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import os
import torch
from PIL import ImageOps
import comfy
import folder_paths
import base64
from io import BytesIO
from .libs.utils import *
class LoadImagesFromDirBatch:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"directory": ("STRING", {"default": ""}),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
"load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
}
}
RETURN_TYPES = ("IMAGE", "MASK", "INT")
FUNCTION = "load_images"
CATEGORY = "image"
@classmethod
def IS_CHANGED(cls, **kwargs):
if 'load_always' in kwargs and kwargs['load_always']:
return float("NaN")
else:
return hash(frozenset(kwargs))
def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0, load_always=False):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory} cannot be found.'")
dir_files = os.listdir(directory)
if len(dir_files) == 0:
raise FileNotFoundError(f"No files in directory '{directory}'.")
# Filter files by extension
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
dir_files = sorted(dir_files)
dir_files = [os.path.join(directory, x) for x in dir_files]
# start at start_index
dir_files = dir_files[start_index:]
images = []
masks = []
limit_images = False
if image_load_cap > 0:
limit_images = True
image_count = 0
has_non_empty_mask = False
for image_path in dir_files:
if os.path.isdir(image_path) and os.path.ex:
continue
if limit_images and image_count >= image_load_cap:
break
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
has_non_empty_mask = True
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
images.append(image)
masks.append(mask)
image_count += 1
if len(images) == 1:
return (images[0], masks[0], 1)
elif len(images) > 1:
image1 = images[0]
mask1 = None
for image2 in images[1:]:
if image1.shape[1:] != image2.shape[1:]:
image2 = comfy.utils.common_upscale(image2.movedim(-1, 1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1, -1)
image1 = torch.cat((image1, image2), dim=0)
for mask2 in masks[1:]:
if has_non_empty_mask:
if image1.shape[1:3] != mask2.shape:
mask2 = torch.nn.functional.interpolate(mask2.unsqueeze(0).unsqueeze(0), size=(image1.shape[2], image1.shape[1]), mode='bilinear', align_corners=False)
mask2 = mask2.squeeze(0)
else:
mask2 = mask2.unsqueeze(0)
else:
mask2 = mask2.unsqueeze(0)
if mask1 is None:
mask1 = mask2
else:
mask1 = torch.cat((mask1, mask2), dim=0)
return (image1, mask1, len(images))
class LoadImagesFromDirList:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"directory": ("STRING", {"default": ""}),
},
"optional": {
"image_load_cap": ("INT", {"default": 0, "min": 0, "step": 1}),
"start_index": ("INT", {"default": 0, "min": 0, "step": 1}),
"load_always": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
}
}
RETURN_TYPES = ("IMAGE", "MASK")
OUTPUT_IS_LIST = (True, True)
FUNCTION = "load_images"
CATEGORY = "image"
@classmethod
def IS_CHANGED(cls, **kwargs):
if 'load_always' in kwargs and kwargs['load_always']:
return float("NaN")
else:
return hash(frozenset(kwargs))
def load_images(self, directory: str, image_load_cap: int = 0, start_index: int = 0, load_always=False):
if not os.path.isdir(directory):
raise FileNotFoundError(f"Directory '{directory}' cannot be found.")
dir_files = os.listdir(directory)
if len(dir_files) == 0:
raise FileNotFoundError(f"No files in directory '{directory}'.")
# Filter files by extension
valid_extensions = ['.jpg', '.jpeg', '.png', '.webp']
dir_files = [f for f in dir_files if any(f.lower().endswith(ext) for ext in valid_extensions)]
dir_files = sorted(dir_files)
dir_files = [os.path.join(directory, x) for x in dir_files]
# start at start_index
dir_files = dir_files[start_index:]
images = []
masks = []
limit_images = False
if image_load_cap > 0:
limit_images = True
image_count = 0
for image_path in dir_files:
if os.path.isdir(image_path) and os.path.ex:
continue
if limit_images and image_count >= image_load_cap:
break
i = Image.open(image_path)
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
images.append(image)
masks.append(mask)
image_count += 1
return images, masks
class LoadImageInspire:
@classmethod
def INPUT_TYPES(s):
input_dir = folder_paths.get_input_directory()
files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
return {"required": {
"image": (sorted(files) + ["#DATA"], {"image_upload": True}),
"image_data": ("STRING", {"multiline": False}),
}
}
CATEGORY = "InspirePack/image"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "load_image"
def load_image(self, image, image_data):
image_data = base64.b64decode(image_data.split(",")[1])
i = Image.open(BytesIO(image_data))
i = ImageOps.exif_transpose(i)
image = i.convert("RGB")
image = np.array(image).astype(np.float32) / 255.0
image = torch.from_numpy(image)[None,]
if 'A' in i.getbands():
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
mask = 1. - torch.from_numpy(mask)
else:
mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
return (image, mask.unsqueeze(0))
class ChangeImageBatchSize:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"image": ("IMAGE",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
"mode": (["simple"],)
}
}
CATEGORY = "InspirePack/Util"
RETURN_TYPES = ("IMAGE", )
FUNCTION = "doit"
@staticmethod
def resize_tensor(input_tensor, batch_size, mode):
if mode == "simple":
if len(input_tensor) < batch_size:
last_frame = input_tensor[-1].unsqueeze(0).expand(batch_size - len(input_tensor), -1, -1, -1)
