Stable Diffusion XL (SDXL) is a very popular text-to-image open source foundation model. This guide will show you how to boost its capabilities with Refiners, using iconic adapters the framework supports out-of-the-box, i.e. without the need for tedious prompt engineering. We'll follow a step by step approach, progressively increasing the number of adapters involved to showcase how simple adapter composition is using Refiners. Our use case will be the generation of an image with "a futuristic castle surrounded by a forest, mountains in the background".
Make sure Refiners is installed in your local environment - see [Getting started](/getting-started/recommended/) - and you have access to a decent GPU.
As Refiners comes with a new model representation - see [Chain](/concepts/chain/) - , you need to download and convert the weights of each module by calling our conversion scripts directly from your terminal (make sure you're in your local `refiners` directory, with your local environment active):
This will download the original weights from https://huggingface.co/ which takes some time. If you already have this repo cloned locally, use the `--from /path/to/stabilityai/stable-diffusion-xl-base-1.0` option instead.
Now, we can write the Python script responsible for inference. Just create a simple `inference.py` file, and open it in your favorite editor.
Start by instantiating a [`StableDiffusion_XL`][refiners.foundationals.latent_diffusion.stable_diffusion_xl.StableDiffusion_XL] model and load it with the converted weights:
```py
import torch
from refiners.fluxion.utils import manual_seed, no_grad
from refiners.foundationals.latent_diffusion.stable_diffusion_xl import StableDiffusion_XL
# Load SDXL
sdxl = StableDiffusion_XL(device="cuda", dtype=torch.float16) # Using half-precision for memory efficiency
It is not really what we prompted the model for, unfortunately. To get a more futuristic-looking castle, you can either go for tedious prompt engineering, or use a pretrainered LoRA tailored to our use case, like the [Sci-fi Environments](https://civitai.com/models/105945?modelVersionId=140624) LoRA available on Civitai. We'll now show you how the LoRA option works with Refiners.
To use the [Sci-fi Environments](https://civitai.com/models/105945?modelVersionId=140624) LoRA, all you have to do is download its weights to disk as a `.safetensors`, and inject them into SDXL using [`SDLoraManager`][refiners.foundationals.latent_diffusion.lora.SDLoraManager] right after instantiating `StableDiffusion_XL`:
```py
from refiners.fluxion.utils import load_from_safetensors
from refiners.foundationals.latent_diffusion.lora import SDLoraManager
# Load LoRA weights from disk and inject them into target
Continuing with our futuristic castle example, we might want to turn it, for instance, into a pixel art.
Again, we could either try some tedious prompt engineering,
or instead use another LoRA found on the web, such as [Pixel Art LoRA](https://civitai.com/models/120096/pixel-art-xl?modelVersionId=135931), found on Civitai.
This is dead simple as [`SDLoraManager`][refiners.foundationals.latent_diffusion.lora.SDLoraManager] allows loading multiple LoRAs:
```py
# Load LoRAs weights from disk and inject them into target
Refiners really shines when it comes to composing different Adapters to fully exploit the possibilities of foundation models.
For instance, IP-Adapter (covered in [a previous blog post](https://blog.finegrain.ai/posts/supercharge-stable-diffusion-ip-adapter/)) is a common choice for practictioners wanting to guide the diffusion process towards a specific prompt image.
In our example, consider this image of the [Neuschwanstein Castle](https://en.wikipedia.org/wiki/Neuschwanstein_Castle):
<figcaption>Credits: Bayerische Schlösserverwaltung, Anton Brandl</figcaption>
</figure>
We would like to guide the diffusion process to align with this image, using IP-Adapter. First, download the image as well as the weights of IP-Adapter by calling the following commands from your terminal (again, make sure in you're in your local `refiners` directory):
Then, in your Python code, simply instantiate a [`SDXLIPAdapter`][refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt.SDXLIPAdapter] targeting our `sdxl.unet`, and inject it using a simple `.inject()` call:
Then, at runtime, we simply compute the embedding of the image prompt through the `ip_adapter` object, and set its embedding calling `.set_clip_image_embedding()`:
Be wary that composing Adapters (especially ones of different natures, such as LoRAs and IP-Adapter) can be tricky, as their respective effects can be adversarial. This is visible in our example below. In the code below, we tuned the LoRAs scales respectively to `1.5` and `1.55`. We invite you to try and test different seeds and scales to find the perfect combination!
??? example "Expand to see the entire end-to-end code"
```py
import torch
from PIL import Image
from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
from refiners.foundationals.latent_diffusion.lora import SDLoraManager
from refiners.foundationals.latent_diffusion.stable_diffusion_xl import StableDiffusion_XL
from refiners.foundationals.latent_diffusion.stable_diffusion_xl.image_prompt import SDXLIPAdapter
T2I-Adapters[^1] are a powerful class of Adapters aiming at controlling the Text-to-Image (T2I) diffusion process with external control signals, such as canny edges or pose estimations inputs.
In this section, we will compose our previous example with the [Depth-Zoe Adapter](https://huggingface.co/TencentARC/t2i-adapter-depth-zoe-sdxl-1.0), providing a depth condition to the diffusion process using the following depth map as input signal:
<figuremarkdown>
<imgsrc="zoe-depth-map-german-castle.png"alt="Input depth map of the initial castle image"width="400">
<figcaption>Input depth map of the initial castle image.</figcaption>
</figure>
First, download the image as well as the weights of T2I-Depth-Zoe-Adapter by calling the following commands:
Finally, at runtime, compute the embedding of the input condition through the `t2i_adapter` object, and set its embedding calling `.set_condition_features()`:
```py
from refiners.fluxion.utils import image_to_tensor, interpolate
As you can see in this guide, composing Adapters on top of foundation models is pretty seamless in Refiners, allowing practitioners to quickly test out different combinations of Adapters for their needs. We encourage you to try out different ones, and even train some yourselves!
