97 lines
2.8 KiB
Python
97 lines
2.8 KiB
Python
import math
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import torch
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import comfy
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class CFE_Aspect_Ratio:
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"""
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Creates an empty latent image that will more easily characterize the resolution process for SD1.5, SDXL, and FLUX
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"""
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def __init__(self):
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self.device = comfy.model_management.intermediate_device()
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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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"type" : (["square", "portrait (w:h)", "landscape (h:w)"],),
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"resolution": ([
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"1:1",
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"2:3",
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"3:4",
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"4:7",
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"8:15",
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"9:7",
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"9:16",
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"11:21",
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"13:18",
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"13:19",
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"14:17",
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"15:16",
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"15:17",
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],),
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"megapixel": ([".5 MP (SD1.5)", "1 MP (SDXL)", "2 MP (FLUX)"],),
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"clip_size": ("FLOAT", {
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"default": 1.0,
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"min": 1.0,
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"max": 10.0,
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"step": 0.5,
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"tooltip": "Multiplies the output width and height values",
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"lazy" : True
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}),
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"batch_size": ("INT", {
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"default": 1,
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"min": 1,
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"max": 4096,
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"tooltip": "The number of latent images in the batch.",
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"lazy" : True
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}),
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},
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}
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RETURN_TYPES = ("LATENT","INT","INT")
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RETURN_NAMES = ("latent", "clip_width","clip_height")
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FUNCTION = "calculate_resolution"
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CATEGORY = "CFE"
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def calculate_resolution(self, type, resolution, megapixel, clip_size, batch_size):
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if type == "square":
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ratio = 1
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else:
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colon = resolution.find(":")
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res0 = float(resolution[:colon])
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res1 = float(resolution[colon + 1:])
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if type.find("portrait") != -1:
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ratio = res0 / res1
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else:
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ratio = res1 / res0
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mp_mark = megapixel[:1]
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if mp_mark == "1":
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mp = 1024 * 1024
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elif mp_mark == "2":
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mp = 2 * 1024 * 1024
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else:
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mp = 512 * 512
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clip_width = int(math.sqrt(math.floor(ratio * mp)))
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off_by = clip_width % 8
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clip_width -= off_by
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clip_height = int(mp / clip_width)
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off_by = clip_height % 8
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clip_height -= off_by
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latent = torch.zeros([batch_size, 4, clip_height // 8, clip_width // 8], device=self.device)
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clip_width = math.floor(clip_width * clip_size)
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clip_height = math.floor(clip_height * clip_size)
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return ({"samples":latent}, clip_width, clip_height)
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