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flyingshutter
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*.pyc
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from custom_nodes.As_ComfyUI_CustomNodes.asnodes import *
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import torch
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from PIL import Image, ImageDraw, ImageFont
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import numpy as np
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import sys
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MAX_RESOLUTION = 8192
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class MaskToImage_AS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"mask": ("MASK",),}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "convert"
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CATEGORY = "ASNodes"
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def convert(self, mask):
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d2, d3 = mask.size()
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print("MASK SIZE:", mask.size())
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new_image = torch.zeros(
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(1, d2, d3, 3),
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dtype=torch.float32,
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)
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new_image[0, :, :, 0] = mask
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new_image[0, :, :, 1] = mask
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new_image[0, :, :, 2] = mask
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print("MaskSize", mask.size())
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print("Tyep New img", type(new_image))
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return (new_image,)
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class ImageToMask_AS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"image": ("IMAGE",),}}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "convert"
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CATEGORY = "ASNodes"
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def convert(self, image):
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return (image.squeeze().mean(2),)
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class LatentMix_AS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples_to": ("LATENT",),
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"samples_from": ("LATENT",),
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"blend": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 1}),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "composite"
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CATEGORY = "ASNodes"
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def composite(self, samples_to, samples_from, blend):
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samples_out = samples_to.copy()
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s_to = samples_to["samples"].clone()
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s_from = samples_from["samples"].clone()
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samples_out["samples"] = s_to * blend / 100 + s_from * (100 - blend) / 100
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return (samples_out,)
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class LatentAdd_AS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples_to": ("LATENT",),
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"samples_from": ("LATENT",),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "composite"
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CATEGORY = "ASNodes"
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def composite(self, samples_to, samples_from):
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samples_out = samples_to.copy()
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s_to = samples_to["samples"].clone()
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s_from = samples_from["samples"].clone()
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samples_out["samples"] = (s_to + s_from)
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return (samples_out,)
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class SaveLatent_AS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "latent_in": ("LATENT",), }}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "doStuff"
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CATEGORY = "ASNodes"
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def doStuff(self, latent_in):
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torch.save(latent_in, 'latent.pt')
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return (latent_in, )
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# a = torch.load("e:/portables/ComfyUI_windows_portable/latent.pt")
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# for idx in range(a['samples'].shape[1]):
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# plt.figure()
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# plt.imshow(a['samples'][0,idx,:,:])
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# plt.show()
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class LoadLatent_AS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "doStuff"
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CATEGORY = "ASNodes"
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def doStuff(self,):
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latent_out = torch.load('latent.pt')
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return (latent_out, )
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class LatentToImages_AS:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "latent_in": ("LATENT",), }}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "doStuff"
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CATEGORY = "ASNodes"
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def doStuff(self, latent_in):
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s = latent_in['samples']
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s = (s - s.min()) / (s.max() - s.min())
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d1, d2, d3, d4 = s.shape
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images_out = torch.zeros(d2, d3, d4, 3)
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for idx in range(s.shape[1]):
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for chan in range(3):
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images_out[idx,:,:,chan] = s[0,idx,:,:]
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return (images_out, )
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class LatentMixMasked_As:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "samples_to": ("LATENT",),
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"samples_from": ("LATENT",),
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"mask": ("MASK",),
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}}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "composite"
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CATEGORY = "ASNodes"
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def composite(self, samples_to, samples_from, mask):
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print(samples_to["samples"].size())
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samples_out = samples_to.copy()
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s_to = samples_to["samples"].clone()
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s_from = samples_from["samples"].clone()
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samples_out["samples"] = s_to * mask + s_from * (1 - mask)
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return (samples_out,)
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class ImageMixMasked_As:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "image_to": ("IMAGE",),
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"image_from": ("IMAGE",),
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"mask": ("MASK",),
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}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "composite"
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CATEGORY = "ASNodes"
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def composite(self, image_to, image_from, mask):
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image_out = image_to.clone()
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image_out[0,:,:,0] = image_to[0,:,:,0] * mask + image_from[0,:,:,0] * (1 - mask)
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image_out[0,:,:,1] = image_to[0,:,:,1] * mask + image_from[0,:,:,1] * (1 - mask)
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image_out[0,:,:,2] = image_to[0,:,:,2] * mask + image_from[0,:,:,2] * (1 - mask)
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return (image_out,)
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class TextToImage_AS:
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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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"text": ("STRING", {"multiline": True}),
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"font": ("STRING", {"multiline": False}),
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"size": ("INT", {"default": 20, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
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"width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
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"height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 64}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "doStuff"
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CATEGORY = "ASNodes"
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def doStuff(self, text, font, size, width, height):
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PIL_image = Image.new("RGB", (width, height), (0, 0, 0))
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draw = ImageDraw.Draw(PIL_image)
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# Set the font and size
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font = ImageFont.truetype(font, size)
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# Get the size of the text
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text_size = draw.textsize(text, font)
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# Calculate the position of the text
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x = (PIL_image.width - text_size[0]) / 2
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y = (PIL_image.height - text_size[1]) / 2
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# Draw the text on the image
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draw.text((x, y), text, font=font, fill=(255,255,255))
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np_image = np.array(PIL_image)
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new_image = torch.zeros(
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(1, height, width, 3),
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dtype=torch.float32,
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)
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new_image[0,:,:,:] = torch.from_numpy(np_image) / 256
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return (new_image,)
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class BatchIndex_AS:
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"batch_index": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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},
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}
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RETURN_TYPES = ("FLOAT",)
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FUNCTION = "doStuff"
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CATEGORY = "ASNodes"
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def doStuff(self, batch_index):
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return (batch_index,)
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class MapRange_AS:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"value": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 0.01}),
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"in_0": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 0.01}),
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"in_1": ("FLOAT", {"default": 1, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 0.01}),
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"out_0": ("FLOAT", {"default": 0, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 0.01}),
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"out_1": ("FLOAT", {"default": 1, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 0.01}),
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},
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}
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RETURN_TYPES = ("FLOAT",)
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FUNCTION = "mapRange"
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CATEGORY = "ASNodes"
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def mapRange(self, value, in_0, in_1, out_0, out_1):
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run_param = (value - in_0) / (in_1 - in_0)
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result = out_0 + run_param * (out_1 - out_0)
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return (result, )
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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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"MaskToImage_AS": MaskToImage_AS,
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"ImageToMask_AS": ImageToMask_AS,
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"LatentMix_AS": LatentMix_AS,
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"LatentAdd_AS": LatentAdd_AS,
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"SaveLatent_AS": SaveLatent_AS,
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"LoadLatent_AS": LoadLatent_AS,
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"LatentToImages_AS": LatentToImages_AS,
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"LatentMixMasked_As": LatentMixMasked_As,
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"ImageMixMasked_As": ImageMixMasked_As,
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"TextToImage_AS": TextToImage_AS,
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"BatchIndex_AS": BatchIndex_AS,
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"MapRange_AS": MapRange_AS,
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}
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