README.md

---
license: creativeml-openrail-m
tags:
  - text-to-image
  - stable-diffusion
  - lora
  - diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: <s0><s1>
inference: false
---
# sdxl-botw LoRA by Julian BILCKE (HF: [jbilcke-hf](https://huggingface.co./jbilcke-hf), Replicate: [jbilcke](https://replicate.com/jbilcke))
### A SDXL LoRA inspired by Breath of the Wild

![lora_image](https://tjzk.replicate.delivery/models_models_cover_image/aea9c0c4-b3d6-425b-9e96-9a615220fa30/link-llama.jpeg)
>

## Inference with Replicate API
Grab your replicate token [here](https://replicate.com/account)
```bash
pip install replicate
export REPLICATE_API_TOKEN=r8_*************************************
```

```py
import replicate

output = replicate.run(
    "sdxl-botw@sha256:bf412da351d41547f117391eff2824ab0301b6ba1c6c010c4b5f766a492d62fc",
    input={"prompt": "Link riding a llama, in the style of TOK"}
)
print(output)
```
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](https://replicate.com/jbilcke/sdxl-botw/api)

## 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>`

```shell
pip install diffusers transformers accelerate safetensors huggingface_hub
git clone https://github.com/replicate/cog-sdxl cog_sdxl
```

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

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)
embhandler.load_embeddings(embedding_path)
prompt="Link riding a llama, in the style of <s0><s1>"
images = pipe(
    prompt,
    cross_attention_kwargs={"scale": 0.8},
).images
#your output image
images[0]
```