Update docs/description.md

docs/description.md

@@ -11,11 +11,12 @@ This demo showcases a lightweight Stable Diffusion model (SDM) for general-purpo
  - This research was accepted to
     - [**ICML 2023 Workshop on Efficient Systems for Foundation Models** (ES-FoMo)](https://es-fomo.com/)
     - [**ICCV 2023 Demo Track**](https://iccv2023.thecvf.com/)
- - Please be aware that your prompts are logged (_without_ any personally identifiable information).
+ - 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).
  - Some demo codes were borrowed from the repo of Stability AI ([stabilityai/stable-diffusion](https://huggingface.co/spaces/stabilityai/stable-diffusion)) and AK ([akhaliq/small-stable-diffusion-v0](https://huggingface.co/spaces/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).