Image Generation

ReFIR enhances Large Restoration Models’ accuracy by incorporating retrieved images as external knowledge, mitigating hallucination without retraining.

RefDrop: A training-free method enhances image and video generation consistency by directly controlling the influence of reference features on the diffusion process, enabling precise manipulation of c…

ReF-LDM uses reference images to improve the accuracy of face image restoration, achieving high-quality results faithful to the subject’s true appearance.

RectifID personalizes image generation by cleverly guiding a diffusion model using off-the-shelf classifiers, achieving identity preservation without needing extra training data.

SRStitcher revolutionizes image stitching by integrating fusion and rectangling into a unified inpainting model, eliminating model training and achieving superior performance and stability.

RealCompo: A novel training-free framework dynamically balances realism and compositionality in text-to-image generation, achieving state-of-the-art results.

CORUN-Colabator: a novel cooperative unfolding network and coherence-based label generator achieves state-of-the-art real-world image dehazing by effectively integrating physical knowledge and generat…

RAW: A novel watermark framework ensures the authenticity of AI-generated images by embedding learnable watermarks directly into the image data, providing provable guarantees even under adversarial at…

PuLID: Lightning-fast, tuning-free ID customization for text-to-image!

PTQ4DiT achieves 8-bit and even 4-bit weight precision for Diffusion Transformers, significantly improving efficiency for image generation without sacrificing quality.

Prune and Repaint: A new content-aware method for superior image retargeting across any aspect ratio, preserving key features and avoiding artifacts.

PromptFix: a novel framework enables diffusion models to precisely follow instructions for diverse image processing tasks, using a new high-frequency guidance sampling method and an auxiliary prompt a…

Prompt-Agnostic Adversarial Perturbation (PAP) defends customized diffusion models against image tampering, achieving superior generalization over prompt-specific methods.

Principled Probabilistic Imaging uses diffusion models as plug-and-play priors for accurate posterior sampling in inverse problems, surpassing existing methods.

PrefPaint: Aligning image inpainting diffusion models with human preferences using reinforcement learning, resulting in significantly improved visual appeal.

Phased Consistency Models (PCMs) revolutionize diffusion model generation by overcoming LCM limitations, achieving superior speed and quality in image and video generation.

PeRFlow accelerates diffusion models by straightening their sampling trajectories using a piecewise reflow operation, enabling fast and high-quality image generation with minimal computational cost.

PaGoDA: Train high-resolution image generators efficiently by progressively growing a one-step generator from a low-resolution diffusion model. This innovative pipeline drastically cuts training cost…

Researchers created an energy-efficient optical system for generating images using light propagation, drastically reducing the latency and energy consumption of diffusion models.

OneActor: One-shot tuning for consistent subject image generation, bypassing laborious backbone tuning via semantic guidance, achieving 4x faster speed.