See the runpod_serverless/ directory for architecture and deployment details.
The original upstream documentation is preserved below.
Be aware, the installation needs technical skills and is not recommended for beginners. In case you are not comfortable using a terminal, our Windows Installer and macOS Installer get you started.
Run the command:
python facefusion.py [commands] [options]
options:
-h, --help show this help message and exit
-v, --version show program's version number and exit
commands:
run run the program
headless-run run the program in headless mode
batch-run run the program in batch mode
force-download force automate downloads and exit
benchmark benchmark the program
job-list list jobs by status
job-create create a drafted job
job-submit submit a drafted job to become a queued job
job-submit-all submit all drafted jobs to become a queued jobs
job-delete delete a drafted, queued, failed or completed job
job-delete-all delete all drafted, queued, failed and completed jobs
job-add-step add a step to a drafted job
job-remix-step remix a previous step from a drafted job
job-insert-step insert a step to a drafted job
job-remove-step remove a step from a drafted job
job-run run a queued job
job-run-all run all queued jobs
job-retry retry a failed job
job-retry-all retry all failed jobs
Read the documentation for a deep dive.
This fork ships a RunPod serverless worker in runpod_serverless/ that runs facefusion.py headless-run for face swap jobs and uploads the result to Cloudflare R2.
RunPod Serverless (简体中文): runpod_serverless/README.md — black-box usage. SETUP_INSTRUCTIONS_CN.md — deploy & integrate. MAINTENANCE_SPECIFICATION_CN.md — spec & ops. English: RUNPOD_SERVERLESS.md.
Quick start:
docker build -t <registry>/worker-facefusion:<tag> .
docker push <registry>/worker-facefusion:<tag>Create a RunPod serverless endpoint from that image, set the R2_* variables in the docs above, and submit jobs via POST https://api.runpod.ai/v2/{endpoint_id}/run. For local testing: python runpod_serverless/handler.py with runpod_serverless/test_input.json, or bash runpod_serverless/tests/run_e2e.sh for a full GPU stack.
