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164 lines
4.4 KiB
164 lines
4.4 KiB
#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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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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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": 8566257, |
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"steps": 20 |
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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": "v1-5-pruned-emaonly.ckpt" |
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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": 512, |
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"width": 512 |
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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": "masterpiece best quality girl" |
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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": "bad hands" |
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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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#set the text prompt for our positive CLIPTextEncode |
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prompt["6"]["inputs"]["text"] = "masterpiece best quality man" |
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#set the seed for our KSampler node |
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prompt["3"]["inputs"]["seed"] = 5 |
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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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#Commented out code to display the output images: |
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# for node_id in images: |
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# for image_data in images[node_id]: |
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# from PIL import Image |
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# import io |
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# image = Image.open(io.BytesIO(image_data)) |
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# image.show() |
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