init

2024-09-03 20:36:30 +08:00
parent ad9b3e25c4
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@@ -0,0 +1,11 @@
+from transformers import PretrainedConfig
+
+class BiRefNetConfig(PretrainedConfig):
+    model_type = "SegformerForSemanticSegmentation"
+    def __init__(
+        self,
+        bb_pretrained=False,
+        **kwargs
+    ):
+        self.bb_pretrained = bb_pretrained
+        super().__init__(**kwargs)
@@ -0,0 +1,59 @@
+# ComfyUI-BiRefNet-Hugo
+
+## 介绍 | Introduction
+
+本仓库将BiRefNet最新模型封装为ComfyUI节点来使用，相较于旧模型，最新模型的抠图精度更高更好。<br>
+This repository wraps the latest BiRefNet model as ComfyUI nodes. Compared to the previous model, the latest model offers higher and better matting accuracy.
+
+## 安装 | Installation 
+
+1. 进入节点目录, `ComfyUI/custom_nodes/`
+2. `git clone https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo.git`
+3. `cd ComfyUI-BiRefNet-Hugo`
+4. `pip install -r requirements.txt`
+___
+
+1. Go to comfyUI custom_nodes folder, `ComfyUI/custom_nodes/`
+2. `git clone https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo.git`
+3. `cd ComfyUI-BiRefNet-Hugo`
+4. `pip install -r requirements.txt`
+
+## 使用 | Usage
+
+示例工作流放置在`ComfyUI-BiRefNet-Hugo/workflow`中<br/>
+The demo workflow placed in `ComfyUI-BiRefNet-Hugo/workflow`
+___
+工作流workflow.json的使用<br/>
+The use of workflow.json
+
+![plot](./assets/d0a22b2a-ceb3-4205-9b4e-f6a68e4337c7.png)
+
+工作流video_workflow.json的使用<br/>
+The use of video_workflow.json
+___
+![plot](./assets/2de5b085-1125-46f9-8ef3-06706743f182.png)
+
+## 效果演示 | Sample Result
+
+![](./assets/demo1.gif)
+
+![](./assets/demo2.gif)
+
+![](./assets/demo3.gif)
+
+## 社交账号 | Social Account Homepage
+- Bilibili：[我的B站主页](https://space.bilibili.com/1303099255)
+
+## 感谢 | Acknowledgments
+
+感谢BiRefNet仓库的所有作者 [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet)
+
+Thanks to BiRefNet repo owner [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet)
+
+部分代码参考了 [ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO) 感谢！
+
+Some of the code references [ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO) Thanks!
+
+## 关注历史 | star history
+
+[![Star History Chart](https://api.star-history.com/svg?repos=MoonHugo/ComfyUI-BiRefNet-Hugo&type=Date)](https://star-history.com/#MoonHugo/ComfyUI-BiRefNet-Hugo&Date)
@@ -0,0 +1,3 @@
+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
@@ -0,0 +1,148 @@
+### config.py
+
+import os
+import math
+from folder_paths import models_dir
+os.environ['HOME'] = os.path.expanduser("~")
+class Config():
+    def __init__(self) -> None:
+        # PATH settings
+        self.sys_home_dir = os.environ['HOME']     # Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
+        # TASK settings
+        self.task = ['DIS5K', 'COD', 'HRSOD', 'DIS5K+HRSOD+HRS10K', 'P3M-10k'][0]
+        self.training_set = {
+            'DIS5K': ['DIS-TR', 'DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4'][0],
+            'COD': 'TR-COD10K+TR-CAMO',
+            '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],
+            'DIS5K+HRSOD+HRS10K': 'DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4+DIS-TR+TE-HRS10K+TE-HRSOD+TE-UHRSD+TR-HRS10K+TR-HRSOD+TR-UHRSD',     # leave DIS-VD for evaluation.
