Instructions to use lora-library/saz with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lora-library/saz with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("andite/anything-v4.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("lora-library/saz") prompt = "saz" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
license: creativeml-openrail-m
base_model: andite/anything-v4.0
instance_prompt: saz
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
LoRA DreamBooth - saz
These are LoRA adaption weights for andite/anything-v4.0. The weights were trained on the instance prompt "saz" using DreamBooth. You can find some example images in the following.