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AI Research
arXiv CS.AI·May 21·6 min read
Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement
TL;DR
Diffusion models (neural networks that generate images by iteratively removing noise) currently rely on external verifiers to improve output quality during inference, which limits scalability. This work introduces iterative partial refinement, a method that refines only selected regions of generated samples without needing external reward models.
✦ Why It Matters
Engineers can now scale diffusion model quality at inference without building or maintaining separate evaluator models, reducing deployment complexity.
Key Takeaways
- Implement iterative partial refinement to scale inference without external reward models.
- Selectively refine image regions during generation to allocate computation efficiently.
- Eliminate external verifier dependency to reduce system complexity and latency.
- Apply self-improvement mechanisms during generation for better sample quality.
Full Summary
Diffusion models generate high-quality images by progressively denoising random noise through many steps. Inference-time scaling—allocating more computation during generation to improve results—has proven effective for reasoning tasks but remains underexplored for diffusion models.
Current scaling methods rely on external verifiers or reward models (separate neural networks trained to score outputs) to select the best samples, creating bottlenecks when such evaluators are unavailable or unreliable. Iterative Partial Refinement addresses this by enabling models to self-improve during generation without external guidance.
The method works by selectively refining image regions across iterations, allowing the model to allocate computation where needed most. This approach demonstrates improved sample quality while eliminating dependency on external evaluators, making inference-time scaling more practical and scalable across diverse applications.