jae
c42cd9a062
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.
550 lines
19 KiB
Python
550 lines
19 KiB
Python
"""Venice.ai Full Lifecycle Video Generation
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Combines video queue and retrieve into a single operation with progress logging.
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Optimized for Agent JAE environment - clear output, efficient polling, agent-friendly responses.
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Usage:
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# CLI
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python generate_video.py "A cat playing piano" --model wan-2.5-preview-text-to-video --duration 5s
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# Python import
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from generate_video import generate_video
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result = generate_video(prompt="A cat playing piano", model="wan-2.5-preview-text-to-video")
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"""
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import os
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import sys
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import time
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import argparse
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import base64
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import requests
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from pathlib import Path
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from datetime import datetime, timedelta
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from typing import Optional, Dict, Any
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from dataclasses import dataclass, field
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# ============================================================================
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# CONFIGURATION
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# ============================================================================
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VENICE_API_KEY = os.getenv("VENICE_API_KEY")
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VENICE_QUEUE_URL = "https://api.venice.ai/api/v1/video/queue"
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VENICE_RETRIEVE_URL = "https://api.venice.ai/api/v1/video/retrieve"
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DEFAULT_OUTPUT_DIR = "/root/venice_videos"
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DEFAULT_MODEL = "wan-2.5-preview-text-to-video"
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PROGRESS_LOG_INTERVAL = 20 # seconds between progress logs
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DEFAULT_POLL_INTERVAL = 5 # seconds between API polls
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DEFAULT_MAX_WAIT = 900 # 15 minutes max wait
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# ============================================================================
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# DATA CLASSES
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# ============================================================================
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@dataclass
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class GenerationResult:
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"""Result of a full video generation lifecycle."""
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success: bool
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video_path: Optional[str] = None
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queue_id: Optional[str] = None
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model: Optional[str] = None
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elapsed_seconds: float = 0.0
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error: Optional[str] = None
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# Timing statistics from API
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api_eta_seconds: Optional[int] = None
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api_progress: Optional[float] = None
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def __str__(self):
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if self.success:
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return f"SUCCESS: Video saved to {self.video_path} (took {self.elapsed_seconds:.1f}s)"
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return f"FAILED: {self.error}"
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@dataclass
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class ProgressInfo:
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"""Progress information for logging."""
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elapsed_seconds: float
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status: str
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api_progress: Optional[float] = None
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api_eta_seconds: Optional[int] = None
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poll_count: int = 0
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def format_progress_bar(self, width: int = 20) -> str:
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"""Generate a text progress bar."""
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if self.api_progress is None:
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return "[" + "?" * width + "]"
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pct = min(100, max(0, self.api_progress))
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filled = int(width * pct / 100)
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return "[" + "=" * filled + "-" * (width - filled) + "]"
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def format_eta(self) -> str:
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"""Format ETA as human-readable string."""
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if self.api_eta_seconds is None:
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return "ETA: unknown"
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if self.api_eta_seconds <= 0:
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return "ETA: completing..."
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mins, secs = divmod(self.api_eta_seconds, 60)
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if mins > 0:
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return f"ETA: {mins}m {secs}s"
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return f"ETA: {secs}s"
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# ============================================================================
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# HELPER FUNCTIONS
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# ============================================================================
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def encode_file_to_base64(file_path: str) -> str:
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"""Read a file and return base64-encoded data URI."""
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path = Path(file_path)
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if not path.exists():
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raise FileNotFoundError(f"File not found: {file_path}")
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suffix = path.suffix.lower()
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mime_types = {
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'.png': 'image/png', '.jpg': 'image/jpeg', '.jpeg': 'image/jpeg',
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'.gif': 'image/gif', '.webp': 'image/webp', '.mp4': 'video/mp4',
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'.webm': 'video/webm', '.mp3': 'audio/mpeg', '.wav': 'audio/wav',
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}
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mime = mime_types.get(suffix, 'application/octet-stream')
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with open(path, 'rb') as f:
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data = base64.b64encode(f.read()).decode('utf-8')
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return f"data:{mime};base64,{data}"
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def log_progress(info: ProgressInfo) -> None:
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"""Log progress in agent-friendly format."""
