Stable Diffusion (SDXL0.9 & 1.0)

Run a Stable Diffusion Text to Image Job

Generically, stable diffusion is what happens when you put a couple of drops of dye into a bucket of water. Given time, the dye randomly disperses and eventually settles into a uniform distribution which colours all the water evenly.

In computer science, you define rules for your (dye) particles to follow and the medium this takes place in.

Stable Diffusion is a machine learning model used for text-to-image processing (like Dall-E) and based on a diffusion probabilistic model that uses a transformer to generate images from text.

[CLI] Running Stable Diffusion SDXL 0.9 and SDXL 1.0

Prerequisites

Before running sdxl, make sure you have the Lilypad CLI installed on your machine and your private key environment variable is set. This is necessary for operations within the Lilypad network.

export WEB3_PRIVATE_KEY=<YOUR_PRIVATE_KEY>

Usage

Lilypad can run SDXL v0.9 or SDXL v1.0 with the option to add tunables to improve or change the model output.

When running SDXL pipelines in Lilypad, you have the choice between using the Base model or the Refiner model. Each serves a unique purpose in the image generation process:

  • Base Model: This is the primary model that generates the initial image based on your input prompt. It focuses on the broad aspects of the image, capturing the main theme and essential elements. The Base model is faster and uses less computational power.

  • Refiner Model: This model takes the image from the Base model and enhances it. It refines details, improves textures, and adjusts colors to increase the visual appeal and realism of the image. The Refiner model is used when you need higher quality and more detailed images.

To run SDXL Pipeline in Lilypad, you can use the following commands:

SDXL v0.9

Base:

lilypad run sdxl-pipeline:v0.9-base-lilypad3 -i Prompt="an astronaut floating against a white background"

Refiner:

lilypad run sdxl-pipeline:v0.9-refiner-lilypad3 -i Prompt="an astronaut floating against a white background"

SDXL 1.0

Base:

lilypad run sdxl-pipeline:v1.0-base-lilypad3 -i Prompt="an astronaut floating against a white background"

Refiner:

lilypad run sdxl-pipeline:v1.0-refiner-lilypad3 -i Prompt="an astronaut floating against a white background"

Specifying tunables

If you wish to specify more than one tunable, such as the number of steps, simply add more -i flags. For example, to improve the quality of the image generated add "Steps=x" with x = (up to 200):

lilypad run sdxl-pipeline -i Prompt="an astronaut floating against a white background" -i Steps=69

See the options and tunables section (below) for more information on what tunables are available.

The output will look like this:

SDXL Output

Take the IPFS link given in the results and paste it into your browser:

https://ipfs.io/ipfs/QmVng1jkMxE9ep4k8mYiiCiWaCRiRLvCeo6bJRXirhz1dZ

Please be patient! IPFS can take some time to propagate and doesn't always work immediately.

You could also check the output folder that would have been downloaded at the end of running the job:

open /tmp/lilypad/data/downloaded-files/QmZuE29GJVmenRUh72FQDgkMUT1Zdp967oEJvzjaDwGGVo

Results of SDXL job on Output Directory

In the /outputs folder, you'll find the image:

The Image in the outputs folder

Since modules are deterministic, running this command with the same text prompt will produce the same image, since the same seed is also used (the default seed is 0).

See this beginner-friendly article on how seeds work for more info on this

To change the image, you can pass in a different seed number:

`lilypad run sdxl-pipeline:v0.9-base-lilypad3 -i Prompt='a gigantic lilypad shaped space station' -i Steps=150` 

Output using different seed.

Options and tunables

The following tunables are available. All of them are optional, and have default settings that will be used if you do not provide them.

NameDescriptionDefaultAvailable options

Prompt

A text prompt for the model

"question mark floating in space"

Any string

Seed

A seed for the model

42

Any valid non-negative integer

Steps

The number of steps to run the model for

50

Any valid non-negative integer

Scheduler

The scheduler to use for the model

normal

normal, karras, exponential, sgm_uniform, simple, ddim_uniform

Sampler

The sampler to use for the model

euler_ancestral

"euler", "euler_ancestral", "heun", "heunpp2", "dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"

Size

The output size requested in px

1024

512, 768, 1024, 2048

Batching

How many images to produce

1

1, 2, 4, 8

See the usage sections for the runner of your choice for more information on how to set and use these variables.

Learn more about this Lilypad module on Github.

CLI Video

In this video, Sam demos the use of SDXL in the Lilypad network.

Looking for Smart Contracts?

Check out our smart contracts docs!

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