support model General-Lite-2K and model Matting
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@@ -25,7 +25,9 @@ Support the use of new and old versions of BiRefNet models
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- General: A pre-trained model for general use cases.
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- General-Lite: A light pre-trained model for general use cases.
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- General-Lite-2K: A light pre-trained model for general use cases in high resolution (2560x1440).
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- Portrait: A pre-trained model for human portraits.
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- Matting: A pre-trained model for general trimap-free matting use.
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- DIS: A pre-trained model for dichotomous image segmentation (DIS).
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- HRSOD: A pre-trained model for high-resolution salient object detection (HRSOD).
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- COD: A pre-trained model for concealed object detection (COD).
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@@ -36,7 +38,9 @@ Model files go here (when use AutoDownloadBiRefNetModel automatically downloaded
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If necessary, they can be downloaded from:
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- [General](https://huggingface.co/ZhengPeng7/BiRefNet/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `General.safetensors`
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- [General-Lite](https://huggingface.co/ZhengPeng7/BiRefNet_T/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `General-Lite.safetensors`
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- [General-Lite-2K](https://huggingface.co/ZhengPeng7/BiRefNet_lite-2K/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `General-Lite-2K.safetensors`
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- [Portrait](https://huggingface.co/ZhengPeng7/BiRefNet-portrait/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `Portrait.safetensors`
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- [Matting](https://huggingface.co/ZhengPeng7/BiRefNet-matting/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `Matting.safetensors`
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- [DIS](https://huggingface.co/ZhengPeng7/BiRefNet-DIS5K/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `DIS.safetensors`
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- [HRSOD](https://huggingface.co/ZhengPeng7/BiRefNet-HRSOD/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `HRSOD.safetensors`
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- [COD](https://huggingface.co/ZhengPeng7/BiRefNet-COD/resolve/main/model.safetensors) ➔ `model.safetensors` must be renamed `COD.safetensors`
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@@ -50,8 +54,8 @@ Some models on GitHub:
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- [BiRefNet-ep480.pth](https://huggingface.co/ViperYX/BiRefNet/resolve/main/BiRefNet-ep480.pth)
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## Weight Models (Optional)
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- [swin_large_patch4_window12_384_22kto1k.pth](https://huggingface.co/ViperYX/BiRefNet/resolve/main/swin_large_patch4_window12_384_22kto1k.pth)(not General-Lite model)
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- [swin_tiny_patch4_window7_224_22kto1k_finetune.pth](https://drive.google.com/drive/folders/1cmce_emsS8A5ha5XT2c_CZiJzlLM81ms)(just General-Lite model)
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- [swin_large_patch4_window12_384_22kto1k.pth](https://huggingface.co/ViperYX/BiRefNet/resolve/main/swin_large_patch4_window12_384_22kto1k.pth)(not General-Lite and General-Lite-2K model)
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- [swin_tiny_patch4_window7_224_22kto1k_finetune.pth](https://drive.google.com/drive/folders/1cmce_emsS8A5ha5XT2c_CZiJzlLM81ms)(just General-Lite and General-Lite-2K model)
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## Nodes
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+6
-2
@@ -25,7 +25,9 @@
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- General: 用于一般用例的预训练模型。
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- General-Lite: 用于一般用例的轻量级预训练模型。
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- General-Lite-2K: 用于一般用例的轻量级预训练模型,适用于高分辨率图像。 (最佳分辨率2560x1440).
