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309
script_examples/gradio_websockets_api_example.py
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309
script_examples/gradio_websockets_api_example.py
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# This is a Gradio example demonstrating using the websocket api and that also decodes preview images
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# Gradio has a lot of idiosyncrasies and I'm definitely not an expert at coding for it
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# I'm sure there are a million and one better ways to code this, but this works pretty well and should get you started
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# I suggest taking the time to check any relevant comments throughout the code
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# For more info on working with Gradio: https://www.gradio.app/docs
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# Ensure that ComfyUI has latent previews enabled
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# If you use Comfy Manager, make sure to set the preview type there because it will override --preview-method auto/latent2rgb/taesd launch flag settings
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# Check or change the preview_method in "/custom_nodes/ComfyUI-Manager/config.ini"
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# If you chose to install Gradio to your ComfyUI python venv, open a command prompt in this script_examples directory and run:
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# ..\..\python_embeded\python.exe -s ..\script_examples\gradio_websockets_api_example.py
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# To launch the app
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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 io
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from io import BytesIO
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import random
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#If you want to use your local ComfyUI python installation, you'll need to navigate to your comfyui/python_embeded folder, open a cmd prompt and run "python.exe -m pip install gradio"
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import gradio as gr
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# adjust to your ComfyUI API settings
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server_address = "127.0.0.1:8188"
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client_id = str(uuid.uuid4())
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#some globals to store previews, active state and progress
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preview_image = None
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active = False
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interrupted = False
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step_current = None
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step_total = None
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def interrupt_diffusion():
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global interrupted, step_current, step_total
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interrupted = True
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step_current = None
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step_total = None
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req = urllib.request.Request("http://{}/interrupt".format(server_address), method='POST')
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return urllib.request.urlopen(req)
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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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global preview_image, active, step_current, step_total
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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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preview_image = None #clear these globals on completion just in case
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step_current = None
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step_total = None
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active = False
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break #Execution is done
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elif message['type'] == 'progress':
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data = message['data']
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step_current = data['value']
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step_total = data['max']
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else:
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bytesIO = BytesIO(out[8:])
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preview_image = Image.open(bytesIO) # This is your preview in PIL image format
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history = get_history(prompt_id)[prompt_id]
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for node_id in history['outputs']:
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node_output = history['outputs'][node_id]
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images_output = []
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if 'images' in node_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 get_prompt_images(prompt):
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global preview_image
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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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outputs = []
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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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outputs.append(image)
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ws.close()
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return outputs
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############################################################################################################################
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# Edit or add your own api workflow here. Make sure to enable dev mode in ComfyUI and to use the "Save(API Format)" option #
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############################################################################################################################
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prompt_text = """
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{
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"3": {
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"class_type": "KSampler",
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"inputs": {
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"cfg": 8,
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"denoise": 1,
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"latent_image": [
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"5",
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0
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],
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"model": [
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"4",
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0
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],
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"negative": [
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"7",
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0
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],
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"positive": [
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"6",
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0
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],
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"sampler_name": "euler",
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"scheduler": "normal",
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"seed": -1,
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"steps": 25
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}
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},
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"4": {
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"class_type": "CheckpointLoaderSimple",
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"inputs": {
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"ckpt_name": "sdxl_base_1.0_0.9vae.safetensors"
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}
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},
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"5": {
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"class_type": "EmptyLatentImage",
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"inputs": {
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"batch_size": 1,
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"height": 1024,
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"width": 1024
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}
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},
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"6": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"clip": [
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"4",
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1
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],
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"text": ""
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}
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},
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"7": {
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"class_type": "CLIPTextEncode",
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"inputs": {
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"clip": [
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"4",
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1
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],
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"text": ""
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}
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},
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"8": {
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"class_type": "VAEDecode",
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"inputs": {
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"samples": [
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"3",
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0
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],
