{"schema":"record/v1","id":"art_01M5NNTXY9F4QSZ4NSZ6SKJ4M9","type":"article","revision":1,"etag":"\"r1.s0\"","title":"For a character LoRA with kohya sd-scripts, which dataset-config keys control repeats and captions, what do the docs say about rank, alpha and learning rate, and how do I watch for overfitting?","summary":"Problem. A character LoRA must learn identity without memorising poses, backgrounds or captions. Conditions. sd-scripts train_network.py (SD 1.x/2.x) with a dataset .toml. Method. (1) Dataset toml: num_repeats per subset (repeats images each epoch), caption_extension='.txt', shuffle_caption=true with keep_tokens=N (or keep_tokens_separator '|||') to pin the trigger token at the front, caption_prefix/caption_suffix, resolution and batch_size per dataset. (2) Network: --network_module=networks.lora, --network_dim (rank; 4-128 commonly used), --network_alpha (docs: generally about half of dim, or equal), example dim=16, alpha=1. (3) Learning rate: --learning_rate=1e-4 in the example; for alpha=1 'relatively higher values (1e-4 to 1e-3)' are common; separate --unet_lr and --text_encoder_lr, text encoder smaller. (4) Overfitting watch: hold out a validation subset with validation_split (per-subset in toml, or global --validation_split), and generate samples during training with --sample_...","status":"active","body":{"text":"Problem. A character LoRA must learn identity without memorising poses, backgrounds or captions. Conditions. sd-scripts train_network.py (SD 1.x/2.x) with a dataset .toml. Method. (1) Dataset toml: num_repeats per subset (repeats images each epoch), caption_extension='.txt', shuffle_caption=true with keep_tokens=N (or keep_tokens_separator '|||') to pin the trigger token at the front, caption_prefix/caption_suffix, resolution and batch_size per dataset. (2) Network: --network_module=networks.lora, --network_dim (rank; 4-128 commonly used), --network_alpha (docs: generally about half of dim, or equal), example dim=16, alpha=1. (3) Learning rate: --learning_rate=1e-4 in the example; for alpha=1 'relatively higher values (1e-4 to 1e-3)' are common; separate --unet_lr and --text_encoder_lr, text encoder smaller. (4) Overfitting watch: hold out a validation subset with validation_split (per-subset in toml, or global --validation_split), and generate samples during training with --sample_prompts and --sample_every_n_steps; save every epoch (--save_every_n_epochs=1) so you can pick an earlier checkpoint. The diffusers DreamBooth README adds that DreamBooth needs only 3-5 images of a subject and that prior-preservation loss is used to avoid overfitting and language drift. Failure modes. Over-trained LoRAs reproduce training backgrounds or poses (inference; the docs say validation loss helps assess generalisation but give no thresholds). Limitations. The sd-scripts docs fetched do not state recommended image counts, repeats-times-epochs totals or overfitting thresholds for identity LoRAs; those numbers are community lore and are not in these primary docs. Version/date. Docs on main, fetched 2026-10-23.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Docs give mechanism and parameter names but not identity-specific dataset size or overfitting thresholds; any such numbers must come from experiment. The 'over-trained reproduces backgrounds' point is inference.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/config_README-en.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"},{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/train_network.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"},{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/validation.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"},{"url":"https://raw.githubusercontent.com/huggingface/diffusers/main/examples/dreambooth/README.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"}]},"categories":["T05","T09"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/config_README-en.md","claim":"Problem. A character LoRA must learn identity without memorising poses, backgrounds or captions. Conditions. sd-scripts train_network.py (SD 1.x/2.x) with a dataset .toml. Method. (1) Dataset toml: num_repeats per subset (repeats images each epoch), caption_extension='.txt', shuffle_caption=true with keep_tokens=N (or keep_tokens_separator '|||') to pin the trigger token at the front, caption_prefix/caption_suffix, resolution and batch_size per dataset. (2) Network: --network_module=networks.lora, --network_dim (rank; 4-128 commonly used), --network_alpha (docs: generally about half of dim, or equal), example dim=16, alpha=1. (3) Learning rate: --learning_rate=1e-4 in the example; for alpha=1 'relatively higher values (1e-4 to 1e-3)' are common; separate --unet_lr and --text_encoder_lr, text encoder smaller. (4) Overfitting watch: hold out a validation subset with validation_split (per-subset in toml, or global --validation_split), and generate samples during training with --sample_prompts and --sample_every_n_steps; save every epoch (--save_every_n_epochs=1) so you can pick an earlier checkpoint. The diffusers DreamBooth README adds that DreamBooth needs only 3-5 images of a subject and that prior-preservation loss is used to avoid overfitting and language drift. Failure modes. Over-trained LoRAs reproduce training backgrounds or poses (inference; the docs say validation loss helps assess generalisation but give no thresholds). Limitations. The sd-scripts docs fetched do not state recommended image counts, repeats-times-epochs totals or overfitting thresholds for identity LoRAs; those numbers are community lore and are not in these primary docs. Version/date. Docs on main, fetched 2026-10-23.","locator":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro","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-cc9d3e3cbf71b2101857ad56a613bf1ae89aebfb/record.json#p-cc9d3e3cbf71b2101857ad56a613bf1ae89aebfb/record","import_batch_id":"pipe-57253f18cad1a5017a0383931951d99e471805ec","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-57253f18cad1a5017a0383931951d99e471805ec","original":{"body":{"text":"Problem. A character LoRA must learn identity without memorising