small changes by Sara

app.py

@@ -248,13 +248,13 @@ def process_and_display(
     use_concpet_from_file_3 = False
 ):
     if base_image is None:
-        raise gr.Error("please upload a base image")
+        raise gr.Error("Please upload a base image")
     
     if concept_image1 is None:
-        raise gr.Error("choose at least one concept image")
+        raise gr.Error("Choose at least one concept image")
 
     if concept_image1 is None:
-        raise gr.Error("choose at least one concept type")
+        raise gr.Error("Choose at least one concept type")
     
     modified_images = process_images(
         base_image, 
@@ -300,14 +300,19 @@ a photo of a person feeling terrified
 """
 with gr.Blocks(css=css) as demo:
         gr.Markdown(f"""# IP Composer 🌅✚🖌️
-### compose new images with visual concepts
-following the algorithm proposed in [*IP-Composer: Semantic Composition of Visual Concepts* by Dorfman et al.](https://arxiv.org/pdf/2502.13951)
-
-#### 1. upload a base image
-#### 2. upload 1-3 concept images
-#### 3. select concept type to extract from each concept image
-
-[[project page](https://ip-composer.github.io/IP-Composer/)] [[arxiv](https://arxiv.org/pdf/2502.13951)]
+### Compose new images with visual concepts
+Following the algorithm proposed in [*IP-Composer: Semantic Composition of Visual Concepts* by Dorfman et al.](https://arxiv.org/pdf/2502.13951)
+(Built on IP-Adapter)
+
+#### 🛠️ How to Use:                                   
+#### 1. Upload a base image  
+#### 2. Upload 1–3 concept images  
+#### 3. Select a concept type to extract from each concept image:  
+    - Choose a **predefined concept type** from the dropdown (e.g. pattern, emotion, pose), **or**  
+    - Upload a **file with text variations of your concept** (e.g. prompts from an LLM).  
+        - 👉 If you're uploading a **new concept**, don't forget to **adjust the "rank" value** under **Advanced Options** for better results.
+                    
+[[Project page](https://ip-composer.github.io/IP-Composer/)] [[arxiv](https://arxiv.org/pdf/2502.13951)]
         """)
         concpet_from_file_1 = gr.State()
         concpet_from_file_2 = gr.State()
@@ -318,61 +323,61 @@ following the algorithm proposed in [*IP-Composer: Semantic Composition of Visua
         with gr.Row():
             with gr.Column():
                 base_image = gr.Image(label="Base Image (Required)", type="numpy")
-                with gr.Tab("concept 1"):      
+                with gr.Tab("Concept 1"):      
                     with gr.Group():
                           concept_image1 = gr.Image(label="Concept Image 1", type="numpy")
                           with gr.Row():  
-                            concept_name1 = gr.Dropdown(concept_options, label="concept 1", value=None, info="pick concept type")
-                            with gr.Accordion("💡 or use a new concept 👇", open=False):
-                                gr.Markdown("1. upload a file with text variations of your concept (e.g. ask an LLM)")
-                                gr.Markdown("2. prefereably with > 100 variations.")
-                                with gr.Accordion("file example for the concept 'emotions'", open=False):
+                            concept_name1 = gr.Dropdown(concept_options, label="Concept 1", value=None, info="Pick concept type")
+                            with gr.Accordion("💡 Or use a new concept 👇", open=False):
+                                gr.Markdown("1. Upload a file with text variations of your concept (e.g. ask an LLM)")
+                                gr.Markdown("2. Prefereably with > 100 variations.")
+                                with gr.Accordion("File example for the concept 'emotions'", open=False):
                                     gr.Markdown(example)
-                                concept_file_1 = gr.File(label="concept variations", file_types=["text"])
+                                concept_file_1 = gr.File(label="Concept variations", file_types=["text"])
                                                     
