🎬 Practical Stable Video Diffusion
Real solutions for generating videos from images without expensive hardware
☁️ Cloud Services
📓 Google Colab
🔄 Replicate API
🚀 Gradio Spaces
☁️ Cloud-Based Solutions
Use existing online services that provide SVD without any setup:
🤗 Hugging Face Spaces
Free, browser-based SVD generation using community-hosted models.
✅ Pros
- Completely free
- No setup required
- Works in browser
- Multiple models available
❌ Cons
- Queue times
- Limited customization
- May have usage limits
Visit
🚀 Try SVD Space
Upload your image and adjust settings (motion bucket, fps, etc.)
Wait for generation (usually 1-5 minutes) and download your video
🎨 Stability AI Official
The official Stability AI platform with high-quality results.
Note: Requires account creation and may have usage credits.
Visit Stability AI Platform
🎭 RunwayML
Professional video generation platform with SVD and other AI models.
| Plan | Cost | Credits |
|---|---|---|
| Free | $0 | Limited trial |
| Standard | $15/month | 625 credits |
📓 Google Colab Solution
Free GPU access for running SVD yourself with full control:
🔬 Complete Colab Notebook
Free GPU Access: Google Colab provides free Tesla T4 GPUs that can run SVD!
Create the notebook: Copy this code into a new Google Colab notebook:
# Install dependencies
!pip install diffusers transformers accelerate torch torchvision xformers
!pip install opencv-python imageio imageio-ffmpeg
# Import libraries
import torch
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import load_image, export_to_video
import numpy as np
from google.colab import files
import os
# Setup pipeline
print("🚀 Loading Stable Video Diffusion...")
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt",
torch_dtype=torch.float16,
variant="fp16"
)
pipe.enable_model_cpu_offload()
print("✅ Pipeline loaded!")
# Upload image
uploaded = files.upload()
image_path = list(uploaded.keys())[0]
# Load and process image
image = load_image(image_path)
image = image.resize((1024, 576))
# Generate video
print("🎬 Generating video...")
generator = torch.manual_seed(42)
frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]
# Save video
export_to_video(frames, "generated_video.mp4", fps=7)
print("✅ Video generated: generated_video.mp4")
# Download video
files.download("generated_video.mp4")
Enable GPU: Runtime → Change runtime type → GPU (T4)
Run cells: Execute each cell in order. Total time: ~5-10 minutes
⚡ Advanced Colab Features
# Advanced settings for better results
settings = {
'motion_bucket_id': 127, # 1-255, higher = more motion
'noise_aug_strength': 0.02, # 0-1, higher = more variation
'num_frames': 25, # 14 or 25 frames
'fps': 7, # Output frame rate
'guidance_scale': 1.8 # How closely to follow input
}
# Generate with custom settings
frames = pipe(
image,
decode_chunk_size=8,
generator=generator,
motion_bucket_id=settings['motion_bucket_id'],
noise_aug_strength=settings['noise_aug_strength'],
num_frames=settings['num_frames'],
guidance_scale=settings['guidance_scale']
).frames[0]
export_to_video(frames, f"video_{settings['motion_bucket_id']}.mp4",
fps=settings['fps'])
🔄 Replicate API
Pay-per-use API service for professional applications:
💳 Replicate Pricing
| Model | Cost per run | Time |
|---|---|---|
| SVD (14 frames) | ~$0.02 | 30-60 seconds |
| SVD-XT (25 frames) | ~$0.03 | 45-90 seconds |
Sign up at replicate.com and get API key
Use this JavaScript code in your app:
// Using Replicate API
const replicate = new Replicate({
auth: "your-api-token"
});
async function generateVideo(imageUrl) {
const output = await replicate.run(
"stability-ai/stable-video-diffusion:3f0457e4619daac51203dedb1a4919c746077925c5e36494e75a73e2688093a5",
{
input: {
image: imageUrl,
fps: 7,
motion_bucket_id: 127,
noise_aug_strength: 0.02
}
}
);
return output;
}
🚀 Build Your Own Gradio Space
Create a custom SVD interface and host it for free:
🏗️ Custom Gradio Interface
Create this Python file (app.py):
import gradio as gr
import torch
from diffusers import StableVideoDiffusionPipeline
from diffusers.utils import export_to_video
import tempfile
import os
# Load pipeline
pipe = StableVideoDiffusionPipeline.from_pretrained(
"stabilityai/stable-video-diffusion-img2vid-xt",
torch_dtype=torch.float16,
variant="fp16"
)
pipe.enable_model_cpu_offload()
def generate_video(image, motion_bucket_id, noise_aug_strength, fps):
# Resize image
image = image.resize((1024, 576))
# Generate video
generator = torch.manual_seed(42)
frames = pipe(
image,
decode_chunk_size=8,
generator=generator,
motion_bucket_id=motion_bucket_id,
noise_aug_strength=noise_aug_strength
).frames[0]
# Save to temporary file
with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp:
export_to_video(frames, tmp.name, fps=fps)
return tmp.name
# Create interface
interface = gr.Interface(
fn=generate_video,
inputs=[
gr.Image(type="pil", label="Input Image"),
gr.Slider(1, 255, value=127, label="Motion Intensity"),
gr.Slider(0, 1, value=0.02, label="Noise Strength"),
gr.Slider(1, 30, value=7, label="FPS")
],
outputs=gr.Video(label="Generated Video"),
title="🎬 Stable Video Diffusion",
description="Transform images into videos using AI"
)
if __name__ == "__main__":
interface.launch()
Create requirements.txt:
diffusers
transformers
torch
torchvision
gradio
accelerate
xformers
Push to Hugging Face Spaces with GPU enabled (free tier available)
🖼️ Try It Now
Upload an image to get started with any of the methods above:
📁
Drop your image here or click to browse
Supports JPG, PNG, WebP formats
📊 Method Comparison
| Method | Cost | Setup Time | Control Level | Best For |
|---|---|---|---|---|
| 🤗 HF Spaces | Free | 0 minutes | Basic | Quick testing |
| 📓 Google Colab | Free | 5 minutes | Full | Learning & experimentation |
| 🔄 Replicate API | $0.02-0.03/video | 10 minutes | Full | Production apps |
| 🚀 Custom Gradio | Free (HF) / $10-50/month | 30 minutes | Full | Custom applications |