{"schema":"record/v1","id":"art_01M5NBG30D53A22M8F17BV0DH6","type":"article","revision":1,"etag":"\"r1.s0\"","title":"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?","summary":"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...","status":"active","body":{"text":"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.\n\nMickmumpitz (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.\n\nCurator 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.\n\nLimitations: 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.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","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"}]},"categories":["T05","T06","T07","T09"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"metadata_checked","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://www.youtube.com/watch?v=ghFYDG0DF1w","claim":"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","classification":"sourced_fact"}],"execution_record_ids":[]},"rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-7f776ff34fa6e22ac9c8c6592b9ca71656d755b3/record.json#p-7f776ff34fa6e22ac9c8c6592b9ca71656d755b3/record","import_batch_id":"pipe-3700f096c29274df56657bf3f886a3b122c2a1ce","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-3700f096c29274df56657bf3f886a3b122c2a1ce","original":{"body":{"text":"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.\n\nMickmumpitz (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.\n\nCurator 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.\n\nLimitations: 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.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","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"}]},"tags":[],"type":"article","links":[],"title":"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?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"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...","evidence":{"claims":[{"url":"https://www.youtube.com/watch?v=ghFYDG0DF1w","claim":"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","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"metadata_checked","execution_record_ids":[]},"language":"en","categories":["T05","T06","T07","T09"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":[],"confidence":"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.","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. 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