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"vae": [
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"4",
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2
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]
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}
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},
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"9": {
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"class_type": "SaveImage",
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"inputs": {
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"filename_prefix": "ComfyUI",
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"images": [
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"8",
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0
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]
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}
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}
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}
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"""
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prompt = json.loads(prompt_text)
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# You can also use the following if you'd rather just load a json, make sure to comment out or remove the line above
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# with open("/path/to/workflow.json", "r", encoding="utf-8") as f:
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# prompt = json.load(f)
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# start and stop timer are used for live updating the preview and progress
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# no point in keeping the timer ticking if it's not currently generating
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def start_timer():
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global active
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active = True
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return gr.Timer(active=True)
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def stop_timer():
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global active
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active = False
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return gr.Timer(active=False)
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def update_preview():
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return gr.Image(value=preview_image)
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# Gradio is somewhat finicky about multiple things trying to change the same output, so we switch between preview and image, while hiding the other
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def window_preview():
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return gr.Image(visible=False, value=None), gr.Image(visible=True, value=None), gr.Button(visible=False), gr.Button(visible=True, value="Stop: Busy")
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def window_final():
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if interrupted: #if we interrupted during the process, put things back to normal
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return gr.Image(visible=True, value=None), gr.Image(visible=False), gr.Button(visible=True), gr.Button(visible=False)
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else:
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return gr.Image(visible=True), gr.Image(visible=False, value=None), gr.Button(visible=True), gr.Button(visible=False)
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# Puts the progress on the stop button
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def update_progress():
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if step_current == 0 or step_current == None:
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x = 0
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else:
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x = int(100 * (step_current / step_total))
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if step_current == None or active == False:
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message = "Stop: Busy"
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else:
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message = f"Stop: {step_current} / {step_total} steps {x}%"
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return gr.Button(value=message)
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# You will need to do a lot of editing here to match your workflow
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def process(pos, neg, width, height, cfg, seed):
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if seed <= -1:
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seed = random.randint(0, 999999999)
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prompt["4"]["inputs"]["ckpt_name"] = "sdxl_base_1.0_0.9vae.safetensors" #if you want to change the model, do it here
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prompt["6"]["inputs"]["text"] = pos
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prompt["7"]["inputs"]["text"] = neg
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prompt["3"]["inputs"]["seed"] = seed
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prompt["3"]["inputs"]["cfg"] = cfg
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prompt["5"]["inputs"]["height"] = height
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prompt["5"]["inputs"]["width"] = width
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global interrupted
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interrupted = False
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images = get_prompt_images(prompt)
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global active
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active = False
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try:
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return gr.Image(value=images[0]) #not covering batch generations in this example because it requires setting the image output to a gr.Gallery, along with some other changes
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except:
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return gr.Image()
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with gr.Blocks(analytics_enabled=False, fill_width=True, fill_height=True,) as example:
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preview_timer = gr.Timer(value=1, active=False) # You can also lower the timer to something like 0.5 to get more frequent updates, but there's not really much point to it
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with gr.Row():
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with gr.Column():
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with gr.Group():
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user_prompt = gr.Textbox(label="Positive Prompt: ", value="orange cat, full moon, vibrant impressionistic painting, bright vivid rainbow of colors", lines=5, max_lines=20)
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user_negativeprompt = gr.Textbox(label="Negative Prompt: ", value="text, watermark", lines=2, max_lines=10,)
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with gr.Group():
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with gr.Row():
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user_width = gr.Slider(label="Width", minimum=512, maximum=1600, step=64, value=1152,)
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user_height = gr.Slider(label="Height", minimum=512, maximum=1600, step=64, value=896,)
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with gr.Row():
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user_cfg = gr.Slider(label="CFG: ", minimum=1.0, maximum=16.0, step=0.1, value=4.5,)
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user_seed = gr.Slider(label="Seed: (-1 for random)", minimum=-1, maximum=999999999, step=1, value=-1,)
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generate = gr.Button("Generate", variant="primary")
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stop = gr.Button("Stop", variant="stop", visible=False)
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with gr.Column():
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output_image = gr.Image(label="Image: ", type="pil", format="jpeg", interactive=False, visible=True)
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output_preview = gr.Image(label="Preview: ", type="pil", format="jpeg", interactive=False, visible=False)
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# On tick, we update the preview and then the progress
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preview_timer.tick(
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fn=update_preview, outputs=output_preview, show_progress="hidden").then(
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fn=update_progress, outputs=stop, show_progress="hidden")
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# On generate we switch windows/buttons, start the update tick, diffuse the image, stop the update tick and then finally, swap the image outputs/buttons back
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generate.click(
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fn=window_preview, outputs=[output_image, output_preview, generate, stop], show_progress="hidden").then(
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fn=start_timer, outputs=preview_timer, show_progress="hidden").then(
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fn=process, inputs=[user_prompt, user_negativeprompt, user_width, user_height, user_cfg, user_seed], outputs=output_image).then(
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fn=stop_timer, outputs=preview_timer, show_progress="hidden").then(
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fn=window_final, outputs=[output_image, output_preview, generate, stop], show_progress="hidden")
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stop.click(fn=interrupt_diffusion, show_progress="hidden")
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# Adjust settings to your needs https://www.gradio.app/docs/gradio/blocks#blocks-launch for more info
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example.queue(max_size=2,) # how many users can queue up in line
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example.launch(share=False, inbrowser=True, server_name="0.0.0.0", server_port=7860, enable_monitoring=False) # good for LAN-only setups
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Reference in New Issue
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