Instructions to use LegoClipStars/Gallus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LegoClipStars/Gallus with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("LegoClipStars/Gallus") prompt = "NEFT" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from LegoClipStars/Gallus: direct link, hf CLI and curl.
- Browser
- Download file 497 Bytes
-
https://huggingface.co/LegoClipStars/Gallus/resolve/main/README.md
- Command line
-
hf download hf://LegoClipStars/Gallus/README.md
-
curl -L -o README.md https://huggingface.co/LegoClipStars/Gallus/resolve/main/README.md
497 Bytes
metadata
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: NEFT
parameters:
negative_prompt: Flying griffon
output:
url: images/FsJVATTacAE4tVY.jpg
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: null
license: cc-by-4.0
Gallus

- Prompt
- NEFT
- Negative Prompt
- Flying griffon
Model description
Here's my RVC 700 epoch voice model of Gallus from MLP:FIM
Download model
Download them in the Files & versions tab.