FFusion Turbo

An experimental weight-adjusted Z-Image Turbo checkpoint, retuned to lean digital / CGI instead of the default photorealistic bias. SFW-oriented.

Drop-in replacement for z_image_turbo_bf16 — same architecture, same 9-step turbo workflow, same VAE and text encoder. Just swap the diffusion model.

🧪 Experimental Notice

This is a weight experiment, not a finetune on new data. The model was adjusted to shift its aesthetic prior toward rendered / CGI output. Results will vary — some prompts respond strongly, others look nearly identical to base turbo. Consider this a sandbox release.

🎨 What it does

• Cleaner 3D render / CGI aesthetic out of the box

• Stronger digital illustration and stylized outputs

• Less aggressive skin-texture / pore / wrinkle bias from the stock turbo

• Still fast — 6–10 steps, same settings as base turbo

Best for: product renders, concept art, stylized characters, abstract compositions, anything that should look made rather than photographed.

⚙️ Usage

SettingValueBaseZ-Image TurboSteps8–10 (9 is the sweet spot)CFG1.0Samplerany turbo-compatible (euler, dpm++ 2m)VAEstock Z-Image ae.safetensorsText encoderstock qwen_3_4b.safetensorsPrecisionBF16

No trigger word needed — it's a base checkpoint, style is always on.

💡 Tips

• Pairs well with CGI / 3D-style LoRAs — stacks their effect instead of fighting it

• If you want to pull back toward photoreal, blend with stock turbo at 0.5 / 0.5

• Works with any Z-Image Turbo ControlNet / workflow unchanged
  
  

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🔬 Model Stats
Full BF16 checkpoint — 453 tensors, 6.155B parameters, no NaN / no Inf. Clean build.



Module
Tensors
Params
% of Total




layers.* (transformer blocks)
390
5.43 B
88.2%


noise_refiner
26
361.8 M
5.9%


context_refiner
22
353.9 M
5.8%


cap_embedder
3
9.8 M
0.2%


final_layer
4
1.2 M
<0.1%


t_embedder
4
0.5 M
<0.1%


x_embedder
2
0.25 M
<0.1%


pad tokens
2
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Total
453
6.155 B
100%



📊 Weight Distribution

Global range: min ≈ −14.00, max ≈ +13.94 — in-line with typical DiT checkpoints
Most active modules (highest std): the deep layers layers.26 through layers.29 — this is where the style adjustment is concentrated. Their ffn_norm2 and attention_norm2 tensors show std up to 3.2 vs. a model average of ~0.32
Most conservative modules: t_embedder (std ~0.005–0.02) — timestep embedding is nearly untouched, as expected
Feed-forward w2 weights carry the largest absolute values (up to ±14), consistent with how Z-Image’s MLP projections store learned priors

✅ File Integrity



Check
Result




NaN tensors
0


Inf tensors
0


Dtype consistency
100% BF16


Architecture match vs. z_image_turbo_bf16
structurally identical (906/906 keys)



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