Training Flux.1 Dev on MI300X with Massive Batch Sizes
Blog post from RunPod
The text explores the process of fine-tuning the Flux.1 Dev model using the AMD MI300X GPU, notable for its substantial VRAM capacity, which supports large batch sizes and resolutions. Released in August 2024, the Flux.1 Dev model, with its 12 billion parameters, offers high-quality image generation and can be self-hosted for training. The guide details setting up a Docker container environment to train Flux LoRAs on Runpod's MI300X GPUs using the kohya-ss/sd-scripts repository, with flexibility to use any PyTorch-compatible scripts. Emphasizing the importance of managing batch sizes and epochs for effective training, it provides instructions to build a container using a rocm/pytorch base image, ensuring compatibility with AMD GPUs, and setting up a Runpod template for efficient training deployment. The process involves configuring environment variables for hyperparameters, deploying training pods, and managing data storage requirements. The text also covers downloading and testing the model using AI image generation tools, recommending practices for optimizing training performance and image quality.