DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models

2026-07-04

AI Summary

This work addresses the limitations of existing conditional image generation methods, which often suffer from task-specific designs or lack training-free guidance, leading to information bottlenecks and error accumulation due to aggressive conditioning signal compression. To overcome these issues, the authors propose a unified, training-free inference framework that injects noisy conditioning signals during early denoising stages and adaptively guides the denoising process to extract task-relevant features. Additionally, they introduce a contrastive trajectory optimization mechanism between adjacent denoising states to refine the generation path. The method demonstrates significant improvements over current baselines across diverse tasks—including style transfer, super-resolution, and deblurring—achieving both high fidelity and superior perceptual quality while exhibiting strong cross-task generalization capabilities.

Abstract

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 introducing task-dependent architectures. DICT introduces Data Injection, where noise-perturbed conditional signals are integrated into early denoising stages; by performing guided denoising on these injected signals, DICT adaptively selects and distills task-salient information from the raw condition, effectively preserving spatial richness and ensuring precise condition-to-generation alignment. Furthermore, DICT applies Contrastive Trajectory Refinement across adjacent denoising states, enabling pairwise comparisons that progressively improve sample quality. These designs keep inference simple while improving cross-task transfer under a unified diffusion formulation. Extensive experiments on conditional image generation tasks (e.g., style transfer, image super-resolution, and image deblurring) show consistent gains in fidelity and perceptual quality over representative task-specific and loss-guided baselines.

Problem

• conditional image generation
• diffusion models
• information bottleneck
• error accumulation
• generalization

Innovation

• Data Injection
• Contrastive Trajectory Refinement
• Training-free Inference
• Conditional Image Generation
• Diffusion Models