Update app.py

app.py

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+from typing import Tuple, Dict
+import requests
+import random
+import numpy as np
+import gradio as gr
+import torch
+from PIL import Image
+from diffusers import FluxInpaintPipeline
+
+# Constants
+MARKDOWN_TEXT = """
+# FLUX.1 Inpainting 🔥
+Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) for 
+creating this amazing model, and a big thanks to [Gothos](https://github.com/Gothos) 
+for taking it to the next level by enabling inpainting with the FLUX.
+"""
+
+MAX_SEED_VALUE = np.iinfo(np.int32).max
+DEFAULT_IMAGE_SIZE = 1024
+DEVICE_TYPE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Model initialization
+pipeline = FluxInpaintPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE_TYPE)
+
+def adjust_image_size(
+    original_size: Tuple[int, int], max_dimension: int = DEFAULT_IMAGE_SIZE
+) -> Tuple[int, int]:
+    width, height = original_size
+    scaling_factor = max_dimension / max(width, height)
+    new_width = int(width * scaling_factor) - (int(width * scaling_factor) % 32)
+    new_height = int(height * scaling_factor) - (int(height * scaling_factor) % 32)
+    return new_width, new_height
+
+def process_images(
+    input_data: Dict,
+    prompt: str,
+    seed: int,
+    randomize_seed: bool,
+    strength: float,
+    num_steps: int,
+    progress=gr.Progress(track_tqdm=True)
+):
+    if not prompt:
+        gr.Info("Please enter a text prompt.")
+        return None, None
+
+    background_img = input_data['background']
+    mask_img = input_data['layers'][0]
+
+    if background_img is None:
+        gr.Info("Please upload an image.")
+        return None, None
+
+    if mask_img is None:
+        gr.Info("Please draw a mask on the image.")
+        return None, None
+
+    new_width, new_height = adjust_image_size(background_img.size)
+    resized_bg = background_img.resize((new_width, new_height), Image.LANCZOS)
+    resized_mask = mask_img.resize((new_width, new_height), Image.LANCZOS)
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED_VALUE)
+    generator = torch.Generator().manual_seed(seed)
+
+    result_image = pipeline(
+        prompt=prompt,
+        image=resized_bg,
+        mask_image=resized_mask,
+        width=new_width,
+        height=new_height,
+        strength=strength,
+        generator=generator,
+        num_inference_steps=num_steps
+    ).images[0]
+    
+    return result_image, resized_mask
+
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown(MARKDOWN_TEXT)
+    
+    with gr.Row():
+        with gr.Column():
+            img_editor = gr.ImageEditor(
+                label='Image',
+                type='pil',
+                sources=["upload", "webcam"],
+                image_mode='RGB',
+                layers=False,
+                brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed")
+            )
+
+            with gr.Row():
+                text_input = gr.Text(
+                    label="Prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                    container=False
+                )
+                submit_btn = gr.Button(
+                    value='Submit', variant='primary', scale=0
+                )
+
+            with gr.Accordion("Advanced Settings", open=False):
+                seed_slider = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED_VALUE,
+                    step=1,
+                    value=42
+                )
+                random_seed_chkbox = gr.Checkbox(
+                    label="Randomize seed", value=True
+                )
+
+                with gr.Row():
+                    strength_slider = gr.Slider(
+                        label="Strength",
+                        info="Indicates extent to transform the reference `image`.",
+                        minimum=0,
+                        maximum=1,
+                        step=0.01,
+                        value=0.85
+                    )
+                    steps_slider = gr.Slider(
+                        label="Number of inference steps",
+                        info="The number of denoising steps.",
+                        minimum=1,
+                        maximum=50,
+                        step=1,
+                        value=20
+                    )
+                    
+        with gr.Column():
+            output_img = gr.Image(
+                type='pil', image_mode='RGB', label='Generated Image', format="png"
+            )
+            with gr.Accordion("Debug", open=False):
+                output_mask = gr.Image(
+                    type='pil', image_mode='RGB', label='Input Mask', format="png"
+                )
+
+    gr.Examples(
+        fn=process_images,
+        examples=[
+            [
+                {
+                    "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
+                    "layers": [Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-2.png", stream=True).raw).convert("RGBA")],
+                    "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-2.png", stream=True).raw),
+                },
+                "little lion",
+                42,
+                False,
+                0.85,
+                30
+            ],
+            [
+                {
+                    "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
+                    "layers": [Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-3.png", stream=True).raw).convert("RGBA")],
+                    "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-3.png", stream=True).raw),
+                },
+                "tribal tattoos",
+                42,
+                False,
+                0.85,
+                30
+            ]
+        ],
+        inputs=[
+            img_editor,
+            text_input,
+            seed_slider,
+            random_seed_chkbox,
+            strength_slider,
+            steps_slider
+        ],
+        outputs=[
+            output_img,
+            output_mask
+        ],
+        run_on_click=True,
+        cache_examples=True
+    )
+
+    submit_btn.click(
+        fn=process_images,
+        inputs=[
+            img_editor,
+            text_input,
+            seed_slider,
+            random_seed_chkbox,
+            strength_slider,
+            steps_slider
+        ],
+        outputs=[
+            output_img,
+            output_mask
+        ]
+    )
+
+demo.launch(debug=False, show_error=True)
+