DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models
Diffusion models have become a dominant paradigm for conditional image generation, yet existing approaches generally follow two directions: task-specific designs that can improve performance but limit generalization, and training-free loss guidance that compresses rich conditions into scalar objectives and applies stepwise guidance, leading to information bottlenecks and error accumulation along the sampling trajectory. Given the urgent need for an effective unified framework across diverse conditional image generation tasks, we propose Data Injection and Contrastive Trajectory Refinement (DICT), a training-free inference method that enhances conditional image generation without introduci...
| Authors | Chunnan Shang, Xin Zhang, Zhizhong Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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