+            'P3M-10k': 'TR-P3M-10k',
+        }[self.task]
+        self.prompt4loc = ['dense', 'sparse'][0]
+
+        # Faster-Training settings
+        self.load_all = True
+        self.compile = True     # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
+                                #   Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
+                                # 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
+                                # 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
+        self.precisionHigh = True
+
+        # MODEL settings
+        self.ms_supervision = True
+        self.out_ref = self.ms_supervision and True
+        self.dec_ipt = True
+        self.dec_ipt_split = True
+        self.cxt_num = [0, 3][1]    # multi-scale skip connections from encoder
+        self.mul_scl_ipt = ['', 'add', 'cat'][2]
+        self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
+        self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
+        self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0]
+
+        # TRAINING settings
+        self.batch_size = 4
+        self.IoU_finetune_last_epochs = [
+            0,
+            {
+                'DIS5K': -50,
+                'COD': -20,
+                'HRSOD': -20,
+                'DIS5K+HRSOD+HRS10K': -20,
+                'P3M-10k': -20,
+            }[self.task]
+        ][1]    # choose 0 to skip
+        self.lr = (1e-4 if 'DIS5K' in self.task else 1e-5) * math.sqrt(self.batch_size / 4)     # DIS needs high lr to converge faster. Adapt the lr linearly
+        self.size = 1024
+        self.num_workers = max(4, self.batch_size)          # will be decrease to min(it, batch_size) at the initialization of the data_loader
+
+        # Backbone settings
+        self.bb = [
+            'vgg16', 'vgg16bn', 'resnet50',         # 0, 1, 2
+            'swin_v1_t', 'swin_v1_s',               # 3, 4
+            'swin_v1_b', 'swin_v1_l',               # 5-bs9, 6-bs4
+            'pvt_v2_b0', 'pvt_v2_b1',               # 7, 8
+            'pvt_v2_b2', 'pvt_v2_b5',               # 9-bs10, 10-bs5
+        ][6]
+        self.lateral_channels_in_collection = {
+            'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
+            'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
+            'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
+            'swin_v1_t': [768, 384, 192, 96], 'swin_v1_s': [768, 384, 192, 96],
+            'pvt_v2_b0': [256, 160, 64, 32], 'pvt_v2_b1': [512, 320, 128, 64],
+        }[self.bb]
+        if self.mul_scl_ipt == 'cat':
+            self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
+        self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
+
+        # MODEL settings - inactive
+        self.lat_blk = ['BasicLatBlk'][0]
+        self.dec_channels_inter = ['fixed', 'adap'][0]
+        self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
+        self.progressive_ref = self.refine and True
+        self.ender = self.progressive_ref and False
+        self.scale = self.progressive_ref and 2
+        self.auxiliary_classification = False       # Only for DIS5K, where class labels are saved in `dataset.py`.
+        self.refine_iteration = 1
+        self.freeze_bb = False
+        self.model = [
+            'BiRefNet',
+        ][0]
+        if self.dec_blk == 'HierarAttDecBlk':
+            self.batch_size = 2 ** [0, 1, 2, 3, 4][2]
+
+        # TRAINING settings - inactive
+        self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
+        self.optimizer = ['Adam', 'AdamW'][1]
+        self.lr_decay_epochs = [1e5]    # Set to negative N to decay the lr in the last N-th epoch.
+        self.lr_decay_rate = 0.5
+        # Loss
+        self.lambdas_pix_last = {
+            # not 0 means opening this loss
+            # original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
+            'bce': 30 * 1,          # high performance
+            'iou': 0.5 * 1,         # 0 / 255
+            'iou_patch': 0.5 * 0,   # 0 / 255, win_size = (64, 64)
+            'mse': 150 * 0,         # can smooth the saliency map
+            'triplet': 3 * 0,
+            'reg': 100 * 0,
+            'ssim': 10 * 1,          # help contours,
+            'cnt': 5 * 0,          # help contours
+            'structure': 5 * 0,    # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
+        }
+        self.lambdas_cls = {
+            'ce': 5.0
+        }
+        # Adv
+        self.lambda_adv_g = 10. * 0        # turn to 0 to avoid adv training
+        self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
+
+        # PATH settings - inactive
+        self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
+        self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights')
+        self.weights = {
+            'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
+            'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
+            'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]),
+            'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]),
+            'swin_v1_t': os.path.join(self.weights_root_dir, ['swin_tiny_patch4_window7_224_22kto1k_finetune.pth'][0]),
+            'swin_v1_s': os.path.join(self.weights_root_dir, ['swin_small_patch4_window7_224_22kto1k_finetune.pth'][0]),
+            'pvt_v2_b0': os.path.join(self.weights_root_dir, ['pvt_v2_b0.pth'][0]),
+            'pvt_v2_b1': os.path.join(self.weights_root_dir, ['pvt_v2_b1.pth'][0]),
+        }
+
+        # Callbacks - inactive
+        self.verbose_eval = True
+        self.only_S_MAE = False
+        self.use_fp16 = False   # Bugs. It may cause nan in training.
