Skip to main content

Face Consistency

Influgen's default face-consistency system is embedding-guided and reference-image-based. It is designed to make a character recognizable across generations without forcing you to train a custom model on day one.

How face-lock works​

When a character has a usable reference image, Influgen creates a face lock that includes:

  • the primary reference image URL
  • a stored face embedding
  • generation strength values
  • optional IP-Adapter settings

The current defaults are:

  • embedding size: 512 dimensions
  • similarity method: cosine similarity
  • minimum acceptable match score: 0.85
  • face-lock strength: 0.9
  • reference-image strength: 0.82

If a generated asset scores below the configured threshold, it can be rejected, regenerated, or held for review depending on the workflow.

[screenshot: Face consistency panel comparing the primary reference photo to a generated image with a similarity score badge]

What happens during generation​

At generation time, Influgen combines:

  1. Your character's reference image
  2. The stored face embedding
  3. Prompt and style instructions
  4. IP-Adapter guidance when the model path supports it

After the model returns an image, Influgen can re-extract an embedding from the generated output and compare it back to the character embedding. That lets the platform score consistency rather than guessing visually.

Why the threshold matters​

The default threshold of 0.85 is a practical quality gate:

  • Above the threshold usually means the face is close enough to feel like the same character.
  • Slightly below the threshold often means the output is attractive but drifts in age, jawline, eye spacing, or overall resemblance.
  • Far below the threshold means the generation is effectively a new person.

This matters most when you generate high volume. A score-based gate is what keeps one strong generation from turning into a week of inconsistent output.

IP-Adapter tier vs. LoRA tier​

New characters start on the ip_adapter face-consistency tier. This is the right default for most workspaces because it:

  • needs less setup
  • works immediately after you upload references
  • avoids the cost of custom training

Custom LoRA changes the tier to lora once training is ready. That usually improves stability further, especially for:

  • specific facial structure
  • repeat outfits and beauty looks
  • difficult lighting conditions
  • video and motion-heavy workflows

See Custom LoRA for the upgrade path.

Improving results without LoRA​

Most face-consistency problems are reference problems, not model problems. Before training a custom model, tighten the source set:

  • Use front-facing photos first.
  • Keep age and styling direction consistent.
  • Avoid heavy filters and beauty retouching.
  • Prefer clear lighting and visible eyes.
  • Remove photos where the face is tiny in frame.

If your character works in close-up portraits but breaks on full-body shots, the issue is usually prompt drift or weak references for body language and styling, not face-lock itself.

When to review manually​

Stay in manual review mode if:

  • you are using fewer than five strong references
  • the niche depends on photorealism
  • the character has unusual features that models tend to smooth away
  • you are switching between beauty, fashion, and sports content too quickly

Manual review is cheaper than publishing an obviously inconsistent post and cleaning up after it.