Darwin-Image-v1 / lora_manifest.yaml
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---
# 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 Γ— 8 modules Γ— 2).
# Direct matrix fusion via `pipeline/manual_fuse.py` β€” 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 (26Γ— other LoRAs) caused over-smoothing,
# destroying portrait/color detail. The LoRA's intended goal β€” JPEG
# artifact removal β€” is unnecessary for Z-Image Turbo (distilled model
# has no JPEG artifacts to remove by design).
# - REDUCED `color` scale 0.4 β†’ 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 β€” 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: ""