Video Generation Systems
Building AI systems that generate high-quality videos from text, images, or other inputs
Video Generation Models
Stable Video Diffusion Implementation
import torch
from diffusers import StableVideoDiffusionPipeline
from PIL import Image
class VideoGenerationService:
def __init__(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load Stable Video Diffusion pipeline
self.pipeline = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid",
torch_dtype=torch.float16,
variant="fp16"
)
self.pipeline.to(self.device)
# Enable memory optimizations
self.pipeline.enable_model_cpu_offload()
self.pipeline.enable_vae_slicing()
def generate_video_from_image(self,
image_path,
num_frames=25,
fps=7,
motion_strength=127):
"""Generate video from input image"""
# Load and preprocess image
image = Image.open(image_path).convert('RGB')
# Generate video frames
frames = self.pipeline(
image=image,
num_frames=num_frames,
fps=fps,
motion_bucket_id=motion_strength,
noise_aug_strength=0.02,
decode_chunk_size=8,
num_inference_steps=25
).frames[0]
return frames
def export_video(self, frames, output_path, fps=7):
"""Export frames to video file"""
import cv2
import numpy as np
height, width = np.array(frames[0]).shape[:2]
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
for frame in frames:
frame_array = np.array(frame)
frame_bgr = cv2.cvtColor(frame_array, cv2.COLOR_RGB2BGR)
out.write(frame_bgr)
out.release()
print(f"Video saved to {output_path}")Performance Optimization
Memory Optimization
- Model CPU offloading
- VAE slicing
- Attention slicing
- Chunked processing
Speed Optimization
- Mixed precision (FP16)
- Compiled models
- Optimized schedulers
- Batch processing
Production Pipeline
import asyncio
from typing import Dict, List, Any
import torch
from datetime import datetime
class ProductionVideoService:
def __init__(self, config):
self.config = config
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load optimized models
self.models = self._load_optimized_models()
# Performance tracking
self.metrics = {
'total_requests': 0,
'avg_generation_time': 0,
'success_rate': 0
}
async def generate_video(self,
request_type: str,
input_data: Dict[str, Any],
**kwargs) -> Dict[str, Any]:
"""Main video generation endpoint"""
start_time = datetime.now()
self.metrics['total_requests'] += 1
try:
if request_type == "image_to_video":
result = await self._generate_from_image(
input_data['image'], **kwargs
)
elif request_type == "text_to_video":
result = await self._generate_from_text(
input_data['prompt'], **kwargs
)
else:
raise ValueError(f"Unsupported request type: {request_type}")
# Calculate metrics
generation_time = (datetime.now() - start_time).total_seconds()
return {
'success': True,
'video_data': result,
'generation_time_seconds': generation_time,
'timestamp': datetime.now().isoformat()
}
except Exception as e:
return {
'success': False,
'error': str(e),
'timestamp': datetime.now().isoformat()
}
async def _generate_from_image(self, image_data, **kwargs):
"""Generate video from image input"""
# Process with chunked generation for memory efficiency
chunk_size = kwargs.get('chunk_size', 8)
total_frames = kwargs.get('num_frames', 25)
all_frames = []
for start_frame in range(0, total_frames, chunk_size):
end_frame = min(start_frame + chunk_size, total_frames)
chunk_frames = end_frame - start_frame
# Generate chunk
chunk_result = self.models['img2vid'].generate(
image=image_data,
num_frames=chunk_frames,
**kwargs
)
all_frames.extend(chunk_result.frames[0])
# Clear GPU memory
torch.cuda.empty_cache()
return all_frames
def get_metrics(self) -> Dict[str, Any]:
"""Get service performance metrics"""
return {
'requests_processed': self.metrics['total_requests'],
'average_generation_time': self.metrics['avg_generation_time'],
'success_rate': self.metrics['success_rate'],
'gpu_memory_usage': torch.cuda.memory_allocated() if torch.cuda.is_available() else 0
}📝 Test Your Understanding
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