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# Copyright 2023 SLAPaper
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import collections.abc as clabc
import torch
class ImageSelector:
"""
Select some of the images and pipe through
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
"""
Input: list of index of selected image, seperated by comma (",")
support colon (":") sperated range (left included, right excluded)
Indexes start with 1 for simplicity
"""
return {
"required": {
"images": ("IMAGE", ),
"selected_indexes": ("STRING", {
"multiline": False,
"default": "1,2,3"
}),
},
}
RETURN_TYPES = ("IMAGE", )
# RETURN_NAMES = ("image_output_name",)
FUNCTION = "run"
OUTPUT_NODE = False
CATEGORY = "image"
def run(self, images: torch.Tensor, selected_indexes: str):
"""
根据 selected_indexes 选择 images 中的图片，支持连续索引和范围索引
Args:
images (torch.Tensor): 输入的图像张量，维度为 [N, C, H, W], 其中 N 为图片数量, C 为通道数, H、W 为图片的高和宽。
selected_indexes (str): 选择的图片索引，支持连续索引和范围索引，例如："0,2,4:6,8" 表示选择第1、3、5张和第2、4、6、8张图片。
Returns:
tuple: 选择的图片张量，维度为 [N', C, H, W]，其中 N' 为选择的图片数量。
"""
shape = images.shape
len_first_dim = shape[0]
selected_index: list[int] = []
total_indexes: list[int] = list(range(len_first_dim))
for s in selected_indexes.strip().split(','):
try:
if ":" in s:
_li = s.strip().split(':', maxsplit=1)
_start = _li[0]
_end = _li[1]
if _start and _end:
selected_index.extend(
total_indexes[int(_start) - 1:int(_end) - 1]
)
elif _start:
selected_index.extend(
total_indexes[int(_start) - 1:]
)
elif _end:
selected_index.extend(
total_indexes[:int(_end) - 1]
)
else:
x: int = int(s.strip()) - 1
if x < len_first_dim:
selected_index.append(x)
except:
pass
if selected_index:
print(f"ImageSelector: selected: {len(selected_index)} images")
return (images[selected_index], )
print(f"ImageSelector: selected no images, passthrough")
return (images, )
class ImageDuplicator:
"""
Duplicate each images and pipe through
"""
def __init__(self):
self._name = "ImageDuplicator"
pass
@classmethod
def INPUT_TYPES(s):
"""
Input: copies you want to get
"""
return {
"required": {
"images": ("IMAGE", ),
"dup_times": ("INT", {
"default": 2,
"min": 1,
"max": 16,
"step": 1,
}),
},
}
RETURN_TYPES = ("IMAGE", )
# RETURN_NAMES = ("image_output_name",)
FUNCTION = "run"
OUTPUT_NODE = False
CATEGORY = "image"
def run(self, images: torch.Tensor, dup_times: int):
"""
对输入的图像张量进行复制多次，并将复制后的张量拼接起来返回。
Args:
images (torch.Tensor): 输入的图像张量，维度为 (batch_size, channels, height, width)。
dup_times (int): 复制的次数。
Returns:
torch.Tensor: 拼接后的图像张量，维度为 (batch_size * dup_times, channels, height, width)。
"""
tensor_list = [images
] + [torch.clone(images) for _ in range(dup_times - 1)]
print(
f"ImageDuplicator: dup {dup_times} times,",
f"return {len(tensor_list)} images",
)
return (torch.cat(tensor_list), )
class LatentSelector:
"""
Select some of the latent images and pipe through
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
"""
Input: list of index of selected image, seperated by comma (",")
support colon (":") sperated range (left included, right excluded)
Indexes start with 1 for simplicity
"""
return {
"required": {
"latent_image": ("LATENT", ),
"selected_indexes": ("STRING", {
"multiline": False,
"default": "1,2,3"
}),
},
}
RETURN_TYPES = ("LATENT", )
# RETURN_NAMES = ("image_output_name",)
FUNCTION = "run"
OUTPUT_NODE = False
CATEGORY = "latent"
def run(self, latent_image: clabc.Mapping[str, torch.Tensor],
selected_indexes: str):
"""
对latent_image进行筛选，根据selected_indexes指定的索引进行筛选
Args:
latent_image: 待筛选的latent_image，Mapping[str, torch.Tensor]，包含'samples'字段
selected_indexes: 待筛选的索引，以逗号分隔，支持连续索引范围以冒号分隔，例如'1,3,5:7,9'
Returns:
筛选后的latent_image，Mapping[str, torch.Tensor]
"""
samples = latent_image['samples']
shape = samples.shape
len_first_dim = shape[0]
selected_index: list[int] = []
total_indexes: list[int] = list(range(len_first_dim))
for s in selected_indexes.strip().split(','):
try:
if ":" in s:
_li = s.strip().split(':', maxsplit=1)
_start = _li[0]
_end = _li[1]
if _start and _end:
selected_index.extend(
total_indexes[int(_start) - 1:int(_end) - 1]
)
elif _start:
selected_index.extend(
total_indexes[int(_start) - 1:]
)
elif _end:
selected_index.extend(
total_indexes[:int(_end) - 1]
)
else:
x: int = int(s.strip()) - 1
if x < len_first_dim:
selected_index.append(x)
except:
pass
if selected_index:
print(f"LatentSelector: selected: {len(selected_index)} latents")
return ({'samples': samples[selected_index, :, :, :]}, )
print(f"LatentSelector: selected no latents, passthrough")
return (latent_image, )
class LatentDuplicator:
"""
Duplicate each latent images and pipe through
"""
def __init__(self):
pass
@classmethod
def INPUT_TYPES(s):
"""
Input: copies you want to get
"""
return {
"required": {
"latent_image": ("LATENT", ),
"dup_times": ("INT", {
"default": 2,
"min": 1,
"max": 16,
"step": 1,
}),
},
}
RETURN_TYPES = ("LATENT", )
# RETURN_NAMES = ("image_output_name",)
FUNCTION = "run"
OUTPUT_NODE = False
CATEGORY = "latent"
def run(self, latent_image: clabc.Mapping[str, torch.Tensor],
dup_times: int):
"""
对latent_image进行复制, 复制次数为dup_times。
Args:
latent_image (clabc.Mapping[str, torch.Tensor]): 输入的latent_image, 包含'samples'键。
dup_times (int): 复制次数。
Returns:
Tuple[Dict[str, torch.Tensor]]: 返回包含samples的字典, samples是一个长度为(dup_times+1)的样本张量。
"""
samples = latent_image['samples']
sample_list = [samples] + [
torch.clone(samples) for _ in range(dup_times - 1)
]
print(
f"LatentDuplicator: dup {dup_times} times,",
f"return {len(sample_list)} images",
)
return ({
'samples': torch.cat(sample_list),
}, )
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"ImageSelector": ImageSelector,
"ImageDuplicator": ImageDuplicator,
"LatentSelector": LatentSelector,
"LatentDuplicator": LatentDuplicator
}
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