Phase 2: Diffusion Models · ~75 minutes · Python

Inpainting, Outpainting & Image Editing

Text-to-image makes new things. Inpainting fixes old ones. In production, 70% of billable image work is editing — swap a background, remove a logo, extend the canvas, regenerate a hand. Inpainting is where diffusion earns its keep.

Hiring signal: Understanding of inpainting, outpainting & image editing internals

What you will learn

Implement inpainting, outpainting & image editing from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 8 · 07 (Latent Diffusion), Phase 8 · 08 (ControlNet & LoRA) Time: ~75 minutes

The Problem

A client sends a perfect product photo with a distracting sign in the background. You want to erase the sign and leave everything else pixel-identical. You cannot run text-to-image from scratch — the result will have a different color, different lighting, different product angle. You want to regenerate only the masked region, and you want the regeneration to respect the surrounding context.

That is inpainting. Variants:

Inpainting. Regenerate inside a mask, keep outside pixels.
Outpainting. Regenerate outside a mask (or beyond the canvas), keep inside.
Image editing. Regenerate the whole image but keep semantic or structural fidelity to the original (SDEdit, InstructPix2Pix).

Every diffusion pipeline in 2026 ships an inpainting mode. Flux.1-Fill, Stable Diffusion Inpaint, SDXL-Inpaint, DALL-E 3 Edit. They work on the same principle.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Pitfalls, Use It, Ship It, Exercises, Key Terms, Production note: edit pipelines are latency-sensitive, Further Reading — plus a hands-on lab, quiz, and project artifact.

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