FORKED FROM BELOW REPO, DESIGNED FOR SALAD PORTAL AND SALAD SCE

https://salad.com

Building your Docker image

• Configure the Dockerfile file near the end to include your desired model, either via a link in the MODEL_ID section (as in the example), or linking to a .CPKT file instead
• Configure the docker file to include your NGROK AUTH token, as well as your NGROK EDGE token, in the specified sections near the end of the Dockerfile
• CD into your working directory
• Run docker build -t dockerName .
• Once built, run docker run --gpus=all -p 50150:50150 dockerName
• Send POST request to http://localhost:50150 to test, using below example POST request:

  {
  "modelInputs": {
    "prompt": "YOUR PROMPT GOES HERE!",
    "num_inference_steps": 50,
    "guidance_scale": 7.5,
    "width": 512,
    "height": 512,
    "seed": 3239022079,
    "num_images_per_prompt": 4 
  },
  "callInputs": {
    "PIPELINE": "StableDiffusionPipeline",
    "SCHEDULER": "LMSDiscreteScheduler",
    "safety_checker": "true"
    }
  }

• Send identical POST request to your designated URL endpoint

Releasing your Docker image

• Once you have a working test Docker built, either push directly to your Docker Repository, or rebuild it with desired tagging and name, and then push your Docker Container to your Docker Repository
• Create your Salad Portal container group, linked to your new Docker Container Tag

Changes to below project:

• Added GET request response
• Included https://github.com/kiri-art/docker-diffusers-api-build-download by default
• Added configuration for network forwarding with NGROK

docker-diffusers-api ("banana-sd-base")

Diffusers / Stable Diffusion in docker with a REST API, supporting various models, pipelines & schedulers. Used by kiri.art, perfect for local, server & serverless.

Copyright (c) Gadi Cohen, 2022. MIT Licensed. Please give credit and link back to this repo if you use it in a public project.

Features

• Models: stable-diffusion, waifu-diffusion, and easy to add others (e.g. jp-sd)
• Pipelines: txt2img, img2img and inpainting in a single container (all diffusers official and community pipelines are wrapped, but untested)
• All model inputs supported, including setting nsfw filter per request
• Permute base config to multiple forks based on yaml config with vars
• Optionally send signed event logs / performance data to a REST endpoint / webhook.
• Can automatically download a checkpoint file and convert to diffusers.
• S3 support, dreambooth training.

Note: This image was created for kiri.art. Everything is open source but there may be certain request / response assumptions. If anything is unclear, please open an issue.

Important Notices

• Official docker-diffusers-api Forum: help, updates, discussion.
• Subscribe ("watch") these forum topics for:
  • notable main branch updates
  • notable dev branch updates
• Always check the CHANGELOG for important updates when upgrading.

Official help in our dedicated forum https://forums.kiri.art/c/docker-diffusers-api/16.

This README refers to the in-development dev branch and may reference features and fixes not yet in the published releases.

v1 has not yet been officially released yet but has been running well in production on kiri.art for almost a month. We'd be grateful for any feedback from early adopters to help make this official. For more details, see Upgrading from v0 to v1. Previous releases available on the dev-v0-final and main-v0-final branches.

Currently only NVIDIA / CUDA devices are supported. Tracking Apple / M1 support in issue #20.

Installation & Setup:

Setup varies depending on your use case.

1. To run locally or on a server, with runtime downloads:

   docker run --gpus all -p 8000:8000 -e HF_AUTH_TOKEN=$HF_AUTH_TOKEN gadicc/diffusers-api.

   See the guides for various cloud providers.

2. To run serverless, include the model at build time:

   1. docker-diffusers-api-build-download ( banana, others)
   2. docker-diffusers-api-runpod, see the guide

3. Building from source.

   1. Fork / clone this repo.
   2. docker build -t gadicc/diffusers-api .
   3. See CONTRIBUTING.md for more helpful hints.

Other configurations are possible but these are the most common cases

Everything is set via docker build-args or environment variables.

Usage:

See also Testing below.

The container expects an HTTP POST request to /, with a JSON body resembling the following:

{
  "modelInputs": {
    "prompt": "Super dog",
    "num_inference_steps": 50,
    "guidance_scale": 7.5,
    "width": 512,
    "height": 512,
    "seed": 3239022079
  },
  "callInputs": {
    // You can leave these out to use the default
    "MODEL_ID": "runwayml/stable-diffusion-v1-5",
    "PIPELINE": "StableDiffusionPipeline",
    "SCHEDULER": "LMSDiscreteScheduler",
    "safety_checker": true,
  },
}

It's important to remember that docker-diffusers-api is primarily a wrapper around HuggingFace's diffusers library. Basic familiarity with diffusers is indespensible for a good experience with docker-diffusers-api. Explaining some of the options above:

• modelInputs - for the most part - are passed directly to the selected diffusers pipeline unchanged. So, for the default StableDiffusionPipeline, you can see all options in the relevant pipeline docs for its __call__ method. The main exceptions are:

  • Only valid JSON values can be given (strings, numbers, etc)
  • seed, a number, is transformed into a generator.
  • images are converted to / from base64 encoded strings.

