Release – Version 0.2 (Unsure which model for your GPU? See Rule of Thumb below.)
What’s new?
Since this is meant to become a semi-realism model, I pushed it further in that direction and added more details. I also intentionally switched to new showcase samplers because different seeds simply looked better in this version. A few images were replaced as well.
(Feedback is highly appreciated!)

Node:
Because this is a checkpoint/LoRA merge (I only use LoRAs that I have trained myself), it can cause issues if you use an additional LoRA with a high epoch. Try starting with a LoRA strength of about 0.3 and increase it gradually from there.

Advanced tip:
In the ModelSamplingAuraFlow node, you can adjust the value between 3.00 and 3.10. This can help if you get images with weird hands or other repeated visual glitches.

• bf16 Diffusion Model (fp8/fp16 coming soon, write me if u need it bad ^^)
• No CLIP and no VAE included (ask me if you need help)
• Recommended settings: CFG 1, 8 steps (max. 15)
• Sampler: Euler A, Scheduler: Simple or Beta (Beta highly recommended)
• Sample images are not upscaled and no Hi-Res Fix was used

Original ComfyUI Models: Link (here you can find CLIP and VAE)

First Release – Version 0.1
This is my first Z-ImageTurbo aka checkpoint LoRA merge release, so it’s still an early version (V0.1).

• bf16/fp8/fp16 Diffusion Model
• No CLIP and no VAE included (Ask me if you need help with that.)
• Recommended settings: CFG 1, 8 steps (max.15)
• Sampler: Euler A, Scheduler: Simple or Beta (Beta highly recommended)
• Sample images are not upscaled and no Hi-Res Fix was used

Original ComfyUI Models: Link (here you can find CLIP and VAE)

I’m still learning and improving, so future updates are planned. Feedback is highly appreciated!

Rule of Thumb

• NVIDIA Turing (RTX 20-series)
  → ❌ no real BF16 support, FP16 is the practical option
  → Quality: usually fine, but a bit more fragile than newer formats

• NVIDIA Ampere (RTX 30-series)
  → ✅ BF16 works well (problems? try to update your PyTorch/CUDA or use fp16)
  → Quality: generally very close to FP32, little noticeable loss

• NVIDIA Ada Lovelace (RTX 40-series)
  → ✅ BF16 stable, FP8 partly possible via software
  → Quality: BF16 ~ FP32; FP8 can show noticeable quality drops depending on workload

• NVIDIA Blackwell (RTX 50-series, e.g., 5090)
  → ✅ BF16 very solid, FP8 better supported but not magic
  → Quality: FP8 is usable, but there is still some quality loss in many cases... not huge, but real

• FP32: still needs to be released by Z-Image

Note: You can load FP8 on basically any GPU and benefit from lower VRAM when loading, but on hardware without proper FP8 support it is automatically converted to FP16/FP32 for computation. That means you don’t get real FP8 speedups, only the memory benefit.