Add Lora Support to T2I and I2I pipelines [70 LPT]

Overview

Our current AI subnet T2I and I2I pipelines only support inference on base models, but we know there's more potential to unlock. Fine-tuning methods like LoRA and Dreambooth allow researchers to build on these base models and adapt them for specific use cases. With our team focused on main subnet core development improvements, we're turning to the builders in the community to take on the exciting challenge of adding support for LoRA to the I2I and T2I pipelines 🔧.

Why LoRA first? Compared to Dreambooth, LoRA is more suited for efficient domain adaptation, while Dreambooth excels in highly specific personalization tasks. By integrating LoRA, we aim to provide developers with more flexible models and give orchestrators a more diverse range of income opportunities, as LoRA can be dynamically downloaded and layered on top of existing base models advertised by orchestrators.

This is a significant improvement that benefits both developers and orchestrators. Are you up for the challenge? If you're ready to take on this important task, let us know below, and we will assign you. Let's enhance our pipelines together and push the boundaries of what's possible! 🌟

Required Skillset

Bounty Level: advanced

High famility with the full subnet AI codebase (i.e. https://github.com/livepeer/ai-worker and https://github.com/livepeer/go-livepeer.
Proficiency in implementing generative inference AI pipelines in Python using pre-trained weights.
Familiarity with FastAPI.
Strong Python programming skills.

Bounty Requirements

Bounty Requirements

To successfully complete this bounty, the participant should:

Dynamic Integration: Design a method to dynamically download and load LoRA weights onto existing base models.
Efficiency: Ensure the process is carried out in an efficient manner to minimize performance overhead.
Abstraction: Abstract this process away from the orchestrators as much as possible, ensuring ease of use and minimal disruption.
Implementation: Deliver a fully functional end-to-end implementation of this pipeline on the Go-Livepeer side.

This bounty does NOT include:

Implementation of other fine-tuned models like Dreambooth.
Testing requirements for the resulting pull request, as tests are not yet implemented.

Implementation Tips

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

Express Your Interest: Comment on this issue to indicate your interest and explain why you're the ideal candidate for the task.
Wait for Review: Our team will review expressions of interest and select the best candidate.
Get Assigned: If selected, we'll assign the GitHub issue to you.
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.
Submit Your Work: Create a pull request in the relevant repository and request a review.
Notify Us: Comment on this GitHub issue when your pull request is ready for review.
Receive Your Bounty: We'll arrange the bounty payment once your pull request is approved.
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.

livepeer / bounties