Add a note on the environment

app.py

@@ -202,7 +202,7 @@ html = f"""<h1>LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to
             <p>2. You can perform multi-round specification by giving ChatGPT follow-up requests (e.g., make the object boxes bigger).</p>
             <p>3. You can also try prompts in Simplified Chinese. If you want to try prompts in another language, translate the first line of last example to your language.</p>
             <p>4. The diffusion model only runs 20 steps by default. You can make it run 50 steps to get higher quality images (or tweak frozen steps/guidance steps for better guidance and coherence).</p>
-            <p>5. Duplicate this space and add GPU to skip the queue and run our model faster. {duplicate_html}</p>
+            <p>5. Duplicate this space and add GPU to skip the queue and run our model faster. (Currently we are using a T4, and you can add a A10G or A100 to make it much faster) {duplicate_html}</p>
             <br/>
             <p>Implementation note: In this demo, we replace the attention manipulation in our layout-guided Stable Diffusion described in our paper with GLIGEN due to much faster inference speed (<b>FlashAttention supported, no backprop needed</b> during inference). Compared to vanilla GLIGEN, we have better coherence. Other parts of text-to-image pipeline, including single object generation and SAM, remain the same. The settings and examples in the prompt are simplified in this demo.</p>"""