t_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 load_textual_inversion():   Copied 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_d