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Upgrade sdxl service #289

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141 changes: 72 additions & 69 deletions bizyair_extras/nodes_ipadapter_plus/nodes_ipadapter_plus.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,10 +3,13 @@

import folder_paths
import torch
from PIL import Image

from bizyair import BizyAirBaseNode, BizyAirNodeIO, create_node_data
from bizyair.data_types import CLIP, CONDITIONING, MODEL

from .utils import T, contrast_adaptive_sharpening

# set the models directory
if "ipadapter" not in folder_paths.folder_names_and_paths:
current_paths = [os.path.join(folder_paths.models_dir, "ipadapter")]
Expand Down Expand Up @@ -1207,76 +1210,76 @@ def INPUT_TYPES(s):
# return (noise,)


# class PrepImageForClipVision:
# @classmethod
# def INPUT_TYPES(s):
# return {
# "required": {
# "image": ("IMAGE",),
# "interpolation": (
# ["LANCZOS", "BICUBIC", "HAMMING", "BILINEAR", "BOX", "NEAREST"],
# ),
# "crop_position": (["top", "bottom", "left", "right", "center", "pad"],),
# "sharpening": (
# "FLOAT",
# {"default": 0.0, "min": 0, "max": 1, "step": 0.05},
# ),
# },
# }

# RETURN_TYPES = ("IMAGE",)
# FUNCTION = "prep_image"

# CATEGORY = "ipadapter/utils"

# def prep_image(
# self, image, interpolation="LANCZOS", crop_position="center", sharpening=0.0
# ):
# size = (224, 224)
# _, oh, ow, _ = image.shape
# output = image.permute([0, 3, 1, 2])

# if crop_position == "pad":
# if oh != ow:
# if oh > ow:
# pad = (oh - ow) // 2
# pad = (pad, 0, pad, 0)
# elif ow > oh:
# pad = (ow - oh) // 2
# pad = (0, pad, 0, pad)
# output = T.functional.pad(output, pad, fill=0)
# else:
# crop_size = min(oh, ow)
# x = (ow - crop_size) // 2
# y = (oh - crop_size) // 2
# if "top" in crop_position:
# y = 0
# elif "bottom" in crop_position:
# y = oh - crop_size
# elif "left" in crop_position:
# x = 0
# elif "right" in crop_position:
# x = ow - crop_size

# x2 = x + crop_size
# y2 = y + crop_size

# output = output[:, :, y:y2, x:x2]

# imgs = []
# for img in output:
# img = T.ToPILImage()(img) # using PIL for better results
# img = img.resize(size, resample=Image.Resampling[interpolation])
# imgs.append(T.ToTensor()(img))
# output = torch.stack(imgs, dim=0)
# del imgs, img

# if sharpening > 0:
# output = contrast_adaptive_sharpening(output, sharpening)

# output = output.permute([0, 2, 3, 1])
class PrepImageForClipVision(BizyAirBaseNode):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"interpolation": (
["LANCZOS", "BICUBIC", "HAMMING", "BILINEAR", "BOX", "NEAREST"],
),
"crop_position": (["top", "bottom", "left", "right", "center", "pad"],),
"sharpening": (
"FLOAT",
{"default": 0.0, "min": 0, "max": 1, "step": 0.05},
),
},
}

# return (output,)
RETURN_TYPES = ("IMAGE",)
FUNCTION = "prep_image"
NODE_DISPLAY_NAME = "Prep Image For ClipVision"
CATEGORY = "ipadapter/utils"

def prep_image(
self, image, interpolation="LANCZOS", crop_position="center", sharpening=0.0
):
size = (224, 224)
_, oh, ow, _ = image.shape
output = image.permute([0, 3, 1, 2])

if crop_position == "pad":
if oh != ow:
if oh > ow:
pad = (oh - ow) // 2
pad = (pad, 0, pad, 0)
elif ow > oh:
pad = (ow - oh) // 2
pad = (0, pad, 0, pad)
output = T.functional.pad(output, pad, fill=0)
else:
crop_size = min(oh, ow)
x = (ow - crop_size) // 2
y = (oh - crop_size) // 2
if "top" in crop_position:
y = 0
elif "bottom" in crop_position:
y = oh - crop_size
elif "left" in crop_position:
x = 0
elif "right" in crop_position:
x = ow - crop_size

x2 = x + crop_size
y2 = y + crop_size

output = output[:, :, y:y2, x:x2]

imgs = []
for img in output:
img = T.ToPILImage()(img) # using PIL for better results
img = img.resize(size, resample=Image.Resampling[interpolation])
imgs.append(T.ToTensor()(img))
output = torch.stack(imgs, dim=0)
del imgs, img

if sharpening > 0:
output = contrast_adaptive_sharpening(output, sharpening)

output = output.permute([0, 2, 3, 1])

return (output,)


# class IPAdapterSaveEmbeds:
Expand Down
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