Self-Evaluation for Any-Step Text-to-Image Generation
Published:Dec 26, 2025 20:42
•1 min read
•ArXiv
Analysis
This paper introduces a novel approach, Self-E, for text-to-image generation that allows for high-quality image generation with a low number of inference steps. The key innovation is a self-evaluation mechanism that allows the model to learn from its own generated samples, acting as a dynamic self-teacher. This eliminates the need for a pre-trained teacher model or reliance on local supervision, bridging the gap between traditional diffusion/flow models and distillation-based approaches. The ability to generate high-quality images with few steps is a significant advancement, enabling faster and more efficient image generation.
Key Takeaways
- •Introduces Self-E, a novel text-to-image generation model.
- •Employs a self-evaluation mechanism for learning.
- •Achieves high-quality image generation with few inference steps.
- •Does not require a pre-trained teacher model.
- •Offers a unified framework for efficient and scalable generation.
Reference
“Self-E is the first from-scratch, any-step text-to-image model, offering a unified framework for efficient and scalable generation.”