app.py

import torch
import gradio as gr
from PIL import Image
from rembg import remove
from diffusers import StableDiffusionPipeline

# -----------------------------------------------------------------------------
# Helper function to adjust image size to multiples of 8.
# -----------------------------------------------------------------------------
def adjust_size(w, h):
    """
    Adjust width and height to be multiples of 8, as required by the Stable Diffusion model.
    """
    new_w = (w // 8) * 8
    new_h = (h // 8) * 8
    return new_w, new_h

# -----------------------------------------------------------------------------
# Core processing function:
#   1. Remove background from the uploaded image.
#   2. Generate a new background image based on the text prompt.
#   3. Composite the foreground onto the generated background.
# -----------------------------------------------------------------------------
def process_image(input_image: Image.Image, bg_prompt: str) -> Image.Image:
    """
    Processes the uploaded image by removing its background and replacing it with a generated one.
    
    Parameters:
        input_image (PIL.Image.Image): The uploaded image.
        bg_prompt (str): Text prompt describing the new background.
    
    Returns:
        PIL.Image.Image: The final composited image.
    """
    if input_image is None:
        raise ValueError("No image provided.")

    # Step 1: Remove the background from the input image.
    print("Removing background from the uploaded image...")
    foreground = remove(input_image)
    foreground = foreground.convert("RGBA")

    # Step 2: Adjust dimensions for background generation.
    orig_w, orig_h = foreground.size
    gen_w, gen_h = adjust_size(orig_w, orig_h)
    print(f"Original size: {orig_w}x{orig_h} | Adjusted size: {gen_w}x{gen_h}")

    # Step 3: Generate a new background using the provided text prompt.
    print("Generating new background using Stable Diffusion...")
    bg_output = pipe(
        bg_prompt,
        height=gen_h,
        width=gen_w,
        num_inference_steps=50,  # Adjust if needed.
        guidance_scale=7.5       # Adjust for more/less prompt adherence.
    )
    # Convert the generated background to RGBA.
    background = bg_output.images[0].convert("RGBA")

    # Step 4: Ensure the foreground matches the background dimensions.
    if foreground.size != background.size:
        print("Resizing foreground to match background dimensions...")
        foreground = foreground.resize(background.size, Image.ANTIALIAS)

    # Step 5: Composite the images.
    print("Compositing images...")
    final_image = Image.alpha_composite(background, foreground)

    return final_image

# -----------------------------------------------------------------------------
# Load the Stable Diffusion pipeline from Hugging Face.
# -----------------------------------------------------------------------------
MODEL_ID = "stabilityai/stable-diffusion-2"  # Change the model if desired.

# Use half precision if GPU is available.
if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

print("Loading Stable Diffusion pipeline...")
pipe = StableDiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch_dtype)
if torch.cuda.is_available():
    pipe = pipe.to("cuda")
print("Stable Diffusion pipeline loaded.")

# -----------------------------------------------------------------------------
# Create the Gradio Interface using the updated API.
# -----------------------------------------------------------------------------
title = "Background Removal & Replacement"
description = (
    "Upload an image (e.g., a person or an animal) and provide a text prompt "
    "describing the new background. The app will remove the original background and "
    "composite the subject onto a generated background."
)

iface = gr.Interface(
    fn=process_image,
    inputs=[
        gr.Image(type="pil", label="Upload Your Image"),
        gr.Textbox(lines=2, placeholder="Describe the new background...", label="Background Prompt")
    ],
    outputs=gr.Image(label="Output Image"),
    title=title,
    description=description,
    allow_flagging="never"
)

# -----------------------------------------------------------------------------
# Launch the app.
# -----------------------------------------------------------------------------
if __name__ == "__main__":
    iface.launch()