Image-to-Image
Diffusers
Safetensors
ZenImageEditPipeline
text-to-image
image-editing
qwen-image
text-encoder
adapter
Instructions to use AiArtLab/zen-image-edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AiArtLab/zen-image-edit 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("AiArtLab/zen-image-edit", 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: 454 Bytes
3a93d0e d264d52 3a93d0e d264d52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_class_name": "ZenImageEditPipeline",
"_diffusers_version": "0.41.0.dev0",
"processor": [
"transformers",
"Qwen3VLProcessor"
],
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Qwen3_5ForConditionalGeneration"
],
"transformer": [
"diffusers",
"QwenImage21FusionTransformer2DModel"
],
"vae": [
"diffusers",
"AutoencoderKLQwenImage21"
]
} |