EACHLABS
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model
Avg Run Time: 70.000s
Model Slug: magic-animate
Playground
Input
Enter a URL or choose a file from your computer.
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image/jpeg, image/png, image/jpg, image/webp (Max 50MB)
Enter a URL or choose a file from your computer.
Click to upload or drag and drop
video/mp4 (Max 50MB)
Output
Example Result
Preview and download your result.
API & SDK
Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Readme
Overview
Magic Animate is a model that animates static human images by applying motion patterns extracted from a reference video. This approach ensures temporal consistency, resulting in smooth and natural animations.
Technical Specifications
Magic Animate employs a diffusion model to animate static human images. By leveraging motion information from a reference video, the Magic Animate generates temporally consistent animations, ensuring smooth transitions and realistic movements.
Key Considerations
Temporal Consistency: The Magic Animate ensures that animations are smooth and free from temporal artifacts.
Motion Alignment: The quality of the output heavily depends on the alignment between the input image and the reference video's motion.
Parameter Sensitivity: Adjusting parameters like num_inference_steps and guidance_scale can significantly impact the animation quality.
Tips & Tricks
Input Image:
- Ensure the image is high-resolution and well-lit.
- The subject should be clearly visible without obstructions.
Reference Video:
- Select videos where the motion aligns with the intended animation.
- Ensure the video's perspective matches that of the input image for seamless integration.
Parameter Settings:
- Number of Inference Steps:
- Range: 1 to 200.
- For detailed and refined animations, consider setting this parameter between 100 and 150.
- Guidance Scale:
- Range: 1 to 50.
- A value between 15 and 25 often provides a good balance between adhering to the input image and incorporating the reference video's motion.
- Seed:
- Setting a specific seed ensures reproducibility of results.
- If variability is desired, use different seed values for each run.
Capabilities
Realistic Animation: Transforms static images into dynamic animations by applying motion from reference videos.
Temporal Consistency: Ensures that the generated animations are smooth and free from temporal artifacts.
Parameter Control: Offers adjustable parameters to fine-tune the animation process according to user preferences.
What Can I Use It For?
Content Creation with Magic Animate: Enhance static images by adding realistic motion for multimedia projects.
Virtual Avatars: Animate character images for use in virtual environments or presentations.
Educational Tools: Create dynamic visual aids from static images to facilitate learning and engagement.
Things to Be Aware Of
Diverse Motions: Experiment with various reference videos to observe how different motions affect the animation.
Parameter Exploration: Adjust num_inference_steps and guidance_scale to see their impact on the animation quality.
Background Simplification: Use images with simple backgrounds to evaluate the Magic Animate's performance in isolating and animating the subject.
Limitations
Pose Compatibility: The Magic Animate performs best when the poses in the input image and reference video are similar.
Complex Backgrounds: Intricate backgrounds in the input image might lead to less accurate animations.
Motion Complexity: Highly complex or rapid motions in the reference video can sometimes result in unnatural animations.
Output Format: MP4
Pricing
Pricing Detail
This model runs at a cost of $0.001540 per second.
The average execution time is 70 seconds, but this may vary depending on your input data.
The average cost per run is $0.107800
Pricing Type: Execution Time
Cost Per Second means the total cost is calculated based on how long the model runs. Instead of paying a fixed fee per run, you are charged for every second the model is actively processing. This pricing method provides flexibility, especially for models with variable execution times, because you only pay for the actual time used.
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