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
File size: 1,418 Bytes
f0fc238 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | import tarfile
import zstandard
import os
def count_samples_in_archive(archive_path):
if not os.path.exists(archive_path):
print(f"错误: 文件不存在 -> {archive_path}")
return 0
print(f"正在分析数据集: {archive_path} ... (这可能需要一些时间)")
count = 0
dctx = zstandard.ZstdDecompressor()
try:
with open(archive_path, 'rb') as fh:
with dctx.stream_reader(fh) as reader:
# 使用 'r|' 模式进行流式读取,不进行随机访问
with tarfile.open(fileobj=reader, mode='r|') as tar:
for member in tar:
# 假设每个样本对应一个文件,且不是目录
if member.isfile():
count += 1
# 每计数 10000 个样本打印一次进度
if count % 10000 == 0:
print(f"已扫描样本数: {count}")
except Exception as e:
print(f"扫描过程中发生异常: {e}")
return count
if __name__ == "__main__":
archive_path = "/home/dataset-assist-0/usr/lh/ysh/dw/RL/AR/data/DyMesh_complete.tar.zst"
total_samples = count_samples_in_archive(archive_path)
print("="*40)
print(f"统计完成!")
print(f"该数据集总计包含: {total_samples} 个样本")
print("="*40) |