update description

app.py

@@ -56,6 +56,11 @@ def accelerate_train_lora(steps):
     return "./output_example/lora_weight.pt"
     
 with gr.Blocks() as demo:
+    gr.Markdown("""<h1><center>LORA - Low-rank Adaptation for Fast Text-to-Image Diffusion Fine-tuning</center></h1>
+    """)
+     gr.Markdown(
+        """**Main Features**<br>- Fine-tune Stable diffusion models twice as faster than dreambooth method, by Low-rank Adaptation.<br>- Get insanely small end result, easy to share and download.<br>- Easy to use, compatible with diffusers.<br>- Sometimes even better performance than full fine-tuning<br>Please refer the Github repo this Space is based on, here - <a href = "https://github.com/cloneofsimo/lora">LORA</a><br>You can also refer this tweet by AK over here to quote/retweet/like, here on <a href="https://twitter.com/_akhaliq/status/1601120767009513472">Twitter</a>.<br>This Gradio Space is an attempt to explore this novel LORA approach to fine-tune Stable diffusion models, using the power and flexibility of Gradio!<br><b>To use this Space well:</b>- First, upload your set of images (4-5), then enter the number of fine-tuning steps, and then press the 'Train LORA model' button.<br>- Enter a prompt, then set the alpha value using the Slider (nearer to 1 implies overfitting to the uploaded images), and then press the 'Inference' button.<br><b>Bonus:</b>Download your fine-tuned model weights from the Gradio file component. The smaller size of LORA models (around 3-4 mb files) is the main highlight of this 'Low-rank Adaptation' approach of fine-tuning.""")
+
     with gr.Row():
         in_images =  gr.File(label="Upload images to fine-tune for LORA", file_count="multiple")
         #in_prompt = gr.Textbox(label="Enter a ")