---
title: lcm-sdxl
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/lcm-sdxl"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/lcm-sdxl.md"
repository: AI-ModelScope/lcm-sdxl
last_updated: 2023-12-13
license: openrail++
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
base_model:
  - stabilityai/stable-diffusion-xl-base-1.0
base_model_relation: finetune
parameters: 5.1B
tensor_type:
  - F32
  - F16
library_name:
  - pytorch
  - lora
  - safetensors
  - diffusers
frameworks:
  - Pytorch
downloads: 752
stars: 5
tags:
  - text-to-image
---

# lcm-sdxl

> lcm-sdxl - AI-ModelScope 在 ModelScope 开源的模型。lcm-sdxl

AI-ModelScope/lcm-sdxl 是 ModelScope 魔搭社区上的 5.1B 参数text-to-image-synthesis模型，采用 openrail++ 许可，基于 stabilityai/stable-diffusion-xl-base-1.0 构建。

- **Repository**: AI-ModelScope/lcm-sdxl
- **License**: openrail++
- **Tasks**: text-to-image-synthesis
- **Parameters**: 5.1B
- **Base model**: stabilityai/stable-diffusion-xl-base-1.0
- **Tags**: text-to-image
- **Downloads**: 752
- **Stars**: 5
- **Last updated**: 2023-12-13

Source: https://www.modelscope.cn/models/AI-ModelScope/lcm-sdxl

---

# Latent Consistency Model (LCM): SDXL

Latent Consistency Model (LCM) was proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) 
by *Simian Luo, Yiqin Tan et al.* and [Simian Luo](https://huggingface.co/SimianLuo), [Suraj Patil](https://huggingface.co/valhalla), and [Daniel Gu](https://huggingface.co/dg845)
succesfully applied the same approach to create LCM for SDXL.

This checkpoint is a LCM distilled version of [`stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) that allows
to reduce the number of inference steps to only between **2 - 8 steps**.


## Usage

LCM SDXL is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first 
install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`.
audio dataset from the Hugging Face Hub:

```bash
pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate peft
```

### Text-to-Image

The model can be loaded with it's base pipeline `stabilityai/stable-diffusion-xl-base-1.0`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps.
Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0.

```python
from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
import torch
from modelscope import snapshot_download

model_dir_lcm = snapshot_download("AI-ModelScope/lcm-sdxl",revision = "master")
model_dir_sdxl = snapshot_download("AI-ModelScope/stable-diffusion-xl-base-1.0",revision = "v1.0.9")

unet = UNet2DConditionModel.from_pretrained(model_dir_lcm, torch_dtype=torch.float16, variant="fp16")
pipe = DiffusionPipeline.from_pretrained(model_dir_sdxl, unet=unet, torch_dtype=torch.float16, variant="fp16")

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")

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

image = pipe(prompt, num_inference_steps=4, guidance_scale=8.0).images[0]
image.save("image.png")
```

![](./image.png)

### Image-to-Image

Works as well! TODO docs

### Inpainting

Works as well! TODO docs

### ControlNet

Works as well! TODO docs

### T2I Adapter

Works as well! TODO docs

## Speed Benchmark

TODO

## Training

TODO