Instructions to use AaronCIH/RAR_modelzoo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AaronCIH/RAR_modelzoo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AaronCIH/RAR_modelzoo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download backbone/train_log.log from AaronCIH/RAR_modelzoo: direct link, hf CLI and curl.
- Browser
- Download file 76.2 MB
-
https://huggingface.co/AaronCIH/RAR_modelzoo/resolve/main/backbone/train_log.log
- Command line
-
hf download hf://AaronCIH/RAR_modelzoo/backbone/train_log.log
-
curl -L -o train_log.log https://huggingface.co/AaronCIH/RAR_modelzoo/resolve/main/backbone/train_log.log
76.2 MB
- Xet hash:
- bf08a67c7786fdf374e17136ccd5a1ffddc38bb3f57e927d1aa4bb101140bdb6
- Size of remote file:
- 76.2 MB
- SHA256:
- 153645fc5d6cca367237ed6ee2d07f3a711dc54b3a69f0c0c36f76b3d06e5667
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