High Level

Regardless of your choice of stable diffusion inference server, models, or extensions, the basic process is as follows:

Get a docker image that runs your inference server
Copy any models and extensions you want into the docker image
Ensure the container is listening on an ipv6 address
Push the new image up to a container registry
Deploy the image as a SaladCloud container group

Find a Docker Image

Find a docker image of ComfyUI. Here’s one we’ve verified works on Salad:

ComfyUI

Git Repo: https://github.com/ai-dock/comfyui
Docker Image: ghcr.io/ai-dock/comfyui:v2-cuda-12.1.1-base-22.04-v0.2.0
Model Directory: /opt/ComfyUI/models
Custom Node Directory: /opt/ComfyUI/custom_nodes/

Note that once deployed, you will be interacting with this as an API, and not through the browser user interface.

Download Your Models and Extensions

Download any model files you plan to use. For our example, we’re going to use Dreamshaper 8, available on Civitai.com (https://blog.salad.com/civitai-salad/)

Create a Dockerfile

Create a new file called Dockerfile and open it in your preferred text editor. At this point, your directory should look like this:
```
.
├── Dockerfile
└── dreamshaper_8.safetensors
```
Save an API-Formatted workflow.json file in the same directory as your Dockerfile. This file will be used to warm up the server before starting it. You can generate this file by using the ComfyUI web interface to create a prompt, and then saving it in API format. See below for more details.

.
├── Dockerfile
├── dreamshaper_8.safetensors
└── workflow_api_dreamshaper.json

Copy the following into your Dockerfile:

# We're going to use this verified comfyui image as a base
FROM ghcr.io/ai-dock/comfyui:v2-cuda-12.1.1-base-22.04-v0.2.0

# Disable the authentication provided by the base image
ENV WEB_ENABLE_AUTH=false

# Now we copy our model into the image
ENV MODEL_DIR=/opt/ComfyUI/models
COPY dreamshaper_8.safetensors ${MODEL_DIR}/checkpoints/dreamshaper_8.safetensors

# We also need to copy the comfyui-api binary into the image, since ComfyUI
# is fully asynchronous by default, and has no convenient way to retrieve
# generated images.
ADD https://github.com/SaladTechnologies/comfyui-api/releases/download/1.4.0/comfyui-api .
RUN chmod +x comfyui-api

# OPTIONAL: Warmup the server by running a workflow before starting the server.
# The comfyui-api supports a warmup mode, where it will run a provided workflow before starting the server.
# This example assumes you have a workflow json file in the same directory as your Dockerfile.
COPY workflow_api_dreamshaper.json .
ENV WARMUP_PROMPT_FILE=workflow_api_dreamshaper.json

CMD ["./comfyui-api"]

Note that we are including a simple wrapper binary to the image to make it easier to retrieve generated images. ComfyUI accepts prompts into a queue, and then eventually saves images to the local filesystem. This makes it difficult to use in a stateless environment like Salad. This additional binary extends the ComfyUI /prompt API to allow either receiving the generated images in the response body, or having complete images submitted to a provided webhook url.

Build and Test Your Docker Image

Build the docker image. You should change the specified tag to suit your purpose. We’ve chosen to include the ComfyUI version (0.2.0), the API version (1.4.0) and the model name (dreamshaper8) in the tag.

docker build -t saladtechnologies/comfyui:comfy0.2.0-api1.4.0-dreamshaper8 .

(Recommended) Run the docker image locally to confirm it works as expected

docker run -it --rm --gpus all -p 3000:3000 -p 8188:8188 --name comfyui \
saladtechnologies/comfyui:comfy0.2.0-api1.4.0-dreamshaper8

Using it here locally, we’re going to expose port 3000, which is required for the wrapper, and port 8188 that will let us access the web ui locally to make it easier to get the prompt object we need for the api.

Go to http://localhost:8188/ in your browser. You should see something like this:
Click “Queue Prompt” to generate an image. Mine came out like this.
Enable Dev Mode Options via the settings menu

You should see a new option in the menu, “Save (API Format)”:

Click the “Save (API Format)” button, and save it. You’ll get a file called “workflow_api.json” that contains everything ComfyUI needs to run that prompt again.

