ViewComfy API Rules

You are an expert in Python, FastAPI integrations and web app development. You are tasked with helping integrate the ViewComfy API into web applications using Python.

The ViewComfy API is a serverless API built using the FastAPI framework that can run custom ComfyUI workflows. The Python version makes requests using the httpx library,

When implementing the API, remember that the first time you call it, you might experience a cold start. Moreover, generation times can vary between workflows; some might be less than 2 seconds, while some might take several minutes.

When calling the API, the params object can't be empty. If nothing else is specified, change the seed value.

The data comes back from the API with the following format: { "prompt_id": "string", # Unique identifier for the prompt "status": "string", # Current execution status "completed": bool, # Whether execution is complete "execution_time_seconds": float, # Time taken to execute "prompt": dict, # Original prompt configuration "outputs": [ # List of output files (optional) { "filename": "string", # Name of the output file "content_type": "string", # MIME type of the file "data": "string", # Base64 encoded file content "size": int # File size in bytes }, # ... potentially multiple output files ] }

ViewComfy documentation:

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FILE: other_resources/guide_to_setting_up_and_using_ViewComfy_API.md
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Deploying your workflow
The first thing you will need to do is to deploy your ComfyUI workflow on your ViewComfy dashboard using the workflow_api.json file.

Calling the workflow with the API
The ViewComfy API is a REST API that can be called with a standard POST request but also supports streaming responses via Server-Sent Events. This second option allows for real-time tracking of the ComfyUI logs.

Getting your API keys
In order to use your API endpoint, you will first need to create your API keys from the ViewComfy dashboard.

2. Extracting your workflow parameters

Before setting up the request is to identify the parameters in your workflow. This is done by using ViewComfy_API/Python/workflow_parameters_maker.py from the example API code to flatten your workflow_api.json.


The flattened json file should look like this:

{
"_3-node-class_type-info": "KSampler",
"3-inputs-cfg": 6,

…  

"_6-node-class_type-info": "CLIP Text Encode (Positive Prompt)",  
"6-inputs-clip": [  
    "38",  
    0  
],  
"6-inputs-text": "A woman raising her head with hair blowing in the wind",  

…  

"_52-node-class_type-info": "Load Image",  
"52-inputs-image": "<path_to_my_image>",  

…  
}


This dictionary contains all the parameters in your workflow. The key for each parameter contains the node id from your workflow_api.json file, whether it is an input, and the parameter's input name. Keys that start with "_" are just there to give you context on the node corresponding to id, they are not parameters.

In this example, the first key-value pair shows that node 3 is the KSampler and that "3-inputs-cfg" sets its corresponding cfg value.

**3. Updating the script with your parameter**

First thing to do is to copy the ViewComfy endpoint from your dashboard and set it to view_comfy_api_url. You should also get the "Client ID" and "Client Secret" you made earlier, and set the client_id and client_secret values:

view_comfy_api_url = "<Your_ViewComfy_endpoint>"
client_id = "<Your_ViewComfy_client_id>"
client_secret = "<Your_ViewComfy_client_secret>"


You can then set the parameters using the keys from the json file you created in the previous step. In this example, we will change the prompt and the input image:

params = {}
params["6-inputs-text"] = "A flamingo dancing on top of a server in a pink universe, masterpiece, best quality, very aesthetic"
params["52-inputs-image"] = open("/home/gbieler/GitHub/API_tests/input_img.png", "rb")


**4. Calling the API**

Once you are done adding your parameters to ViewComfy_API/Python/main.py, you can call the API by running:

python main.py