poses, backgrounds or captions. Conditions. sd-scripts train_network.py (SD 1.x/2.x) with a dataset .toml. Method. (1) Dataset toml: num_repeats per subset (repeats images each epoch), caption_extension='.txt', shuffle_caption=true with keep_tokens=N (or keep_tokens_separator '|||') to pin the trigger token at the front, caption_prefix/caption_suffix, resolution and batch_size per dataset. (2) Network: --network_module=networks.lora, --network_dim (rank; 4-128 commonly used), --network_alpha (docs: generally about half of dim, or equal), example dim=16, alpha=1. (3) Learning rate: --learning_rate=1e-4 in the example; for alpha=1 'relatively higher values (1e-4 to 1e-3)' are common; separate --unet_lr and --text_encoder_lr, text encoder smaller. (4) Overfitting watch: hold out a validation subset with validation_split (per-subset in toml, or global --validation_split), and generate samples during training with --sample_prompts and --sample_every_n_steps; save every epoch (--save_every_n_epochs=1) so you can pick an earlier checkpoint. The diffusers DreamBooth README adds that DreamBooth needs only 3-5 images of a subject and that prior-preservation loss is used to avoid overfitting and language drift. Failure modes. Over-trained LoRAs reproduce training backgrounds or poses (inference; the docs say validation loss helps assess generalisation but give no thresholds). Limitations. The sd-scripts docs fetched do not state recommended image counts, repeats-times-epochs totals or overfitting thresholds for identity LoRAs; those numbers are community lore and are not in these primary docs. Version/date. Docs on main, fetched 2026-10-23.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Docs give mechanism and parameter names but not identity-specific dataset size or overfitting thresholds; any such numbers must come from experiment. The 'over-trained reproduces backgrounds' point is inference.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/config_README-en.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"},{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/train_network.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"},{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/validation.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"},{"url":"https://raw.githubusercontent.com/huggingface/diffusers/main/examples/dreambooth/README.md","note":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro"}]},"tags":[],"type":"article","links":[],"title":"For a character LoRA with kohya sd-scripts, which dataset-config keys control repeats and captions, what do the docs say about rank, alpha and learning rate, and how do I watch for overfitting?","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":"Problem. A character LoRA must learn identity without memorising poses, backgrounds or captions. Conditions. sd-scripts train_network.py (SD 1.x/2.x) with a dataset .toml. Method. (1) Dataset toml: num_repeats per subset (repeats images each epoch), caption_extension='.txt', shuffle_caption=true with keep_tokens=N (or keep_tokens_separator '|||') to pin the trigger token at the front, caption_prefix/caption_suffix, resolution and batch_size per dataset. (2) Network: --network_module=networks.lora, --network_dim (rank; 4-128 commonly used), --network_alpha (docs: generally about half of dim, or equal), example dim=16, alpha=1. (3) Learning rate: --learning_rate=1e-4 in the example; for alpha=1 'relatively higher values (1e-4 to 1e-3)' are common; separate --unet_lr and --text_encoder_lr, text encoder smaller. (4) Overfitting watch: hold out a validation subset with validation_split (per-subset in toml, or global --validation_split), and generate samples during training with --sample_...","evidence":{"claims":[{"url":"https://raw.githubusercontent.com/kohya-ss/sd-scripts/main/docs/config_README-en.md","claim":"Problem. A character LoRA must learn identity without memorising poses, backgrounds or captions. Conditions. sd-scripts train_network.py (SD 1.x/2.x) with a dataset .toml. Method. (1) Dataset toml: num_repeats per subset (repeats images each epoch), caption_extension='.txt', shuffle_caption=true with keep_tokens=N (or keep_tokens_separator '|||') to pin the trigger token at the front, caption_prefix/caption_suffix, resolution and batch_size per dataset. (2) Network: --network_module=networks.lora, --network_dim (rank; 4-128 commonly used), --network_alpha (docs: generally about half of dim, or equal), example dim=16, alpha=1. (3) Learning rate: --learning_rate=1e-4 in the example; for alpha=1 'relatively higher values (1e-4 to 1e-3)' are common; separate --unet_lr and --text_encoder_lr, text encoder smaller. (4) Overfitting watch: hold out a validation subset with validation_split (per-subset in toml, or global --validation_split), and generate samples during training with --sample_prompts and --sample_every_n_steps; save every epoch (--save_every_n_epochs=1) so you can pick an earlier checkpoint. The diffusers DreamBooth README adds that DreamBooth needs only 3-5 images of a subject and that prior-preservation loss is used to avoid overfitting and language drift. Failure modes. Over-trained LoRAs reproduce training backgrounds or poses (inference; the docs say validation loss helps assess generalisation but give no thresholds). Limitations. The sd-scripts docs fetched do not state recommended image counts, repeats-times-epochs totals or overfitting thresholds for identity LoRAs; those numbers are community lore and are not in these primary docs. Version/date. Docs on main, fetched 2026-10-23.","locator":"Option list: num_repeats; Option list: keep_tokens_separator; LoRA Parameters: network_dim; LoRA Parameters: network_alpha; learning_rate; text encoder LR; Introduction; Config table; Prior-preservation section; intro","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T05","T09"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":["CAT:T06.AIC.02"],"confidence":"Docs give mechanism and parameter names but not identity-specific dataset size or overfitting thresholds; any such numbers must come from experiment. 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