Project Description
Generate a personalized avatar artwork images by fine-tuning stable diffusion on personal pictures
Goal for this Hackweek
Get a new fancy and unique avatar!
Resources
- https://huggingface.co/docs/diffusers/using-diffusers/sdxl
- https://huggingface.co/docs/diffusers/training/dreambooth
- https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sdxl.md
- https://civitai.com/models/133005/juggernaut-xl?modelVersionId=198530
Looking for hackers with the skills:
This project is part of:
Hack Week 23
Activity
Comments
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almost 2 years ago by STorresi | Reply
These are generated after a bespoke LoRA training using DreamBooth over the JuggernautXL model, which in turn is based on SDXL 1.0.
As you can see, hands are still tricky (a known issue of diffusion models, apparently), but I didn't try inpainting and img2img fine-tuning, which are supposed to be the go-to way to solve small issues like that. I must say the overall experience was quite painful due to the hardware requirements of SDXL and the amount of memory leaks in pytorch. A high-end consumer grade GPU like an NVIDIA 4080 with 16GB of VRAM often wasn't enough and ran OOM.
Similar Projects
Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios
Description
Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.
This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.
The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.
Goals
By the end of Hack Week, we aim to have a single, working Python script that:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- New panel in our Dashboard listing the Flaky tests
Resources
- Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
- Data Source: Our internal Prometheus server.
- Key Metric:
jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}. - Existing Query for Reference:
count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range])). - AI Model: The Google Gemini API.
- Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
- Visualization: Our internal Grafana Dashboard.
- Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring