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Character Consistency — Same Face Across Images and Videos

One of the biggest problems in AI video:

The character changes every time.

Different face, different hair, different identity.

This guide shows how to keep the same person across images and videos.

1. Why It Happens

Stable Diffusion does NOT remember identity.

Each generation is random.

Even with the same prompt, you get a different person.

2. Core Principle

You must control identity using:

  • seed
  • reference image
  • LoRA or embeddings
  • consistent prompt

Without this, consistency is impossible.

3. Method 1 — Fixed Seed (Basic)

Use the same seed:

Seed: 123456

Result: - similar composition - but NOT reliable for faces

👉 Good for testing, not production

Best beginner approach.

Steps:

  1. Generate a strong base image
  2. Reuse it as input

Example workflow

  • Load Image node
  • Connect to KSampler
  • Use low denoise (0.3–0.5)

Result: - same face - same identity

5. Method 3 — IPAdapter (Better Control)

IPAdapter lets you control identity from image.

You provide a face → model follows it.

Steps: - Load reference image - Connect IPAdapter - Adjust weight

Result: - strong identity preservation - works across poses

6. Method 4 — LoRA (Production)

Train a LoRA for your character.

Use: - 10–20 images of same person - consistent angles

Then:

<lora:character_name:1>

Result: - repeatable identity - scalable for videos

7. Prompt Consistency

Never change core description.

Good:

male, 35 years old, construction worker, beard, yellow helmet, serious face

Bad:

man, worker, guy

👉 Small changes break identity

8. Negative Prompt

Use stable negative prompt:

blurry, deformed face, extra limbs, low quality, bad anatomy

9. Face Lock Strategy (Important)

For videos:

  1. Generate ONE perfect face
  2. Use it as base for all frames
  3. Apply motion later

Pipeline:

Prompt → Base Image → Variations → Video Model → Lip Sync → Output

Do NOT generate random frames.

Always anchor to base image.

11. Common Mistakes

Changing prompt

Even small changes = new person

High denoise

0.8–1.0 → new face

Use:

0.3–0.5

No reference

Without reference → no consistency

Mixing models

Different models = different faces

12. Practical Setup

Keep structure:

/opt/models/loras
/opt/models/checkpoints
/opt/projects/characters

Save: - base images - LoRA files - prompts

13. Real Workflow (Production)

  1. Create character image
  2. Save seed + prompt
  3. Generate variations
  4. Use image-to-video model (Wan / LTX)
  5. Apply lip sync (VoxCPM / SadTalker)
  6. Export video

14. Why This Matters

Without consistency: - videos look fake - characters change - brand is lost

With consistency: - you can build series - recognizable characters - real content pipeline

15. Next Step

Now build full pipeline:

👉 Prompt → Image