- z-image training

- No sfw export for chroma

MANIFEST.knf.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dbea508609557d68236652ddb95f796eff2a13f6dab2665ecf5274613ea45c83
-size 14890902
+oid sha256:dd8ff41cd69ca60b7ec86918c599e52a9be45d25f9ed71f3321e000a9770c3e4
+size 14890787

configs/CHROMA.knf.yaml

@@ -31,7 +31,7 @@ mappings:
     - "2-HEAD"            # Face/head detail examples.
 
 # > Also export manifest rows marked sfw into an additional generated sfw subset.
-export_sfw_subset: true # < Add generated sfw subset from rows marked sfw.
+export_sfw_subset: false # < Add generated sfw subset from rows marked sfw.
 
 # ==============================================================
 # == Images

configs/CHROMA_BASE.knf.yaml

@@ -31,7 +31,7 @@ mappings:
     - "2-HEAD"            # Face/head detail examples.
 
 # > Also export manifest rows marked sfw into an additional generated sfw subset.
-export_sfw_subset: true # < Add generated sfw subset from rows marked sfw.
+export_sfw_subset: false # < Add generated sfw subset from rows marked sfw.
 
 # ==============================================================
 # == Images

configs/Z_IMAGE.knf.yaml

@@ -0,0 +1,298 @@
+# ==============================================================
+# == 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.
+  fur:
+    - "7-FUR" # Fur texture crops for coat, mane, spots, paws, tail, and anatomy-adjacent texture.
+  head:
+    - "2-HEAD"            # Face/head detail examples.
+  # > Focused fur-texture crops derived from high-resolution Rook sources.
+
+# > 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. The upper bound
+# preserves more source detail than Chroma while staying below roughly 1024x1024.
+image_resize:
+  min_pixel_area: 512   # Upscale tiny images below roughly 512x512 area.
+  max_pixel_area: 1024  # Downscale large images above roughly 1024x1024 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: false  # Z-Image captions are natural prose, not domain-tag tails.
+
+# Default caption sidecar layout for every exported subset.
+caption_outputs:
+  mode: hybrid_txt  # One .txt sidecar per image containing selected formats.
+  formats: [z-image]  # Z-Image-profile single-line captions for FLUX.2 Klein training.
+  # tag_scope: supplemental  # Ignored by Z-Image; default is supplemental.
+
+# 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: [Z-Image]
+#   genitals:
+#     mode: hybrid_txt
+#     formats: [Z-Image]
+#   fullbody:
+#     mode: hybrid_txt
+#     formats: [Z-Image]
+#   body_details:
+#     mode: hybrid_txt
+#     formats: [Z-Image]
+#   fur:
+#     mode: hybrid_txt
+#     formats: [Z-Image]
+#   sfw:
+#     mode: hybrid_txt
+#     formats: [Z-Image]
+
+# ==============================================================
+# == Publishing
+# ==============================================================
+
+publishing:
+  huggingface:
+    pretty_name: Ladybug Felkin # Human-readable dataset-card base title.
+    version: v1.0-z-image # Public dataset version label.
+    optimized_for_model: FLUX.2 Klein Base 4B # Rendered as [For FLUX.2 Klein Base 4B].
+    tags: [Z-Image, Z-Image-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.
+
+    # -- Generated artifact filenames written into TRAINING/<config-id>_<run>.
+    config_filename: simpletuner-config.json                  # Top-level SimpleTuner config.
+    data_backend_filename: simpletuner-multidatabackend.json  # Multi-databackend config.
+
+    # Generated cache/output directories. Relative paths resolve below TRAINING/<config-id>_<run>.
+    cache_root: .simpletuner-cache     # Shared model, text, and VAE cache root.
+    output_dir: _simpletuner-output    # Normal full-run checkpoint output dir.
+
+    # Subset-specific SimpleTuner dataloader overrides. Any key here is merged
+    # into the subset backend entry after the dataset defaults below.
+    subsets:
+      felkin_anchor:
+        probability: 1       # Sample anchor identity images less often.
+        resolution: 1024        # High-resolution identity anchors support 1024 area.
+        # minimum_image_size: 1024 # Avoid using weakly upscaled anchors.
+        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.
+        resolution: 1024        # Full-body images are the strongest high-res subset.
+        crop: false             # Preserve full-body composition.
+        # repeats: 0
+        # disabled: false
+      body_details:
+        probability: 3 # Sample general detail subset at normal probability.
+        crop: false     # Preserve detail framing; many samples are already close crops.
+        # repeats: 0
+        # disabled: false
