File size: 4,544 Bytes
edb09f2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | from PIL import Image, ImageOps
import numpy as np
import torch
MAX_RESOLUTION = 32768
class ImageSimpleResize:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"size": (
"INT",
{"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 1},
),
"edge": (["largest", "smallest", "all", "width", "height"],),
},
"optional": {
"size_override": ("INT", {"forceInput": True}),
"vae": ("VAE",),
},
}
RETURN_TYPES = ("IMAGE", "LATENT")
OUTPUT_NODE = False
FUNCTION = "imagesimpleresize"
CATEGORY = "Chibi-Nodes/Image"
def imagesimpleresize(self, image, size, edge, size_override=None, vae=None):
if size_override:
size = size_override
width = image.shape[2]
height = image.shape[1]
ratio = height / width
image = Image.fromarray(
np.clip(255.0 * image[0].cpu().numpy(), 0, 255).astype(np.uint8)
)
if edge == "largest":
if width > height:
if size < width:
image = ImageOps.contain(
image, (size, MAX_RESOLUTION), Image.LANCZOS
)
else:
image = image.resize(
(round(size), round(size * ratio)), Image.LANCZOS
)
if width < height:
if size < height:
image = ImageOps.contain(
image, (MAX_RESOLUTION, size), Image.LANCZOS
)
else:
image = image.resize(
(round(size / ratio), round(size)), Image.LANCZOS
)
if width == height:
if size < width:
image = ImageOps.contain(
image, (size, size), Image.LANCZOS)
else:
image = image.resize(
(round(size), round(size)), Image.LANCZOS)
if edge == "smallest":
if width > height:
if size < height:
image = ImageOps.contain(
image, (MAX_RESOLUTION, size), Image.LANCZOS
)
else:
image = image.resize(
(round(size / ratio), round(size)), Image.LANCZOS
)
if width < height:
if size < width:
image = ImageOps.contain(
image, (size, MAX_RESOLUTION), Image.LANCZOS
)
else:
image = image.resize(
(round(size), round(size * ratio)), Image.LANCZOS
)
if width == height:
if size < width:
image = ImageOps.contain(
image, (size, size), Image.LANCZOS)
else:
image = image.resize(
(round(size), round(size)), Image.LANCZOS)
if edge == "all":
image = image.resize((round(size), round(size)), Image.LANCZOS)
if edge == "width":
image = image.resize((round(size), round(height)), Image.LANCZOS)
if edge == "height":
image = image.resize((round(width), round(size)), Image.LANCZOS)
image = ImageOps.exif_transpose(image)
image = image.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if vae is not None:
latent = image
x = (latent.shape[1] // 8) * 8
y = (latent.shape[2] // 8) * 8
if latent.shape[1] is not x or latent.shape[2] is not y:
x_offset = (latent.shape[1] % 8) // 2
y_offset = (latent.shape[2] % 8) // 2
latent = latent[:, x_offset: x +
x_offset, y_offset: y + y_offset, :]
latent = vae.encode(latent[:, :, :, :3])
return (image, {"samples": latent})
else:
return (
image,
None,
)
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