+        self.SDPA_enabled = False    # Bugs. Slower and errors occur in multi-GPUs
+
+        # others
+        self.device = [0, 'cpu'][0]     # .to(0) == .to('cuda:0')
+
+        self.batch_size_valid = 1
+        self.rand_seed = 7
+        # run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f]
+        # with open(run_sh_file[0], 'r') as f:
+        #     lines = f.readlines()
+        #     self.save_last = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'val_last=' in l][0].split('val_last=')[-1].split()[0])
+        #     self.save_step = int([l.strip() for l in lines if '"{}")'.format(self.task) in l and 'step=' in l][0].split('step=')[-1].split()[0])
+        # self.val_step = [0, self.save_step][0]
@@ -0,0 +1,95 @@
+from transformers import AutoModelForImageSegmentation
+import torch
+from torchvision import transforms
+import numpy as np
+from PIL import Image
+import torch.nn.functional as F
+from .config import Config
+
+Config()
+
+torch.set_float32_matmul_precision(["high", "highest"][0])
+
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+
+birefnet.to("cuda")
+
+transform_image = transforms.Compose(
+    [
+        transforms.Resize((1024, 1024)),
+        transforms.ToTensor(),
+        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+    ]
+)
+
+def tensor2pil(image):
+    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
+
+def pil2tensor(image):
+    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+
+def resize_image(image):
+    image = image.convert('RGB')
+    model_input_size = (1024, 1024)
+    image = image.resize(model_input_size, Image.BILINEAR)
+    return image
+
+
+class BiRefNet_Hugo:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE", "MASK",)
+    RETURN_NAMES = ("image", "mask",)
+    FUNCTION = "background_remove"
+    CATEGORY = "🔥BiRefNet"
+  
+    def background_remove(self, image):
+        processed_images = []
+        processed_masks = []
+        for image in image:
+            orig_image = tensor2pil(image)
+            w,h = orig_image.size
+            image = resize_image(orig_image)
+            im_tensor = transform_image(image).unsqueeze(0)
+            if torch.cuda.is_available():
+                im_tensor=im_tensor.cuda()
+            with torch.no_grad():
+                result = birefnet(im_tensor)[-1].sigmoid().cpu()
+            result = torch.squeeze(F.interpolate(result, size=(h,w)))
+            ma = torch.max(result)
+            mi = torch.min(result)
+            result = (result-mi)/(ma-mi)    
+            im_array = (result*255).cpu().data.numpy().astype(np.uint8)
+            pil_im = Image.fromarray(np.squeeze(im_array))
+            new_im = Image.new("RGBA", pil_im.size, (0,0,0,0))
+            new_im.paste(orig_image, mask=pil_im)
+            new_im_tensor = pil2tensor(new_im)
+            pil_im_tensor = pil2tensor(pil_im)
+            processed_images.append(new_im_tensor)
+            processed_masks.append(pil_im_tensor)
+
+        new_ims = torch.cat(processed_images, dim=0)
+        new_masks = torch.cat(processed_masks, dim=0)
+
+        return new_ims, new_masks
+
+
+NODE_CLASS_MAPPINGS = {
+    "BiRefNet_Hugo": BiRefNet_Hugo
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "BiRefNet_Hugo": "🔥BiRefNet"
+}
@@ -0,0 +1,2 @@
+numpy
+timm
@@ -0,0 +1,378 @@
+{
+  "last_node_id": 15,
+  "last_link_id": 16,
+  "nodes": [
+    {
+      "id": 11,
+      "type": "VHS_LoadVideo",
+      "pos": [
+        840,
+        -90
+      ],
+      "size": [
+        210,
+        480
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null,
+          "label": "批次管理"
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            12
+          ],
+          "shape": 3,
+          "label": "图像",
+          "slot_index": 0
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3,
+          "label": "帧计数"
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "links": null,
+          "shape": 3,
+          "label": "音频"
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3,
