Input: master product photo and label close-up
Output: a clip whose first, middle, and last frames match the SKU
Aspect: same ratio across all variants
Build log
1. Set a five-point invariant
Shape, color, label, material, and logo position. I put this list beside the prompt and never describe those features as changeable.
2. Generate a 5-second probe
I use the shortest legal clip to test consistency. This avoids paying a 10-20 second render before the product is stable.
3. Compare three frames
I align first, middle, and last frames with the original. A clip can look correct while moving but drift in the last 20%.
4. Retry one variable
If color drifts, remove colored props first. If the label blurs, shorten the motion or move the label away from motion blur. If the shape changes, return to a tighter reference crop.
5. Save the winning reference set
I save the approved product photo, prompt, ratio, and model settings in the asset library, then use that set for every SKU variant.
Complete execution record
Task boundary and source gate
Treat AI product video consistency as a deliverable, not a definition. The job receives master product photo and label close-up and owes a clip whose first, middle, and last frames match the SKU in same ratio across all variants. Write the rejection reason first, then turn it into the first hard constraint.
Do not start until the source photo has a visible product code, a note on material and color, the approved claim, and a named target module. Check packaging damage, sticker state, lighting direction, and background rights before generation. Do not invent certifications, competitor claims, price windows, or a model using the product in an unsafe way. If the seller cannot prove it, the image does not carry it.
The imgmov route
Open Asset Library first, attach the master SKU as the locked reference, then build the module or shot around that reference. Keep offer copy, platform text, and CTA outside the visual prompt. In imgmov, the approved image becomes the source of truth and generation supplies only the requested scene, angle, or motion.
Keep platform requirements outside the model prompt. The prompt can describe a scene; a named checklist records the crop, claim, consent, and console check that make it deliverable.
Lock the variables that cannot drift
Lock product silhouette, label spelling, logo position, material, primary color, accessory count, contact shadow, and the platform safe area. Scene, props, and motion may change; these fields may not.
Put the locked fields at the top of the prompt, not at the end: Keep one master product reference and change one variable per retry. At 5 seconds, Agnes Video costs 10 credits, Seedance 2.5 costs 90 credits at 480p or 195 credits at 720p, and Kling v3 Omni costs 195 credits without audio. Do not pay for a new model until the same reference passes three frames. Save that prompt with the reference, ratio, and model so the next run starts from a decision instead of a guess.
Step-by-step build path
1) Set a five-point invariant: Shape, color, label, material, and logo position. I put this list beside the prompt and never describe those features as changeable. Leave one checkable artifact from this step; do not start the next until it exists. 2) Generate a 5-second probe: I use the shortest legal clip to test consistency. This avoids paying a 10-20 second render before the product is stable. Leave one checkable artifact from this step; do not start the next until it exists. 3) Compare three frames: I align first, middle, and last frames with the original. A clip can look correct while moving but drift in the last 20%. Leave one checkable artifact from this step; do not start the next until it exists. 4) Retry one variable: If color drifts, remove colored props first. If the label blurs, shorten the motion or move the label away from motion blur. If the shape changes, return to a tighter reference crop. Leave one checkable artifact from this step; do not start the next until it exists. 5) Save the winning reference set: I save the approved product photo, prompt, ratio, and model settings in the asset library, then use that set for every SKU variant. Leave one checkable artifact from this step; do not start the next until it exists.
Change one named variable per rerun: prompt, reference, camera, duration, model, ratio, or export crop. If two variables change together, a better output cannot be reused because nobody knows which fix worked.
Review gates and evidence
Review at 100%. Read small text, count buttons and parts, compare color against the source, inspect the four corners, and overlay the platform crop. A thumbnail is only a concept check, not a marketplace check.
Keep source, approved wording, rejected version, correction, credit cost, and final crop next to the asset. The record exists so the next reviewer can reproduce the decision without a chat thread.
Failure diagnosis and retry ladder
Name the failure first: label redraw, color drift, shape drift, missing accessory, wrong contact shadow, or cropped CTA. Stop prompting for more letters, reduce the variable count, return to the reference, and only then upgrade the model.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
Versioning and handoff
Deliver by module and ratio, not one stretched master. Name files with SKU, module, crop, version, and approval state. Keep the rejected render and the correction line in the same folder.
The folder is boring on purpose: source, approved reference, generation settings, caption file, platform cuts, QA screenshots, and one correction line. A reusable asset is boring in the right way.
Why this is not a generic answer
The imgmov advantage is the chain: Asset Library preserves the subject reference, Canvas fixes shot order and first/last frames, the workspace routes Agnes to Seedance, Kling, or Veo only after proof, and your external editor adds captions and CTA without regenerating the media.
Do not invent certifications, competitor claims, price windows, or a model using the product in an unsafe way. If the seller cannot prove it, the image does not carry it. If the request only asks what ai product video consistency means, a search page is faster. This page is useful when someone must deliver a clip whose first, middle, and last frames match the sku under real constraints.
Frequently asked questions
Why does the last frame change?
Long camera moves accumulate drift. Shorten the motion or add a still hold before the last frame.
The workflow is specific about its stopping point: it starts with master product photo and label close-up and stops at a clip whose first, middle, and last frames match the SKU. In imgmov, upload the source, lock it as a reference, set same ratio across all variants, run one proof, and save the passing settings. If a reviewer cannot tell which version is approved, the workflow has failed even when the media looks good.
What should I check first?
Label text and logo position. Viewers forgive background drift faster than a wrong product.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
How much does one retry cost?
At 5 seconds, Agnes Video is 10 credits, Seedance 2.5 is 90 credits at 480p or 195 credits at 720p, and Kling is 195 credits without audio.
The check is not “does it look AI-nice?” It is: Review at 100%. Read small text, count buttons and parts, compare color against the source, inspect the four corners, and overlay the platform crop. A thumbnail is only a concept check, not a marketplace check. Then the source, approved copy, rejected version, correction, and final crop stay in the same handoff folder.
Should I switch models immediately?
No. If one variable fixes it, keep the cheaper model. Change models only after a correct prompt fails twice.
Name the failure first: label redraw, color drift, shape drift, missing accessory, wrong contact shadow, or cropped CTA. Stop prompting for more letters, reduce the variable count, return to the reference, and only then upgrade the model. Change one named variable, keep the old version, and record the credit cost. If the same failure repeats, fix the reference or scope rather than asking the prompt for forgiveness.
Does the end frame help?
Yes. End frames are supported by Agnes, Kling, Veo, and Seedance, which is useful when the last frame must return to the packaging.
The imgmov advantage is the chain: Asset Library preserves the subject reference, Canvas fixes shot order and first/last frames, the workspace routes Agnes to Seedance, Kling, or Veo only after proof, and your external editor adds captions and CTA without regenerating the media. The handoff is reusable only when the source, reference ID, prompt, model, cost, rejection reason, and approved cut are linked. That chain is what makes the next ai product video consistency task faster.