AI product image generation with SKU-level safeguards

Separate creative scene freedom from SKU truth

List the product properties that must remain exact: shape, proportions, packaging, labels, color, material, included accessories, and legally relevant claims. Then name the areas where invention is allowed, such as a background surface, shadow, or decorative context. Review criteria should mirror that split.

A plausible image can still be commercially false. Tie every request to an approved SKU version and publication channel, and define whether the output is a concept, internal draft, or customer-facing asset. Keep a deterministic photography or compositing fallback for protected products.

Ground edits in approved product references

Use controlled reference photography and authoritative product data, not marketplace screenshots or filenames. Preserve source resolution, color treatment, and checksum. If multiple views or masks are required, label their semantic roles and record which approved product version they depict.

Do not send confidential launch products or unnecessary customer data to a provider without an approved data path. Review provider region, retention, model terms, and access. Prompts should describe permitted scene changes without inviting the model to redesign protected product attributes.

Choose compositing when exact pixels must survive

Use exact compositing when the product, text, regulatory mark, or packaging cannot change. Background removal and deterministic layout can often place approved source pixels into a new scene more reliably than generation. Reserve inpainting for bounded regions where review can detect unwanted changes.

Compare generation with the deterministic baseline on correction time and publishability, not visual novelty. A generated shadow or reflection may still contradict material or scale. Product owners should approve the protected-fact checklist before a creative reviewer judges aesthetics.

Use masks to define the intended edit region

The example uses two uploads with semantic image and mask roles for documented inpainting. Keep the mask aligned with the source and verify its dimensions and intended polarity. The prompt requests a background change, but a reviewer must still confirm that the product pixels remained acceptable.

Store source, mask, prompt version, model, candidate, SKU version, and review state. Create delivery derivatives only from the approved candidate. A failed export should retry independently, and a late result for an obsolete SKU image must not replace a newer approved asset.

{
  "steps": {
    ":original": { "robot": "/upload/handle" },
    "scene_candidate": {
      "use": [
        { "name": ":original", "as": "image" },
        { "name": ":original", "as": "mask" }
      ],
      "robot": "/image/generate",
      "model": "stability-ai/stable-diffusion-inpainting",
      "prompt": "Replace only the masked background with a neutral studio set. Keep the product, packaging, labels, colors, and geometry unchanged.",
      "format": "png"
    }
  }
}

Review identity, materials, color, and accessories

Compare candidate and source for silhouette, dimensions, labels, spelling, logos, color, finish, texture, openings, controls, included items, and reflections. Review every target crop and compressed output. Use objective color or geometry checks where possible, but retain qualified visual review.

Record rejection reasons per product category because failure patterns differ for transparent items, jewelry, textiles, reflective packaging, and products with small text. Acceptance rate without SKU-fact errors and correction time can hide candidates that are attractive but unusable.

Reject candidates that change a material product fact

Reject rather than retouch candidates that add features, alter quantity, change packaging, obscure required warnings, or imply an unsupported environment or use. Keep the approved source and choose compositing, reshooting, or a different creative concept when model freedom conflicts with product truth.

Verify rights, talent consent, trademark treatment, disclosure, and claim substantiation for the intended territory and channel. Protect reviewers and credentials, sanitize errors, and provide a correction and takedown path for published assets.

Invalidate derivatives when the source SKU changes

Version the SKU reference, masks, prompt, model settings, review rubric, finishing Steps, and approved outputs. When packaging or product facts change, mark dependent generated media stale and block republication until it is reviewed against the new source.

Monitor rejection categories, correction time, stale-result rejection, provider errors, cost per accepted SKU, and later replacement. Keep prior approved assets available during rollout, and make callbacks and publication idempotent so completion order cannot select product truth.

Frequently asked questions