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progress_str = f"{info.api_progress:.0f}%" if info.api_progress is not None else "---%"
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bar = info.format_progress_bar()
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eta = info.format_eta()
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timestamp = datetime.now().strftime("%H:%M:%S")
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print(f"[{timestamp}] PROGRESS: {info.elapsed_seconds:>6.0f}s elapsed | "
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f"{bar} {progress_str:>4} | {eta} | status: {info.status}")
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sys.stdout.flush()
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def log_event(event_type: str, message: str) -> None:
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"""Log an event in agent-friendly format."""
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timestamp = datetime.now().strftime("%H:%M:%S")
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print(f"[{timestamp}] {event_type}: {message}")
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sys.stdout.flush()
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# ============================================================================
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# CORE API FUNCTIONS
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# ============================================================================
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def queue_video(
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model: str,
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prompt: str,
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duration: str = "5s",
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aspect_ratio: Optional[str] = None,
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resolution: str = "720p",
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audio: Optional[bool] = None,
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negative_prompt: Optional[str] = None,
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image_path: Optional[str] = None,
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image_url: Optional[str] = None,
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) -> Dict[str, Any]:
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"""Queue a video for generation. Returns dict with model and queue_id."""
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if not VENICE_API_KEY:
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raise ValueError("VENICE_API_KEY environment variable not set")
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headers = {
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"Authorization": f"Bearer {VENICE_API_KEY}",
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"Content-Type": "application/json"
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}
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# Handle image input
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if image_path and not image_url:
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image_url = encode_file_to_base64(image_path)
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# Build request with only provided fields
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request_data = {
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"model": model,
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"prompt": prompt,
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"duration": duration,
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"resolution": resolution,
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}
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if aspect_ratio is not None:
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request_data["aspect_ratio"] = aspect_ratio
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if audio is not None:
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request_data["audio"] = audio
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if negative_prompt:
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request_data["negative_prompt"] = negative_prompt
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if image_url:
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request_data["image_url"] = image_url
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response = requests.post(
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VENICE_QUEUE_URL,
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headers=headers,
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json=request_data,
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timeout=60
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)
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if not response.ok:
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try:
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error_detail = response.json()
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except:
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error_detail = response.text
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raise RuntimeError(f"Queue API Error {response.status_code}: {error_detail}")
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return response.json()
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def retrieve_video_status(
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model: str,
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queue_id: str,
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delete_on_completion: bool = False
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) -> Dict[str, Any]:
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"""Single retrieve request. Returns status dict or video bytes."""
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headers = {
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"Authorization": f"Bearer {VENICE_API_KEY}",
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"Content-Type": "application/json"
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}
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request_data = {
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"model": model,
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"queue_id": queue_id,
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"delete_media_on_completion": delete_on_completion
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}
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response = requests.post(
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VENICE_RETRIEVE_URL,
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headers=headers,
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json=request_data,
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timeout=120
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)
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if not response.ok:
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return {
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"status": "error",
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"error": f"HTTP {response.status_code}: {response.text[:200]}"
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}
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content_type = response.headers.get("Content-Type", "")
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# Check if response is video data (binary)
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if "video" in content_type or response.content[:4] == b'\x00\x00\x00' or b'ftyp' in response.content[:20]:
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return {
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"status": "completed",
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"video_data": response.content
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}
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# Try to parse as JSON
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try:
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data = response.json()
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# Normalize status to lowercase
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if "status" in data:
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data["status"] = data["status"].lower()
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return data
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except:
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# Might be binary video without proper content-type
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if len(response.content) > 1000:
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return {
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"status": "completed",
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"video_data": response.content
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}
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return {
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"status": "error",
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"error": "Failed to parse response"
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}
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# ============================================================================
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# MAIN GENERATION FUNCTION
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# ============================================================================
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def generate_video(
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prompt: str,
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model: str = DEFAULT_MODEL,
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duration: str = "5s",
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aspect_ratio: Optional[str] = "16:9",
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resolution: str = "720p",
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audio: Optional[bool] = None,
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negative_prompt: Optional[str] = None,
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image_path: Optional[str] = None,
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image_url: Optional[str] = None,
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output_path: Optional[str] = None,
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output_dir: str = DEFAULT_OUTPUT_DIR,
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max_wait: int = DEFAULT_MAX_WAIT,
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poll_interval: int = DEFAULT_POLL_INTERVAL,
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progress_interval: int = PROGRESS_LOG_INTERVAL,
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verbose: bool = True,
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delete_on_completion: bool = False,
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) -> GenerationResult:
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"""
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Full lifecycle video generation: queue, poll with progress, retrieve, save.
