You can also merge different adapter checkpoints for inference to blend their styles together.  
Once again, use the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method to activate the `pixel` and `toy` adapters and specify the weights for how they should be merged.  
```python
pipe.set_adapters(["pixel", "toy"], adapter_weights=[0.5, 1.0])
```  
<Tip>  
LoRA checkpoints in the diffusion community are almost always obtained with [DreamBooth](https://huggingface.co/docs/diffusers/main/en/training/dreambooth). DreamBooth training often relies on "trigger" words in the input text prompts in order for the generation results to look as expected. When you combine multiple LoRA checkpoints, it's important to ensure the trigger words for the corresponding LoRA checkpoints are present in the input text prompts.  
</Tip>  
Remember to use the trigger words for [CiroN2022/toy-face](https://hf.co/CiroN2022/toy-face) and [nerijs/pixel-art-xl](https://hf.co/nerijs/pixel-art-xl) (these are found in their repositories) in the prompt to generate an image.  
```python
prompt = "toy_face of a hacker with a hoodie, pixel art"
image = pipe(
prompt, num_inference_steps=30, cross_attention_kwargs={"scale": 1.0}, generator=torch.manual_seed(0)
).images[0]
image
```  
![toy-face-pixel-art](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_16_1.png)  
Impressive! As you can see, the model generated an image that mixed the characteristics of both adapters.  
> [!TIP]
> Through its PEFT integration, Diffusers also offers more efficient merging methods which you can learn about in the [Merge LoRAs](../using-diffusers/merge_loras) guide!  
To return to only using one adapter, use the [`~loaders.peft.PeftAdapterMixin.set_adapters`] method to activate the `"toy"` adapter:  
```python
pipe.set_adapters("toy")

prompt = "toy_face of a hacker with a hoodie"
lora_scale = 0.9
image = pipe(
prompt, num_inference_steps=30, cross_attention_kwargs={"scale": lora_scale}, generator=torch.manual_seed(0)
).images[0]
image
```  
Or to disable all adapters entirely, use the [`~loaders.peft.PeftAdapterMixin.disable_lora`] method to return the base model.  
```python
pipe.disable_lora()

prompt = "toy_face of a hacker with a hoodie"
image = pipe(prompt, num_inference_steps=30, generator=torch.manual_seed(0)).images[0]
image
```  
![no-lora](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/peft_integration/diffusers_peft_lora_inference_20_1.png)
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/tutorials/using_peft_for_inference.md