Midjourney vs Leonardo AI for ecommerce product photos

Short recommendation
Pick Leonardo AI if your top priorities are deterministic, template-driven batch generation and built-in inpainting/control for consistent white-background product shots at scale. Pick Midjourney if you need the most creative, high-end lifestyle art direction and can accept more manual QA and tweaking per SKU.

Decision criteria (how to choose)
- Output consistency vs. artistry: choose Leonardo for tight, repeatable templates and inpainting; choose Midjourney for expressive lifestyle visuals.
- Automation & throughput: Leonardo usually has easier programmatic batch/export options; Midjourney requires Discord automation or unofficial wrappers and more human tuning.
- Budget & team skills: if you have dev resources and need true bulk export, prefer the tool with an API and lower per-image cost; for small teams wanting premium single images, Midjourney subscription + manual workflow may suffice.
- Final quality bar: if you require pixel-perfect product placement, color accuracy, and identical shadows across hundreds of SKUs, favor a template + inpainting pipeline (Leonardo or a hybrid workflow with real photos).

Practical tips: prompts, seeds, and toolchains
- Build one canonical prompt template for white-background shots: e.g. “Product centered, studio photo, pure white background #FFFFFF, soft natural key light from top-right, cast shadow on ground, photorealistic, ultra-detailed, true-to-color, neutral lens, 50mm, ISO low, no watermark, no text.” Add a negative prompt: “no extra objects, no logo, no watermark, no people.”
- Use a separate lifestyle prompt family: keep the core product descriptor identical, then append different scene modifiers ("casual table setting, warm morning light, shallow depth of field").
- Seeds & references: lock a seed when supported (e.g. --seed 12345 in Midjourney) to keep composition stable. More reliable: use the same reference image (photo or 3D render) and inpaint/mask the background—that preserves product pose and scale across variants.
- Use image-to-image or ControlNet/inpainting for consistent angles: feed the same mask and reference for each SKU, only swap the product region (or the product texture). This gives reproducible camera and shadow behavior.
- Batch prompt generation: keep a CSV with SKU fields (title, color, descriptor, background-type). Generate per-SKU prompts via a small script or using ChatGPT to template and expand prompts automatically.

Toolchain checklist (practical)
1) Prep: gather true-color reference photos or renders for each SKU. 2) Create prompt templates and negative-prompts. 3) Automate prompt CSV → API/Discord calls (Leonardo API or Midjourney via bot). 4) Lock seed / reference image / mask for white shots. 5) Export high-res PNG (specify aspect and size). 6) Run automated QA: color-check, histogram, shadow presence, remove images that fail. 7) Post-process: minor color-correct, resize, name files to SKU. 8) Store metadata (prompt, seed, tool, timestamp) for reproducibility.

Best-for / Avoid-if
- Midjourney: Best for high-end lifestyle images and creative brand direction; Avoid if you need strict, repeatable product photos at scale.
- Leonardo: Best for template-driven catalogs, inpainting-based consistency, and easier programmatic export; Avoid if you need extremely stylized editorial effects that Midjourney produces more naturally.

Final note
For 500 SKUs I recommend prototype 10 SKUs first: test reference-based inpainting + locked seed, measure pass rate, then scale. Use ChatGPT to produce prompt variations from your CSV and Leonardo/Midjourney for generation depending on which prototype gives higher pass rate. Good metadata and QA automation are more important than the generator choice when you reach this scale.

Compare Midjourney and Leonardo AI