cjwbw / waifu-diffusion

Stable Diffusion on Danbooru images

Cold

Public
1.1M runs
A100 (80GB)
GitHub

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=cjwbw/waifu-diffusion

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 cjwbw/waifu-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/waifu-diffusion:25d2f75ecda0c0bed34c806b7b70319a53a1bccad3ade1a7496524f013f48983",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "touhou 1girl komeiji_koishi portrait",
      num_outputs: 1,
      guidance_scale: 7.5,
      num_inference_steps: 50
    }
  }
);
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 cjwbw/waifu-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/waifu-diffusion:25d2f75ecda0c0bed34c806b7b70319a53a1bccad3ade1a7496524f013f48983",
    input={
        "width": 512,
        "height": 512,
        "prompt": "touhou 1girl komeiji_koishi portrait",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "num_inference_steps": 50
    }
)
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 cjwbw/waifu-diffusion 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": "25d2f75ecda0c0bed34c806b7b70319a53a1bccad3ade1a7496524f013f48983",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "touhou 1girl komeiji_koishi portrait",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

4.9 seconds

Tweak itReport

This example was created by a different version, cjwbw/waifu-diffusion:9e767fba.

Examples

View more examples

Run time and cost

This model costs approximately $0.039 to run on Replicate, or 25 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 A100 (80GB) GPU hardware. Predictions typically complete within 28 seconds.

Readme

This is a Cog implementation of waifu-diffusion. Code used for the demo can be found here.

waifu-diffusion - Diffusion for Weebs

waifu-diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning.

Model Description

The model originally used for fine-tuning is Stable Diffusion V1-4, which is a latent image diffusion model trained on LAION2B-en.

The current model has been fine-tuned with a learning rate of 5.0e-6 for 4 epochs on 56k Danbooru text-image pairs which all have an aesthetic rating greater than 6.0.

Training Data & Annotative Prompting

The data used for fine-tuning has come from a random sample of 56k Danbooru images, which were filtered based on CLIP Aesthetic Scoring where only images with an aesthetic score greater than 6.0 were used.

Captions are Danbooru-style captions.

License

This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:

You can’t use the model to deliberately produce nor share illegal or harmful outputs or content
The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here

Team Members and Acknowledgements

This project would not have been possible without the incredible work by the CompVis Researchers.

In order to reach us, you can join our Discord server.