lllyasviel / lllyasviel/FramePack
Considerations regarding accessibility of FramePack on devices with very low GPU VRAM (e.g. 2 GB VRAM and lower) or supporting only CPU and how to accelerate CPU inference
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- Dominant language
- Python
- Stars
- 17.3k
- Forks
- 1.7k
- PR merge metrics
- No merged PRs in 30d
Description
As of now, only CUDA works. I only have an ASUS VivoBook X512DA with an AMD Ryzen 5 3500U CPU with an integrated Radeon Vega 8 Mobile Graphics GPU, which unfortunately has 2048 MB VRAM only, and this is, in my opinion, insufficient for me. I can't afford to get a newer device because I lack a substantial source of revenue, and my autism is preventing me from having a job. I don't know what to do, CPU inference is extremely slow, and I don't know if I can make it faster. I feel like I'm out of options, and others may be in the same position as I do.
Could there be some way for me to help?
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
The issue names no files, tests, or entry points. Start by locating the CPU inference path and the CUDA/VRAM requirements, then determine whether a concrete low-VRAM or CPU-acceleration change is feasible. Done would require an agreed implementation target and validation on hardware around 2 GB VRAM or CPU-only, neither of which the issue specifies.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100