- Virtual Reality (VR) and Augmented Reality (AR)The multi-view generation feature creates 360-degree panoramic images, enhancing the immersive experience in VR and AR applications.
- Movies and EntertainmentIn film production, it is used to generate preliminary sketches of special effects scenes or to quickly preview scene layouts.