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-playground 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-playground", 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-playground", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Creative playground variant of Zen3 Image tuned for artistic and experimental generation.
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-playground"
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-playground",
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