Added more examples, added about info

app.py

@@ -26,16 +26,39 @@ p_params = replicate(params)
 title = "# 🧨 ControlNet on Segment Anything 🤗"
 description = """This is a demo on 🧨 ControlNet based on Meta's [Segment Anything Model](https://segment-anything.com/).
 
-                Upload a Segment Anything Segmentation Map, write a prompt, and generate images 🤗 This demo is still Work in Progress, so don't expect it to work well for now !! 
+                Upload a Segment Anything Segmentation Map, write a prompt, and generate images 🤗 This demo is still a Work in Progress, so don't expect it to work well for now !! 
 
-                
-                Test some of the examples below to give it a try ⬇️
+                ⌛️ It takes about 30~ seconds to generate 4 samples, to get faster results, don't forget to reduce the Nº Samples to 1.
               """
 
+about = """
+
+
+        # 👨‍💻 About the model
+
+        This model is based on the [ControlNet Model](https://huggingface.co/blog/controlnet), which allow us to generate Images using some sort of condition image. For this model, we selected the segmentation maps produced by Meta's new segmentation model called [Segment Anything Model](https://github.com/facebookresearch/segment-anything) as the condition image. We then trained the model to generate images based on the structure of the segmentation maps and the text prompts given.
+
+
+        # 💾 About the dataset
+
+        For the training, we generated a segmented dataset based on the [COYO-700M](https://huggingface.co/datasets/kakaobrain/coyo-700m) dataset. The dataset provided us with the images, and the text prompts. For the segmented images, we used [Segment Anything Model](https://github.com/facebookresearch/segment-anything). We then created 8k samples train our model on, which isn't a lot, but as a team, we have been very busy with many other responsibilities and time constraints, which made it challenging to dedicate a lot of time to generating a larger dataset.  Despite the constraints we faced, we have still managed to achieve some nice results 🙌
+
+        You can check the generated datasets below ⬇️
+        - [sam-coyo-2k](https://huggingface.co/datasets/mfidabel/sam-coyo-2k)
+        - [sam-coyo-2.5k](https://huggingface.co/datasets/mfidabel/sam-coyo-2.5k)
+        - [sam-coyo-3k](https://huggingface.co/datasets/mfidabel/sam-coyo-3k)
+
+"""
+
 examples = [["contemporary living room of a house", "low quality", "examples/condition_image_1.png"],
             ["new york buildings,  Vincent Van Gogh starry night ", "low quality, monochrome", "examples/condition_image_2.png"],
-            ["contemporary living room,  high quality, 4k, realistic", "low quality, monochrome, low res", "examples/condition_image_3.png"]]
+            ["contemporary living room,  high quality, 4k, realistic", "low quality, monochrome, low res", "examples/condition_image_3.png"],
+            ["internal stairs of a japanese house", "low quality,  low res,  people, kids", "examples/condition_image_4.png"],
+            ["a photo of a girl taking notes", "low quality,  low res,  painting", "examples/condition_image_5.png"],
+            ["painting of an hot air ballon flying over a valley, The Great Wave off Kanagawa style, blue and white colors", "low quality,  low res", "examples/condition_image_6.png"],
+            ["painting of families enjoying the sunset,  The Garden of Earthly Delights style,  joyful", "low quality,  low res", "examples/condition_image_7.png"]]
 
+css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"
 
 # Inference Function
 def infer(prompts, negative_prompts, image, num_inference_steps = 50, seed = 4, num_samples = 4):
@@ -93,7 +116,7 @@ prompt = gr.Textbox(lines=1, label="Prompt", value=default_example[0])
 negative_prompt = gr.Textbox(lines=1, label="Negative Prompt", value=default_example[1])
 
 
-with gr.Blocks(css="h1 { text-align: center }") as demo:
+with gr.Blocks(css=css) as demo:
     with gr.Row():
         with gr.Column():
             # Title
@@ -103,10 +126,12 @@ with gr.Blocks(css="h1 { text-align: center }") as demo:
 
         with gr.Column():
             # Examples
+            gr.Markdown("Try some of the examples below ⬇️")
             gr.Examples(examples=examples,
                     inputs=[prompt, negative_prompt, cond_img],
                     outputs=output,
-                    fn=infer)
+                    fn=infer,
+                    examples_per_page=4)
 
     # Images
     with gr.Row(variant="panel"):
@@ -130,6 +155,8 @@ with gr.Blocks(css="h1 { text-align: center }") as demo:
             submit = gr.Button("Generate")
             # TODO: Download Button
 
+    with gr.Row():
+        gr.Markdown(about, elem_classes="about")
     
     submit.click(infer, 
                  inputs=[prompt, negative_prompt, cond_img, num_steps, seed, num_samples],