Instructions to use GenghisHan0911/hari-zit-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GenghisHan0911/hari-zit-lora 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("GenghisHan0911/hari-zit-lora") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Hari

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Model description
๐ Model Description
This is a LoRA (Low-Rank Adaptation) model fine-tuned on the Z-Image Turbo (ZIT) architecture. It is exclusively designed to generate consistent facial features, styling, and identity for the virtual AI influencer, "Hari (ํ๋ฆฌ)".
- Base Model: Z-Image Turbo (ZIT) - Single-Stream Diffusion Transformer
- Task: Text-to-Image Generation (Character Consistency)
- Resolution: Highly optimized for 1024x1024
โ๏ธ Usage Guidelines
[WARNING] Since this LoRA is based on the ZIT architecture, it is NOT compatible with SDXL, SD 1.5, or SD 2.1. Please ensure you are using a ZIT-compatible pipeline (e.g., ComfyUI with ZIT nodes).
- Trigger Word(s): `[hari]`
- Recommended Weight: `[1.0]` (Adjust based on desired likeness and prompt flexibility. Higher weights may over-saturate the image.)
Trigger words
You should use hari to trigger the image generation.
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Model tree for GenghisHan0911/hari-zit-lora
Base model
Tongyi-MAI/Z-Image-Turbo