Stable Diffusion XL (SDXL) can also use textual inversion vectors for inference. In contrast to Stable Diffusion 1 and 2, SDXL has two text encoders so you'll need two textual inversion embeddings - one for each text encoder model.  
Let's download the SDXL textual inversion embeddings and have a closer look at it's structure:  
```py
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file

file = hf_hub_download("dn118/unaestheticXL", filename="unaestheticXLv31.safetensors")
state_dict = load_file(file)
state_dict
```  
```
{'clip_g': tensor([[ 0.0077, -0.0112,  0.0065,  ...,  0.0195,  0.0159,  0.0275],
...,
[-0.0170,  0.0213,  0.0143,  ..., -0.0302, -0.0240, -0.0362]],
'clip_l': tensor([[ 0.0023,  0.0192,  0.0213,  ..., -0.0385,  0.0048, -0.0011],
...,
[ 0.0475, -0.0508, -0.0145,  ...,  0.0070, -0.0089, -0.0163]],
```  
There are two tensors, `"clip_g"` and `"clip_l"`.
`"clip_g"` corresponds to the bigger text encoder in SDXL and refers to
`pipe.text_encoder_2` and `"clip_l"` refers to `pipe.text_encoder`.  
Now you can load each tensor separately by passing them along with the correct text encoder and tokenizer
to [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`]:  
```py
from diffusers import AutoPipelineForText2Image
import torch

pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", torch_dtype=torch.float16)
pipe.to("cuda")

pipe.load_textual_inversion(state_dict["clip_g"], token="unaestheticXLv31", text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
pipe.load_textual_inversion(state_dict["clip_l"], token="unaestheticXLv31", text_encoder=pipe.text_encoder, tokenizer=pipe.tokenizer)

# the embedding should be used as a negative embedding, so we pass it as a negative prompt
generator = torch.Generator().manual_seed(33)
image = pipe("a woman standing in front of a mountain", negative_prompt="unaestheticXLv31", generator=generator).images[0]
image
```
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/textual_inversion_inference.md