-                with gr.Tab("concept 2 (optional)"):            
+                with gr.Tab("Concept 2 (Optional)"):            
                     with gr.Group():    
                           concept_image2 = gr.Image(label="Concept Image 2", type="numpy")
                           with gr.Row():
-                              concept_name2 = gr.Dropdown(concept_options, label="concept 2", value=None, info="pick concept type")
-                              with gr.Accordion("💡 or use a new concept 👇", open=False):
-                                    gr.Markdown("1. upload a file with text variations of your concept (e.g. ask an LLM)")
-                                    gr.Markdown("2. prefereably with > 100 variations.")
-                                    with gr.Accordion("file example for the concept 'emotions'", open=False):
+                              concept_name2 = gr.Dropdown(concept_options, label="Concept 2", value=None, info="Pick concept type")
+                              with gr.Accordion("💡 Or use a new concept 👇", open=False):
+                                    gr.Markdown("1. Upload a file with text variations of your concept (e.g. ask an LLM)")
+                                    gr.Markdown("2. Prefereably with > 100 variations.")
+                                    with gr.Accordion("File example for the concept 'emotions'", open=False):
                                         gr.Markdown(example)
-                                    concept_file_2 = gr.File(label="concept variations", file_types=["text"])
+                                    concept_file_2 = gr.File(label="Concept variations", file_types=["text"])
                           
                 
-                with gr.Tab("concept 3 (optional)"):
+                with gr.Tab("Concept 3 (optional)"):
                     with gr.Group(): 
                           concept_image3 = gr.Image(label="Concept Image 3", type="numpy")
                           with gr.Row():
-                            concept_name3 = gr.Dropdown(concept_options, label="concept 3", value= None, info="pick concept type")
-                            with gr.Accordion("💡 or use a new concept 👇", open=False):
-                                gr.Markdown("1. upload a file with text variations of your concept (e.g. ask an LLM)")
-                                gr.Markdown("2. prefereably with > 100 variations.")
-                                with gr.Accordion("file example for the concept 'emotions'", open=False):
+                            concept_name3 = gr.Dropdown(concept_options, label="Concept 3", value= None, info="Pick concept type")
+                            with gr.Accordion("💡 Or use a new concept 👇", open=False):
+                                gr.Markdown("1. Upload a file with text variations of your concept (e.g. ask an LLM)")
+                                gr.Markdown("2. Prefereably with > 100 variations.")
+                                with gr.Accordion("File example for the concept 'emotions'", open=False):
                                     gr.Markdown(example)
-                                concept_file_3 = gr.File(label="concept variations", file_types=["text"])
+                                concept_file_3 = gr.File(label="Concept variations", file_types=["text"])
                           
                
             
                 with gr.Accordion("Advanced options", open=False):
                     prompt = gr.Textbox(label="Guidance Prompt (Optional)", placeholder="Optional text prompt to guide generation")
-                    num_inference_steps = gr.Slider(minimum=1, maximum=50, value=30, step=1, label="num steps")
+                    num_inference_steps = gr.Slider(minimum=1, maximum=50, value=30, step=1, label="Num steps")
                     with gr.Row():
                         scale = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Scale")
                         randomize_seed = gr.Checkbox(value=True, label="Randomize seed")
                         seed = gr.Number(value=0, label="Seed", precision=0)
                     with gr.Column():
-                        gr.Markdown("if concept is not showing enough try to increase the rank")
+                        gr.Markdown("If a concept is not showing enough, try to increase the rank")
                         with gr.Row():
-                            rank1 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 1")
-                            rank2 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 2")
-                            rank3 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="rank concept 3")
+                            rank1 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="Rank concept 1")
+                            rank2 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="Rank concept 2")
+                            rank3 = gr.Slider(minimum=1, maximum=150, value=30, step=1, label="Rank concept 3")
             
             with gr.Column():
-                output_image = gr.Image(label="composed output", show_label=True)
+                output_image = gr.Image(label="Composed output", show_label=True)
                 submit_btn = gr.Button("Generate")
         
         gr.Examples(