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: 2,460 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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | 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 |