Instructions to use Attonos/MRA_coder_local with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Attonos/MRA_coder_local with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Attonos/MRA_coder_local") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 307f8b0baed3b565346506e9f0be8f8e6f989097f307ec16cb9b311a3805e8df
- Size of remote file:
- 49.9 MB
- SHA256:
- a296ca4e400fc704b8e8adcd3850df54822d0ef320a11f2c61911f9ea47c3cf3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.