Optimization suggestions
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Quiet
- Domain
- machine-learning, performance
Research direction
The issue names no files, tests, or entry points. First clarify whether Tiny Auto Encoder support, GGUF quantization support, or both are in scope, then locate the relevant inference path and establish compatibility requirements. Done would require an agreed implementation scope plus evidence for the claimed memory and latency improvements.
Written by the indexing model from the issue text.
Description
Have you considered Tiny Auto Encoder for Hunyuan, Wan variant? It's a direct drop in for the vae you use that takes up a fraction of the memory and latency bandwidth. I successfully subbed it in myself to great success.
and
Did you know there are GGUF quantization options for Wan based models? I'm testing compatibility now, but I see no reason why a Q4 quant wouldn't run.
The combination of both would likely bring real time inference down to 4090 scale and trajectory rollout to laptop scale.
Just thoughts, I appreciate the project regardless.
- Dominant language
- Python
- Stars
- 2.3k
- Forks
- 254
- PR merge metrics
- No merged PRs in 30d
Contributor guide
No contributing guide indexed for this repository
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.
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