Instructions to use Shakker-Labs/AWPortrait-Z with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Shakker-Labs/AWPortrait-Z 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("Shakker-Labs/AWPortrait-Z") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
AWPortrait-Z
AWPortrait-Z is a portrait-beauty LoRA meticulously built on the Z-Image.
- Native-noise reduction: fixed Zimage’s chronic grain—those downy, high-frequency artifacts that plagued skin tones—so complexions now look flawlessly real.
- Relit lighting: tamed the base model’s excessive HDR, restoring punchy contrast and saturation; re-engineered artificial-light behavior so studio strobes sit naturally in-scene instead of floating above it.
- Diverse faces: expanded multi-ethnic feature coverage, breaking the “same-face” barrier and delivering portraits that are both authentic and unmistakably individual.
Showcases
Comparison
Acknowledgements
This model is released by DynamicWang.
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