Instructions to use mikkoph/mikkoph-zimage-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mikkoph/mikkoph-zimage-turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("mikkoph/mikkoph-zimage-turbo") prompt = "A striking fashion portrait of an exteremly beautiful, fair skinned russian young woman draped in voluminous, sheer black tulle fabric. She wears a wide, elastic nude-colored headband completely covering her hair, while the black fabric flows dramatically around her head like fog and wraps around her torso and legs, creating an ethereal, layered effect. Her curves are well visible. She has a tiny black heart tattoo on her shoulder and wears dramatic, smoky eye makeup. Her eyes are blue. Her left hand rests on her thigh as she looks directly at the camera with a serious, intense gaze against a soft, neutral grey studio background. The frame is cut right under the hips and the top of the head is cropped" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
The comparison chart looks very effective.
I haven't tried it yet, but after looking at the comparison chart, I can see that it significantly improves the quality of professional photography portraits (and doesn't affect other features). If there is a z-image base version without the specific trigger words (which might require longer training), this should be adopted by many people who like to perform LORA merging in models with "realism" labels.
The effectiveness of your first training has exceeded the quality of many models that have gained attention in the community.
thank you! The LoRA has be trained on the base model (not de-turbo) but the samples have been generated on Z-Image Turbo. The trigger word doesn't have any effect actually, about half of the samples do not include it and it seems to make no difference