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 scripts/train.sh from xfcghj/AR: direct link, hf CLI and curl.
- Browser
- Download file 375 Bytes
-
https://huggingface.co/xfcghj/AR/resolve/main/scripts/train.sh
- Command line
-
hf download hf://xfcghj/AR/scripts/train.sh
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curl -L -o train.sh https://huggingface.co/xfcghj/AR/resolve/main/scripts/train.sh
375 Bytes
| export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 | |
| torchrun \ | |
| --nproc_per_node=8 \ | |
| --master_port=29898 \ | |
| train.py \ | |
| --num_refine_groups 3 \ | |
| --batch_size 6 \ | |
| --lr 1e-4 \ | |
| --epochs 100 \ | |
| --save_every_k_epochs 1 \ | |
| --last_ckpt /home/dataset-assist-0/usr/lh/ysh/dw/RL/AR/checkpoints/20260617_190742/model_step_4500.pth \ | |
| --auto_resume |