12 citations · 22 across the 15 of their papers we have counts for
17 papers
Refinement Is Inherently Editable: Training-Free Prompt-to-Prompt Image Editing with Generative Refinement Network
Yulong Chen, Ziqian Zhang, Haoyu Zhang +4
Text-guided image editing must introduce the requested changes while preserving unrelated source content. In training-free editing, diffusion editors often use spatial controls who…
Training-free image inversion for one-step diffusion models
Tao Wu, Senmao Li, Yaxing Wang +3
In this work, we introduce a novel training-free inversion (TFinv) framework for one-step diffusion models,addressing key challenges in real image inversion and editing. We first i…
WaDi: Weight Direction-aware Distillation for One-step Image Synthesis
Lei Wang, Yang Cheng, Senmao Li +3
Despite the impressive performance of diffusion models such as Stable Diffusion (SD) in image generation, their slow inference limits practical deployment. Recent works accelerate…
Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device
Abdelrahman Shaker, Ahmed Heakl, Jaseel Muhammad +8
Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry and too heavy for deployment on…
FasterVAR: Plug-and-Play Acceleration for Visual Autoregressive Models
Senmao Li, Kai Wang, Salman Khan +3
Visual Autoregressive (VAR) modeling departs from the next-token prediction paradigm of traditional Autoregressive (AR) models through next-scale prediction, enabling high-quality…
Diversity Has Always Been There in Your Visual Autoregressive Models
Tong Wang, Guanyu Yang, Nian Liu +6
Visual Autoregressive (VAR) models have recently garnered significant attention for their innovative next-scale prediction paradigm, offering notable advantages in both inference e…
Adversarial Concept Distillation for One-Step Diffusion Personalization
Yixiong Yang, Tao Wu, Senmao Li +4
Recent progress in accelerating text-to-image diffusion models enables high-fidelity synthesis within a single denoising step. However, customizing the fast one-step models remains…
From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
Tao Liu, Dafeng Zhang, Gengchen Li +7
Face aging has become a crucial task in computer vision, with applications ranging from entertainment to healthcare. However, existing methods struggle with achieving a realistic a…
One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models
Senmao Li, Lei Wang, Kai Wang +7
Text-to-Image (T2I) diffusion models have made remarkable advancements in generative modeling; however, they face a trade-off between inference speed and image quality, posing chal…
Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability
Lei Wang, Senmao Li, Fei Yang +5
The diffusion models, in early stages focus on constructing basic image structures, while the refined details, including local features and textures, are generated in later stages.…
InterLCM: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration
Senmao Li, Kai Wang, Joost van de Weijer +6
Diffusion priors have been used for blind face restoration (BFR) by fine-tuning diffusion models (DMs) on restoration datasets to recover low-quality images. However, the naive app…
Free-Lunch Color-Texture Disentanglement for Stylized Image Generation
Jiang Qin, Senmao Li, Alexandra Gomez-Villa +4
Recent advances in Text-to-Image (T2I) diffusion models have transformed image generation, enabling significant progress in stylized generation using only a few style reference ima…
One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt
Tao Liu, Kai Wang, Senmao Li +6
Text-to-image generation models can create high-quality images from input prompts. However, they struggle to support the consistent generation of identity-preserving requirements f…
Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models
Senmao Li, Joost van de Weijer, Taihang Hu +4
The success of recent text-to-image diffusion models is largely due to their capacity to be guided by a complex text prompt, which enables users to precisely describe the desired c…
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model Inference
Senmao Li, Taihang Hu, Joost van de Weijer +7
One of the main drawback of diffusion models is the slow inference time for image generation. Among the most successful approaches to addressing this problem are distillation metho…
3D-Aware Multi-Class Image-to-Image Translation with NeRFs
Senmao Li, Joost van de Weijer, Yaxing Wang +3
Recent advances in 3D-aware generative models (3D-aware GANs) combined with Neural Radiance Fields (NeRF) have achieved impressive results. However no prior works investigate 3D-aw…
StyleDiffusion: Prompt-Embedding Inversion for Text-Based Editing
Senmao Li, Joost van de Weijer, Taihang Hu +5
A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the…