Instructions to use MLXCreator/MLXCreator-ACEStep-1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MLXCreator/MLXCreator-ACEStep-1.5 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MLXCreator/MLXCreator-ACEStep-1.5", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - MLX
How to use MLXCreator/MLXCreator-ACEStep-1.5 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir MLXCreator-ACEStep-1.5 MLXCreator/MLXCreator-ACEStep-1.5
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- e1c5ae75d50677cf0cce3ad0441fbf25396122838224b9473c03004e92077538
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.