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sdxl-botw LoRA by Julian BILCKE (HF: jbilcke-hf, Replicate: jbilcke)

A SDXL LoRA inspired by Breath of the Wild


Inference with Replicate API

Grab your replicate token here

pip install replicate
export REPLICATE_API_TOKEN=r8_*************************************
import replicate

output = replicate.run(
    input={"prompt": "Link riding a llama, in the style of TOK"}

You may also do inference via the API with Node.js or curl, and locally with COG and Docker, check out the Replicate API page for this model

Inference with 🧨 diffusers

Replicate SDXL LoRAs are trained with Pivotal Tuning, which combines training a concept via Dreambooth LoRA with training a new token with Textual Inversion. As diffusers doesn't yet support textual inversion for SDXL, we will use cog-sdxl TokenEmbeddingsHandler class.

The trigger tokens for your prompt will be <s0><s1>

pip install diffusers transformers accelerate safetensors huggingface_hub
git clone https://github.com/replicate/cog-sdxl cog_sdxl
import torch
from huggingface_hub import hf_hub_download
from diffusers import DiffusionPipeline
from cog_sdxl.dataset_and_utils import TokenEmbeddingsHandler
from diffusers.models import AutoencoderKL

pipe = DiffusionPipeline.from_pretrained(

load_lora_weights("jbilcke-hf/sdxl-botw", weight_name="lora.safetensors")

text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
tokenizers = [pipe.tokenizer, pipe.tokenizer_2]

embedding_path = hf_hub_download(repo_id="jbilcke-hf/sdxl-botw", filename="embeddings.pti", repo_type="model")
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
prompt="Link riding a llama, in the style of <s0><s1>"
images = pipe(
    cross_attention_kwargs={"scale": 0.8},
#your output image
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