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# == Directories
# ==============================================================
# > Dataset sync derives SOURCE/, MANIFEST.knf.xlsx, and HF/<config-id>/ from this filename.
# -- Map exported subset names to source subfolders in MANIFEST.knf.xlsx.
mappings:
# > Anchor identity examples get sampled at a lower probability below.
felkin_anchor:
- "0-ANCHOR" # Source folder for anchor identity images.
# > Explicit anatomy close-up subsets are kept separate for sampling control.
genitals:
- "6-DICK" # Source folder for exposed anatomy close-ups.
- "6-SHEATH" # Source folder for sheathed anatomy close-ups.
# > Complete Body
fullbody:
- "0-FULLBODY" # Full-body identity examples; also included in rest subset.
- "99-VERSION-LEANER" # Alternate leaner full-body examples.
# > General identity, body, face, hand, leg, tail, and leaner variant images.
body_details:
- "1-TORSO" # Torso detail examples.
- "3-HANDS" # Hand detail examples.
- "4-LEGS" # Leg/foot detail examples.
- "5-TAIL" # Tail detail examples.
# > Focused fur-texture crops derived from high-resolution Rook sources.
fur:
- "7-FUR" # Fur texture crops for coat, mane, spots, paws, tail, and anatomy-adjacent texture.
head:
- "2-HEAD" # Face/head detail examples.
# > Also export manifest rows marked sfw into an additional generated sfw subset.
export_sfw_subset: false # < Add generated sfw subset from rows marked sfw.
# ==============================================================
# == Images
# ==============================================================
# Export-time mirroring writes fixed extra image/caption pairs before
# SimpleTuner sees the dataset. flip_only keeps kneifftools from applying its
# own random crop, rotation, or color jitter.
augmentations:
mirrored_extra: true # Add one mirrored copy per selected source image.
mirrored_transform: flip_only # Exact horizontal mirror, no kneifftools crop/jitter.
seed: 12345 # Makes mirrored export naming deterministic.
# Export image area bounds before SimpleTuner sees the dataset. Chroma1-Base
# starts from SimpleTuner's 512px Chroma example; validation still renders at 1024.
image_resize:
min_pixel_area: 512 # Upscale tiny images below roughly 512x512 area.
max_pixel_area: 512 # Downscale large images above roughly 512x512 area.
# ==============================================================
# == Captions
# ==============================================================
# Controlled caption rendering context used by all generated sidecars.
caption:
subject_sex: male # Adds male subject wording to prose renderers.
add_domains_to_tags: true # Emits domain tags, e.g. meta:shaded.
# Default caption sidecar layout for every exported subset.
caption_outputs:
mode: hybrid_txt # One .txt sidecar per image containing selected formats.
formats: [chroma] # Chroma-profile single-line captions for Chroma training.
tag_scope: supplemental # Kept explicit for consistency with shipped overlays.
# Optional per-subset caption output overrides. Keep commented unless a subset
# needs different caption files or formats.
# caption_outputs_overrides:
# felkin_anchor:
# mode: hybrid_txt
# formats: [chroma]
# genitals:
# mode: hybrid_txt
# formats: [chroma]
# fullbody:
# mode: hybrid_txt
# formats: [chroma]
# body_details:
# mode: hybrid_txt
# formats: [chroma]
# fur:
# mode: hybrid_txt
# formats: [chroma]
# head:
# mode: hybrid_txt
# formats: [chroma]
# sfw:
# mode: hybrid_txt
# formats: [chroma]
# ==============================================================
# == Publishing
# ==============================================================
publishing:
huggingface:
pretty_name: Ladybug Felkin # Human-readable dataset-card base title.
version: v5.4-chroma-base # Public dataset version label.
optimized_for_model: Chroma1-Base # Rendered as [For Chroma1-Base].
tags: [chroma, chroma1-base, lora, diffusion-training, image-captioning]
adult_content: true # Render the adult-content notice in README.md.
