# sdxl-upside-down LoRA by [fofr](https://replicate.com/fofr)
### SDXL fine-tuned on upside down photos of people

![lora_image](https://replicate.delivery/pbxt/qYTj7uYe3eobL0cucLja3HTuZMfCIwdkceYu2QUpEsf6OSmOC/out-0.png)
>

## 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-upside-down@sha256:96ca22ee353563144869b523f36a05692f8f00a90557654f2b75be17a37bad94",
    input={"prompt": "A TOK photo of a person"}
)
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/fofr/sdxl-upside-down/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")

pipe.load_lora_weights("fofr/sdxl-upside-down", 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="fofr/sdxl-upside-down", filename="embeddings.pti", repo_type="model")
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
prompt="A <s0><s1> photo of a person"
images = pipe(
    prompt,
    cross_attention_kwargs={"scale": 0.8},
).images
#your output image
images[0]
```

{"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "pivotal_tuning": true, "textual_embeddings": "embeddings.pti", "instance_prompt": "<s0><s1>", "inference": false}
diffusers text-to-image stable-diffusion lora base_model:stabilityai/stable-diffusion-xl-base-1.0 license:creativeml-openrail-m region:us