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cs.CV2024

TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps

Qingsong Xie, Zhenyi Liao, Zhijie Deng +2

Distilling latent diffusion models (LDMs) into ones that are fast to sample from is attracting growing research interest. However, the majority of existing methods face two critica…

cs.CV2024

PEA-Diffusion: Parameter-Efficient Adapter with Knowledge Distillation in non-English Text-to-Image Generation

Jian Ma, Chen Chen, Qingsong Xie +1

Text-to-image diffusion models are well-known for their ability to generate realistic images based on textual prompts. However, the existing works have predominantly focused on Eng…

cs.CV2024

Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning

Jian Ma, Junhao Liang, Chen Chen +1

Recent progress in personalized image generation using diffusion models has been significant. However, development in the area of open-domain and non-fine-tuning personalized image…

cs.CV2024

MCAD: Multi-teacher Cross-modal Alignment Distillation for efficient image-text retrieval

Youbo Lei, Feifei He, Chen Chen +4

Due to the success of large-scale visual-language pretraining (VLP) models and the widespread use of image-text retrieval in industry areas, it is now critically necessary to reduc…

cs.CV2024

MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers

Sijia Li, Chen Chen, Haonan Lu

Diffusion-model-based text-guided image generation has recently made astounding progress, producing fascinating results in open-domain image manipulation tasks. Few models, however…