Instructions to use shellypeng/AtomicXL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shellypeng/AtomicXL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shellypeng/AtomicXL", 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
- Local Apps
- Draw Things
- DiffusionBee
Rename vae/diffusion_pytorch_model.safetensors to vae/diffusion_pytorch_model.fp16.safetensors
906a58a verified - Xet hash:
- ea738416d38b265de5e0a0fb3ecce826e70d047293c0818d0e42952377b4123a
- Size of remote file:
- 167 MB
- SHA256:
- 39a9822aaa359fe37ca5d3cff8a06b9505f29a018b2331d126b36472e77665a6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.