AI Briefingdiffusers ControlNet Training Guide
Key point
Hugging Face's diffusers library provides a step-by-step guide for training custom ControlNet models.
Details
ControlNet is a neural network architecture that adds additional conditioning to Diffusion Models to enable fine-grained control. This guide explains the process of training your own ControlNet using the diffusers library.
The training process is broadly divided into three steps:
- Planning a Condition: Decide what kind of condition to use. Representative examples include pose, depth maps, and canny edges.
- Building a Dataset: You need to construct a dataset that includes three elements for training: ground truth image (image), conditioning image (conditioning_image), and prompt.
- Training the Model: Use the training script provided by
diffusers. A GPU with at least 8GB or more of VRAM is required for smooth training.
The guide covers, as an example, the process of creating a model that uses facial landmarks as the condition, utilizing Microsoft's FaceSynthetics dataset.
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