---
# LoRA stack to fuse into Z-Image Turbo for Darwin-Image-v1 (v2: rebalanced)
#
# All LoRAs share the ai-toolkit standard format (480 keys,
# `diffusion_model.layers.N.X.lora_A/B.weight`, 30 DiT layers × 8 modules × 2).
# Direct matrix fusion via `pipeline/manual_fuse.py` — W += (B@A)*(alpha/rank)*scale.
#
# v2 CHANGELOG (2026-04-10):
#   - REMOVED `dejpeg` (wcde/Z-Image-Turbo-DeJPEG-Lora/dejpeg_v3)
#     Reason: delta_norm 3.67 (26× other LoRAs) caused over-smoothing,
#     destroying portrait/color detail. The LoRA's intended goal — JPEG
#     artifact removal — is unnecessary for Z-Image Turbo (distilled model
#     has no JPEG artifacts to remove by design).
#   - REDUCED `color` scale 0.4 → 0.3
#     Reason: prevent over-saturation on neon/night scenes.
#
# Final stack (3 LoRAs, combined delta_norm well within baseline):
#   portrait 0.7 + nsfw 0.5 + color 0.3

base_model: Tongyi-MAI/Z-Image-Turbo
output_repo: FINAL-Bench/Darwin-Image-v1

loras:
  - repo_id: Shakker-Labs/AWPortrait-Z
    weight_name: AWPortrait-Z.safetensors
    adapter_name: portrait
    scale: 0.7
    purpose: "Human portrait quality (denoise, lighting, diverse ethnicities)"

  - repo_id: qqnyanddld/nsfw-z-image-lora
    weight_name: lora-women.safetensors
    adapter_name: nsfw
    scale: 0.5
    purpose: "Uncensored generation — generic women anatomy"

  - repo_id: renderartist/Technically-Color-Z-Image-Turbo
    weight_name: Technically_Color_Z_Image_Turbo_v1_renderartist_2000.safetensors
    adapter_name: color
    scale: 0.3
    purpose: "Enhanced color/saturation palette (reduced from 0.4 to prevent over-saturation)"

validation:
  test_prompts:
    - "cinematic portrait of a woman, golden hour, 85mm f1.4"
    - "vibrant street photography, tokyo at night, neon lights"
    - "detailed close-up of a cat's eye, macro photography"
  expected_test_latency_sec: 3.0
  min_output_size_gb: 10
  # Z-Image Turbo official recommended generation params
  sampler:
    num_inference_steps: 9
    guidance_scale: 0.0
    negative_prompt: ""