openclaw-home-pc/skills/image-edit/inpainting.md

Inpainting

Replace or remove parts of an image using AI.

How It Works

1. Provide source image
2. Create mask (white = area to change)
3. Optionally describe replacement content
4. AI fills masked area matching surrounding context

Tools

DALL-E 2 (OpenAI)

from openai import OpenAI
client = OpenAI()

response = client.images.edit(
    model="dall-e-2",
    image=open("image.png", "rb"),
    mask=open("mask.png", "rb"),
    prompt="A sunny beach with palm trees",
    size="1024x1024"
)

Requirements:

• Image must be square PNG
• Mask: transparent areas = edit zone
• Max 4MB per file

Stable Diffusion Inpaint

from diffusers import StableDiffusionInpaintPipeline
import torch

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

result = pipe(
    prompt="A fluffy cat",
    image=init_image,
    mask_image=mask,
    num_inference_steps=30,
    guidance_scale=7.5
).images[0]

Key parameters:

• strength — How much to change (0.5-1.0)
• guidance_scale — Prompt adherence (5-15)

IOPaint (Local, Free)

# Install
pip install iopaint

# Run web UI
iopaint start --model lama --port 8080

Models:

• lama — Fast, good for object removal
• ldm — Better quality, slower
• sd — Stable Diffusion backend

Best Practices

• Extend mask slightly — cover edges of object to remove
• Describe surroundings — "grassy field" helps context
• Multiple passes — for large areas, edit in chunks
• Clean up edges — blend modes in photo editor

Object Removal (No Prompt)

For pure removal without replacement:

• Use LaMa model (designed for removal)
• Leave prompt empty or minimal
• AI infers from surrounding context

Common Issues

• Visible seams — feather mask edges
• Wrong content — be more specific in prompt
• Repeating patterns — edit in smaller sections
• Color mismatch — adjust levels after inpainting