Update docs/description.md

docs/description.md

@@ -10,6 +10,8 @@ This demo showcases a lightweight Stable Diffusion model (SDM) for general-purpo
 ### Notice
  - This research is accepted to [**ICCV 2023 Demo Track**](https://iccv2023.thecvf.com/) — title: Architecturally Compressed Stable Diffusion for Efficient Text-to-Image Generation.
  - Please be aware that your prompts are logged (_without_ any personally identifiable information).
+ - To generate different images with the same prompt, please change _Random Seed_ in Advanced Settings (because this demo only uses the firstly sampled latent code for each seed).
+ - Many parts of the demo codes were borrowed from [stabilityai/stable-diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion) and [akhaliq/small-stable-diffusion-v0](https://huggingface.co/spaces/akhaliq/small-stable-diffusion-v0). Thanks, Stability AI ([@stabilityai](https://huggingface.co/stabilityai)) and AK ([@akhaliq](https://huggingface.co/akhaliq))!
 
 ### Updates
  - [May/31/2023] The demo is running on **T4-small** (4 vCPU · 15 GB RAM · 16GB VRAM). It takes 5~10 seconds for the original model to generate a 512×512 image with 25 denoising steps. Our compressed model accelerates inference speed while preserving visually compelling results.