Uni-DAD: Unified Distillation and Adaptation of Diffusion Models for Few-step Few-shot Image Generation

1. 1Implement unified distillation and adaptation of diffusion models
2. 2Use Uni-DAD for few-step few-shot image generation
3. 3Evaluate the quality and diversity of generated images
4. 4Compare Uni-DAD with two-stage pipelines (Adapt-then-Distill or Distill-then-Adapt)

AI engineers and researchers working on computer vision and image generation tasks can benefit from Uni-DAD, as it simplifies the process of adapting diffusion models to new domains

💡 Uni-DAD simplifies the process of adapting diffusion models to new domains, reducing design complexity and improving quality or diversity

💡 Uni-DAD: a single-stage method for fast and high-quality image generation using unified distillation and adaptation of diffusion models