docs/description.md

This demo showcases a lightweight Stable Diffusion model (SDM) for general-purpose text-to-image synthesis. Our model BK-SDM-Small achieves 36% reduced parameters and latency. This model is bulit with (i) removing several residual and attention blocks from the U-Net of SDM-v1.4 and (ii) distillation pretraining on only 0.22M LAION pairs (fewer than 0.1% of the full training set). Despite very limited training resources, our model can imitate the original SDM by benefiting from transferred knowledge.

Notice

• The model weights are available at BK-SDM-{Base, Small, Tiny} and can be easily used with 🤗 Diffusers.
• This research was accepted to
  • ICML 2023 Workshop on Efficient Systems for Foundation Models (ES-FoMo)
  • ICCV 2023 Demo Track
• Please be aware that your prompts are logged, without any personally identifiable information.
• For different images with the same prompt, please change Random Seed in Advanced Settings (because of using the firstly sampled latent code per seed).

Acknowledgments

• We thank Microsoft for Startups Founders Hub for supporting this research.
• Some demo codes were borrowed from the repo of Stability AI (stabilityai/stable-diffusion) and AK (akhaliq/small-stable-diffusion-v0). Thanks!

Demo Environment

• Regardless of machine types, our compressed model achieves speedups while preserving visually compelling results.
• [June/30/2023] Free CPU-basic (2 vCPU · 16 GB RAM) — 7~10 min slow inference of the original SDM.
  • Because free CPU resources are dynamically allocated with other demos, it may take much longer, depending on the server situation.
• [May/31/2023] NVIDIA T4-small (4 vCPU · 15 GB RAM · 16GB VRAM) — 5~10 sec inference of the original SDM (for a 512×512 image with 25 denoising steps).