jyoung105 / flash-sdxl

Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation

Cold

Public
34 runs
L40S
GitHub
Weights
Paper
License

Run with an API

Playground API Examples README Versions

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/flash-sdxl

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

const output = await replicate.run(
  "jyoung105/flash-sdxl:5b026f92273c153de127f7e164354d51f748989118443a6fd9a29802bbd85f36",
  {
    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/flash-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "jyoung105/flash-sdxl:5b026f92273c153de127f7e164354d51f748989118443a6fd9a29802bbd85f36",
    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/flash-sdxl 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/flash-sdxl:5b026f92273c153de127f7e164354d51f748989118443a6fd9a29802bbd85f36",
    "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

3.0 seconds

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This output was created using a different version of the model, jyoung105/flash-sdxl:a18e80da.

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

Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation

Flash Diffusion is a diffusion distillation method proposed in Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation by Clément Chadebec, Onur Tasar, Eyal Benaroche, and Benjamin Aubin from Jasper Research.

Model Descriptions:

This model is a 108M LoRA distilled version of SDXL model that is able to generate images in 4 steps. The main purpose of this model is to reproduce the main results of the paper.

See our live demo and official Github repo.

BibTeX

@misc{2406.02347,
Author = {Clement Chadebec and Onur Tasar and Eyal Benaroche and Benjamin Aubin},
Title = {Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation},
Year = {2024},
Eprint = {arXiv:2406.02347},
}