• callInputs affect which model, pipeline, scheduler and other lower level options are used to construct the final pipeline. Notably:

  • SCHEDULER: any scheduler included in diffusers should work out the box, provided it can loaded with its default config and without requiring any other explicit arguments at init time. In any event, the following schedulers are the most common and most well tested: DPMSolverMultistepScheduler (fast! only needs 20 steps!), LMSDiscreteScheduler, DDIMScheduler, PNDMScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler.

  • PIPELINE: the most common are StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline, and the community lpw_stable_diffusion which allows for long prompts (more than 77 tokens) and prompt weights (things like ((big eyes)), (red hair:1.2), etc), and accepts a custom_pipeline_method callInput with values text2img ("text", not "txt"), img2img and inpaint. See these links for all the possible modelInputs's that can be passed to the pipeline's __call__ method.

  • MODEL_URL (optional) can be used to retrieve the model from locations other than HuggingFace, e.g. an HTTP server, S3-compatible storage, etc. For more info, see the storage docs and this post for info on how to use and store optimized models from your own cloud.

Examples and testing

There are also very basic examples in test.py, which you can view and call python test.py if the container is already running on port 8000. You can also specify a specific test, change some options, and run against a deployed banana image:

$ python test.py
Usage: python3 test.py [--banana] [--xmfe=1/0] [--scheduler=SomeScheduler] [all / test1] [test2] [etc]

# Run against http://localhost:8000/ (Nvidia Quadro RTX 5000)
$ python test.py txt2img
Running test: txt2img
Request took 5.9s (init: 3.2s, inference: 5.9s)
Saved /home/dragon/www/banana/banana-sd-base/tests/output/txt2img.png

# Run against deployed banana image (Nvidia A100)
$ export BANANA_API_KEY=XXX
$ BANANA_MODEL_KEY=XXX python3 test.py --banana txt2img
Running test: txt2img
Request took 19.4s (init: 2.5s, inference: 3.5s)
Saved /home/dragon/www/banana/banana-sd-base/tests/output/txt2img.png

# Note that 2nd runs are much faster (ignore init, that isn't run again)
Request took 3.0s (init: 2.4s, inference: 2.1s)

The best example of course is https://kiri.art/ and it's source code.

Help on Official Forums.

Adding other Models

You have two options.

1. For a diffusers model, simply set MODEL_ID build-var / call-arg to the name of the model hosted on HuggingFace, and it will be downloaded automatically at build time.

2. For a non-diffusers model, simply set the CHECKPOINT_URL build-var / call-arg to the URL of a .ckpt file, which will be downloaded and converted to the diffusers format automatically at build time. CHECKPOINT_CONFIG_URL can also be set.

Troubleshooting

• 403 Client Error: Forbidden for url

  Make sure you've accepted the license on the model card of the HuggingFace model specified in MODEL_ID, and that you correctly passed HF_AUTH_TOKEN to the container.

Event logs / web hooks / performance data

Set SEND_URL (and optionally SIGN_KEY) environment variable(s) to send event and timing data on init, inference and other start and end events. This can either be used to log performance data, or for webhooks on event start / finish.

The timing data is now returned in the response payload too, like this: { $timings: { init: timeInMs, inference: timeInMs } }, with any other events (such a training, upload, etc).

You can go to https://webhook.site/ and use the provided "unique URL" as your SEND_URL to see how it works, if you don't have your own REST endpoint (yet).

If SIGN_KEY is used, you can verify the signature like this (TypeScript):

import crypto from "crypto";

async function handler(req: NextApiRequest, res: NextApiResponse) {
  const data = req.body;

  const containerSig = data.sig as string;
  delete data.sig;

  const ourSig = crypto
    .createHash("md5")
    .update(JSON.stringify(data) + process.env.SIGN_KEY)
    .digest("hex");

  const signatureIsValid = containerSig === ourSig;
}

Acknowledgements

• The container image is originally based on https://github.com/bananaml/serverless-template-stable-diffusion.

• CompVis, Stability AI, LAION and RunwayML for their incredible time, work and efforts in creating Stable Diffusion, and no less so, their decision to release it publicly with an open source license.

• HuggingFace - for their passion and inspiration for making machine learning more accessibe to developers, and in particular, their Diffusers library.