# latent-consistency-model
**Repository Path**: chuang_lin/latent-consistency-model
## Basic Information
- **Project Name**: latent-consistency-model
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-15
- **Last Updated**: 2025-12-15
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Latent Consistency Models
Official Repository of the paper: [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378).
Official Repository of the paper: [LCM-LoRA: A Universal Stable-Diffusion Acceleration Module](https://arxiv.org/abs/2311.05556).
Project Page: https://latent-consistency-models.github.io
### Try our Demos:
🤗 **Hugging Face Demo**: [](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model) 🔥🔥🔥
**Replicate Demo**: [](https://replicate.com/cjwbw/latent-consistency-model)
**OpenXLab Demo**: [](https://openxlab.org.cn/apps/detail/Latent-Consistency-Model/Latent-Consistency-Model)
**LCM Community**: Join our LCM discord channels
for discussions. Coders are welcome to contribute.
## Breaking News 🔥🔥!!
- (🤖New) 2023/12/1 **Pixart-α X LCM** is out, a high quality image generative model. see [here](https://huggingface.co/spaces/PixArt-alpha/PixArt-LCM).
- (❤️New) 2023/11/10 **Training Scripts** are released!! Check [here](https://github.com/luosiallen/latent-consistency-model/tree/main/LCM_Training_Script/consistency_distillation).
- (🤯New) 2023/11/10 **Training-free acceleration LCM-LoRA** is born! See our technical report [here](https://arxiv.org/abs/2311.05556) and Hugging Face blog [here](https://huggingface.co/blog/lcm_lora).
- (⚡️New) 2023/11/10 LCM has a major update! We release **3 LCM-LoRA (SD-XL, SSD-1B, SD-V1.5)**, see [here](https://huggingface.co/latent-consistency/lcm-lora-sdxl).
- (🚀New) 2023/11/10 LCM has a major update! We release **2 Full Param-tuned LCM (SD-XL, SSD-1B)**, see [here](https://huggingface.co/latent-consistency/lcm-sdxl).
## News
- (🔥New) 2023/11/10 We support LCM Inference with C# and ONNX Runtime now! Thanks to [@saddam213](https://github.com/saddam213)! Check the link [here](https://github.com/saddam213/OnnxStack).
- (🔥New) 2023/11/01 **Real-Time Latent Consistency Models** is out!! Github link [here](https://github.com/radames/Real-Time-Latent-Consistency-Model). Thanks [@radames](https://github.com/radames) for the really cool Huggingface🤗 demo [Real-Time Image-to-Image](https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model), [Real-Time Text-to-Image](https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model-Text-To-Image). Twitter/X [Link](https://x.com/radamar/status/1718783886413709542?s=20).
- (🔥New) 2023/10/28 We support **Img2Img** for LCM! Please refer to "🔥 Image2Image Demos".
- (🔥New) 2023/10/25 We have official [**LCM Pipeline**](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_consistency_models) and [**LCM Scheduler**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lcm.py) in 🧨 Diffusers library now! Check the new "Usage".
- (🔥New) 2023/10/24 Simple **Streamlit UI** for local use: See the [link](https://github.com/akx/lcm_test) Thanks for [@akx](https://github.com/akx).
- (🔥New) 2023/10/24 We support **SD-Webui** and **ComfyUI** now!! Thanks for [@0xbitches](https://github.com/0xbitches). See the link: [SD-Webui](https://github.com/0xbitches/sd-webui-lcm) and [ComfyUI](https://github.com/0xbitches/ComfyUI-LCM).
- (🔥New) 2023/10/23 Running on **Windows/Linux CPU** is also supported! Thanks for [@rupeshs](https://github.com/rupeshs) See the [link](https://github.com/rupeshs/fastsdcpu).
- (🔥New) 2023/10/22 **Google Colab** is supported now. Thanks for [@camenduru](https://github.com/camenduru) See the link: [Colab](https://github.com/camenduru/latent-consistency-model-colab)
- (🔥New) 2023/10/21 We support **local gradio demo** now. LCM can run locally!! Please refer to the "**Local gradio Demos**".
- (🔥New) 2023/10/19 We provide a demo of LCM in 🤗 Hugging Face Space. Try it [here](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model).
- (🔥New) 2023/10/19 We provide the LCM model (Dreamshaper_v7) in 🤗 Hugging Face. Download [here](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7).