+          "label": "视频信息"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_LoadVideo"
+      },
+      "widgets_values": {
+        "video": "6月28日(1).mp4",
+        "force_rate": 0,
+        "force_size": "Disabled",
+        "custom_width": 512,
+        "custom_height": 512,
+        "frame_load_cap": 0,
+        "skip_first_frames": 0,
+        "select_every_nth": 1,
+        "choose video to upload": "image",
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "frame_load_cap": 0,
+            "skip_first_frames": 0,
+            "force_rate": 0,
+            "filename": "6月28日(1).mp4",
+            "type": "input",
+            "format": "video/mp4",
+            "select_every_nth": 1
+          },
+          "muted": false
+        }
+      }
+    },
+    {
+      "id": 1,
+      "type": "BiRefNet_Hugo",
+      "pos": [
+        1110,
+        -60
+      ],
+      "size": {
+        "0": 210,
+        "1": 60
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 12,
+          "slot_index": 0,
+          "label": "image"
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            14
+          ],
+          "slot_index": 0,
+          "shape": 3,
+          "label": "image"
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "links": [
+            15
+          ],
+          "slot_index": 1,
+          "shape": 3,
+          "label": "mask"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "BiRefNet_Hugo"
+      }
+    },
+    {
+      "id": 14,
+      "type": "MaskToImage",
+      "pos": [
+        1410,
+        30
+      ],
+      "size": {
+        "0": 210,
+        "1": 30
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 15,
+          "label": "遮罩"
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            16
+          ],
+          "shape": 3,
+          "label": "图像",
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "MaskToImage"
+      }
+    },
+    {
+      "id": 15,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        1410,
+        120
+      ],
+      "size": [
+        360,
+        660
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 16,
+          "label": "图像"
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": null,
+          "label": "音频"
+        },
+        {
+          "name": "meta_batch",
+          "type": "VHS_BatchManager",
+          "link": null,
+          "label": "批次管理"
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": null
+        }
+      ],
+      "outputs": [
+        {
+          "name": "Filenames",
+          "type": "VHS_FILENAMES",
+          "links": null,
+          "shape": 3,
+          "label": "文件名"
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VHS_VideoCombine"
+      },
+      "widgets_values": {
+        "frame_rate": 30,
+        "loop_count": 0,
+        "filename_prefix": "AnimateDiff",
+        "format": "video/h265-mp4",
+        "pix_fmt": "yuv420p10le",
+        "crf": 22,
+        "save_metadata": true,
+        "pingpong": false,
+        "save_output": true,
+        "videopreview": {
+          "hidden": false,
+          "paused": false,
+          "params": {
+            "filename": "AnimateDiff_00005.mp4",
+            "subfolder": "",
+            "type": "output",
+            "format": "video/h265-mp4",
+            "frame_rate": 30
+          },
+          "muted": false
+        }
+      }
+    },
+    {
+      "id": 13,
+      "type": "VHS_VideoCombine",
+      "pos": [
+        1830,
+        -180
+      ],
+      "size": [
+        360,
+        660
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 14,
+          "label": "图像"
+        },
+        {
+          "name": "audio",
+          "type": "AUDIO",
+          "link": null,
+          "label": "音频"
+        },
+        {
+          "name": "meta_batch",
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+          "shape": 3,
+          "label": "文件名"
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+            "type": "output",
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+}
@@ -0,0 +1,231 @@
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