Update app.py

app.py

@@ -87,7 +87,21 @@ for image, prompt, negative_prompt in zip(e_images, e_prompts, e_negative_prompt
   examples.append([prompt, negative_prompt, image])
     
 title = " # ControlLight: Light control through ControlNet and Depth Maps conditioning"
+info = '''
+# ControlLight: Light control through ControlNet and Depth Maps conditioning
+We propose a ControlNet using depth maps conditioning that is capable of controlling the light direction in a scene while trying to maintain the scene integrity.
+The model was trained on [VIDIT dataset](https://huggingface.co/datasets/Nahrawy/VIDIT-Depth-ControlNet) and [
+A Dataset of Flash and Ambient Illumination Pairs from the Crowd](https://huggingface.co/datasets/Nahrawy/FAID-Depth-ControlNet) as a part of the [Jax Diffusers Event](https://huggingface.co/jax-diffusers-event).
 
+Due to the limited available data the model is clearly overfit, but it serves as a proof of concept to what can be further achieved using enough data.
+
+A large part of the training data is synthetic so we encourage further training using synthetically generated scenes, using Unreal engine for example.
+
+The WandB training logs can be found [here](https://wandb.ai/hassanelnahrawy/controlnet-VIDIT-FAID), it's worth noting that the model was left to overfit for experimentation and it's advised to use the 8K steps weights or prior weights.
+
+This project is a joint work between [ParityError](https://huggingface.co/ParityError) and [Nahrawy](https://huggingface.co/Nahrawy).
+
+'''
 with gr.Blocks() as demo:
     gr.Markdown(title)
     prompts = gr.Textbox(label='prompts')
@@ -105,6 +119,7 @@ with gr.Blocks() as demo:
                    outputs=out_image,
                    fn=infer,
                    cache_examples=True)
+        gr.Makdown(info)
     btn.click(fn=infer, inputs=[prompts,negative_prompts, in_image] , outputs=out_image)
     
 demo.launch()