7 citations · 13 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
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…
Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models
Ruichen Wang, Zekang Chen, Chen Chen +3
Recent text-to-image (T2I) diffusion models show outstanding performance in generating high-quality images conditioned on textual prompts. However, they fail to semantically align…
Edit Everything: A Text-Guided Generative System for Images Editing
Defeng Xie, Ruichen Wang, Jian Ma +5
We introduce a new generative system called Edit Everything, which can take image and text inputs and produce image outputs. Edit Everything allows users to edit images using simpl…
GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
Jian Ma, Mingjun Zhao, Chen Chen +4
Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user i…