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Jiwon Kang

4 accepted papers

2026

A Noise is Worth Diffusion Guidance

ICLR 2026poster

Diffusion models have demonstrated remarkable image generation capabilities, but their performance heavily relies on classifier-free guidance (CFG). While CFG significantly enhances image quality, evaluating both conditional and unconditional models at every denoising step leads to substantial compu…

Cited by 0SourcecodeScholar
2026

Attribute-Preserving Pseudo-Labeling for Diffusion-Based Face Swapping

CVPR 2026

Face swapping aims to transfer the identity of a source face onto a target face while preserving target-specific attributes such as pose, expression, lighting, skin tone, and makeup. However, since real ground truth for face swapping is unavailable, achieving both accurate identity transfer and high

Cited by 0SourceScholar
2025

Identity-preserving Distillation Sampling by Fixed-Point Iterator

CVPR 2025poster

Score distillation sampling (SDS) demonstrates a powerful capability for text-conditioned 2D image and 3D object generation by distilling the knowledge from learned score functions. However, SDS often suffers from blurriness caused by noisy gradients. When SDS meets the image editing, such degradati…

Cited by 0SourcePDFScholar
2025

Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models

NeurIPS 2025poster

Recent guidance methods in diffusion models steer reverse sampling by perturbing the model to construct an implicit weak model and guide generation away from it. Among these approaches, attention perturbation has demonstrated strong empirical performance in unconditional scenarios where classifier-f…

Cited by 0SourceScholar