Implementation Blueprint

Prerequisites

# Install diffusers and image processing tools
pip install diffusers transformers accelerate pillow

Simple Inpainting API (Python + FastAPI)

from fastapi import FastAPI, UploadFile, File
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
import torch

app = FastAPI()
pipe = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting", 
    torch_dtype=torch.float16
).to("cuda")

@app.post("/inpaint")
async def inpaint(image: UploadFile = File(...), mask: UploadFile = File(...), prompt: str = ""):
    init_image = Image.open(image.file).convert("RGB").resize((512, 512))
    mask_image = Image.open(mask.file).convert("RGB").resize((512, 512))
    
    result = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
    result.save("inpainted.png")
    return {"url": "/inpainted.png"}

Scaling Strategy

• Tiled Inpainting: For high-resolution images, use a tiled approach to process the masked area and its immediate vicinity at native resolution, then stitch it back into the original large file.
• Mask Pre-processing: Use a separate lightweight segmentation model (like Segment Anything) to automatically generate perfect masks based on user clicks, reducing manual work.
• Concurrent Processing: Load-balance across multiple GPU nodes to handle batch restoration of large image datasets.

Backup & Safety

• Mask Archiving: Store the binary masks used for każda generation to allow for reproducibility and quality auditing.
• Safety Verification: Use an automated CLIP filter to ensure that the inpainted content does not violate safety policies or generate deepfakes.
• Hardware Health: monitor GPU temperatures; inpainting requires multiple denoising loops and can be intensive on older hardware.