Instructions to use nphSi/Z-Image-Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nphSi/Z-Image-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,Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nphSi/Z-Image-Lora") prompt = "Alexandra Chando (vrtlAlexandraChando)" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
LoRas for Anima?
#42
by DanVance - opened
Is it feasible to create LoRAs(Real or 3D/2D) for Anima?
I did not really follow Anima and there is no support for it in OneTrainer yet because its not available in HF diffusers which is required for OT support. See https://github.com/Nerogar/OneTrainer/issues/1278
I guess output would have a cell shading look at best. Anyway i will test it when support arrives.
I think one of the biggest advantages of Anima is that it uses relatively little VRAM for high-resolution images.