Instructions to use Akiyue/awwl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Akiyue/awwl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Akiyue/awwl", 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
| # prepare_ref_data.py | |
| import os | |
| from torchvision.datasets import CIFAR10 | |
| from tqdm import tqdm | |
| from PIL import Image | |
| def save_cifar10_images(output_dir="cifar10_train_ref"): | |
| # Tải dataset CIFAR-10 (Train split) | |
| dataset = CIFAR10(root="./data", train=True, download=True) | |
| os.makedirs(output_dir, exist_ok=True) | |
| print(f"Saving {len(dataset)} CIFAR-10 training images to '{output_dir}'...") | |
| for idx, (img, label) in enumerate(tqdm(dataset)): | |
| # img là PIL Image | |
| img.save(f"{output_dir}/{idx:05d}.png") | |
| print("✅ Done! Reference images are ready.") | |
| if __name__ == "__main__": | |
| save_cifar10_images() |