README.md

---
license: openrail++
tags:
- text-to-image
- stable-diffusion
library_name: diffusers
inference: false
---

# SDXS-512-DreamShaper-Anime

SDXS is a model that can generate high-resolution images in real-time based on prompt texts, trained using score distillation and feature matching. 
For more information, please refer to our research paper: [SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions](https://arxiv.org/abs/2403.16627). 
We open-source the model as part of the research.

SDXS-512-DreamShaper-Anime is the anime-style **LoRA** for [SDXS-512-DreamShaper](https://huggingface.co/IDKiro/sdxs-512-dreamshaper).
Watch [our repo](https://github.com/IDKiro/sdxs) for any updates.


## Diffusers Usage

![](output.png)

```python
import torch
from diffusers import StableDiffusionPipeline
import peft

repo = "IDKiro/sdxs-512-dreamshaper"
lora_repo = "IDKiro/sdxs-512-dreamshaper-anime"
seed = 42
weight_type = torch.float16     # or float32

# Load model.
pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type)
pipe.unet = PeftModel.from_pretrained(pipe.unet, lora_repo)
pipe.to("cuda")

prompt = "a close-up picture of an old man standing in the rain"

# Ensure using 1 inference step and CFG set to 0.
image = pipe(
    prompt, 
    num_inference_steps=1, 
    guidance_scale=0,
    generator=torch.Generator(device="cuda").manual_seed(seed)
).images[0]

image.save("output.png")
```

## Cite Our Work

```
@article{song2024sdxs,
  author    = {Yuda Song, Zehao Sun, Xuanwu Yin},
  title     = {SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions},
  journal   = {arxiv},
  year      = {2024},
}
```