import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image, export_to_video
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("zenlm/zen-video-i2v", dtype=torch.bfloat16, device_map="cuda")
pipe.to("cuda")
prompt = "A man with short gray hair plays a red electric guitar."
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
)
output = pipe(image=image, prompt=prompt).frames[0]
export_to_video(output, "output.mp4")Configuration Parsing Warning:Invalid JSON for config file config.json
Zen Video I2v
Image-to-video generation model for animating static images at 720p.
Overview
Built on Zen MoDE (Mixture of Distilled Experts) architecture with 13B parameters.
Developed by Hanzo AI and the Zoo Labs Foundation.
Quick Start
from diffusers import AutoPipelineForImage2Video
from PIL import Image
import torch
model_id = "zenlm/zen-video-i2v"
pipe = AutoPipelineForImage2Video.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")
image = Image.open("input.jpg")
video_frames = pipe(image, prompt="A gentle wind blows through the scene").frames[0]
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-video-i2v",
prompt="A drone flying over a tropical coastline at golden hour",
size="1280x720",
)
print(response.data[0].url)
Model Details
| Attribute | Value |
|---|---|
| Parameters | 13B |
| Architecture | Zen MoDE |
| License | Apache 2.0 |
License
Apache 2.0
- Downloads last month
- -