LCM 的全称是 Latent Consistency Models（潜在一致性模型），由清华大学交叉信息研究院的研究者们构建。通过一些创新性的方法，LCM 只用少数的几步推理就能生成高分辨率图像，将主流文生图模型的效率提高 5-10 倍，所以能呈现出实时的效果。

目前LCM只可以使用一个模型Dreamshaper_v7，基于SD版本Dreamshaper微调而来的。

LCM模型下载：

https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7

项目源码：

GitHub - luosiallen/latent-consistency-model: Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step InferenceLatent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference - GitHub - luosiallen/latent-consistency-model: Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inferencehttps://github.com/luosiallen/latent-consistency-model

安装依赖库

pip install --upgrade diffusers  # make sure to use at least diffusers >= 0.22
pip install transformers accelerate

运行模型

文生图

from diffusers import DiffusionPipeline

import torch

pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")

# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
pipe.to(torch_device="cuda", torch_dtype=torch.float32)

prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
num_inference_steps = 4
images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images
images[0].save("image.png")

图生图

from diffusers import AutoPipelineForImage2Image
import torch
import PIL

pipe = AutoPipelineForImage2Image.from_pretrained("SimianLuo/LCM_Dreamshaper_v7")
# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
pipe.to(torch_device="cuda", torch_dtype=torch.float32)

prompt = "High altitude snowy mountains"
image = PIL.Image.open("./snowy_mountains.png")

# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
num_inference_steps = 4
images = pipe(prompt=prompt, image=image, num_inference_steps=num_inference_steps, guidance_scale=8.0).images

images[0].save("image.png")

在线demo

https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Modelhttps://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model

OpenXLab浦源 - 应用中心应用中心提供应用托管的服务，用户只需遵循平台规范，通过简单的前端封装组件（Gradio）即可构建模型推理应用演示demo，应用中心提供免费应用部署的能力，普通用户也可在应用中心中交互式体验模型的能力，更好帮助用户寻找想要的学术模型或应用服务。通过前端封装组件和平台的SDK工具，帮助AI开发者简单快速构建人工智能应用。https://openxlab.org.cn/apps/detail/Latent-Consistency-Model/Latent-Consistency-Model

将LCM集成到Stable Diffusion WebUI中

SD WebUI 的 LCM 插件源码

GitHub - 0xbitches/sd-webui-lcm: Latent Consistency Model for AUTOMATIC1111 Stable Diffusion WebUILatent Consistency Model for AUTOMATIC1111 Stable Diffusion WebUI - GitHub - 0xbitches/sd-webui-lcm: Latent Consistency Model for AUTOMATIC1111 Stable Diffusion WebUIhttps://github.com/0xbitches/sd-webui-lcm选择“Extensions”->“Install from URL”，安装LCM插件。

生成的图片将会保存到outputs/txt2img-images/LCM

LCM插件提供了txt2img、img2img、vid2vid三个选项卡。

Img2Img和Vid2Vid的输出高度和宽度将与输入相同，目前不可更改。

生成的视频将保存到outputs/LCM-vid2vid

​​​​​​​