FLUX.1 Training Solution for Low Memory Environments
For devices with less than 24GB of video memory, training can be achieved by following the steps below:
- Enabling quantitative mode: Setting in the configuration file
quantize: trueThis option compresses the model parameter footprint and reduces the video memory requirement by about 40%. - Activate low memory mode: In the configuration file, add
low_vram: trueThe system automatically shifts some of the calculations to the CPU. - Resizing batches: Modify the configuration file of the
batch_sizefor 1-2, and bygradient_accumulation_stepsControl of total number of batches - Training with Layer Selection: By
only_if_containsParameters train only key network layers (e.g., transformer blocks 7 and 20 in the example)
Options:
1. Using RunPod Cloud A40 instances (48GB video memory)
2. Switch to Stable Diffusion base model training, which has lower video memory requirements (8-16 GB)
This answer comes from the articleAI Toolkit by Ostris: Stable Diffusion with FLUX.1 Model Training ToolkitThe
May not be reproduced without permission:AI productivity tools " How to solve the challenge of running FLUX.1 model training on low graphics memory devices?
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