Image-to-Image
Diffusers
Safetensors
MageFlowPipeline
image-editing
instruction-based-editing
diffusion
rectified-flow
mage-flow
Instructions to use microsoft/Mage-Flow-Edit-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use microsoft/Mage-Flow-Edit-Base with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("microsoft/Mage-Flow-Edit-Base", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d9fc02abea32ce851bafafdf68e19051057488953a664e951853505b6aee4469
- Size of remote file:
- 345 MB
- SHA256:
- 34e076dc1e8a15321e1e07be5111d59cf16dd10b804b7c7e20b4de29013427e0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.