Image-to-Image
Diffusers
Safetensors
MageFlowPipeline
image-editing
instruction-based-editing
diffusion
rectified-flow
mage-flow
Instructions to use SceneWorks/Mage-Flow-Edit-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SceneWorks/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("SceneWorks/Mage-Flow-Edit-Base", 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
Download text_encoder/tokenizer.json from SceneWorks/Mage-Flow-Edit-Base: direct link, hf CLI and curl.
- Browser
- Download file 7.03 MB
-
https://huggingface.co/SceneWorks/Mage-Flow-Edit-Base/resolve/main/text_encoder/tokenizer.json
- Command line
-
hf download hf://SceneWorks/Mage-Flow-Edit-Base/text_encoder/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SceneWorks/Mage-Flow-Edit-Base/resolve/main/text_encoder/tokenizer.json
7.03 MB
File too large to display, you can check the raw version instead.