Condition Errors Refinement in Autoregressive Image Generation with Diffusion Loss
Recent studies have explored autoregressive models for image generation, with promising results, and have combined diffusion models with autoregressive frameworks to optimize image generation via diffusion losses. In this study, we present a theoretical analysis of diffusion and autoregressive models with diffusion loss, highlighting the latter's advantages. We present a theoretical comparison of conditional diffusion and autoregressive diffusion with diffusion loss, demonstrating that patch denoising optimization in autoregressive models effectively mitigates condition errors and leads to a stable condition distribution. Our analysis also reveals that autoregressive condition generation refines the condition, causing the condition error influence to decay exponentially. In addition, we introduce a novel condition refinement approach based on Optimal Transport (OT) theory to address ``condition inconsistency''. We theoretically demonstrate that formulating condition refinement as a Wasserstein Gradient Flow ensures convergence toward the ideal condition distribution, effectively mitigating condition inconsistency. Experiments demonstrate the superiority of our method over diffusion and autoregressive models with diffusion loss methods.
698c2ee36052d3bed9630c8e
Yucheng Zhou
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2026-02-11T11:13:43.761Z
636f37fa93d9a0c987e092fa
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Yucheng Zhou
YCZhou
user
YCZhou
698c2ee36052d3bed9630c8f
Hao Li
698c2ee36052d3bed9630c90
Jianbing Shen
Condition Errors Refinement in Autoregressive Image Generation with Diffusion Loss
Recent studies have explored autoregressive models for image generation, with promising results, and have combined diffusion models with autoregressive frameworks to optimize image generation via diffusion losses. In this study, we present a theoretical analysis of diffusion and autoregressive models with diffusion loss, highlighting the latter's advantages. We present a theoretical comparison of conditional diffusion and autoregressive diffusion with diffusion loss, demonstrating that patch denoising optimization in autoregressive models effectively mitigates condition errors and leads to a stable condition distribution. Our analysis also reveals that autoregressive condition generation refines the condition, causing the condition error influence to decay exponentially. In addition, we introduce a novel condition refinement approach based on Optimal Transport (OT) theory to address ``condition inconsistency''. We theoretically demonstrate that formulating condition refinement as a Wasserstein Gradient Flow ensures convergence toward the ideal condition distribution, effectively mitigating condition inconsistency. Experiments demonstrate the superiority of our method over diffusion and autoregressive models with diffusion loss methods.
Autoregressive models with diffusion loss outperform traditional diffusion models by effectively mitigating condition errors through patch denoising optimization and condition refinement using Optimal Transport theory.
autoregressive models diffusion models diffusion loss patch denoising optimization condition error condition distribution Optimal Transport (OT) theory Wasserstein Gradient Flow condition refinement conditional diffusion