+      fur:
+        probability: 1.0 # Give focused fur crops extra exposure without changing other subsets.
+        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.
+        resolution: 1024        # Preserve detail in high-res head crops.
+        crop: false             # Preserve detail framing; many samples are close crops.
+      genitals:
+        probability: 4   # 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: 1024             # Default target area edge for mixed-resolution subsets.
+      minimum_image_size: 512      # Keep smaller detail/anatomy crops after export upscaling.
+      maximum_image_size: 1024     # Upper bound for input image sizing.
+      target_downsample_size: 1024 # 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                  # Disable SimpleTuner cropping for this Z-Image 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 MANIFEST.knf.xlsx.
+    validation_prompts:
+      enabled: true
+      filename: simpletuner-validation-prompts.json
+      from_prompt_pack:
+        prompts: rook-kaefer
+        caption_styles: [nlg]
+        uses: [training_validation]
+      from_manifest:
+        enabled: true
+        seed: 42
+        profile: z-image
+        activation_token: Rook_Kaefer
+        species_token: felkin
+        negative_control_species: wolf
+        exchanges:
+          - [erection, fully_sheathed]
+
+    # Model and LoRA settings merged into the top-level SimpleTuner config.
+    model:
+      model_type: lora       # Train a LoRA adapter.
+      model_family: z_image    
+      model_flavour: base    # base is Undistilled
+      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   # Ask SimpleTuner to save ComfyUI-style LoRA keys.
+
+      # Base FLUX.2 Klein asset. Klein models use the bundled Qwen3 text
+      # encoder, so no separate T5/text-encoder path is set here.
+      # !! We don't have the 16 bit
+      # pretrained_model_name_or_path: /mnt/wsl/comfyui-models/models/diffusion_models/Chroma/chroma_v10HD.safetensors
+
+      # Precision defaults for FLUX.2 Klein 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.
+    
+    
+    # == Curricular warmup
+    subset_warmup:
+      enabled: true
+      subsets: [genitals, fullbody, head]
+      steps: 800   # !! First steps of the total run are ONLY listed subsets!
+
+    
+    # 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-z_image-1                # Full-run tracker name.
+      hub_model_id: ladybird-z_image-1-lora               # Optional Hub model id if push_to_hub is enabled.
+
+      optimizer: adamw_bf16        # BF16 AdamW optimizer.
+      learning_rate: 0.00008        # 1.2e-4; BFL recommends 8e-5 to 1e-4 for LoRA.
+      lr_scheduler: constant_with_warmup # Ramp up LR to avoid weird color biases.
+      lr_warmup_steps: 100
+      seed: 42                     # Training seed.
+      gradient_checkpointing: true # Reduce VRAM by recomputing activations.
+      max_grad_norm: 0.1           # Clip gradients for stability. Simpletuner recommends 0.01 for z-image
+      grad_clip_method: norm       # Clip by gradient norm.
+
+      train_batch_size: 4             # Per-device batch size; 32 GB VRAM should handle this first.
+      gradient_accumulation_steps: 1  # Keep optimizer steps direct when VRAM allows batch 2.
+      num_train_epochs: 0             # Step-based training; max_train_steps controls run length.
+      max_train_steps: 2600           # First full Z-Image character run.
+
+      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: 200    # Save checkpoint every 200 steps.
+      validation_step_interval: 200    # Run validation every 200 steps.
+      validation_on_startup: 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: 1024x1024       # Keep validation affordable for the first Z-Image run.
+      validation_num_inference_steps: 35     # Full-run validation sampling steps.
+      validation_guidance: 4.0               # Inference guidance used by validation.
+      validation_guidance_real: null         # Remove Chroma default guidance-real from generated config.
+      validation_guidance_rescale: 0.0       # Guidance rescale; 0 disables rescale.
+      validation_seed: 42                    # Deterministic validation seed.
+      num_eval_images: 1                     # Images per validation 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:
+      validation_on_startup: false
+      tracker_run_name: ladybird-f2k-1-testrun # Separate tracker/log name for smoke runs.
+      output_dir: _simpletuner-output-testrun  # Keep smoke checkpoints away from full run.
+      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.