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 scripts/test.sh from xfcghj/AR: direct link, hf CLI and curl.
- Browser
- Download file 367 Bytes
-
https://huggingface.co/xfcghj/AR/resolve/main/scripts/test.sh
- Command line
-
hf download hf://xfcghj/AR/scripts/test.sh
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curl -L -o test.sh https://huggingface.co/xfcghj/AR/resolve/main/scripts/test.sh
367 Bytes
| export CUDA_VISIBLE_DEVICES=1,2,3 | |
| python test.py \ | |
| --checkpoint_path /home/dataset-assist-0/usr/lh/ysh/dw/RL/AR/checkpoints/20260614_235422/model_epoch_7.pth \ | |
| --archive_path /home/dataset-assist-0/usr/lh/ysh/dw/RL/AR/data/shards/shard_0000.tar.zst \ | |
| --num_refine_groups 3 \ | |
| --model_device cuda:0 \ | |
| --vae_device cuda:0 \ | |
| --clip_device cuda:0 |