Identity consistency
Compare the same eye, brow, mouth, jaw, and age cues across every result.
Batch workflow guide
Consistency is a batch workflow, not one setting
A photo set amplifies small input differences. Group similar targets, lock the identity reference, test a representative frame, and review the set with one acceptance standard.
Plan the set
Group targets by the visual problem they share
Do not treat a mixed set as one uniform input. Separate targets by pose, lighting, expression, resolution, or occlusion so one reference is not forced across conflicting conditions.
| Grouping signal | Keep together | Split into another test |
|---|---|---|
| Pose | Front-facing and slight turns | Extreme profiles or steep head tilt |
| Lighting | Similar soft or directional light | Night scenes, colored light, or hard backlight |
| Expression | Neutral and mildly expressive faces | Closed eyes, wide mouth shapes, or extreme expression |
| Resolution | Targets with comparable face size and detail | Tiny, compressed, or heavily filtered faces |
| Occlusion | Clear faces or similar accessories | Hands, hair, glasses glare, masks, or props over landmarks |
Pilot run
Test the middle and the edge case
- Choose one representative target: use the image that best reflects the majority of the set.
- Choose one difficult target: include the most important profile, shadow, expression, or occlusion case.
- Hold the reference constant: compare target conditions before swapping identity references.
- Review with one rubric: use the same identity, scene, and technical checks for both results.
- Split or scale: process the full group only when the pilot passes; move difficult images to a separate group.
The face swap quality guide explains which reference and target conflicts to fix first.
Quality control
Review the set as a sequence
Scene preservation
Check that hair, accessories, hands, lighting, and framing still belong to each target.
Outlier detection
Sort failures by pose, light, expression, resolution, or occlusion instead of retrying the whole set blindly.
Retry discipline
Change one input variable at a time and keep approved outputs separate from the retry group.
Operational rule: a batch is ready when its weakest important image meets the intended use, not when the average result looks acceptable.
Cost and privacy
Know the output count before you submit
The current batch face swap workflow charges 2 credits per target image, with each image limited to 30 MB and the batch limited to 200 MB. The selected files are checked locally for readiness before upload. Uploaded and generated media is removed from DeepSwapAI servers within 24 hours.
Trial image exports from an account with no completed credit purchase visibly contain DeepSwapAI.com and the account's full email address. The interface discloses the exact address before generation. After any completed one-time credit purchase, future image exports are watermark-free. Review the privacy and consent checklist before uploading or sharing another person's likeness.
How this guide was made
A repeatable production checklist, not a volume claim
The DeepSwapAI Product Team organized the guide around the current batch workflow and variables a user can observe and change. It does not invent throughput, accuracy, or customer-count claims. Cost, limit, privacy, and watermark facts were reviewed on July 13, 2026; see the verification methodology.
Pilot two images before the full set
Use one representative target and one difficult edge case. Scale only after both meet the same review standard.