Input

prompt

string

Shift + Return to add a new line

Input prompt, text of what you want to generate.

negative_prompt

string

Shift + Return to add a new line

Input negative prompt, text of what you don't want to generate.

width

integer

(minimum: 1, maximum: 2048)

Width of the output image.

Default: 1024

height

integer

(minimum: 1, maximum: 2048)

Height of the output image.

Default: 1024

num_images

integer

(minimum: 1, maximum: 4)

Number of output images.

Default: 1

steps

integer

(minimum: 1, maximum: 50)

Number of denoising steps.

Default: 4

eta

number

(minimum: 0, maximum: 1)

Stochastic parameter to control the randomness.

Default: 0

guidance_scale

number

(minimum: 0, maximum: 20)

Scale for classifier-free guidance.

Default: 0

seed

integer

Random seed. Leave blank to randomize the seed.

clip_skip

integer

Number of the layers to skip in CLIP.

Default: 0

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

npx create-replicate --model=jyoung105/dmd2

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";
import fs from "node:fs";

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

Run jyoung105/dmd2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "jyoung105/dmd2:5535bd99ab9e80d5ef6567e420ac8c9091ef7dd56456f274da6b11721b0fb692",
  {
    input: {
      eta: 0,
      steps: 4,
      width: 1024,
      height: 1024,
      prompt: "A man with hoodie on, illustration",
      clip_skip: 0,
      num_images: 1,
      guidance_scale: 0
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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 jyoung105/dmd2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "jyoung105/dmd2:5535bd99ab9e80d5ef6567e420ac8c9091ef7dd56456f274da6b11721b0fb692",
    input={
        "eta": 0,
        "steps": 4,
        "width": 1024,
        "height": 1024,
        "prompt": "A man with hoodie on, illustration",
        "clip_skip": 0,
        "num_images": 1,
        "guidance_scale": 0
    }
)
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 jyoung105/dmd2 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": "jyoung105/dmd2:5535bd99ab9e80d5ef6567e420ac8c9091ef7dd56456f274da6b11721b0fb692",
    "input": {
      "eta": 0,
      "steps": 4,
      "width": 1024,
      "height": 1024,
      "prompt": "A man with hoodie on, illustration",
      "clip_skip": 0,
      "num_images": 1,
      "guidance_scale": 0
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

4.3 seconds

Tweak it ShareReport

This output was created using a different version of the model, jyoung105/dmd2:d718f19c.

Examples

View more examples

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

Improved Distribution Matching Distillation for Fast Image Synthesis

Improved Distribution Matching Distillation for Fast Image Synthesis,
Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman

BibTeX

@article{yin2024improved,
    title={Improved Distribution Matching Distillation for Fast Image Synthesis},
    author={Yin, Tianwei and Gharbi, Micha{\"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T},
    journal={arXiv:2405.14867},
    year={2024}
}
@inproceedings{yin2024onestep,
    title={One-step Diffusion with Distribution Matching Distillation},
    author={Yin, Tianwei and Gharbi, Micha{\"e}l and Zhang, Richard and Shechtman, Eli and Durand, Fr{\'e}do and Freeman, William T and Park, Taesung},
    booktitle={CVPR},
    year={2024}
}

jyoung105 / dmd2

Input

Output

Examples

Run time and cost

Readme

Improved Distribution Matching Distillation for Fast Image Synthesis

BibTeX

Logs (mb5r6wwbx5rgg0cjthhb152mv8)