output_tensor = torch.concat((input_tensor, last_frame), dim=0)
else:
output_tensor = input_tensor[:batch_size, :, :, :]
return output_tensor
else:
print(f"[WARN] ChangeImage(Latent)BatchSize: Unknown mode `{mode}` - ignored")
return input_tensor
@staticmethod
def doit(image, batch_size, mode):
res = ChangeImageBatchSize.resize_tensor(image, batch_size, mode)
return (res,)
class ChangeLatentBatchSize:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "step": 1}),
"mode": (["simple"],)
}
}
CATEGORY = "InspirePack/Util"
RETURN_TYPES = ("LATENT", )
FUNCTION = "doit"
@staticmethod
def doit(latent, batch_size, mode):
res_latent = latent.copy()
samples = res_latent['samples']
samples = ChangeImageBatchSize.resize_tensor(samples, batch_size, mode)
res_latent['samples'] = samples
return (res_latent,)
class ImageBatchSplitter:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"images": ("IMAGE",),
"split_count": ("INT", {"default": 4, "min": 0, "max": 50, "step": 1}),
},
}
RETURN_TYPES = ByPassTypeTuple(("IMAGE", ))
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, images, split_count):
cnt = min(split_count, len(images))
res = [image.unsqueeze(0) for image in images[:cnt]]
if split_count >= len(images):
lack_cnt = split_count - cnt + 1 # including remained
empty_image = empty_pil_tensor()
for x in range(0, lack_cnt):
res.append(empty_image)
elif cnt < len(images):
remained_cnt = len(images) - cnt
remained_image = images[-remained_cnt:]
res.append(remained_image)
return tuple(res)
class LatentBatchSplitter:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"latent": ("LATENT",),
"split_count": ("INT", {"default": 4, "min": 0, "max": 50, "step": 1}),
},
}
RETURN_TYPES = ByPassTypeTuple(("LATENT", ))
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, latent, split_count):
samples = latent['samples']
latent_base = latent.copy()
del latent_base['samples']
cnt = min(split_count, len(samples))
res = []
for single_samples in samples[:cnt]:
item = latent_base.copy()
item['samples'] = single_samples.unsqueeze(0)
res.append(item)
if split_count >= len(samples):
lack_cnt = split_count - cnt + 1 # including remained
item = latent_base.copy()
item['samples'] = empty_latent()
for x in range(0, lack_cnt):
res.append(item)
elif cnt < len(samples):
remained_cnt = len(samples) - cnt
remained_latent = latent_base.copy()
remained_latent['samples'] = samples[-remained_cnt:]
res.append(remained_latent)
return tuple(res)
def top_k_colors(image_tensor, k, min_pixels):
flattened_image = image_tensor.view(-1, image_tensor.size(-1))
unique_colors, counts = torch.unique(flattened_image, dim=0, return_counts=True)
sorted_counts, sorted_indices = torch.sort(counts, descending=True)
sorted_colors = unique_colors[sorted_indices]
filtered_colors = sorted_colors[sorted_counts >= min_pixels]
return filtered_colors[:k]
def create_mask(image_tensor, color):
mask_tensor = torch.zeros_like(image_tensor[:, :, :, 0])
mask_tensor = torch.where(torch.all(image_tensor == color, dim=-1, keepdim=False), 1, mask_tensor)
return mask_tensor
class ColorMapToMasks:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"color_map": ("IMAGE",),
"min_pixels": ("INT", {"default": 500, "min": 1, "max": 0xffffffffffffffff, "step": 1}),
"max_count": ("INT", {"default": 5, "min": 0, "max": 1000, "step": 1}),
},
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, color_map, max_count, min_pixels):
if len(color_map) > 0:
print(f"[Inspire Pack] WARN: ColorMapToMasks - Sure, here's the translation: `color_map` can only be a single image. Only the first image will be processed. If you want to utilize the remaining images, convert the Image Batch to an Image List.")
top_colors = top_k_colors(color_map[0], max_count, min_pixels)
masks = None
for color in top_colors:
this_mask = create_mask(color_map, color)
if masks is None:
masks = this_mask
else:
masks = torch.concat((masks, this_mask), dim=0)
if masks is None:
masks = torch.zeros_like(color_map[0, :, :, 0])
masks.unsqueeze(0)
return (masks,)
class SelectNthMask:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"masks": ("MASK",),
"idx": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "step": 1}),
},
}
RETURN_TYPES = ("MASK",)
FUNCTION = "doit"
CATEGORY = "InspirePack/Util"
def doit(self, masks, idx):
return (masks[idx].unsqueeze(0),)
NODE_CLASS_MAPPINGS = {
"LoadImagesFromDir //Inspire": LoadImagesFromDirBatch,
"LoadImageListFromDir //Inspire": LoadImagesFromDirList,
"LoadImage //Inspire": LoadImageInspire,
"ChangeImageBatchSize //Inspire": ChangeImageBatchSize,
"ChangeLatentBatchSize //Inspire": ChangeLatentBatchSize,
"ImageBatchSplitter //Inspire": ImageBatchSplitter,
"LatentBatchSplitter //Inspire": LatentBatchSplitter,
"ColorMapToMasks //Inspire": ColorMapToMasks,
"SelectNthMask //Inspire": SelectNthMask
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LoadImagesFromDir //Inspire": "Load Image Batch From Dir (Inspire)",
"LoadImageListFromDir //Inspire": "Load Image List From Dir (Inspire)",
"LoadImage //Inspire": "Load Image (Inspire)",
"ChangeImageBatchSize //Inspire": "Change Image Batch Size (Inspire)",
"ChangeLatentBatchSize //Inspire": "Change Latent Batch Size (Inspire)",
"ImageBatchSplitter //Inspire": "Image Batch Splitter (Inspire)",
"LatentBatchSplitter //Inspire": "Latent Batch Splitter (Inspire)",
"ColorMapToMasks //Inspire": "Color Map To Masks (Inspire)",
"SelectNthMask //Inspire": "Select Nth Mask (Inspire)"
}
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