Instructions to use angshineee/dogs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use angshineee/dogs with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("angshineee/dogs") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-100/optimizer.bin from angshineee/dogs: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/angshineee/dogs/resolve/main/checkpoint-100/optimizer.bin
- Command line
-
hf download hf://angshineee/dogs/checkpoint-100/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/angshineee/dogs/resolve/main/checkpoint-100/optimizer.bin
6.59 MB
- Xet hash:
- bf7b3a6b366426b5313ebd5921852d48e2b0849cf32606389647d09a40e732e1
- Size of remote file:
- 6.59 MB
- SHA256:
- f1cfdcdd3cbe2a44755d3d488e28a86b30eae10046a0819ecabfadf596200da5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.