File size: 3,331 Bytes
0f3328a 2adbf2a 0f3328a 2adbf2a c7f9b67 2adbf2a c7f9b67 2adbf2a c4c7950 786fa1a c4c7950 2adbf2a c4c7950 2adbf2a cda1c7e 2adbf2a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
---
license: mit
language:
- en
pipeline_tag: text-to-image
tags:
- text-to-image
---
# Latent Consistency Models
Official Repository of the paper: *[Latent Consistency Models](https://arxiv.org/abs/2310.04378)*.
Project Page: https://latent-consistency-models.github.io
## Try our Hugging Face demos:
[](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model)
## Model Descriptions:
Distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable-Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours).
## Generation Results:
<p align="center">
<img src="teaser.png">
</p>
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.
<p align="center">
<img src="speed_fid.png">
</p>
## Usage
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
```
## Usage (Deprecated)
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, output_type="pil").images
```
## BibTeX
```bibtex
@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}
}
``` |