T2I-Adapters are also composable, allowing you to use more than one adapter to impose multiple control conditions on an
image. For example, you can use a pose map to provide structural control and a depth map for depth control. This is
enabled by the [`MultiAdapter`] class.  
Let's condition a text-to-image model with a pose and depth adapter. Create and place your depth and pose image and in a list.  
```py
from diffusers.utils import load_image

pose_image = load_image(
"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"
)
depth_image = load_image(
"https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"
)
cond = [pose_image, depth_image]
prompt = ["Santa Claus walking into an office room with a beautiful city view"]
```  
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/depth_sample_input.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">depth image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/diffusers/docs-images/resolve/main/t2i-adapter/keypose_sample_input.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">pose image</figcaption>
</div>
</div>  
Load the corresponding pose and depth adapters as a list in the [`MultiAdapter`] class.  
```py
import torch
from diffusers import StableDiffusionAdapterPipeline, MultiAdapter, T2IAdapter

adapters = MultiAdapter(
[
T2IAdapter.from_pretrained("TencentARC/t2iadapter_keypose_sd14v1"),
T2IAdapter.from_pretrained("TencentARC/t2iadapter_depth_sd14v1"),
]
)
adapters = adapters.to(torch.float16)
```  
Finally, load a [`StableDiffusionAdapterPipeline`] with the adapters, and pass your prompt and conditioned images to
it. Use the [`adapter_conditioning_scale`] to adjust the weight of each adapter on the image.  
```py
pipeline = StableDiffusionAdapterPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
adapter=adapters,
).to("cuda")

image = pipeline(prompt, cond, adapter_conditioning_scale=[0.7, 0.7]).images[0]
image
```  
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/t2i-multi.png"/>
</div>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/t2i_adapter.md