# ==============================================================
# == LoRA Training
# ==============================================================
training:
enabled: true # Enables kneifftools training artifact generation.
backend: simpletuner # Selects the SimpleTuner LoRA backend.
simpletuner:
# enabled: true # Optional backend-specific switch; defaults to enabled.
# Subset-specific SimpleTuner dataloader overrides. Any key here is merged
# into the subset backend entry after the dataset defaults below.
subsets:
felkin_anchor:
probability: 0.2 # Sample anchor identity images at steady exposure.
crop: false # Preserve full source composition.
# repeats: 0 # Optional per-subset repeat count.
# disabled: false # Optional per-subset on/off switch.
fullbody:
probability: 8 # Sample full-body subset at high probability.
crop: false # Preserve full-body composition.
# repeats: 0
# disabled: false
body_details:
probability: 4 # Sample body detail subset at normal probability.
crop: false # Preserve detail framing; many samples are already close crops.
# repeats: 0
# disabled: false
fur:
probability: 1 # Give focused fur crops extra exposure.
crop: false # Preserve texture crop intent; do not random-crop fur samples.
# repeats: 0
# disabled: false
head:
probability: 2 # Sample head/face details at extra exposure.
crop: false # Preserve detail framing; many samples are close crops.
genitals:
probability: 3 # Sample anatomy subset at normal probability.
crop: false # Preserve anatomy crop framing.
# repeats: 0
# disabled: false
# Dataset backend defaults copied into every image subset entry.
dataset:
type: local # SimpleTuner local filesystem backend.
dataset_type: image # Image dataset backend, not text embeds.
resolution: 512 # First Chroma1-Base profile follows SimpleTuner's 512px example.
minimum_image_size: 512 # Keep smaller detail/anatomy crops after export upscaling.
maximum_image_size: 512 # Upper bound for input image sizing.
target_downsample_size: 512 # Downsample target for cache generation.
resolution_type: pixel_area # Area-based buckets, not smaller-edge resize.
caption_strategy: textfile # Read captions from exported .txt sidecars.
metadata_backend: discovery # Discover images/captions from directories.
crop: false # Preserve source framing for the Chroma run.
crop_style: center # Inert while crop is false; avoids random-crop defaults.
crop_aspect: preserve # Preserve original aspect instead of square crop.
# repeats: 0 # Dataset-level repeat count; subset can override.
# disabled: false # Dataset-level backend switch.
# Optional shared text-embedding backend overrides. Commented keys show the
# kneifftools-owned defaults for the generated text_embeds backend.
# text_embeds:
# id: text-embeds # Backend id used by SimpleTuner.
# type: local # Store embeddings on local filesystem.
# dataset_type: text_embeds
# default: true # Marks this as the shared embedding backend.
# cache_dir: .simpletuner-cache/text # Override generated text cache dir.
# disabled: false
# Generate a SimpleTuner user_prompt_library JSON from the curated Rook
# showcase prompts plus manifest-derived controls.
validation_prompts:
enabled: true
from_prompts:
caption_styles: [nlg]
from_manifest:
enabled: true
seed: 42
profile: chroma
# Model and LoRA settings merged into the top-level SimpleTuner config.
model:
model_type: lora # Train a LoRA adapter.
model_family: chroma # Use SimpleTuner's Chroma model family.
model_flavour: base # Chroma1-Base foundation variant.
lora_rank: 64 # LoRA rank/capacity.
lora_alpha: 64 # LoRA alpha scaling.
lora_type: standard # Standard LoRA, not an alternate adapter type.
lora_format: comfyui # Convert output naming/layout for ComfyUI.
flux_lora_target: all # Train all Flux/Chroma LoRA target modules.
# Base Chroma assets. The active transformer is loaded from the HF repo
# because no local Chroma1-Base single-file checkpoint is staged yet.
pretrained_model_name_or_path: lodestones/Chroma1-Base
pretrained_transformer_model_name_or_path: null
pretrained_transformer_subfolder: null
pretrained_vae_model_name_or_path: /mnt/wsl/comfyui-models/models/vae/ae.safetensors
pretrained_t5_model_name_or_path: /mnt/wsl/comfyui-models/models/text_encoders/t5xxl/flanT5XXLTextEncorder_fp16.safetensors
# Precision defaults for Chroma LoRA training.
base_model_precision: no_change
base_model_default_dtype: bf16
mixed_precision: bf16
# init_lora: /path/to/existing.safetensors # Optional LoRA resume/init weights.
# lycoris_config: /path/to/lycoris.json # Optional LyCORIS config path.
# == Curriculum
curriculum:
enabled: true
phases:
- name: focused_start
start_step: 0
subsets: [genitals, fullbody, head]
- name: full_mix
start_step: 1000 # !! From this step onward, all subsets are active.
