CLIP-VQDiffusion: Language-Free Training of Text To Image Generation Using CLIP and Vector Quantized Diffusion Model
Proposing CLIP-VQDiffusion for language-free training in text-to-image generation, outperforming state-of-the-art methods on FFHQ dataset.
InstructCV: Unified Language Interface for Computer Vision Tasks
InstructCV introduces a unified language interface for computer vision tasks, leveraging text-to-image generative models to enhance generalization capabilities.
SELMA: Improving Text-to-Image Models with Skill-Specific Expert Learning and Merging
SELMA introduces a novel paradigm to enhance the faithfulness of Text-to-Image models by fine-tuning on auto-generated, multi-skill datasets with skill-specific expert learning and merging.
TCIG: Two-Stage Controlled Image Generation with Quality Enhancement through Diffusion
Two-stage method combining controllability and high quality in image generation.
InstructCV: Unified Language Interface for Computer Vision Tasks
InstructCV introduces a unified language interface for computer vision tasks, leveraging text-to-image generative models to enhance generalization capabilities across diverse datasets and user instructions.
Two-Stage Controlled Image Generation with Quality Enhancement Through Diffusion
A two-stage method is proposed to combine controllability and high quality in image generation by leveraging pre-trained models and diffusion models, achieving outstanding results.
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