---
license: mit
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - lora
inference: true
---

# sdxl-wrong-lora

A LoRA for SDXL 1.0 Base which improves output image quality after loading it and using `wrong` as a negative prompt during inference.

Benefits of using this LoRA:

- Higher color saturation and vibrance
- Higher detail in textures/fabrics
- Higher sharpness for blurry/background objects
- Better at anatomically-correct hands
- Less likely to have random artifacts
- Appears to allow the model to follow the input prompt with a more expected behavior

## Usage

The LoRA can be loaded using `load_lora_weights` like any other LoRA in `diffusers`:

```py
import torch
from diffusers import DiffusionPipeline, AutoencoderKL

vae = AutoencoderKL.from_pretrained(
    "madebyollin/sdxl-vae-fp16-fix",
    torch_dtype=torch.float16
)
base = DiffusionPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    vae=vae,
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True
)

base.load_lora_weights("minimaxir/sdxl-wrong-lora")

_ = base.to("cuda")
```

During inference, use `wrong` as the negative prompt.

## Examples

Left is the base model output (no LoRA) + refiner, right is base + LoRA and refiner. The generations use the same seed.

## Methodology

The methodology and motivation for creating this LoRA is similar to my [wrong SD 2.0 textual inversion embedding](https://huggingface.co/minimaxir/wrong_embedding_sd_2_0) by training on a balanced variety of undesirable outputs, except trained as a LoRA since textual inversion on SDXL is complicated. The base images were generated from SDXL itself, with some prompt weighting to emphasize undesirable attributes for test images.

## Notes

- The intuitive way to think about how this LoRA works
- It's possible to use `not wrong` in the prompt itself but in testing it has no effect.
- You can use other negative prompts in conjunction with the `wrong` prompt but you may want to weight them appropriately.