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", torch_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
File size: 381 Bytes
f0fc238 394918c f0fc238 | 1 2 3 4 5 6 7 8 9 10 11 12 | # 定义你要清理的卡号
target_gpus="6,7"
# 通过 nvidia-smi 查询,-i 指定卡号,--query 过滤进程 PID
pids=$(nvidia-smi --query-compute-apps=pid --format=csv,noheader -i $target_gpus | sort -u)
if [ -n "$pids" ]; then
echo "正在终止占用 GPU $target_gpus 的进程: $pids"
kill -9 $pids
else
echo "未发现占用 GPU $target_gpus 的进程"
fi |