64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
import folder_paths
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from PIL import Image, ImageOps
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import numpy as np
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import torch
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from server import PromptServer, BinaryEventTypes
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import asyncio
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from globals import send_image
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class ComfyDeployWebscoketImageOutput:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"output_id": (
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"STRING",
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{"multiline": False, "default": "output_id"},
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),
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"images": ("IMAGE", ),
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},
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"optional": {
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"client_id": (
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"STRING",
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{"multiline": False, "default": ""},
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),
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}
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# "hidden": {"client_id": "CLIENT_ID"},
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}
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OUTPUT_NODE = True
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RETURN_TYPES = ()
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RETURN_NAMES = ("text",)
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FUNCTION = "run"
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CATEGORY = "output"
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def run(self, output_id, images, client_id):
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results = []
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prompt_server = PromptServer.instance
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loop = prompt_server.loop
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def schedule_coroutine_blocking(target, *args):
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future = asyncio.run_coroutine_threadsafe(target(*args), loop)
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return future.result() # This makes the call blocking
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for tensor in images:
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array = 255.0 * tensor.cpu().numpy()
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image = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
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schedule_coroutine_blocking(send_image, ["PNG", image, None], client_id)
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print("Image sent")
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# loop.run_until_complete(send_image(["PNG", image, None], client_id))
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results.append(
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{"source": "websocket", "content-type": "image/png", "type": "output"}
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)
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return {"ui": {"images": results}}
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NODE_CLASS_MAPPINGS = {"ComfyDeployWebscoketImageOutput": ComfyDeployWebscoketImageOutput}
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NODE_DISPLAY_NAME_MAPPINGS = {"ComfyDeployWebscoketImageOutput": "Image Websocket Output (ComfyDeploy)"} |