subsets: all
# Full-run trainer config. trainer_testrun below overrides selected keys
# only when knf lora sync/train is called with --testrun.
trainer:
tracker_project_name: kneifftools-lora-training # Experiment tracker project.
tracker_run_name: ladybird-chroma-base # Full-run tracker name.
hub_model_id: ladybird-chroma-base-lora # Optional Hub model id if push_to_hub is enabled.
optimizer: adamw_bf16 # BF16 AdamW optimizer.
learning_rate: 0.00005 # Conservative Chroma LoRA starting point.
lr_scheduler: constant_with_warmup # ramp up LR to avoid weird color biases.
lr_warmup_steps: 200
seed: 42 # Training seed.
gradient_checkpointing: true # Reduce VRAM by recomputing activations.
max_grad_norm: 1.0 # Clip gradients for stability.
grad_clip_method: norm # Clip by gradient norm.
train_batch_size: 4 # Per-device batch size.
gradient_accumulation_steps: 1 # Effective batch multiplier.
num_train_epochs: 0 # Step-based training; max_train_steps controls run length.
max_train_steps: 4000 # ANIMA-aligned Chroma character run with extended curriculum.
ignore_final_epochs: true # This fixes early stop due to weird epoch calculation by simpletuner
caption_dropout_probability: 0.0 # Disable caption dropout; keep trigger behavior stable.
data_backend_sampling: auto-weighting # Auto-balance dataloader sampling based on subset probabilities and sizes.
# push_to_hub: false # Upload final artifacts to Hugging Face Hub.
# push_checkpoints_to_hub: false # Upload intermediate checkpoints.
checkpoints_total_limit: 5 # Keep latest 5 checkpoints.
checkpoint_step_interval: 400 # Save checkpoint every 200 steps.
validation_step_interval: 400 # Run validation every 200 steps.
disable_benchmark: false # Render baseline validation before training.
validation_prompt_library: false # Disable SimpleTuner built-in prompt library.
validation_disable_unconditional: true # Skip unconditional validation branch.
validation_resolution: 512x512 # Keep Base validation at native Chroma/Flux image size.
validation_num_inference_steps: 40 # Full-run validation sampling steps.
validation_guidance: 3 # CFG guidance used by validation.
validation_guidance_real: 3 # Chroma/flow guidance-real default.
validation_guidance_rescale: 0.0 # Guidance rescale; 0 disables rescale.
validation_seed: 42 # Deterministic validation seed.
num_eval_images: 1 # Images per validation prompt.
validation_negative_prompt: "signature, worst quality, low quality, score_1, score_2, score_3, artist name" # Shared validation negative prompt.
report_to: tensorboard # Write TensorBoard logs.
logging_dir: logs # TensorBoard log dir relative to TRAINING/<config-id>_<run>.
# Additional kneifftools-owned SimpleTuner defaults. Leave commented to
# use defaults from src/kneiff/training/lora/simpletuner.py.
# use_ema: false
# vae_batch_size: 1
# data_backend_sampling: uniform
# allow_dataset_oversubscription: true
aspect_bucket_alignment: 64
tokenizer_max_length: 512
t5_padding: zero
# fully_unload_text_encoder: true
# offload_during_startup: true
# keep_vae_loaded: false
# resume_from_checkpoint: /path/to/checkpoint
# Short smoke-run overrides. These merge last only with --testrun.
trainer_testrun:
disable_benchmark: true
tracker_run_name: ladybird-chroma-base-testrun # Separate tracker/log name for smoke runs.
max_train_steps: 12 # Tiny run to verify plumbing.
checkpoint_step_interval: 12 # Save at the end of the smoke run.
validation_step_interval: 12 # Validate at the end of the smoke run.
validation_num_inference_steps: 12 # Faster validation for smoke runs.
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