88 lines
2.2 KiB
Python
88 lines
2.2 KiB
Python
from server import PromptServer
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from io import BytesIO
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from PIL import Image
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import numpy as np
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import base64
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import torch
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class ImageOutput:
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def __init__(self):
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pass
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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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"images": ("IMAGE", {"default": None, "forceInput": True}),
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"tag": ("STRING", {"default": None})}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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FUNCTION = "Proc"
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OUTPUT_NODE = True
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CATEGORY = "Knodes"
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def Proc(self, images, tag = ""):
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outs = []
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for single_image in images:
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img = np.asarray(single_image * 255., dtype=np.uint8)
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img = Image.fromarray(img)
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buffered = BytesIO()
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img.save(buffered, format="PNG")
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img_str = base64.b64encode(buffered.getvalue()).decode()
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outs.append(img_str)
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PromptServer.instance.send_sync("knodes", {"images": outs, "text": tag})
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return (images,)
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class LoadImageBase64:
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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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"image": ("STRING", {"multiline": False})}
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}
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RETURN_TYPES = ("IMAGE",)
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CATEGORY = "Knodes"
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FUNCTION = "Proc"
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def Proc(self, image):
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# image is a base64 encoded JObject.
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jObject = base64.b64decode(image)
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# jObject is a JObject with the following structure: [image, image, ...]
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# where each image is a base64 encoded string.
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images = []
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for single_image in jObject:
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imgdata = base64.b64decode(single_image)
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img = Image.open(BytesIO(imgdata))
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img = img.convert("RGB")
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img = np.array(img).astype(np.float32) / 255.0
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img = torch.from_numpy(img)[None,]
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images.append(img)
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return (images,)
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NODE_CLASS_MAPPINGS = {
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"Image(s) To Websocket (Base64)": ImageOutput,
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"Load Image(s) From Websocket (Base64)": LoadImageBase64
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ImageOutput": "Image(s) To Websocket (Base64)",
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"LoadImageBase64": "Load Image(s) From Websocket (Base64)"
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}
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