mirror of
https://github.com/comfyanonymous/ComfyUI.git
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139 lines
5.0 KiB
Python
139 lines
5.0 KiB
Python
#This is an example that uses the websockets api to know when a prompt execution is done
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#Once the prompt execution is done it downloads the images using the /history endpoint
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import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
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import uuid
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import json
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import urllib.request
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import urllib.parse
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from PIL import Image
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import base64
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import io
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import os
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from PIL import ImageFile
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ImageFile.LOAD_TRUNCATED_IMAGES = True
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from custom_scripts_for_nodes.Rainbow import extract_rainbow
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import runpod
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server_address = "127.0.0.1:8188"
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client_id = str(uuid.uuid4())
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def queue_prompt(prompt):
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p = {"prompt": prompt, "client_id": client_id}
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data = json.dumps(p).encode('utf-8')
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req = urllib.request.Request("http://{}/prompt".format(server_address), data=data)
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return json.loads(urllib.request.urlopen(req).read())
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def get_image(filename, subfolder, folder_type):
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data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
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url_values = urllib.parse.urlencode(data)
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with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
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return response.read()
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def get_history(prompt_id):
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with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
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return json.loads(response.read())
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def get_images(ws, prompt):
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prompt_id = queue_prompt(prompt)['prompt_id']
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output_images = {}
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while True:
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out = ws.recv()
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if isinstance(out, str):
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message = json.loads(out)
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if message['type'] == 'executing':
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data = message['data']
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if data['node'] is None and data['prompt_id'] == prompt_id:
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break #Execution is done
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else:
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continue #previews are binary data
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history = get_history(prompt_id)[prompt_id]
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for o in history['outputs']:
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for node_id in history['outputs']:
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node_output = history['outputs'][node_id]
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if 'images' in node_output:
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images_output = []
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for image in node_output['images']:
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image_data = get_image(image['filename'], image['subfolder'], image['type'])
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images_output.append(image_data)
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output_images[node_id] = images_output
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return output_images
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def run_prompt(job):
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data = {'images':[],'rainbow':"None",'lama':"None"}
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#Inferring from the Rainbow Script
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image_string = job['input']['image_string_lama']
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mask_string = job['input']['mask_string_lama']
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prompt_text = job["input"]["prompt"]
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rainbow = job["input"]["rainbow"]
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if image_string != 'None' and mask_string != 'None':
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print("Running this part")
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# Decode the base64 string into bytes
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decoded_bytes = base64.b64decode(image_string)
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# Convert the bytes to an in-memory file-like object using io.BytesIO
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image_data = io.BytesIO(decoded_bytes)
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image = Image.open(image_data)
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image.save("./lama-with-refiner/input/image.png")
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# Decode the base64 string into bytes
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mask_decoded_bytes = base64.b64decode(mask_string)
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# Convert the bytes to an in-memory file-like object using io.BytesIO
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mask_data = io.BytesIO(mask_decoded_bytes)
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mask = Image.open(mask_data)
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mask.save("./lama-with-refiner/input/image_mask.png")
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new_working_directory = "./lama-with-refiner"
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os.chdir(new_working_directory)
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cmd = "python3 bin/predict.py model.path=$(pwd)/big-lama indir=$(pwd)/LaMa_test_images outdir=$(pwd)/output"
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os.system(cmd)
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new_working_directory = "../"
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os.chdir(new_working_directory)
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image = Image.open('./lama-with-refiner/output/image_mask.png')
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im_file = io.BytesIO()
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image.save(im_file, format="JPEG")
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im_bytes = im_file.getvalue() # im_bytes: image in binary format.
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im_b64 = base64.b64encode(im_bytes)
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im_b64 = str(im_b64)
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data['lama'] = im_b64
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if prompt_text != "None":
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prompt = prompt_text
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ws = websocket.WebSocket()
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ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
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images = get_images(ws, prompt)
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for node_id in images:
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for image_data in images[node_id]:
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image = Image.open(io.BytesIO(image_data))
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#NOTE This is for the Rainbow Script Will be updating it later
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if rainbow != 'None':
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rnbw = extract_rainbow()
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rnbw_values = rnbw.main(image)
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data['rainbow'] = rnbw_values
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im_file = io.BytesIO()
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image.save(im_file, format="JPEG")
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im_bytes = im_file.getvalue() # im_bytes: image in binary format.
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im_b64 = base64.b64encode(im_bytes)
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im_b64 = str(im_b64)
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data['images'].append(im_b64)
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return data
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runpod.serverless.start({"handler":run_prompt})
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