Ordered-AR Unlocks Any-Order Generation for Pretrained Autoregressive Image Models
Summary:Ordered-AR introduces a novel approach to adapt pretrained autoregressive (AR) image models for any-order generation using future-aware positional embeddings and an ordering curriculum. Experiments on ImageNet demonstrate superior performance in inpainting and outpainting compared to MaskGIT, while maintaining compatibility with the standard AR inference stack. This research highlights the potential of unlocking AR models' flexibility without the need for retraining, offering a new direction for
Technical Mechanism Analysis
Autoregressive (AR) image generation models are powerful in theory but face inflexibility in practical applications. Traditional training methods use a fixed raster-scan order, hardcoding a single factorization of the image distribution into the model, which makes it incapable of inpainting, outpainting, or editing at inference time. Existing solutions, such as discrete diffusion models, require training from scratch with a mixture of token orderings, resulting in roughly 3x the compute cost of standard raster-scan training. Additionally, discrete diffusion models sacrifice compatibility with the AR inference ecosystem (e.g., KV caching, vLLM).
Ordered-AR introduces a novel approach using two key components to adapt pretrained AR models for any-order generation:
- Future-aware positional embeddings: Resolve the target-position identifiability problem with negligible overhead.
- Ordering curriculum: Trace a stable optimization path from the pretrained solution to a good any-order basin.
Engineering Trade-offs and Performance
Applied to four base models (LlamaGen-L/XL, RAR-L/XL), the method significantly outperforms MaskGIT on ImageNet inpainting (FID 6.21 vs. 10.44) and outpainting, while remaining competitive with MAR-L. It trades a 1.2-1.9 increase in class-conditional FID for this flexibility and incurs only 13-33% additional compute beyond pretraining, without modifying the standard AR inference stack.
Developer Implementation and Deployment Recommendations
The key advantage of this approach is its compatibility with existing pretrained models, allowing developers to unlock any-order generation without retraining. Recommended applications include:
- Image inpainting and outpainting: Suitable for scenarios requiring flexible generation capabilities.
- Multimodal interaction: Provides higher flexibility in tasks requiring cross-modal generation.
- Resource-constrained environments: More advantageous in scenarios with limited computational resources due to the absence of retraining.
Additionally, developers should consider:
- Model fine-tuning: Fine-tuning on specific tasks to further enhance generation quality.
- Performance optimization: Combining with other optimization techniques (e.g., quantization, pruning) to reduce inference costs.
Conclusion
The Ordered-AR method demonstrates the potential of pretrained AR image models in any-order generation, offering a new technical direction for AI image generation.
— END —Source: Hugging Face Daily Papers (2026-10-29)
Tags: #Autoregressive Models #Image Generation #Any-Order Generation #Ordered-AR
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