ConsisID requires about 44 GB of GPU memory to decode 49 frames (6 seconds of video at 8 FPS) with output resolution 720x480 (W x H), which makes it not possible to run on consumer GPUs or free-tier T4 Colab. The following memory optimizations could be used to reduce the memory footprint. For replication, you can refer to [this](https://gist.github.com/SHYuanBest/bc4207c36f454f9e969adbb50eaf8258) script.  
| Feature (overlay the previous) | Max Memory Allocated | Max Memory Reserved |
| :----------------------------- | :------------------- | :------------------ |
| -                              | 37 GB                | 44 GB               |
| enable_model_cpu_offload       | 22 GB                | 25 GB               |
| enable_sequential_cpu_offload  | 16 GB                | 22 GB               |
| vae.enable_slicing             | 16 GB                | 22 GB               |
| vae.enable_tiling              | 5 GB                 | 7 GB                |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/consisid.md