More than one IP-Adapter can be used at the same time to generate specific images in more diverse styles. For example, you can use IP-Adapter-Face to generate consistent faces and characters, and IP-Adapter Plus to generate those faces in a specific style.  
> [!TIP]
> Read the [IP-Adapter Plus](../using-diffusers/loading_adapters#ip-adapter-plus) section to learn why you need to manually load the image encoder.  
Load the image encoder with [`~transformers.CLIPVisionModelWithProjection`].  
```py
import torch
from diffusers import AutoPipelineForText2Image, DDIMScheduler
from transformers import CLIPVisionModelWithProjection
from diffusers.utils import load_image

image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16,
)
```  
Next, you'll load a base model, scheduler, and the IP-Adapters. The IP-Adapters to use are passed as a list to the `weight_name` parameter:  
* [ip-adapter-plus_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter#ip-adapter-for-sdxl-10) uses patch embeddings and a ViT-H image encoder
* [ip-adapter-plus-face_sdxl_vit-h](https://huggingface.co/h94/IP-Adapter#ip-adapter-for-sdxl-10) has the same architecture but it is conditioned with images of cropped faces  
```py
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16,
image_encoder=image_encoder,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.load_ip_adapter(
"h94/IP-Adapter",
subfolder="sdxl_models",
weight_name=["ip-adapter-plus_sdxl_vit-h.safetensors", "ip-adapter-plus-face_sdxl_vit-h.safetensors"]
)
pipeline.set_ip_adapter_scale([0.7, 0.3])
pipeline.enable_model_cpu_offload()
```  
Load an image prompt and a folder containing images of a certain style you want to use.  
```py
face_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png")
style_folder = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/style_ziggy"
style_images = [load_image(f"{style_folder}/img{i}.png") for i in range(10)]
```  
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/women_input.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter image of face</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_style_grid.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">IP-Adapter style images</figcaption>
</div>
</div>  
Pass the image prompt and style images as a list to the `ip_adapter_image` parameter, and run the pipeline!  
```py
generator = torch.Generator(device="cpu").manual_seed(0)

image = pipeline(
prompt="wonderwoman",
ip_adapter_image=[style_images, face_image],
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50, num_images_per_prompt=1,
generator=generator,
).images[0]
image
```  
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip_multi_out.png" />
</div>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/ip_adapter.md