The AI Toolkit is an experimental repository containing various AI scripts, primarily focused on Stable Diffusion. Developed by Ostris, it supports FLUX.1 training and offers tools for LoRA extraction, rescaling, and slider training.
Recent development has focused heavily on cloud integration and training enhancements. Pull requests #80 and #115 added support for RunPod and Modal cloud platforms respectively, significantly expanding the toolkit's accessibility and scalability. Additionally, PR #95 introduced Weights & Biases logging integration for improved training monitoring, while PR #109 added the ability to push trained models directly to Hugging Face.
The project has seen a flurry of activity aimed at improving usability and expanding functionality. PRs #124 and #125 addressed critical compatibility issues with the diffusers library, highlighting the challenges of maintaining compatibility in the rapidly evolving AI field. Meanwhile, PRs #120, #41, and #25 focused on enhancing documentation and usability, demonstrating the maintainers' attentiveness to user needs.
Recent development team activities:
Jaret Burkett (jaretburkett):
martintomov:
apolinario:
liaoliaojun:
fofr:
| Timespan | Opened | Closed | Comments | Labeled | Milestones |
|---|---|---|---|---|---|
| 7 Days | 38 | 60 | 128 | 38 | 1 |
| 30 Days | 83 | 79 | 258 | 83 | 1 |
| 90 Days | 85 | 79 | 260 | 85 | 1 |
| 1 Year | 103 | 83 | 283 | 103 | 1 |
| All Time | 110 | 90 | - | - | - |
Like all software activity quantification, these numbers are imperfect but sometimes useful. Comments, Labels, and Milestones refer to those issues opened in the timespan in question.
| Developer | Avatar | Branches | PRs | Commits | Files | Changes |
|---|---|---|---|---|---|---|
| Jaret Burkett |  |
1 | 0/0/0 | 61 | 36 | 3681 |
| martintomov |  |
1 | 2/2/0 | 2 | 8 | 952 |
| apolinário |  |
1 | 1/1/0 | 1 | 4 | 140 |
| liaoliaojun |  |
1 | 1/1/0 | 1 | 1 | 9 |
| fofr |  |
1 | 1/1/0 | 1 | 1 | 2 |
| Ilya (chaous) | 0 | 1/0/1 | 0 | 0 | 0 | |
| AngelBottomless (aria1th) | 0 | 1/0/0 | 0 | 0 | 0 | |
| None (boopage) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Wandel (ewandel) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Plat (p1atdev) | 0 | 1/0/0 | 0 | 0 | 0 | |
| Ikko Eltociear Ashimine (eltociear) | 0 | 1/0/0 | 0 | 0 | 0 | |
| pixelprotest (pixelprotest) | 0 | 1/0/1 | 0 | 0 | 0 | |
| Blueprint Coding (BlueprintCoding) | 0 | 1/0/1 | 0 | 0 | 0 |
PRs: created by that dev and opened/merged/closed-unmerged during the period
Here is a brief analysis of the recent GitHub issues activity for the ai-toolkit project:
Recent Activity Analysis:
There has been steady activity on the project's GitHub issues over the past week, with several new issues opened and existing ones closed. The issues cover a range of topics including installation problems, training errors, feature requests, and general usage questions.
Some notable issues:
Several users have reported problems with model downloads and initialization, particularly for the FLUX models. This seems to be a common pain point, likely due to large file sizes and potential network issues.
There are multiple requests and questions about multi-GPU support, indicating this is a desired feature that is not yet fully implemented.
Installation issues on Windows systems appear fairly frequently, suggesting potential compatibility challenges on that platform.
Several users have asked about how to use trained models or continue training from checkpoints, indicating a need for clearer documentation or examples on model usage and transfer learning.
There are a few reports of out-of-memory errors or slow training on GPUs with less VRAM, highlighting the resource-intensive nature of the toolkit.
