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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 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 = """
{
"1": {
"inputs": {
"sam_model": "sam_vit_h (2.56GB)",
"grounding_dino_model": "GroundingDINO_SwinT_OGC (694MB)",
"threshold": 0.3,
"detail_method": "VITMatte",
"detail_erode": 6,
"detail_dilate": 6,
"black_point": 0.01,
"white_point": 0.99,
"process_detail": false,
"prompt": "shirt",
"device": "cuda",
"max_megapixels": 2,
"cache_model": true,
"image": [
"2",
0
]
},
"class_type": "LayerMask: SegmentAnythingUltra V2",
"_meta": {
"title": "LayerMask: SegmentAnythingUltra V2"
}
},
"2": {
"inputs": {
"image": "q.jpg",
"upload": "image"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"3": {
"inputs": {
"image": "tshirt.jpeg",
"upload": "image"
},
"class_type": "LoadImage",
"_meta": {
"title": "Load Image"
}
},
"5": {
"inputs": {
"mask_grow": 25,
"mixed_precision": "fp16",
"seed": 95593377186337,
"steps": 40,
"cfg": 2.5,
"image": [
"2",
0
],
"mask": [
"1",
1
],
"refer_image": [
"3",
0
]
},
"class_type": "CatVTONWrapper",
"_meta": {
"title": "CatVTON Wrapper"
}
},
"6": {
"inputs": {
"images": [
"5",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
}
}"""
prompt = json.loads(prompt_text)
prompt["2"]["inputs"]["image"] = "\\ put your input person pose image"
prompt["3"]["inputs"]["image"] = "\\ put your input cloth image"
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.save("output.jpg")
# image.show()
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