[T2I-Adapter](https://hf.co/papers/2302.08453) is a lightweight adapter for controlling and providing more accurate
structure guidance for text-to-image models. It works by learning an alignment between the internal knowledge of the
text-to-image model and an external control signal, such as edge detection or depth estimation.  
The T2I-Adapter design is simple, the condition is passed to four feature extraction blocks and three downsample
blocks. This makes it fast and easy to train different adapters for different conditions which can be plugged into the
text-to-image model. T2I-Adapter is similar to [ControlNet](controlnet) except it is smaller (~77M parameters) and
faster because it only runs once during the diffusion process. The downside is that performance may be slightly worse
than ControlNet.  
This guide will show you how to use T2I-Adapter with different Stable Diffusion models and how you can compose multiple
T2I-Adapters to impose more than one condition.  
> [!TIP]
> There are several T2I-Adapters available for different conditions, such as color palette, depth, sketch, pose, and
> segmentation. Check out the [TencentARC](https://hf.co/TencentARC) repository to try them out!  
Before you begin, make sure you have the following libraries installed.  
```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers accelerate controlnet-aux==0.0.7
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/t2i_adapter.md