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RefurnishAI
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#This is an example that uses the websockets api to know when a prompt execution is done
#Once the prompt execution is done it downloads the images using the /history endpoint

import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
import uuid
import json
import urllib.request
import urllib.parse

server_address = "127.0.0.1:8188"
client_id = str(uuid.uuid4())

def queue_prompt(prompt):
    p = {"prompt": prompt, "client_id": client_id}
    data = json.dumps(p).encode('utf-8')
    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
    return json.loads(urllib.request.urlopen(req).read())

def get_image(filename, subfolder, folder_type):
    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
    url_values = urllib.parse.urlencode(data)
    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
        return response.read()

def get_history(prompt_id):
    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
        return json.loads(response.read())

def get_images(ws, prompt):
    prompt_id = queue_prompt(prompt)['prompt_id']
    output_images = {}
    while True:
        out = ws.recv()
        if isinstance(out, str):
            message = json.loads(out)
            if message['type'] == 'executing':
                data = message['data']
                if data['node'] is None and data['prompt_id'] == prompt_id:
                    break #Execution is done
        else:
            # If you want to be able to decode the binary stream for latent previews, here is how you can do it:
            # bytesIO = BytesIO(out[8:])
            # preview_image = Image.open(bytesIO) # This is your preview in PIL image format, store it in a global
            continue #previews are binary data

    history = get_history(prompt_id)[prompt_id]
    for node_id in history['outputs']:
        node_output = history['outputs'][node_id]
        images_output = []
        if 'images' in node_output:
            for image in node_output['images']:
                image_data = get_image(image['filename'], image['subfolder'], image['type'])
                images_output.append(image_data)
        output_images[node_id] = images_output

    return output_images

prompt_text = """
{
    "3": {
        "class_type": "KSampler",
        "inputs": {
            "cfg": 8,
            "denoise": 1,
            "latent_image": [
                "5",
                0
            ],
            "model": [
                "4",
                0
            ],
            "negative": [
                "7",
                0
            ],
            "positive": [
                "6",
                0
            ],
            "sampler_name": "euler",
            "scheduler": "normal",
            "seed": 8566257,
            "steps": 20
        }
    },
    "4": {
        "class_type": "CheckpointLoaderSimple",
        "inputs": {
            "ckpt_name": "v1-5-pruned-emaonly.safetensors"
        }
    },
    "5": {
        "class_type": "EmptyLatentImage",
        "inputs": {
            "batch_size": 1,
            "height": 512,
            "width": 512
        }
    },
    "6": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "4",
                1
            ],
            "text": "masterpiece best quality girl"
        }
    },
    "7": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "4",
                1
            ],
            "text": "bad hands"
        }
    },
    "8": {
        "class_type": "VAEDecode",
        "inputs": {
            "samples": [
                "3",
                0
            ],
            "vae": [
                "4",
                2
            ]
        }
    },
    "9": {
        "class_type": "SaveImage",
        "inputs": {
            "filename_prefix": "ComfyUI",
            "images": [
                "8",
                0
            ]
        }
    }
}
"""

prompt = json.loads(prompt_text)
#set the text prompt for our positive CLIPTextEncode
prompt["6"]["inputs"]["text"] = "masterpiece best quality man"

#set the seed for our KSampler node
prompt["3"]["inputs"]["seed"] = 5

ws = websocket.WebSocket()
ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
images = get_images(ws, prompt)
ws.close() # for in case this example is used in an environment where it will be repeatedly called, like in a Gradio app. otherwise, you'll randomly receive connection timeouts
#Commented out code to display the output images:

# for node_id in images:
#     for image_data in images[node_id]:
#         from PIL import Image
#         import io
#         image = Image.open(io.BytesIO(image_data))
#         image.show()