Instructions to use xfcghj/AR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xfcghj/AR with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("xfcghj/AR", 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 mesh/scripts/train.sh from xfcghj/AR: direct link, hf CLI and curl.
- Browser
- Download file 194 Bytes
-
https://huggingface.co/xfcghj/AR/resolve/main/mesh/scripts/train.sh
- Command line
-
hf download hf://xfcghj/AR/mesh/scripts/train.sh
-
curl -L -o train.sh https://huggingface.co/xfcghj/AR/resolve/main/mesh/scripts/train.sh
194 Bytes
| export CUDA_VISIBLE_DEVICES=4,5,6,7 | |
| torchrun \ | |
| --nproc_per_node=4 \ | |
| --master_port=29889 \ | |
| train.py \ | |
| --batch_size 2 \ | |
| --epochs 100 \ | |
| --save_every_k_epochs 1 |