luosiallen / latent-consistency-model

Synthesizing High-Resolution Images with Few-Step Inference

Cold

Public
1.1M runs
L40S
GitHub
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

Self-portrait oil painting, a beautiful cyborg with golden hair, 8kSelf-portrait oil painting, a beautiful cyborg with golden hair, 8k

Input prompt

Default: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

width

integer

Width of output image. Lower the setting if out of memory.

Default: 768

height

integer

Height of output image. Lower the setting if out of memory.

Default: 768

num_images

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 50)

Number of denoising steps. Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.

Default: 8

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 8

seed

integer

Random seed. Leave blank to randomize the seed

Run this model in Node.js with one line of code:

npx create-replicate --model=luosiallen/latent-consistency-model

or set up a project from scratch

Install Replicate’s Node.js client library

npm install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run luosiallen/latent-consistency-model using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "luosiallen/latent-consistency-model:553803fd018b3cf875a8bc774c99da9b33f36647badfd88a6eec90d61c5f62fc",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      num_images: 1,
      guidance_scale: 8,
      num_inference_steps: 8
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library

pip install replicate

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client

import replicate

Run luosiallen/latent-consistency-model using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "luosiallen/latent-consistency-model:553803fd018b3cf875a8bc774c99da9b33f36647badfd88a6eec90d61c5f62fc",
    input={
        "width": 768,
        "height": 768,
        "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
        "num_images": 1,
        "guidance_scale": 8,
        "num_inference_steps": 8
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run luosiallen/latent-consistency-model using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "553803fd018b3cf875a8bc774c99da9b33f36647badfd88a6eec90d61c5f62fc",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      "num_images": 1,
      "guidance_scale": 8,
      "num_inference_steps": 8
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Generated in

3.0 seconds

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.0089 to run on Replicate, or 112 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 10 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Latent Consistency Models

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.

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}
}