# How do you build a consistent multi-character cast with LoRAs (dataset, captions, training steps) for image and video models, per a July 2026 local workflow?

- id: art_01M5NBG30D53A22M8F17BV0DH6
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T05, T06, T07, T09 · tags: none
- updated: 2026-10-23T21:43:29.031Z
- url: https://veyrul.com/articles/art_01M5NBG30D53A22M8F17BV0DH6

Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated. Mickmumpitz (2026-07-17) gives five LoRA rules: (1) a made-up trigger word absorbs everything not captioned; (2) vary what should generalize (include wide shots, lighting, poses, clothing), or the character only exists as trained (e.g. close-ups); (3) decide what stays fixed versus editable; (4) captions decide editability: captioned clothes stay changeable but must then be re-prompted; (5) captions are reverse prompts, so match the target model's prompt style (long natural language for Krea 2; structured JSON with bounding boxes for Ideogram 4). Pipeline: an edit-model "character creator" generates a varied synthetic dataset from one or two images (add close-ups of det...

Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated.

Mickmumpitz (2026-07-17) gives five LoRA rules: (1) a made-up trigger word absorbs everything not captioned; (2) vary what should generalize (include wide shots, lighting, poses, clothing), or the character only exists as trained (e.g. close-ups); (3) decide what stays fixed versus editable; (4) captions decide editability: captioned clothes stay changeable but must then be re-prompted; (5) captions are reverse prompts, so match the target model's prompt style (long natural language for Krea 2; structured JSON with bounding boxes for Ideogram 4). Pipeline: an edit-model "character creator" generates a varied synthetic dataset from one or two images (add close-ups of details that drift, such as necklaces; optional face detailer for small faces), an LLM tagger writes captions (trigger words per character; describe position and interaction), and AI Toolkit trains (RunPod RTX Pro 6000 about 80 minutes for 3,000 steps; best checkpoints around 1,250-2,500 steps, last is not best; rank 64 for three characters). For multi-character LoRAs, include generated images of the characters together so the model learns they are separate entities. Ideogram 4 LoRAs use JSON captions with bounding boxes (rotated-box format that LLMs wrongly "correct"); Wan 2.1 LoRAs trained on images (sample frames = 1) also work on Wan 2.2. Licences verified on Hugging Face cards: Ideogram 4 weights non-commercial (gated); Krea 2 under a Krea-2 community licence.

Curator qualifications: Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated. Video metadata checked; documentation excerpts (if any) located; creator statements not curator-verified.

Limitations: Creator statements were read by discovery from auto-captions (ASR) in a browser panel; not re-verified by the curator. ASR: "Craya/Crea 2"=Krea 2, "IDOG/idoggram 4"=Ideogram 4, "Ostrus"=Ostris. Training costs and step ranges are single-creator experience. Synthetic datasets of real people require consent.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://www.youtube.com/watch?v=ghFYDG0DF1w",
      "note": "0:00-3:00; 0:00-3:00; 15:10; 15:10-18:15; 24:18; cardData.license_name; cardData.license_name"
    },
    {
      "url": "https://huggingface.co/api/models/ideogram-ai/ideogram-4-fp8",
      "note": "0:00-3:00; 0:00-3:00; 15:10; 15:10-18:15; 24:18; cardData.license_name; cardData.license_name"
    },
    {
      "url": "https://huggingface.co/api/models/Comfy-Org/Krea-2",
      "note": "0:00-3:00; 0:00-3:00; 15:10; 15:10-18:15; 24:18; cardData.license_name; cardData.license_name"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Mickmumpitz (2026-07-17) gives five LoRA rules: (1) a made-up trigger word absorbs everything not captioned; (2) vary what should generalize (include wide shots, lighting, poses, clothing), or the character only exists as trained (e.g. close-ups); (3) decide what stays fixed versus editable; (4) captions decide editability: captioned clothes stay changeable but must then be re-prompted; (5) captions are reverse prompts, so match the target model's prompt style (long natural language for Krea 2; structured JSON with bounding boxes for Ideogram 4). Pipeline: an edit-model "character creator" generates a varied synthetic dataset from one or two images (add close-ups of details that drift, such as necklaces; optional face detailer for small faces), an LLM tagger writes captions (trigger words per character; describe position and interaction), and AI Toolkit trains (RunPod RTX Pro 6000 about 80 minutes for 3,000 steps; best checkpoints around 1,250-2,500 steps, last is not best; rank 64 for three characters). For multi-character LoRAs, include generated images of the characters together so the model learns they are separate entities. Ideogram 4 LoRAs use JSON captions with bounding boxes (rotated-box format that LLMs wrongly "correct"); Wan 2.1 LoRAs trained on images (sample frames = 1) also work on Wan 2.2. Licences verified on Hugging Face cards: Ideogram 4 weights non-commercial (gated); Krea 2 under a Krea-2 community licence. · locator: 0:00-3:00; 0:00-3:00; 15:10; 15:10-18:15; 24:18; cardData.license_name; cardData.license_name · source: https://www.youtube.com/watch?v=ghFYDG0DF1w

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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