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- Portrait: 人物肖像预训练模型。
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- Matting: 一种使用无trimap matting的预训练模型。
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- DIS: 一种用于二分图像分割(DIS)的预训练模型。
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- HRSOD: 一种用于高分辨率显著目标检测(HRSOD)的预训练模型。
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- COD: 一种用于隐蔽目标检测(COD)的预训练模型。
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@@ -36,7 +38,9 @@
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也可以手动下载模型:
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- [General](https://huggingface.co/ZhengPeng7/BiRefNet/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `General.safetensors`
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- [General-Lite](https://huggingface.co/ZhengPeng7/BiRefNet_T/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `General-Lite.safetensors`
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- [General-Lite-2K](https://huggingface.co/ZhengPeng7/BiRefNet_lite-2K/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `General-Lite-2K.safetensors`
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- [Portrait](https://huggingface.co/ZhengPeng7/BiRefNet-portrait/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `Portrait.safetensors`
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- [Matting](https://huggingface.co/ZhengPeng7/BiRefNet-matting/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `Matting.safetensors`
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- [DIS](https://huggingface.co/ZhengPeng7/BiRefNet-DIS5K/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `DIS.safetensors`
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- [HRSOD](https://huggingface.co/ZhengPeng7/BiRefNet-HRSOD/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `HRSOD.safetensors`
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- [COD](https://huggingface.co/ZhengPeng7/BiRefNet-COD/resolve/main/model.safetensors) ➔ `model.safetensors` 重命名为 `COD.safetensors`
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@@ -52,8 +56,8 @@ GitHub上的模型:
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## 权重模型(非必须)
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下载放在`models/BiRefNet`
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- [swin_large_patch4_window12_384_22kto1k.pth](https://huggingface.co/ViperYX/BiRefNet/resolve/main/swin_large_patch4_window12_384_22kto1k.pth)(非General-Lite模型)
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- [swin_tiny_patch4_window7_224_22kto1k_finetune.pth](https://drive.google.com/drive/folders/1cmce_emsS8A5ha5XT2c_CZiJzlLM81ms)(仅General-Lite模型)
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- [swin_large_patch4_window12_384_22kto1k.pth](https://huggingface.co/ViperYX/BiRefNet/resolve/main/swin_large_patch4_window12_384_22kto1k.pth)(非General-Lite和General-Lite-2K模型)
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- [swin_tiny_patch4_window7_224_22kto1k_finetune.pth](https://drive.google.com/drive/folders/1cmce_emsS8A5ha5XT2c_CZiJzlLM81ms)(仅General-Lite和General-Lite-2K模型)
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## 节点
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+15
-6
@@ -8,20 +8,29 @@ class Config:
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def __init__(self, bb_index: int = 6) -> None:
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# PATH settings
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# Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
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# self.sys_home_dir = [os.path.expanduser('~'), '/mnt/data'][1] # Default, custom
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# self.sys_home_dir = [os.path.expanduser('~'), '/mnt/data'][0] # Default, custom
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# self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
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# TASK settings
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self.task = ['DIS5K', 'COD', 'HRSOD', 'General', 'General-2K', 'Matting'][0]
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self.testsets = {
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# Benchmarks
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'DIS5K': ','.join(['DIS-VD', 'DIS-TE1', 'DIS-TE2', 'DIS-TE3', 'DIS-TE4']),
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'COD': ','.join(['CHAMELEON', 'NC4K', 'TE-CAMO', 'TE-COD10K']),
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'HRSOD': ','.join(['DAVIS-S', 'TE-HRSOD', 'TE-UHRSD', 'DUT-OMRON', 'TE-DUTS']),
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# Practical use
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'General': ','.join(['DIS-VD', 'TE-P3M-500-NP']),
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'General-2K': ','.join(['DIS-VD', 'TE-P3M-500-NP']),
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'Matting': ','.join(['TE-P3M-500-NP', 'TE-AM-2k']),
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}[self.task]
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# datasets_all = '+'.join([ds for ds in (os.listdir(os.path.join(self.data_root_dir, self.task)) if os.path.isdir(os.path.join(self.data_root_dir, self.task)) else []) if ds not in self.testsets.split(',')])
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self.training_set = {
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'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
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'COD': 'TR-COD10K+TR-CAMO',
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'HRSOD': ['TR-DUTS', 'TR-HRSOD', 'TR-UHRSD', 'TR-DUTS+TR-HRSOD', 'TR-DUTS+TR-UHRSD', 'TR-HRSOD+TR-UHRSD', 'TR-DUTS+TR-HRSOD+TR-UHRSD'][5],
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# 'General': '+'.join([ds for ds in os.listdir(os.path.join(self.data_root_dir, self.task)) if ds not in ['DIS-VD', 'TE-P3M-500-NP']]), # leave DIS-VD,TE-P3M-500-NP for evaluation.
