Instructions to use Adminhuggingface/OUTPUTA_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Adminhuggingface/OUTPUTA_2 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("Adminhuggingface/OUTPUTA_2") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-12500/optimizer.bin from Adminhuggingface/OUTPUTA_2: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/Adminhuggingface/OUTPUTA_2/resolve/main/checkpoint-12500/optimizer.bin
- Command line
-
hf download hf://Adminhuggingface/OUTPUTA_2/checkpoint-12500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/Adminhuggingface/OUTPUTA_2/resolve/main/checkpoint-12500/optimizer.bin
6.59 MB
- Xet hash:
- 405413f6b8e7de5f6b9a343bffdacd463cb4abaeb588349efa77e675fc82e3cf
- Size of remote file:
- 6.59 MB
- SHA256:
- 9cb44bdf01bed4e51564d06b2489097f79fcb33d22e2f77f940ece0c67869ff5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.