Diffusion transformers have demonstrated remarkable generation quality, albeit requiring longer training iterations and numerous inference steps. In each denoising step, diffusion transformers encode the noisy inputs to extract the lower-frequency semantic component and then decode the higher frequency with identical modules. This scheme creates an inherent optimization dilemma: encoding low-frequency semantics necessitates reducing high-frequency components, creating tension between semantic encoding and high-frequency decoding. To resolve this challenge, we propose a new \color ddtD ecoupled \color ddtD iffusion \color ddtT ransformer(\color ddtDDT ), with a decoupled design of a dedicated condition encoder for semantic extraction alongside a specialized velocity decoder. Our experiments reveal that a more substantial encoder yields performance improvements as model size increases. For ImageNet 256x256, Our DDT-XL/2 achieves a new state-of-the-art performance of 1.31 FID (nearly 4xfaster training convergence compared to previous diffusion transformers). For ImageNet 512x512, Our DDT-XL/2 achieves a new state-of-the-art FID of 1.28. Additionally, as a beneficial by-product, our decoupled architecture enhances inference speed by enabling the sharing self-condition between adjacent denoising steps. To minimize performance degradation, we propose a novel statistical dynamic programming approach to identify optimal sharing strategies.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A key bottleneck is the lack of a robust general reward model for all editing tasks. Existing edit reward models usually give overall scores without detailed checks, ignoring different instruction requirements and causing biased rewards. To address this, we propose the Verifier-Based Reasoning Reward Model (RRM), which breaks instructions into verifiable principles, evaluates the edited images against each principle, and aggregates fine-grained scores to reduce hallucinations and provide more interpretable criteria. To address this, we argue the key is to move from a simple scorer to a reasoning verifier. We introduce Edit-R1, a framework to build a chain-of-thought (CoT) verifier-based reasoning reward model (RRM) and leverage it into the downstream editing task. The Edit-RRM breaks instructions into distinct principles, evaluates the edited image against each, and aggregates these checks to provide an interpretable, fine-grained reward. To build such an RRM, we first apply supervised fine-tuning (SFT) as a "cold-start" to generate CoT reward trajectories. Then, we introduce Group Contrastive Preference Optimization (GCPO), a reinforcement learning algorithm that leverages human pairwise preference data to reinforce our pointwise RRM. After building the RRM, we use GRPO to train editing models with this non-differentiable yet powerful reward model. Extensive experiments demonstrate that our Edit-RRM surpasses powerful VLMs such as Seed-1.5-VL and Seed-1.6-VL as an editing-specific reward model, and we observe a clear scaling trend, with performance consistently improving from 3B to 7B parameters. Moreover, Edit-R1 delivers gains to editing models like FLUX.1-kontext, highlighting its effectiveness in enhancing image editing.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表