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Args:
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prompt: Text description of the video to generate
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model: Venice model ID (default: wan-2.5-preview-text-to-video)
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duration: Video duration (e.g., "5s", "10s")
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aspect_ratio: Aspect ratio (e.g., "16:9", "9:16", "1:1") - omit for some models
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resolution: Video resolution (e.g., "720p", "1080p")
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audio: Enable audio generation (model-dependent)
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negative_prompt: What to avoid in generation
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image_path: Local path to input image (for image-to-video)
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image_url: URL/base64 of input image (for image-to-video)
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output_path: Full path for output video (auto-generated if not provided)
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output_dir: Directory for output (default: /root/venice_videos)
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max_wait: Maximum seconds to wait for completion (default: 900)
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poll_interval: Seconds between API polls (default: 5)
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progress_interval: Seconds between progress logs (default: 20)
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verbose: Print progress logs (default: True)
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delete_on_completion: Delete from Venice servers after download
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Returns:
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GenerationResult with success status, video path, timing info
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"""
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start_time = time.time()
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result = GenerationResult(success=False, model=model)
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# ========== PHASE 1: QUEUE ==========
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if verbose:
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log_event("START", f"Queueing video generation with model: {model}")
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log_event("CONFIG", f"duration={duration}, resolution={resolution}, aspect_ratio={aspect_ratio}")
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try:
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queue_response = queue_video(
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model=model,
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prompt=prompt,
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duration=duration,
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aspect_ratio=aspect_ratio,
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resolution=resolution,
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audio=audio,
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negative_prompt=negative_prompt,
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image_path=image_path,
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image_url=image_url,
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)
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queue_id = queue_response.get("queue_id")
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if not queue_id:
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result.error = f"No queue_id in response: {queue_response}"
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return result
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result.queue_id = queue_id
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if verbose:
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log_event("QUEUED", f"queue_id={queue_id}")
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except Exception as e:
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result.error = f"Queue failed: {e}"
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result.elapsed_seconds = time.time() - start_time
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if verbose:
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log_event("ERROR", result.error)
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return result
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# ========== PHASE 2: POLL WITH PROGRESS ==========
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poll_count = 0
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error_count = 0
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last_progress_log = 0
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if verbose:
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log_event("POLLING", f"Waiting for video completion (max {max_wait}s, logging every {progress_interval}s)")
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while True:
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elapsed = time.time() - start_time
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# Check timeout
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if elapsed > max_wait:
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result.error = f"Timeout after {max_wait}s"
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result.elapsed_seconds = elapsed
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if verbose:
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log_event("TIMEOUT", result.error)
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return result
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# Poll API
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poll_count += 1
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status_response = retrieve_video_status(model, queue_id, delete_on_completion)
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status = status_response.get("status", "unknown").lower()
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api_progress = status_response.get("progress")
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api_eta = status_response.get("eta")
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# Update result with latest API timing info
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result.api_progress = api_progress
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result.api_eta_seconds = api_eta
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# Log progress at intervals
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if verbose and (elapsed - last_progress_log >= progress_interval):
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info = ProgressInfo(
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elapsed_seconds=elapsed,
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status=status,
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api_progress=api_progress,
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api_eta_seconds=api_eta,
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poll_count=poll_count
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)
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log_progress(info)
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last_progress_log = elapsed
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# Check completion
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if status in ["completed", "complete"]:
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video_data = status_response.get("video_data")
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video_url = status_response.get("video_url")
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if video_data or video_url:
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# Save video
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if output_path:
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save_path = Path(output_path)
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else:
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Path(output_dir).mkdir(parents=True, exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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save_path = Path(output_dir) / f"video_{timestamp}_{queue_id[:8]}.mp4"
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save_path.parent.mkdir(parents=True, exist_ok=True)
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if video_data:
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with open(save_path, "wb") as f:
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f.write(video_data)
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elif video_url:
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if video_url.startswith("data:"):
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header, encoded = video_url.split(",", 1)
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with open(save_path, "wb") as f:
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f.write(base64.b64decode(encoded))
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else:
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dl_response = requests.get(video_url, timeout=120)
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dl_response.raise_for_status()
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with open(save_path, "wb") as f:
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f.write(dl_response.content)
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result.success = True
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result.video_path = str(save_path)
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result.elapsed_seconds = time.time() - start_time
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if verbose:
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log_event("COMPLETE", f"Video saved to {save_path}")
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log_event("TIMING", f"Total time: {result.elapsed_seconds:.1f}s")
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return result
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else:
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result.error = "Completed but no video data received"
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result.elapsed_seconds = time.time() - start_time
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return result
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# Check failure
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if status == "failed":
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result.error = f"Generation failed: {status_response.get('error', 'unknown')}"
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result.elapsed_seconds = time.time() - start_time
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if verbose:
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log_event("FAILED", result.error)
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return result
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# Handle transient errors
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if status == "error":
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error_count += 1
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if error_count > 10:
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result.error = f"Too many API errors: {status_response.get('error')}"
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result.elapsed_seconds = time.time() - start_time
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return result
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if verbose and error_count == 1:
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log_event("RETRY", f"API error, retrying... ({status_response.get('error', '')})")
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else:
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error_count = 0
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# Wait before next poll
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time.sleep(poll_interval)
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# ============================================================================
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# CLI INTERFACE
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# ============================================================================
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def main():
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parser = argparse.ArgumentParser(
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description="Venice.ai Full Lifecycle Video Generation",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Basic text-to-video
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python generate_video.py "A cat playing piano"
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# With specific model and duration
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python generate_video.py "Ocean waves at sunset" --model kling-2.6-pro-text-to-video --duration 10s
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# Image-to-video
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python generate_video.py "Make this image come alive" --image /path/to/image.png --model wan-2.5-preview-image-to-video
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# Custom output path
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python generate_video.py "Dancing robot" --output /root/my_video.mp4
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"""
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)
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parser.add_argument("prompt", help="Text description of the video to generate")
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parser.add_argument("--model", "-m", default=DEFAULT_MODEL,
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help=f"Venice model ID (default: {DEFAULT_MODEL})")
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parser.add_argument("--duration", "-d", default="5s",
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help="Video duration, e.g., 5s, 10s (default: 5s)")
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parser.add_argument("--resolution", "-r", default="720p",
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help="Video resolution (default: 720p)")
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parser.add_argument("--aspect-ratio", "-a", default="16:9",
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help="Aspect ratio, e.g., 16:9, 9:16, 1:1 (default: 16:9)")
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parser.add_argument("--audio", action="store_true", default=None,
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help="Enable audio generation")
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parser.add_argument("--no-audio", action="store_true",
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help="Disable audio generation")
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parser.add_argument("--negative-prompt", "-n",
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help="What to avoid in generation")
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parser.add_argument("--image", "-i",
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help="Input image path (for image-to-video models)")
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parser.add_argument("--output", "-o",
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help="Output video file path")
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parser.add_argument("--output-dir", default=DEFAULT_OUTPUT_DIR,
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help=f"Output directory (default: {DEFAULT_OUTPUT_DIR})")
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parser.add_argument("--max-wait", type=int, default=DEFAULT_MAX_WAIT,
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help=f"Maximum wait time in seconds (default: {DEFAULT_MAX_WAIT})")
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parser.add_argument("--quiet", "-q", action="store_true",
|
|
help="Suppress progress output")
|
|
|
|
args = parser.parse_args()
|
|
|
|
# Handle audio flag
|
|
audio = None
|
|
if args.audio:
|
|
audio = True
|
|
elif args.no_audio:
|
|
audio = False
|
|
|
|
# Run generation
|
|
result = generate_video(
|
|
prompt=args.prompt,
|
|
model=args.model,
|
|
duration=args.duration,
|
|
aspect_ratio=args.aspect_ratio,
|
|
resolution=args.resolution,
|
|
audio=audio,
|
|
negative_prompt=args.negative_prompt,
|
|
image_path=args.image,
|
|
output_path=args.output,
|
|
output_dir=args.output_dir,
|
|
max_wait=args.max_wait,
|
|
verbose=not args.quiet,
|
|
)
|
|
|
|
# Final output for agent parsing
|
|
print("")
|
|
print("=" * 70)
|
|
if result.success:
|
|
print(f"RESULT: SUCCESS")
|
|
print(f"VIDEO_PATH: {result.video_path}")
|
|
print(f"ELAPSED_SECONDS: {result.elapsed_seconds:.1f}")
|
|
print(f"QUEUE_ID: {result.queue_id}")
|
|
print(f"MODEL: {result.model}")
|
|
else:
|
|
print(f"RESULT: FAILED")
|
|
print(f"ERROR: {result.error}")
|
|
print(f"ELAPSED_SECONDS: {result.elapsed_seconds:.1f}")
|
|
print("=" * 70)
|
|
|
|
return 0 if result.success else 1
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|