Zen3 Generation
Collection
Zen 3 generation — legacy zen-3-*, zen3-*, omni, nano, guard, image, reranker, 3d. • 12 items • Updated
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]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]Developer variant of Zen3 Image for research and fine-tuning workflows.
Built on Zen MoDE (Mixture of Distilled Experts) architecture with 12B parameters.
Developed by Hanzo AI and the Zoo Labs Foundation.
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")
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)
| Attribute | Value |
|---|---|
| Parameters | 12B |
| Architecture | Zen MoDE |
| Max Resolution | 1024px |
| License | Apache 2.0 |
Apache 2.0