Running FaceFusion on Google Colab sounds great—free GPU, no hardware to buy. But once you actually try it, you'll run into some frustrating issues: GPU memory blowing up, sessions disconnecting, dependency conflicts, and storage running out.
Colab's free tier gives you limited GPU resources—usually a Tesla T4 with 15GB VRAM—which often isn't enough for high-res or long videos. Plus, FaceFusion doesn't automatically release GPU memory after a job finishes, so you'll need to manually restart the runtime to free it up. These aren't FaceFusion bugs—they're just what happens when you run heavy software in a constrained cloud environment.
What you might be experiencing
- "FaceFusion runs out of memory on Colab."
- "CUDA out of memory even with a T4 GPU."
- "My Colab session disconnected in the middle of processing."
- "GPU memory won't release after the job finishes."
- "JSON error when using occlusion mask on Colab."
- "FaceFusion worked yesterday but crashes today."
- "How do I free up VRAM without restarting the runtime?"
If you're dealing with any of these, you're in the right place.
When this happens most often
Colab-specific issues usually pop up in these scenarios:
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Processing long videos: Anything longer than a few minutes can exhaust GPU memory, especially with face enhancers or high-resolution models enabled.
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Using HyperSwap models: The 256px HyperSwap models eat way more VRAM than Inswapper. Colab's limited GPU allocation might not handle it.
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Running multiple jobs without restarting: FaceFusion keeps models in memory after a job completes. Run a few jobs back-to-back and your memory just keeps piling up until it crashes.
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Free tier GPU lottery: Colab's free tier assigns GPUs randomly—you might get a T4 today and a K80 tomorrow. That notebook that worked fine yesterday? Might crash today with a different GPU.
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Session idle timeouts: Colab disconnects if you're idle too long. Long-running jobs can get killed if you don't interact with the notebook occasionally.
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Dependency version conflicts: Colab's pre-installed packages might clash with what FaceFusion needs, especially ONNX Runtime versions.
Why this happens
1. GPU Memory doesn't release automatically
FaceFusion keeps models loaded in GPU memory for performance. When a job finishes, that memory stays occupied unless you explicitly clear it or restart the runtime. This is efficient for local use but problematic on Colab where memory is limited.
2. Colab's resource allocation is dynamic
Google Colab assigns GPU resources based on availability and your tier:
| Tier | Typical GPU | VRAM |
|---|---|---|
| Free | T4, K80, or P100 | 12-16GB |
| Pro | T4, P100, or V100 | 16-32GB |
| Pro+ | A100 | 40GB |
Free users might get different GPUs on different days. What runs fine on a T4 might fail on a K80 with less memory.
3. Sessions have time limits
Colab sessions have timeout limits:
- Idle timeout: 30-90 minutes of inactivity = disconnected
- Max runtime: Free tier caps out at around 12 hours
Long video processing can exceed these limits, killing your job before it finishes.
4. ONNX Runtime version conflicts
Colab's pre-installed packages might not match what FaceFusion expects. Many users report JSON errors and segmentation faults with features like occlusion masks—usually traced back to incompatible ONNX or onnxruntime-gpu versions.
5. Storage is limited and temporary
Colab's local storage is temporary and not that big. Large video files or multiple outputs can fill it up fast. And everything disappears when your session ends—unless you save to Google Drive.
Trade-offs you'll face
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Free access vs resource limits: Colab gives you free GPU, but with memory, time, and stability constraints that don't exist on local hardware.
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Convenience vs control: Colab requires no local setup, but you can't manage memory, install custom CUDA versions, or persist state between sessions.
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HyperSwap quality vs Colab compatibility: Higher-resolution models produce better results but might not fit in Colab's free tier GPU memory.
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Long jobs vs session stability: Processing long videos risks session timeouts. Splitting into shorter segments is safer but adds workflow complexity.
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Pre-installed packages vs custom dependencies: Colab's environment might conflict with FaceFusion. Forcing specific versions might break other Colab functionality.
Frequently asked questions
Q: Why does FaceFusion run out of memory on Colab? A: Colab GPUs have limited VRAM, and FaceFusion keeps models in memory without auto-releasing. Long videos or high-res models can easily exceed what's available.
Q: How do I free GPU memory on Colab? A: The most reliable way is to restart the runtime. This clears all GPU memory but also resets the Python environment—you'll need to re-run your setup cells.
Q: Why did FaceFusion work yesterday but fail today? A: Colab assigns GPUs dynamically. You might have gotten a bigger GPU yesterday and a smaller one today. Run a diagnostic command to check which GPU you got.
Q: Can I use HyperSwap on Colab free tier? A: Depends on which GPU you get and how long your video is. HyperSwap needs more VRAM than Inswapper. Short videos might work; long ones will likely crash.
Q: How do I prevent session disconnections? A: Keep the browser tab active and interact with the notebook occasionally. For long jobs, consider Colab Pro for extended limits, or split your processing into smaller batches.
Q: Why do I get JSON errors with occlusion masks? A: Usually ONNX Runtime version conflicts. Colab's pre-installed packages might not match what FaceFusion expects. Reinstalling specific onnxruntime-gpu versions might help.
Related articles
- Why FaceFusion Installation Often Fails – Dependency issues that also affect Colab
- What FaceFusion Execution Providers Do and When They Fail – GPU execution provider issues in cloud environments
- FaceFusion HyperSwap vs Inswapper – Model selection considering VRAM constraints
Final thoughts
Google Colab is a convenient way to run FaceFusion without buying a GPU, but it's not the same as having your own workstation. Memory limits, session timeouts, random GPU assignments, dependency conflicts—these are all baked into the platform. If Colab feels unreliable for FaceFusion, you're not hitting software bugs—you're hitting the boundaries of a shared, resource-constrained cloud environment. For consistent results, local installation or a paid cloud GPU service with guaranteed resources will be more predictable.