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'General': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TR-HRSOD+TE-HRSOD+TR-HRS10K+TE-HRS10K+TR-UHRSD+TE-UHRSD+TR-P3M-10k+TE-P3M-500-P+TR-humans+DIS-VD-ori',
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# 'General-2K': '+'.join([ds for ds in os.listdir(os.path.join(self.data_root_dir, self.task)) if ds not in ['DIS-VD', 'TE-P3M-500-NP']]),
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'General-2K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TR-HRSOD+TE-HRSOD+TR-HRS10K+TE-HRS10K+TR-UHRSD+TE-UHRSD+TR-P3M-10k+TE-P3M-500-P+TR-humans+DIS-VD-ori',
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'Matting': 'TR-P3M-10k+TE-P3M-500-NP+TR-humans+TR-Distrinctions-646',
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'General': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TR-HRSOD+TE-HRSOD+TR-HRS10K+TE-HRS10K+TR-UHRSD+TE-UHRSD+TR-P3M-10k+TE-P3M-500-P+TR-humans+DIS-VD-ori', # datasets_all
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'General-2K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TR-HRSOD+TE-HRSOD+TR-HRS10K+TE-HRS10K+TR-UHRSD+TE-UHRSD+TR-P3M-10k+TE-P3M-500-P+TR-humans+DIS-VD-ori', # datasets_all
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'Matting': 'TR-P3M-10k+TE-P3M-500-NP+TR-humans+TR-Distrinctions-646', # datasets_all
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}[self.task]
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self.prompt4loc = ['dense', 'sparse'][0]
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+6
-4
@@ -20,14 +20,16 @@ models_path_default = folder_paths.get_folder_paths(models_dir_key)[0]
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usage_to_weights_file = {
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'General': 'BiRefNet',
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'General-Lite': 'BiRefNet_T',
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'General-Lite-2K': 'BiRefNet_lite-2K',
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'Portrait': 'BiRefNet-portrait',
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'Matting': 'BiRefNet-matting',
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'DIS': 'BiRefNet-DIS5K',
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'HRSOD': 'BiRefNet-HRSOD',
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'COD': 'BiRefNet-COD',
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'DIS-TR_TEs': 'BiRefNet-DIS5K-TR_TEs'
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}
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modelNameList = ['General', 'General-Lite', 'Portrait', 'DIS', 'HRSOD', 'COD', 'DIS-TR_TEs']
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modelNameList = ['General', 'General-Lite', 'General-Lite-2K', 'Portrait', 'Matting', 'DIS', 'HRSOD', 'COD', 'DIS-TR_TEs']
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def get_model_path(model_name):
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@@ -94,7 +96,7 @@ class AutoDownloadBiRefNetModel:
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DESCRIPTION = "Auto download BiRefNet model from huggingface to models/BiRefNet/{model_name}.safetensors"
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def load_model(self, model_name, device):
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bb_index = 3 if model_name == "General-Lite" else 6
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bb_index = 3 if model_name == "General-Lite" or model_name == "General-Lite-2K" else 6
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biRefNet_model = BiRefNet(bb_pretrained=False, bb_index=bb_index)
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model_file_name = f'{model_name}.safetensors'
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model_full_path = folder_paths.get_full_path(models_dir_key, model_file_name)
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@@ -138,7 +140,7 @@ class LoadRembgByBiRefNetModel:
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biRefNet_model = OldBiRefNet(bb_pretrained=use_weight)
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else:
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version = VERSION[1]
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bb_index = 3 if model == "General-Lite.safetensors" else 6
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bb_index = 3 if model == "General-Lite.safetensors" or model == "General-Lite-2K.safetensors" else 6
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biRefNet_model = BiRefNet(bb_pretrained=use_weight, bb_index=bb_index)
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model_path = folder_paths.get_full_path(models_dir_key, model)
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@@ -199,7 +201,7 @@ class RembgByBiRefNet:
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image = apply_mask_to_image(image.cpu(), mask.cpu())
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_images.append(image)
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_masks.append(mask)
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_masks.append(mask.squeeze(0))
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out_images = torch.cat(_images, dim=0)
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out_masks = torch.cat(_masks, dim=0)
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+1
-1
@@ -1,7 +1,7 @@
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[project]
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name = "comfyui_birefnet_ll"
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description = "Sync with version of BiRefNet. NODES:AutoDownloadBiRefNetModel, LoadRembgByBiRefNetModel, RembgByBiRefNet."
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version = "1.0.3"
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version = "1.0.4"
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license = {file = "LICENSE"}
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dependencies = ["numpy<2", "opencv-python", "scipy", "timm"]
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