Update app.py

app.py

@@ -23,6 +23,13 @@ pipe.fuse_lora()
 # optimize_pipeline_(pipe, image=Image.new("RGB", (512, 512)), prompt='prompt')
 
 
+import os
+
+EXAMPLES_DIR = "examples"
+BASE_EXAMPLES = [os.path.join(EXAMPLES_DIR, "base", f) for f in sorted(os.listdir(os.path.join(EXAMPLES_DIR, "base")))]
+FACE_EXAMPLES = [os.path.join(EXAMPLES_DIR, "face", f) for f in sorted(os.listdir(os.path.join(EXAMPLES_DIR, "face")))]
+
+
 def add_overlay(base_img, overlay_img, margin=20):
     """
     Pastes an overlay image onto the top-right corner of a base image.
@@ -67,104 +74,6 @@ def add_overlay(base_img, overlay_img, margin=20):
     return base
 
 
-# def add_overlay(base_img, overlay_img, margin=20, target_width=200):
-#     if base_img is None or overlay_img is None:
-#         return base_img
-    
-#     base = base_img.convert("RGBA")
-#     overlay = overlay_img.convert("RGBA")
-    
-#     # Keep aspect ratio, resize overlay to target width
-#     w, h = overlay.size
-#     new_height = int(h * (target_width / w))
-#     overlay = overlay.resize((target_width, new_height), Image.LANCZOS)
-
-#     # Position: top-right with margin
-#     x = base.width - overlay.width - margin
-#     y = margin
-
-#     # Paste overlay on base with transparency
-#     base.paste(overlay, (x, y), overlay)
-#     return base
-
-
-# @spaces.GPU
-# def infer(input_image, input_image_upload, overlay_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
-#     """
-#     Perform image editing using the FLUX.1 Kontext pipeline.
-    
-#     This function takes an input image and a text prompt to generate a modified version
-#     of the image based on the provided instructions. It uses the FLUX.1 Kontext model
-#     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.
-#         steps (int, optional): Controls how many steps to run the diffusion model for.
-#             Range: 1-30. Defaults to 28.
-#         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
-#         ... )
-#     """
-#     if randomize_seed:
-#         seed = random.randint(0, MAX_SEED)
-
-#     if input_image_upload is not None:
-#         input_image_upload = input_image
-#     elif "composite" in input_image and input_image["composite"] is not None:
-#         input_image = input_image["composite"]
-#     elif "background" in input_image and input_image["background"] is not None:
-#         input_image = input_image["background"]
-#     else:
-#         raise ValueError("No valid image found in EditorValue dict (both 'composite' and 'background' are None)")
-    
-    
-#     if input_image is not None:
-#         if overlay_image is not None:
-#             input_image = add_overlay(input_image, overlay_image)
-            
-#         input_image = input_image.convert("RGB")
-#         image = pipe(
-#             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]
-#     else:
-#         image = pipe(
-#             prompt=prompt,
-#             guidance_scale=guidance_scale,
-#             num_inference_steps=steps,
-#             generator=torch.Generator().manual_seed(seed),
-#         ).images[0]
-#     return image, input_image, seed, gr.Button(visible=True)
-
 
 @spaces.GPU
 def infer(input_image, input_image_upload, overlay_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
@@ -272,8 +181,16 @@ Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro
                                 )
                         with gr.TabItem("Upload"):
                             input_image_upload = gr.Image(label="Upload the drawing", type="pil")
+                    gr.Examples(
+                        examples=[[img] for img in BASE_EXAMPLES],
+                        inputs=[input_image_upload],
+                    )
                 with gr.Row():
                     overlay_image = gr.Image(label="Upload face photo", type="pil")
+                    gr.Examples(
+                        examples=[[img] for img in FACE_EXAMPLES],
+                        inputs=[overlay_image],
+                    )
                 with gr.Row():
                     prompt = gr.Text(
                         label="Prompt",