added more details

app.py

@@ -1,13 +1,12 @@
+import spaces
 import gradio as gr
 import numpy as np
 import random
-import spaces
 import torch
 from diffusers import DiffusionPipeline
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
-
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -19,30 +18,68 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_in
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
-            prompt = prompt, 
-            width = width,
-            height = height,
-            num_inference_steps = num_inference_steps, 
-            generator = generator,
-            guidance_scale=0.0
-    ).images[0] 
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        guidance_scale=0.0
+    ).images[0]
     return image, seed
 
+# Example prompt
+example_prompt = "A vibrant red origami crane on a white background, intricate paper folds, studio lighting"
+
 # Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# FLUX.1 [schnell] Image Generator")
+    
     with gr.Row():
-        with gr.Column():
-            prompt = gr.Textbox(label="Prompt")
+        with gr.Column(scale=2):
+            gr.Markdown("""
+            ## About FLUX.1 [schnell]
+            - Fast text-to-image model optimized for local development and personal use
+            - Part of the FLUX.1 model family by Black Forest Labs
+            - Open-source: Available under Apache 2.0 license
+            - Supports resolutions between 0.1 and 2.0 megapixels
+            - Outperforms many larger models in quality and prompt adherence
+            - Uses advanced transformer architecture with flow matching techniques
+            - Capable of generating high-quality images in just a few inference steps
+            """)
+        
+        with gr.Column(scale=3):
+            prompt = gr.Textbox(label="Prompt", placeholder="Enter your image description here...", value=example_prompt)
             run_button = gr.Button("Generate")
-        with gr.Column():
             result = gr.Image(label="Generated Image")
+    
+    gr.Markdown("""
+    ## Example Prompt
+    Try this example prompt or modify it to see how FLUX.1 [schnell] performs:
+    ```
+    A vibrant red origami crane on a white background, intricate paper folds, studio lighting
+    ```
+    """)
+    
     with gr.Accordion("Advanced Settings", open=False):
         seed = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, label="Seed", randomize=True)
         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
         width = gr.Slider(minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, label="Width")
         height = gr.Slider(minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024, label="Height")
         num_inference_steps = gr.Slider(minimum=1, maximum=50, step=1, value=4, label="Number of inference steps")
+        
+        gr.Markdown("""
+        **Note:** FLUX.1 [schnell] is optimized for speed and can produce high-quality results with just a few inference steps.
+        Adjust the number of steps based on your speed/quality preference. More steps may improve quality but will increase generation time.
+        """)
+    
+    gr.Markdown("""
+    ## Additional Information
+    - FLUX.1 [schnell] is based on a hybrid architecture of multimodal and parallel diffusion transformer blocks
+    - It supports various aspect ratios within the 0.1 to 2.0 megapixel range
+    - The model uses bfloat16 precision for efficient computation
+    - For optimal performance, running on a CUDA-enabled GPU is recommended
+    - For more details and other FLUX.1 variants, visit [Black Forest Labs](https://blackforestlabs.ai)
+    """)
     
     run_button.click(
         infer,