most citedImagine yourself: Tuning-Free Personalized Image Generation

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cs.CV2025

Movie Weaver: Tuning-Free Multi-Concept Video Personalization with Anchored Prompts

Feng Liang, Haoyu Ma, Zecheng He +10

Video personalization, which generates customized videos using reference images, has gained significant attention. However, prior methods typically focus on single-concept personal…

cs.CV2025

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation

Philippe Hansen-Estruch, David Yan, Ching-Yao Chung +7

Visual tokenization via auto-encoding empowers state-of-the-art image and video generative models by compressing pixels into a latent space. Although scaling Transformer-based gene…

cs.CV2024

Pixel-Space Post-Training of Latent Diffusion Models

Christina Zhang, Simran Motwani, Matthew Yu +6

Latent diffusion models (LDMs) have made significant advancements in the field of image generation in recent years. One major advantage of LDMs is their ability to operate in a com…

cs.CV20241 cited

Imagine yourself: Tuning-Free Personalized Image Generation

Zecheng He, Bo Sun, Felix Juefei-Xu +14

Diffusion models have demonstrated remarkable efficacy across various image-to-image tasks. In this research, we introduce Imagine yourself, a state-of-the-art model designed for p…

cs.CV2024

Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation

Jonas Kohler, Albert Pumarola, Edgar Schönfeld +4

Diffusion models are a powerful generative framework, but come with expensive inference. Existing acceleration methods often compromise image quality or fail under complex conditio…