Update app.py

app.py

@@ -8,12 +8,14 @@ from PIL import Image
 from diffusers import FluxKontextPipeline
 from diffusers.utils import load_image
 
+# down to 22 steps to try and keep this ~<30 seconds so it will generally work in claude.ai - which doesn't reset timeout with notifications.
+
 MAX_SEED = np.iinfo(np.int32).max
 
 pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
 
 @spaces.GPU
-def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=20, progress=gr.Progress(track_tqdm=True)):
     """
     Perform image editing using the FLUX.1 Kontext pipeline.
     
@@ -22,36 +24,20 @@ def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5
     for contextual image editing tasks.
     
     Args:
-        input_image (PIL.Image.Image): The input image to be edited. Will be converted
-            to RGB format if not already in that format.
-        prompt (str): Text description of the desired edit to apply to the image.
-            Examples: "Remove glasses", "Add a hat", "Change background to beach".
-        seed (int, optional): Random seed for reproducible generation. Defaults to 42.
-            Must be between 0 and MAX_SEED (2^31 - 1).
-        randomize_seed (bool, optional): If True, generates a random seed instead of
-            using the provided seed value. Defaults to False.
-        guidance_scale (float, optional): Controls how closely the model follows the
-            prompt. Higher values mean stronger adherence to the prompt but may reduce
-            image quality. Range: 1.0-10.0. Defaults to 2.5.
+        input_image (PIL.Image.Image): The path to the input image to be edited.
+        prompt (str): Text description of the desired edit to apply to the image. Examples: "Remove glasses", "Add a hat", "Change background to beach".
+        seed (int, optional): Random seed for reproducible generation. 
+            Must be between 0 and MAX_SEED (2^31 - 1). Defaults to 42.
+        randomize_seed (bool, optional): If True, generates a random seed instead of using the provided seed value. 
+            Defaults to False.
+        guidance_scale (float, optional): Controls how closely the model follows the prompt. Higher values mean stronger adherence to the prompt but may reduce image quality. Range: 1.0-10.0. Defaults to 2.5.
         steps (int, optional): Controls how many steps to run the diffusion model for.
-            Range: 1-30. Defaults to 28.
+            Range: 1-30. Defaults to 20.
         progress (gr.Progress, optional): Gradio progress tracker for monitoring
             generation progress. Defaults to gr.Progress(track_tqdm=True).
     
     Returns:
-        tuple: A 3-tuple containing:
-            - PIL.Image.Image: The generated/edited image
-            - int: The seed value used for generation (useful when randomize_seed=True)
-            - gr.update: Gradio update object to make the reuse button visible
-    
-    Example:
-        >>> edited_image, used_seed, button_update = infer(
-        ...     input_image=my_image,
-        ...     prompt="Add sunglasses",
-        ...     seed=123,
-        ...     randomize_seed=False,
-        ...     guidance_scale=2.5
-        ... )
+        The modified image and seed used for generation.
     """
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -62,6 +48,8 @@ def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5
             image=input_image, 
             prompt=prompt,
             guidance_scale=guidance_scale,
+            width = input_image.size[0],
+            height = input_image.size[1],
             num_inference_steps=steps,
             generator=torch.Generator().manual_seed(seed),
         ).images[0]
@@ -72,7 +60,12 @@ def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5
             num_inference_steps=steps,
             generator=torch.Generator().manual_seed(seed),
         ).images[0]
-    return image, seed, gr.update(visible=True)
+    return image, seed, gr.Button(visible=True)
+
+@spaces.GPU(duration=25)
+def infer_example(input_image, prompt):
+    image, seed, _ = infer(input_image, prompt)
+    return image, seed
 
 css="""
 #col-container {
@@ -85,7 +78,7 @@ with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""# FLUX.1 Kontext [dev]
-Image editing and manipulation model.
+Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro], [[blog]](https://bfl.ai/announcements/flux-1-kontext-dev) [[model]](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)
         """)
         with gr.Row():
             with gr.Column():
@@ -123,7 +116,7 @@ Image editing and manipulation model.
                         label="Steps",
                         minimum=1,
                         maximum=30,
-                        value=28,
+                        value=20,
                         step=1
                     )
                     
@@ -131,17 +124,30 @@ Image editing and manipulation model.
                 result = gr.Image(label="Result", show_label=False, interactive=False)
                 reuse_button = gr.Button("Reuse this image", visible=False)
         
-        
+            
+        examples = gr.Examples(
+            examples=[
+                ["flowers.png", "turn the flowers into sunflowers"],
+                ["monster.png", "make this monster ride a skateboard on the beach"],
+                ["cat.png", "make this cat happy"]
+            ],
+            inputs=[input_image, prompt],
+            outputs=[result, seed],
+            fn=infer_example,
+            cache_examples="lazy"
+        )
+            
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
         inputs = [input_image, prompt, seed, randomize_seed, guidance_scale, steps],
         outputs = [result, seed, reuse_button]
     )
-    reuse_button.click(
-        fn = lambda image: image,
-        inputs = [result],
-        outputs = [input_image]
-    )
+    
+    # reuse_button.click(
+    #    fn = lambda image: image,
+    #    inputs = [result],
+    #    outputs = [input_image]
+    # )
 
 demo.launch(mcp_server=True)