This time, i did a training using Z-Image De-Turbo, an undistilled version of ZIT. I also raised the LR to 0.0002 and made a 64 dim LoRA before reducing the rank to 32 and carefully merge it with the previous LoRA. I also removed the trigger word, the only constant remaining is the fact that it was training on "Digital illustrations" with an "anime-like" "semi-realistic style".

This helped me achieve better results even if i raise the CFG to 1.2 (distilled models don't have a CFG and should be used with CFG = 1, aka no negative prompt).

All showcase pictures are made with 9 steps, CFG 1.2, Euler/Beta (aka Smoothstep in stable-diffusion.cpp) using the Q4_K quant, the ablitered version of Qwen3-4B for the TE and the regular VAE, plus a flow shift at 3. They got an img2img pass at 0.5 denoise with the same parameters to smooth some details.

Below a part of my ugly python wrapper script to parse multiple prompt ;-)

Enjoy! 💜