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
File size: 497 Bytes
40e7979 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"_class_name": "MageFlowPipeline",
"_mage_flow_version": "0.1.0",
"transformer": [
"mage_flow",
"MageFlow"
],
"vae": [
"mage_flow",
"MageVAE"
],
"text_encoder": [
"transformers",
"Qwen3VLForConditionalGeneration"
],
"tokenizer": [
"transformers",
"AutoProcessor"
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"_text_encoder_path": "text_encoder",
"_vae_source": "vae/diffusion_pytorch_model.safetensors"
} |