Machine Learning Advanced Pro

Case Study: Generative Fill — Inpainting, Outpainting and Object Removal

Design Photoshop-style Generative Fill — the mask-conditioned cascade behind Adobe Firefly and Canva's editing tools

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Model interview answer for Generative Fill (remove / extend / generate-with-prompt): why the right design is a cascade — a LaMa-style single-pass GAN for object removal, mask-conditioned latent diffusion for prompt-driven fill, outpainting as inpainting with the mask outside the frame — argued against the GAN-vs-diffusion comparison table from this track's GAN foundation subject; crop-around-the-mask as the scaling trick that makes 8K interactive editing possible; seam-blending and a masked-region-specific evaluation suite (FID is not enough); and a system design with content-credential signing as a first-class pipeline stage.

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Practice questions (6)

Arguing the Cascade Against a Single-Model Proposal

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Fixing a Design That Diffuses the Whole Canvas

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Why a Great FID Score Didn't Predict the Complaint Spike

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Diagnosing a Model That Fails on Real User Masks

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Building an Offline Evaluation Set for Outpainting

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Quizzes (1)

Case Study: Generative Fill Quiz

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