87 lines
2.0 KiB
Python
87 lines
2.0 KiB
Python
import base64
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from PIL import Image
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import torch
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import numpy as np
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import io
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class Base64ImageInput:
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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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"bas64_image": ("STRING", {
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"multiline": False,
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"default": ""
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}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "test"
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CATEGORY = "A8R8"
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def test(self, bas64_image):
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if bas64_image:
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image_bytes = base64.b64decode(bas64_image)
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# Open the image from bytes
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image = Image.open(io.BytesIO(image_bytes))
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image = image.convert("RGB")
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image = np.array(image).astype(np.float32) / 255.0
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image = torch.from_numpy(image)[None,]
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return (image,)
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class Base64ImageOutput:
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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 {"required":
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{"images": ("IMAGE", ), },
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}
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RETURN_TYPES = ()
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FUNCTION = "test"
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OUTPUT_NODE = True
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CATEGORY = "A8R8"
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def test(self, images: list[torch.Tensor]):
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image = images[0]
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i = 255. * image.cpu().numpy()
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img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
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buffered = io.BytesIO()
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img.save(buffered, optimize=False,
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format='png', compress_level=4)
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base64_image = base64.b64encode(buffered.getvalue()).decode()
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return {"ui": {"images": [base64_image]}}
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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NODE_CLASS_MAPPINGS = {
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"Base64ImageInput": Base64ImageInput,
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"Base64ImageOutput": Base64ImageOutput
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Base64ImageInput": "Base64Image Input Node",
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"Base64ImageOutput": "Base64Image Output Node"
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}
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