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Found 6 papers, 1 papers with code
Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance
This paper presents a general approach for text-to-image diffusion models to address the mutual interference between different subjects and their attachments in complex scenes, pursuing better text-image consistency.
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data
Typical diffusion models and modern large-scale conditional generative models like text-to-image generative models are vulnerable to overfitting when fine-tuned on extremely limited data.
Few-shot 3D Shape Generation
Our approach only needs the silhouettes of few-shot target samples as training data to learn target geometry distributions and achieve generated shapes with diverse topology and textures.
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators.
Few-shot Image Generation with Diffusion Models
Then we fine-tune DDPMs pre-trained on large source domains to solve the overfitting problem when training data is limited.
Few-shot Image Generation via Masked Discrimination
It strengthens global image discrimination and guides adapted GANs to preserve more information learned from source domains for higher image quality.