{
  "3": {
    "inputs": {
      "seed": 712610403220747,
      "steps": 20,
      "cfg": 8,
      "sampler_name": "euler",
      "scheduler": "normal",
      "denoise": 1,
      "model": ["4", 0],
      "positive": ["6", 0],
      "negative": ["7", 0],
      "latent_image": ["5", 0]
    },
    "class_type": "KSampler",
    "_meta": {
      "title": "KSampler"
    }
  },
  "4": {
    "inputs": {
      "ckpt_name": "dreamshaper_8.safetensors"
    },
    "class_type": "CheckpointLoaderSimple",
    "_meta": {
      "title": "Load Checkpoint"
    }
  },
  "5": {
    "inputs": {
      "width": 512,
      "height": 512,
      "batch_size": 1
    },
    "class_type": "EmptyLatentImage",
    "_meta": {
      "title": "Empty Latent Image"
    }
  },
  "6": {
    "inputs": {
      "text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
      "clip": ["4", 1]
    },
    "class_type": "CLIPTextEncode",
    "_meta": {
      "title": "CLIP Text Encode (Prompt)"
    }
  },
  "7": {
    "inputs": {
      "text": "text, watermark",
      "clip": ["4", 1]
    },
    "class_type": "CLIPTextEncode",
    "_meta": {
      "title": "CLIP Text Encode (Prompt)"
    }
  },
  "8": {
    "inputs": {
      "samples": ["3", 0],
      "vae": ["4", 2]
    },
    "class_type": "VAEDecode",
    "_meta": {
      "title": "VAE Decode"
    }
  },
  "9": {
    "inputs": {
      "filename_prefix": "ComfyUI",
      "images": ["8", 0]
    },
    "class_type": "SaveImage",
    "_meta": {
      "title": "Save Image"
    }
  }
}

You might notice this is kind of an unintuitive prompting format, but it does capture the nodes and connections used by ComfyUI. In my experience, the ComfyUI web ui is the best way to design your prompts, rather than trying to create a workflow json file like this from scratch.

Submit the prompt to the wrapper API on port 3000, using Postman or any http request tool of your choice.

You should submit a POST request to http://localhost:3000/prompt with a JSON request body like this, where the value of “prompt” is that workflow json we created previously.
```
{
	"prompt": { ... }
}
```

In a couple seconds you should receive a response like this:

{
   "id": "random-uuid",
   "prompt": { ... },
   "images": ["base64encodedimage"]
}

Decode the base64 encoded string into your image. You can do this in a free browser tool such as https://codebeautify.org/base64-to-image-converter

or using CLI tools like jq and base64. For this method, first save your response to a file called response.json. Then, run the following command:
```
jq -r '.images[0]' response.json | base64 -d > image.png
```

Push and Deploy Your Docker Image

Push your docker image up to docker hub (or the container registry of your choice.)

docker push saladtechnologies/comfyui:comfy0.2.0-api1.4.0-dreamshaper8

Deploy your image on Salad, using either the Portal or the SaladCloud Public API

We’re going to name our container group something obvious, and fill in the configuration form. Since this is a stable diffusion 1.5 based model, we’re gong to give ourselves fairly modest hardware: 4 vCPUs, 12GB ram, an RTX 3060 Ti GPU, and a reserved 1 GB of local storage for temporary storage of images as they are being generated. We’re going to use 3 replicas, to ensure coverage during node interruptions and reallocations.

Additionally, we will want to configure our startup and readiness probes (endpoints provided by the wrapper), and enable the container gateway on port 3000. We’ve disabled authentication for this example, but you may want to enable it. If you enable authentication, requests must be submitted with your SaladCloud API Key in the Salad-Api-Key header.

Click Deploy, and wait for the deployment to come up.

Interact with Your Deployment

Wait for the deployment to be ready.
1. First, SaladCloud pulls your container image into our own internal high-performance cache.
2. Next, SaladCloud locates eligible nodes for your workload, based on the configuration you provided.
3. Next, SaladCloud begins downloading the cached container image to the nodes that have been assigned to your workload.
  
  This step can take tens of minutes in some cases, depending on the size of the image, and the internet speed of the individual nodes. Note that our progress bars are only estimates, and do not necessarily reflect real-time download status. These slow cold starts, and the possibility of nodes being interrupted by their host without warning, are why we always want to provision multiple replicas.
4. Eventually, you will see instances listed as “running”, with a green check in the “ready” column.
Submit your prompt to the provided Access Domain Name. You will get back a json response within a few seconds. See above for how to submit the request and process the response.

How-to Guides

How to Deploy Stable Diffusion (ComfyUI)

High Level

Find a Docker Image

Download Your Models and Extensions

Create a Dockerfile

Build and Test Your Docker Image

Push and Deploy Your Docker Image

Interact with Your Deployment

How-to Guides

​High Level

​Find a Docker Image

​Download Your Models and Extensions

​Create a Dockerfile

​Build and Test Your Docker Image

​Push and Deploy Your Docker Image

​Interact with Your Deployment

High Level

Find a Docker Image

Download Your Models and Extensions

Create a Dockerfile

Build and Test Your Docker Image

Push and Deploy Your Docker Image

Interact with Your Deployment