Instructions to use iskandre/output2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use iskandre/output2 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("iskandre/output2") prompt = "a photo of harito cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download vae/diffusion_pytorch_model.bin from iskandre/output2: direct link, hf CLI and curl.
- Browser
- Download file 335 MB
-
https://huggingface.co/iskandre/output2/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://iskandre/output2/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/iskandre/output2/resolve/main/vae/diffusion_pytorch_model.bin
335 MB
- Xet hash:
- 16b3d5d6b914117425b7a5bf913b7e61ff9444e6e3f2bf8e3aaee21fb1328fa5
- Size of remote file:
- 335 MB
- SHA256:
- af27ea858349760ebe3311953e0bfe8d6fd257dc9537ae0b2b938c262132a2c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.