OpenVideo
Open-source AI video generation library without API keys. Generate videos from text prompts or images using open-source models.
Features
- Text-to-Video: Generate videos from text descriptions
- Image-to-Video: Animate static images into videos
- Multiple Models: Support for various open-source models (Zeroscope, ModelScope, AnimateDiff)
- No API Keys Required: Fully local generation using open-source models
- CLI Interface: Easy command-line usage
- Python API: Programmatic access for developers
Installation
pip install openvideo
For development installation:
pip install openvideo[dev]
Requirements
- Python 3.9+
- CUDA-capable GPU (recommended for performance)
- 8GB+ VRAM for video generation
Quick Start
CLI Usage
# Text to Video
openvideo text2video -p "a cat walking in the snow" -o output.mp4
# Image to Video
openvideo image2video -i input.png -o output.mp4 -p "camera panning"
Python API
from openvideo import VideoGenerator
# Initialize generator
generator = VideoGenerator(model_name="zeroscope")
# Generate from text
frames = generator.generate(
prompt="a beautiful sunset over the ocean",
num_frames=24,
width=512,
height=512,
)
# Save video
generator.save_video(frames, "output.mp4", fps=24)
# Generate from image
from PIL import Image
img = Image.open("input.png")
frames = generator.generate_from_image(
image=img,
prompt="gentle camera pan",
num_frames=24,
)
generator.save_video(frames, "output.mp4")
Supported Models
| Model | Description | VRAM |
|---|---|---|
| zeroscope | High-quality text-to-video | 8GB |
| modelscope | Fast text-to-video | 6GB |
| animatediff | Animation from SD models | 6GB |
Configuration
GPU Settings
# Use specific device
generator = VideoGenerator(device="cuda")
# Use float16 for less VRAM
generator = VideoGenerator(dtype="float16")
Generation Parameters
| Parameter | Default | Description |
|---|---|---|
| num_frames | 24 | Number of frames |
| width | 512 | Frame width |
| height | 512 | Frame height |
| num_inference_steps | 25 | Denoising steps |
| guidance_scale | 7.5 | CFG scale |
| seed | None | Random seed |
Advanced Usage
Custom Model Loading
from openvideo.models.text2video import TextToVideoModel
model = TextToVideoModel(model_name="zeroscope")
model.load()
frames = model.generate(prompt="your prompt")
Batch Generation
prompts = [
"a dog running in park",
"a bird flying in sky",
"rain falling on window",
]
for i, prompt in enumerate(prompts):
frames = generator.generate(prompt=prompt)
generator.save_video(frames, f"video_{i}.mp4")
Troubleshooting
Out of Memory
- Reduce resolution (width/height)
- Use float16 dtype
- Reduce num_frames
- Enable attention slicing
Slow Generation
- Use GPU instead of CPU
- Reduce num_inference_steps
- Use smaller models
License
MIT License
Credits
- Uses Hugging Face Diffusers
- Model weights from various open-source projects
Metadata
Release files for openvideo-ai 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| openvideo_ai-0.1.0.tar.gz | 4.6 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openvideo_ai-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 8.5 kB
Release files / openvideo_ai-0.1.0.tar.gz
| Download URL | openvideo_ai-0.1.0.tar.gz |
|---|---|
| Size | 4.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / openvideo_ai-0.1.0-py3-none-any.whl
| Download URL | openvideo_ai-0.1.0-py3-none-any.whl |
|---|---|
| Size | 3.9 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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