Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use Loyola/model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Loyola/model3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Loyola/model3", dtype=torch.bfloat16, device_map="cuda") prompt = "A Loyolatic painting" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from Loyola/model3: direct link, hf CLI and curl.
- Browser
- Download file 6.88 GB
-
https://huggingface.co/Loyola/model3/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://Loyola/model3/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Loyola/model3/resolve/main/checkpoint-500/optimizer.bin
6.88 GB
- Xet hash:
- f9ee2a0d9984442a53dd41ee6235acd7267c5bacd7fe7c601c53b9a565f461ea
- Size of remote file:
- 6.88 GB
- SHA256:
- ca00a824e60f08f9c6b15ade05f4bff40488b7e3c75ffa3c033d6932373442e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.