Image-to-Image
Diffusers
Safetensors
English
QwenImageEditPlusPipeline
image-edit
zen
zenlm
hanzo
image-editing
diffusion
Instructions to use zenlm/zen-image-edit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zenlm/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("zenlm/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
metadata
language: en
license: apache-2.0
tags:
- image-to-image
- zen
- zenlm
- hanzo
- image-editing
- diffusion
pipeline_tag: image-to-image
library_name: diffusers
Zen Image Edit
Instruction-following image editing model for targeted modifications and inpainting.
Overview
Built on Zen MoDE (Mixture of Distilled Experts) architecture with 7B parameters.
Developed by Hanzo AI and the Zoo Labs Foundation.
Quick Start
from diffusers import AutoPipelineForText2Image
import torch
model_id = "zenlm/zen-image-edit"
pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
image = pipe("A serene mountain landscape at sunset, photorealistic").images[0]
image.save("output.png")
API Access
from openai import OpenAI
client = OpenAI(base_url="https://api.hanzo.ai/v1", api_key="your-api-key")
response = client.images.generate(
model="zen-image-edit",
prompt="A serene mountain landscape at sunset",
size="1024px",
)
print(response.data[0].url)
Model Details
| Attribute | Value |
|---|---|
| Parameters | 7B |
| Architecture | Zen MoDE |
| Max Resolution | 1024px |
| License | Apache 2.0 |
License
Apache 2.0