Implement frame interpolation pipeline at AI worker side [60 LPT]

[rickstaa]

Overview

To enhance the output quality of our image-to-video pipelines, we aim to implement a frame interpolation pipeline. This new pipeline will increase the number of frames, thereby improving the smoothness and or length of the videos produced. Since @yondon previously implemented a similar task in an earlier version of the AI subnet, this work can be ported to the new software relatively quickly.

We are calling on the community to help implement this crucial pipeline on the AI-worker side of the AI subnet. Achieving this will not only improve the existing image-to-video pipeline but also introduce a new pipeline that, with further optimization, could potentially be integrated with the transcoding network to upscale videos 🚀.

Required Skillset

• Familiarity with FastAPI.
• Strong Python programming skills.

Bounty Requirements

1. Implementation: Develop a working /frame_interpolation route and pipeline in the AI worker repository. This pipeline should be accessible on port 8006.
2. Functionality: The pipeline must accept a batch of images and return a new batch with interpolated frames, enhancing the smoothness and continuity of the images.

Scope Exclusions

• This bounty does NOT cover the complete end-to-end implementation of this pipeline on the go-livepeer side, including payment logic and job routing. These aspects will be addressed by the AI SPE team or in a future bounty.

Implementation Tips

To understand how to create a new AI worker pipeline, you can refer to recent pull requests where new pipelines were added:

• Pull Request #96
• Pull Request #103

Additionally, make sure to:

• Utilize Earlier Work: Don't forget to review the frame interpolation implementation done by @yondon in an earlier version of the AI subnet. This can provide valuable insights and a foundation for your work.
• Utilize Developer Documentation: Check out our developer documentation for the worker and runner. These resources provide valuable tips for speeding up your development process by mocking pipelines and enabling direct debugging.
• Generate OpenAPI Spec: Run the runner/gen_openapi.py file to generate the updated OpenAPI spec.
• Generate Go-Livepeer Bindings: In the main repository folder, run the make command to generate the necessary go-livepeer bindings, ensuring your implementation works seamlessly with the go-livepeer repository.

How to Apply

1. Express Your Interest: Comment on this issue to indicate your interest and explain why you're the ideal candidate for the task.
2. Wait for Review: Our team will review expressions of interest and select the best candidate.
3. Get Assigned: If selected, we'll assign the GitHub issue to you.
4. Start Working: Dive into your task! If you need assistance or guidance, comment on the issue or join the discussions in the #🛋│developer-lounge channel on our Discord server.
5. Submit Your Work: Create a pull request in the relevant repository and request a review.
6. Notify Us: Comment on this GitHub issue when your pull request is ready for review.
7. Receive Your Bounty: We'll arrange the bounty payment once your pull request is approved.
8. Gain Recognition: Your valuable contributions will be showcased in our project's changelog.

Thank you for your interest in contributing to our project 💛!

Warning

Please wait for the issue to be assigned to you before starting work. To prevent duplication of effort, submissions for unassigned issues will not be accepted.

[rickstaa]

@JJassonn69 as promised 🔥.

[JJassonn69]

Here, Here