Instructions to use btmccarthy15/SD2LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use btmccarthy15/SD2LORA with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("btmccarthy15/SD2LORA") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-600/optimizer.bin from btmccarthy15/SD2LORA: direct link, hf CLI and curl.
- Browser
- Download file 13 MB
-
https://huggingface.co/btmccarthy15/SD2LORA/resolve/main/checkpoint-600/optimizer.bin
- Command line
-
hf download hf://btmccarthy15/SD2LORA/checkpoint-600/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/btmccarthy15/SD2LORA/resolve/main/checkpoint-600/optimizer.bin
13 MB
- Xet hash:
- b176ee07e3490696b90f660df985b15a0c1e845c350adfc8ae771a9606b31a0c
- Size of remote file:
- 13 MB
- SHA256:
- 2427f42b603af3b949836ee94668b67a16aca02ff4bf90bcd79d3904d73edd8d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.