Instructions to use zenlm/zen3-image-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use zenlm/zen3-image-dev with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("zenlm/zen3-image-dev", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 1,280 Bytes
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language: en
license: apache-2.0
tags:
- zen
- zenlm
- hanzo
- zen3
- text-to-image
- diffusion
pipeline_tag: text-to-image
library_name: diffusers
---
# Zen3 Image Dev
Developer variant of Zen3 Image for research and fine-tuning workflows.
## Overview
Built on **Zen MoDE (Mixture of Distilled Experts)** architecture with 12B parameters.
Developed by [Hanzo AI](https://hanzo.ai) and the [Zoo Labs Foundation](https://zoo.ngo).
## Quick Start
```python
from diffusers import AutoPipelineForText2Image
import torch
model_id = "zenlm/zen3-image-dev"
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
```python
from openai import OpenAI
client = OpenAI(base_url="https://api.hanzo.ai/v1", api_key="your-api-key")
response = client.images.generate(
model="zen3-image-dev",
prompt="A serene mountain landscape at sunset",
size="1024px",
)
print(response.data[0].url)
```
## Model Details
| Attribute | Value |
|-----------|-------|
| Parameters | 12B |
| Architecture | Zen MoDE |
| Max Resolution | 1024px |
| License | Apache 2.0 |
## License
Apache 2.0
|