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
| import torch | |
| from tqdm import tqdm # 推荐安装 tqdm 以显示进度条 | |
| from utils.autodataloader_v3 import get_mesh_dataloader | |
| def count_dataset_samples(dataloader): | |
| print("正在统计数据总数,请稍候...") | |
| count = 0 | |
| # 使用 tqdm 包装 dataloader 可以直观看到进度,如果不方便安装,可去掉 tqdm | |
| for batch in tqdm(dataloader): | |
| # 这里的 batch 是由 collate_fn 返回的字典 | |
| # 由于 batch_size 可能大于 1,每次累加当前 batch 的大小 | |
| # 假设 batch['vertices'] 是一个列表,其长度即为当前批次的样本数 | |
| batch_size = len(batch['vertices']) | |
| # print(f"batch_size:{batch_size}") | |
| count += batch_size | |
| return count | |
| if __name__ == "__main__": | |
| # 实例化 DataLoader | |
| # 注意:如果数据量非常巨大,请确保 num_workers 设置合理,否则可能会因为读取速度瓶颈卡住 | |
| dataloader = get_mesh_dataloader(batch_size=32, num_workers=16) | |
| total_samples = count_dataset_samples(dataloader) | |
| print(f"\n✅ 数据集总样本数: {total_samples}") |