Fractal Generative Models
- Published
- Source
- arXiv
- Paper number
- 045
- Field
- Image Generation
- arXiv ID
- 2502.17437
Key points
- Traditional generative models struggle with the computational burden and artificial sequentialization required by high-dimensional, non-sequential data such as raw images.
- Achieving high-fidelity pixel-level image generation at high resolution is computationally infeasible and difficult because of the enormous dimensionality.
- Modularity in existing deep learning is often limited to layers or simple blocks, leaving it without the higher-level abstractions needed to compose full generative models.
- It proposes a new generative modeling paradigm, 'fractal generative models,' that recursively calls existing generative models as atomic generation modules to form self-similar structures.
- The framework is instantiated as autoregressive (AR) models for pixel-level image generation, FractalAR and FractalMAR, using a divide-and-conquer strategy over image patches.
- At each hierarchical level, it uses multi-scale patching and local attention mechanisms to greatly reduce the quadratic computational cost of high-resolution image generation.
Paper links
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