Build a contact sheet for generated image review

Freeze the review contract before generating

A future AI generation contact sheet starts before the first image. Write one brief, record the required placement ratio, and define the decision the sheet must support. Keep the subject, required elements, framing target, and delivery context fixed. Then name the single variable intended to change. Google’s experiment guidance recommends a baseline, small changes, and reproducibility metadata; applying that principle is a proposed review method, not evidence that this page produced controlled variants. The actual model ratio must remain unresolved until current model-specific Studio evidence exists.

Create a manifest row for every candidate, including its filename, generation ID, model or system name, known system version, complete positive and negative prompt information, aspect ratio, seed when the service exposes one, and generation time. The IPTC 2025.1 standard provides dedicated fields for AI Prompt Information, AI Prompt Writer Name, AI System Used, and AI System Version Used. Keep operational fields that do not fit embedded metadata in a sidecar CSV or JSON file tied to the same candidate ID.

Fixed brief

One subject, purpose, ratio, and required-element listA reviewer should be able to state what all candidates were trying to solve.

Allowed delta

One named variable per comparison roundIf model, prompt, ratio, and composition all change together, the sheet cannot explain why one result differs.

Candidate record

ID, file, model, version, prompt, ratio, seed if available, timeRecord unknown or unavailable values explicitly instead of reconstructing them later.

Set the human gate before seeing the candidates

Write the rubric before a future sheet is assembled. Use criteria that follow the brief: required elements, focal hierarchy at delivery size, protected copy space, observable visual integrity, and finishability. A small anchored scale can be proposed, but this page has no populated scores or real defect rows. Add a hard-reject column for rights, safety, identity, or brand problems that should not be averaged away by an aesthetic score.

Review first at equal thumbnail size, then inspect only the finalists at full resolution. Hide filenames or model names during the first pass when they could bias the choice. Record a short reason for every advance and rejection. The goal is not to declare a universally best model; it is to select the candidate that best satisfies this disclosed brief under this disclosed rubric.

Brief fit

0–2Are the required subject, action, setting, and exclusions represented?

Composition

0–2Does hierarchy survive at intended display size, with usable crop and copy zones?

Technical integrity

0–2Check anatomy, geometry, repeated objects, edges, texture, lighting, and unwanted text.

Finishability

0–2Estimate whether normal retouching can finish the image without changing its core concept.

Hard reject

Yes / noFlag rights, identity, safety, disclosure, or brand failures separately from the numeric score.

Specify a future baseline and visible delta

For a future authorized batch, save the exact baseline prompt as plain text and separate invariant requirements from one experimental instruction. An illustrative record could keep product, camera distance, background, copy-space requirement, and exclusions fixed while changing only “soft window light” to “hard late-afternoon side light.” Record the placement-ratio requirement separately until the selected model's Studio compatibility is verified. No baseline or delta output was generated here.

Do not call a sheet controlled when hidden settings differ. A provider may not expose every parameter, and a model can still produce stochastic variation. Mark unavailable values as unavailable, retain every result from the declared run, and avoid regenerating only the weakest cell. Google notes that initialization and small evaluation sets can introduce noise; one attractive output is therefore a candidate, not proof that a prompt or model is generally superior.

Invariant

Product on pale stone, three-quarter view, upper-right copy spaceIllustrative brief structure only; no Studio ratio or output is implied.

Baseline

Soft north-window light, restrained shadow, no letteringStore this exact text with every baseline candidate.

Single delta

Hard late-afternoon side lightChange only this instruction for the comparison row.

Real output evidence available in this research pass

No new generation was run for this page, and the research did not recover a public, controlled same-brief output set with complete model metadata. That gap matters: this draft can document the review method, but it cannot claim that one candidate, prompt, or model won a controlled comparison. A future evidence pass should attach the unedited full batch, the manifest, the assembled sheet, and the completed rubric before this page is considered review-ready.

OfflineCreator Studio’s public homepage contains labeled AI-generated examples for several listed models. Their labels describe different workflows and formats, but they do not disclose a shared brief, complete settings, rejected outputs, or one review rubric. They are public references, not a controlled contact sheet or benchmark. The image-generator interface also displays ratio controls, but that page does not establish per-model compatibility and real generation requires sign-in. This draft infers neither output quality nor executable ratios from those interfaces.

Public reference

FLUX 1.1 Pro Ultra · 4:5A labeled homepage example, but not matched to the other examples by prompt or task.

Public reference

Kling 2.6 Pro Motion · 9:16An image-to-video example, so it cannot be compared as an image-generation variant.

Public reference

Veo 3.1 Fast · 16:9A text-to-video example with a different workflow and ratio.

Missing acceptance artifact

Controlled image batch plus completed score sheetRequired before any comparative result or model recommendation is added.

Assemble, score, and preserve the decision

Export every candidate at the same thumbnail dimensions and color treatment, place them in generation order, and print a short neutral ID below each cell. Adobe Lightroom Classic’s Print module supports Single Image/Contact Sheet layouts for one or more photos and controls for the text and objects printed with them. A comparable layout can be built elsewhere, but equal cell dimensions and readable IDs are the essential review properties.

Run the thumbnail pass, record scores without deleting weak outputs, and move only rubric-qualified candidates to a full-resolution inspection. After selection, preserve the original files, contact sheet, manifest, rubric scores, final decision, and any later edit history together. If the publishing pipeline supports Content Credentials, C2PA guidance says a manifest records creation or editing actions and uses digitalSourceType for the asset’s source; generative-AI input and model information can also be recorded. Provenance complements the working manifest rather than replacing it.

1. Collect

Keep the full declared batchDo not remove weak generations before review.

2. Normalize

Equal cells, one color treatment, neutral IDsAvoid making a preferred candidate larger or more polished.

3. Score

Apply the prewritten rubricCapture criterion scores, hard rejects, and short reasons.

4. Inspect

Open finalists at full resolutionConfirm edge, texture, anatomy, geometry, and finishability details.

5. Archive

Files, sheet, manifest, scores, decision, edit historyKeep the evidence package together so another reviewer can reconstruct the choice.

Choose the next step from the review result

Return to the workflow directory if the test exposed a model-choice problem. Use the podcast-cover guide when the approved direction is a square cover concept. Move to the proof-set guide when the winning direction is ready for a small representative validation set rather than immediate scale.

Editorial ownership and evidence boundary

This page owns the review method for “ai generation contact sheet workflow”: controlled inputs, candidate metadata, equal presentation, a precommitted selection rubric, and a preserved decision record. The parent workflow directory owns broad model routing, while the proof-set guide owns validation after a direction is selected.

The current draft remains researched but not review-ready because its recent community coverage is degraded and no controlled same-brief image batch was produced. Its overlap guard therefore requires the actionable review method to remain consolidated in the parent workflow hub. Do not add a winning-model claim, quality ranking, benchmark, defect example, or customer outcome until a complete output set and reciprocal evidence record support it.