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Skills included: - venice-chat: Chat with Venice LLM models, vision, reasoning - venice-chat-benchmark: Benchmark chat models with infographics - venice-image-gen: Generate images via Venice API - venice-list-image-models: List available image models - venice-list-text-models: List available text models - venice-list-video-models: List available video models - venice-tts: Text-to-speech via Venice API - venice-video-generate: Generate videos from text/images - venice-video-queue: Queue video generation jobs - venice-video-quote: Get video generation cost quotes - venice-video-retrieve: Retrieve completed videos All rebranded from Agent Zero paths to Agent JAE (~/.jae/agent/skills/). Requires VENICE_API_KEY environment variable.
88 lines
2.5 KiB
Markdown
88 lines
2.5 KiB
Markdown
# Venice Video Queue
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Queue videos for generation on [Venice.ai](https://venice.ai/). Supports text-to-video, image-to-video, and video-to-video. Returns a `queue_id` for later retrieval with `venice-video-retrieve`.
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> For a simpler all-in-one workflow, use [venice-video-generate](../venice-video-generate/) instead.
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## Features
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- **Text-to-video**, **image-to-video**, and **video-to-video** support
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- Automatic file-to-base64 encoding for local image/video/audio inputs
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- CLI with full argparse support
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- JSON output mode for scripting
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- Convenience functions for common workflows
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## Prerequisites
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```bash
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pip install requests pydantic
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export VENICE_API_KEY="your_venice_api_key"
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```
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## Usage
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### Text-to-video
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```bash
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python scripts/queue_video.py "A cat playing piano in a jazz club" \
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--duration 5s --resolution 720p --aspect-ratio 16:9
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```
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### Image-to-video
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```bash
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python scripts/queue_video.py "Animate this scene with gentle motion" \
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--image /path/to/image.png \
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--model wan-2.5-preview-image-to-video
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```
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### JSON output (for scripting)
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```bash
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python scripts/queue_video.py "Cinematic sunset" --json
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# Output: {"model": "wan-2.5-preview-text-to-video", "queue_id": "abc123..."}
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```
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## Options
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| Option | Short | Default | Description |
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|--------|-------|---------|-------------|
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| `prompt` | -- | *(required)* | Text description |
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| `--model` | `-m` | `wan-2.5-preview-text-to-video` | Model ID |
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| `--duration` | `-d` | `5s` | Duration (`5s`, `10s`) |
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| `--resolution` | `-r` | `720p` | Resolution |
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| `--aspect-ratio` | `-a` | None | Aspect ratio (omit if model doesn't support) |
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| `--negative-prompt` | `-n` | None | What to avoid |
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| `--image` | `-i` | None | Input image path |
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| `--video` | `-v` | None | Input video path |
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| `--audio` | -- | None | Audio file path |
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| `--with-audio` | -- | off | Enable audio generation |
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| `--json` | -- | off | JSON output |
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## After Queuing
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Use the returned `queue_id` with `venice-video-retrieve`:
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```bash
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python ../venice-video-retrieve/scripts/retrieve_video.py MODEL QUEUE_ID
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```
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## Python Import
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```python
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from queue_video import queue_video, queue_text_to_video, queue_image_to_video
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# Text-to-video
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result = queue_text_to_video(prompt="A cat playing piano", duration="5s")
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print(f"Queue ID: {result.queue_id}")
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# Image-to-video
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result = queue_image_to_video(prompt="Animate this", image_path="/path/to/img.png")
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print(f"Queue ID: {result.queue_id}")
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```
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## Environment Variables
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| Variable | Required | Description |
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|----------|----------|-------------|
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| `VENICE_API_KEY` | Yes | Venice.ai API key |
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