- (🔥New) 2023/10/19 LCM is integrated in 🧨 Diffusers library. Please refer to the "Usage".
## 🔥 Image2Image Demos (Image-to-Image):
We support **Img2Img** now! Try the impressive img2img demos here: [Replicate](https://replicate.com/fofr/latent-consistency-model), [SD-webui](https://github.com/0xbitches/sd-webui-lcm), [ComfyUI](https://github.com/0xbitches/ComfyUI-LCM), [Colab](https://github.com/camenduru/latent-consistency-model-colab/)
Local gradio for img2img is on the way!
## 🔥 Local gradio Demos (Text-to-Image):
To run the model locally, you can download the "local_gradio" folder:
1. Install Pytorch (CUDA). MacOS system can download the "MPS" version of Pytorch. Please refer to: [https://pytorch.org](https://pytorch.org). Install [Intel Extension for Pytorch](https://intel.github.io/intel-extension-for-pytorch/xpu/latest/) as well if you're using Intel GPUs.
2. Install the main library:
```
pip install diffusers transformers accelerate gradio==3.48.0
```
3. Launch the gradio: (For MacOS users, need to set the device="mps" in app.py; For Intel GPU users, set `device="xpu"` in app.py)
```
python app.py
```
## Demos & Models Released
Ours Hugging Face Demo and Model are released ! Latent Consistency Models are supported in 🧨 [diffusers](https://github.com/huggingface/diffusers).
**LCM Model Download**: [LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7)
LCM模型已上传到始智AI(wisemodel) 中文用户可在此下载，[下载链接](https://www.wisemodel.cn/organization/Latent-Consistency-Model).
For Chinese users, download LCM here: (中文用户可以在此下载LCM模型) [](https://openxlab.org.cn/models/detail/Latent-Consistency-Model/LCM_Dreamshaper_v7_4k.safetensors)
Hugging Face Demo: [](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model)
Replicate Demo: [](https://replicate.com/cjwbw/latent-consistency-model)
OpenXLab Demo: [](https://openxlab.org.cn/apps/detail/Latent-Consistency-Model/Latent-Consistency-Model)
Tungsten Demo: [](https://tungsten.run/mjpyeon/lcm)
Novita.AI Demo: [](https://novita.ai/product/lcm-txt2img)
By distilling classifier-free guidance into the model's input, LCM can generate high-quality images in very short inference time. We compare the inference time at the setting of 768 x 768 resolution, CFG scale w=8, batchsize=4, using a A800 GPU.
## Usage
We have official [**LCM Pipeline**](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_consistency_models) and [**LCM Scheduler**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lcm.py) in 🧨 Diffusers library now! The older usages will be deprecated.
You can try out Latency Consistency Models directly on:
[](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model)
To run the model yourself, you can leverage the 🧨 Diffusers library:
1. Install the library:
```
pip install --upgrade diffusers # make sure to use at least diffusers >= 0.22
pip install transformers accelerate
```
2. Run the model:
```py
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
```
For more information, please have a look at the official docs:
👉 https://huggingface.co/docs/diffusers/api/pipelines/latent_consistency_models#latent-consistency-models
## Usage (Deprecated)
We have official [**LCM Pipeline**](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/latent_consistency_models) and [**LCM Scheduler**](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lcm.py) in 🧨 Diffusers library now! The older usages will be deprecated. But you can still use the older usages by adding ```revision="fb9c5d1"``` from ```from_pretrained(...)```
To run the model yourself, you can leverage the 🧨 Diffusers library:
1. Install the library:
```
pip install diffusers transformers accelerate
```
2. Run the model:
```py
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main", revision="fb9c5d")
# 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
```
### Our Contributors :
## BibTeX
```bibtex
LCM:
@misc{luo2023latent,
title={Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference},
author={Simian Luo and Yiqin Tan and Longbo Huang and Jian Li and Hang Zhao},
year={2023},
eprint={2310.04378},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
LCM-LoRA:
@article{luo2023lcm,
title={LCM-LoRA: A Universal Stable-Diffusion Acceleration Module},
author={Luo, Simian and Tan, Yiqin and Patil, Suraj and Gu, Daniel and von Platen, Patrick and Passos, Apolin{\'a}rio and Huang, Longbo and Li, Jian and Zhao, Hang},
journal={arXiv preprint arXiv:2311.05556},
year={2023}
}
```