Z-Image-Turbo_clear

Guide (External site): English | Japanese
Hugging Face
VAE: Z-Image_clear_vae

Compare to Z-Image-Turbo (Original)

Z-Image-Turbo_clear | Z-Image-Turbo

The Z-Image-Turbo_clear model is a fine-tuned version of Z-Image-Turbo that improves details compared to the original.

For better results, please use it with the dedicated VAE, Z-Image_clear_vae.

Which Precision Should I Use?

+----------+------+----------+----------+--------------------+-----------+
| Format   | Sign | Exponent | Mantissa | Significant Digits | Precision |
+----------+------+----------+----------+--------------------+-----------+
| FP32     | 1bit | 8bit     | 23bit    | 7–8 digits         | ✅        |
| BF16     | 1bit | 8bit     | 7bit     | 2–3 digits         | 👍        |
| FP16     | 1bit | 5bit     | 10bit    | 3–4 digits         | 👍        |
| FP8(e5m2)| 1bit | 5bit     | 2bit     | 1–2 digits         | ⚠️        |
| FP8(e4m3)| 1bit | 4bit     | 3bit     | 1–2 digits         | ⚠️        |
+----------+------+----------+----------+--------------------+-----------+

Exponent: Stability
Mantissa: Quality

Anime Illustration Comparison

Hand Close-up

FP32: Highest precision, high VRAM usage
BF16: Most popular in Z-Image-Turbo model, good for LoRA compatibility
FP16: Slightly better precision than BF16, less performance degradation on pre-RTX 2000 or non-NVIDIA GPUs
FP8(e4m3): Lower precision, low VRAM usage, accelerated on RTX 4000 series and later

If you are unsure which precision to use, it is safest to choose the BF16 format.

Recomended Settings

Sampler:
- euler
- res_multistep
Scheduler: beta
Steps: 9
ModelSamplingAuraFlow: shift 7.5 (3-9)

Upscaler

your choice, examples:

Tips (External Sites)

ComfyUI VRAM management: English | Japanese
Steps and Scheduler: English | Japanese
Color Correct custom nodes in ComfyUI: English | Japanese

License

Apache-2.0

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