Issue Details:
Most recently created: #127: "Does not work with m1 mac" (open, created 0 days ago) #126: "502 on Cloudflare" (open, created 1 day ago)
Most recently updated: #127: "Does not work with m1 mac" (open, updated 0 days ago) #121: "How to use locally downloaded models？" (open, updated 0 days ago)
The issues span a range of priorities from critical functionality problems to minor feature requests. Many are still open, indicating ongoing development and community engagement.
The provided data includes information on 17 pull requests (5 open, 12 closed) for the ostris/ai-toolkit repository, covering various improvements, bug fixes, and feature additions.
#125 (closed): Fixed requirements.txt to address breaking changes in diffusers library. #124 (closed): Specified diffusers version 0.30.0 to resolve compatibility issues. #120 (closed): Added instructions for downloading Flux.1-Dev file via command prompt. #115 (closed): Added Modal cloud training support, fixed typos, and improved Colab support. #109 (closed): Added ability to push trained models directly to Hugging Face. #107 (closed): Improved error reporting for image encoding failures. #95 (open): Added Weights & Biases (wandb) logging integration for monitoring training. #86 (open): Fixed issue with WEBP format caching using incorrect values. #80 (closed): Addressed issue #76 and added RunPod cloud training setup. #69 (open): Suggested changes to learning rate and steps in training configuration. #61 (closed): Fixed image name in captions section of README. #60 (closed): Attempted to add Telegram bot feature for training updates. #41 (open): Fixed typo in README.md. #25 (open): Fixed minor typos in train_slider.example.yml.
Active Development: The repository shows signs of active development with frequent pull requests addressing various aspects of the project. This includes bug fixes, feature additions, and documentation improvements.
Cloud Integration: There's a clear focus on improving cloud integration, with PRs adding support for RunPod (#80) and Modal (#115) cloud platforms. This indicates an effort to make the toolkit more accessible and scalable.
Training Enhancements: Several PRs (#95, #109, #115) focus on improving the training process, including adding logging capabilities, push-to-hub functionality, and support for different training environments (local, cloud, Colab).
Documentation and Usability: There's an ongoing effort to improve documentation and usability, as seen in PRs #120, #41, and #25. This suggests the maintainers are attentive to user needs and are working to make the toolkit more accessible.
Performance and Compatibility: PRs #124 and #125 address compatibility issues with the diffusers library, highlighting the challenges of maintaining compatibility with rapidly evolving dependencies in the AI field.
Experimental Features: The addition of features like Telegram bot integration (#60) shows willingness to experiment with new functionalities, even if they aren't always merged.
Community Contributions: The variety of contributors suggests an active community around the project, with users contributing everything from major features to minor typo fixes.
Ongoing Challenges: Some PRs (#86, #107) reveal ongoing challenges with image processing and error handling, indicating areas where the toolkit might need further refinement.
Configuration Tweaking: PR #69, which remains open, suggests that there's ongoing discussion and experimentation with training parameters, indicating that the optimal configuration is still being fine-tuned.
Long-standing PRs: Some PRs (#41, #25) have been open for a long time, which might indicate a need for more regular review and management of contributions.
In conclusion, the ai-toolkit project appears to be in active development with a focus on cloud integration, training improvements, and usability enhancements. The project seems to balance between adding new features and refining existing functionality, with input from a diverse set of contributors. However, there may be room for improvement in managing long-standing pull requests and ensuring timely reviews.
The primary developer for the AI Toolkit project appears to be Jaret Burkett (GitHub username: jaretburkett). Here's a summary of recent development activity:
Jaret Burkett (jaretburkett):
martintomov:
apolinario:
Summary
Quantified Reports
Detailed Reportspush_to_hub functionality to the trainerliaoliaojun:
fofr:
Patterns and Themes: 1. Focus on FLUX model training and optimization 2. Ongoing improvements to LoRA and adapter training methods 3. Cloud platform integration (RunPod, Modal) 4. Collaborative efforts to improve documentation and usability 5. Rapid iteration with frequent bug fixes and feature additions
The project is seeing active development with a mix of core feature work, bug fixes, and usability improvements. The main developer, Jaret Burkett, is driving most of the changes, with occasional contributions from other developers for specific features or fixes.