Phase 2: Image Generation · 50 min · Python · fal.ai API · Hugging Face Diffusers

The FLUX Model Family

FLUX.1 Pro for quality, FLUX.1 Dev for open-weights flexibility, FLUX.1 Schnell for speed — knowing which to use when is image generation engineering.

What you will learn

Generate images with FLUX.1 Pro (API, highest quality), FLUX.1 Dev (open weights, non-commercial), and FLUX.1 Schnell (open weights, 1-4 step, Apache 2.0)
Choose the right FLUX variant for speed, quality, or cost
Understand FLUX.2 improvements: better prompt adherence and text rendering
Compare FLUX vs SDXL: FLUX for quality and speed, SDXL for ecosystem (LoRAs, ControlNets)

Objective

Produce and compare image candidates against an explicit creative brief. In the paired studio lab, generate the same prompt with FLUX.1 Schnell (via fal.ai), FLUX.1 Dev (via Hugging Face), and FLUX.1 Pro (via fal.ai). Measure generation time, cost, and compare output quality. Write a decision guide for when to use each variant.

The Problem

A team uses FLUX.1 Pro for every image generation. At 10,000 images/day, they're spending $500/day on API calls ($0.05/image). They could use FLUX.1 Schnell at $0.003/image ($30/day) for 90% of their use cases and reserve Pro for hero images. But they don't know the difference between the variants, so they overpay by 16x.

FLUX model selection is a standard interview question because it tests whether you understand the speed-quality-cost trade-off — the core engineering decision in image generation.

What you'll build
Generate the same prompt with FLUX.1 Schnell, FLUX.1 Dev, and FLUX.1 Pro. Measure generation time, cost, and compare output quality. Write a decision guide for when to use each variant.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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