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 check_text.py from xfcghj/AR: direct link, hf CLI and curl.
- Browser
- Download file 2.46 kB
-
https://huggingface.co/xfcghj/AR/resolve/main/check_text.py
- Command line
-
hf download hf://xfcghj/AR/check_text.py
-
curl -L -o check_text.py https://huggingface.co/xfcghj/AR/resolve/main/check_text.py
2.46 kB
| import io | |
| import tarfile | |
| import pickle | |
| import zstandard | |
| # 只拿第一个包来做实验 | |
| archive_path = "/home/dataset-assist-0/usr/lh/ysh/dw/RL/AR/data/DyMesh_50000v_16f_0001_part_00" | |
| print("正在抽取前 3 个文件窥探内部完整的文本和数据结构...\n") | |
| with open(archive_path, 'rb') as fh: | |
| dctx = zstandard.ZstdDecompressor() | |
| with dctx.stream_reader(fh) as reader: | |
| with tarfile.open(fileobj=reader, mode='r|') as tar: | |
| count = 0 | |
| for member in tar: | |
| if member.isfile(): | |
| count += 1 | |
| print(f"=== [文件 {count}] {member.name} ===") | |
| f = tar.extractfile(member) | |
| if f is not None: | |
| mem_file = io.BytesIO(f.read()) | |
| data = pickle.load(mem_file) | |
| if isinstance(data, dict): | |
| # 1. 打印里面所有的键 | |
| print(f" 📦 字典包含的所有 Key: {list(data.keys())}") | |
| # 2. 自动寻找并打印文本内容 | |
| text_keys = ['caption', 'text', 'prompt', 'description'] | |
| found_text = False | |
| for tk in text_keys: | |
| if tk in data: | |
| print(f" 📝 文本标注 ({tk}): \"{data[tk]}\"") | |
| found_text = True | |
| break | |
| if not found_text: | |
| # 如果不在常用词里,打印出所有不是数组的纯文本/字符串字段 | |
| for k, v in data.items(): | |
| if isinstance(v, str): | |
| print(f" 📝 发现文本字段 ({k}): \"{v}\"") | |
| # 3. 顺便打印一下矩阵的大小 | |
| if 'vertices' in data: | |
| print(f" 📐 顶点矩阵 Shape: {data['vertices'].shape}") | |
| else: | |
| print(" ❌ 该文件不是字典格式") | |
| print("-" * 60 + "\n") | |
| if count >= 100: # 只看前 3 个就退出 | |
| break |