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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:
continue #previews are binary data
history = get_history(prompt_id)[prompt_id]
for o in history['outputs']:
for node_id in history['outputs']:
node_output = history['outputs'][node_id]
if 'images' in node_output:
images_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.ckpt"
}
},
"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)
#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()
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