feat: support onnx model and rename
This commit is contained in:
duhaifeng
@@ -1,4 +1,4 @@
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<h1 align="center">ComfyUI-BiRefNet-lite</h1>
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<h1 align="center">ComfyUI-BiRefNet-Super</h1>
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<p align="center">
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<br><font size=5>English</font> | <a href="README_CN.md">中文</a>
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@@ -18,14 +18,17 @@ Supports putalpha, naive, and alpha_matting cropping methods.
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<br>
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## News
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- Nov 19, 2023: Add `mask_precision_threshold` parameter to control the accuracy threshold of the mask, default to 0.1<br>
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- Nov 19, 2024: Add `mask_precision_threshold` parameter to control the accuracy threshold of the mask, default to 0.1<br>
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- Dec 10, 2024:
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- Add `BiRefNet_onnx` node to Support `onnx` model <br>
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- Modify the name of repository from `ComfyUI-BiRefNet-lite` to `ComfyUI-BiRefNet-Super`<br>
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## Installation
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#### Method 1:
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1. Navigate to the node directory, `ComfyUI/custom_nodes/`
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2. `git clone https://github.com/rubi-du/ComfyUI-BiRefNet-lite.git`
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3. `cd ComfyUI-BiRefNet-lite`
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2. `git clone https://github.com/rubi-du/ComfyUI-BiRefNet-Super.git`
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3. `cd ComfyUI-BiRefNet-Super`
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4. `pip install -r requirements.txt`
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5. Restart ComfyUI
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@@ -33,11 +36,13 @@ Supports putalpha, naive, and alpha_matting cropping methods.
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Directly download the node source code package, unzip it into the `custom_nodes` directory, and then restart ComfyUI.
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#### Method 3:
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Install via ComfyUI-Manager by searching for "ComfyUI-BiRefNet-lite".
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Install via ComfyUI-Manager by searching for "ComfyUI-BiRefNet-Super".
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## Usage
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### BiRefNet_Super | BiRefNet_Lite
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Example workflows are placed in `ComfyUI-BiRefNet-lite/workflow`.<br/>
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Example workflows are placed in `ComfyUI-BiRefNet-Super/workflow`.<br/>
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There are two options for loading models: one is to automatically download and load a remote model, and the other is to load a local model (in which case you need to set `load_local_model` to true and set `local_model_path` to the local model path under the models/birefnet directory, for example, the BiRefNet folder).<br/>
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@@ -46,7 +51,7 @@ ___
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Model download links:<br/>
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#### Model download links:<br/>
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BiRefNet: https://huggingface.co/ZhengPeng7/BiRefNet/tree/main<br/>
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BiRefNet_lite-2K: https://huggingface.co/ZhengPeng7/BiRefNet_lite-2K/tree/main<br/>
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BiRefNet-portrait: https://huggingface.co/ZhengPeng7/BiRefNet-portrait/tree/main<br/>
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@@ -60,10 +65,39 @@ Usage of workflow.json<br/>
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___
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Usage of video_workflow.json<br/>
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[Workflow Address](./workflow/video_workflow.json)
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[Workflow Address](./workflow/video_workflow.json)
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### BiRefNet_onnx
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Example workflows are placed in [workflow](./workflow/workflow-onnx.json).<br/>
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Place the model file in `ComfyUI/models/birefnet`.<br/>
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Model file name should be one of the following:
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- birefnet-general.onnx
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- birefnet-general-lite.onnx
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- birefnet-portrait.onnx
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- birefnet_massive.onnx
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- birefnet-hrsod.onnx
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- birefnet-dis.onnx
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- birefnet-cod.onnx
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#### Model download links:<br/>
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birefnet-genernal: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-epoch_244.onnx<br/>
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birefnet-genernal-lite: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-bb_swin_v1_tiny-epoch_232.onnx<br/>
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birefnet-portrait: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-portrait-epoch_150.onnx <br/>
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birefnet_massive: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-massive-TR_DIS5K_TR_TEs-epoch_420.onnx<br/>
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birefnet-hrsod: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-HRSOD_DHU-epoch_115.onnx <br/>
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birefnet-dis: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-DIS-epoch_590.onnx <br/>
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birefnet-cod: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-COD-epoch_125.onnx <br/>
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## Acknowledgments
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Thanks to all the authors of the BiRefNet repository [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet).
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Some code was referenced from [MoonHugo/ComfyUI-BiRefNet-Hugo](https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo). Thank you!
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Some code was referenced from [MoonHugo/ComfyUI-BiRefNet-Hugo](https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo). Thank you!
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Some code was referenced from [danielgatis/rembg](https://github.com/danielgatis/rembg). Thank you!
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+39
-7
@@ -1,4 +1,4 @@
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<h1 align="center">ComfyUI-BiRefNet-lite</h1>
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<h1 align="center">ComfyUI-BiRefNet-Super</h1>
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<p align="center">
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<br> <a href="README.md">English</a> | <font size=5>中文</font>
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@@ -26,8 +26,8 @@
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#### 方法1:
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1. 进入节点目录, `ComfyUI/custom_nodes/`
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2. `git clone https://github.com/rubi-du/ComfyUI-BiRefNet-lite.git`
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3. `cd ComfyUI-BiRefNet-lite`
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2. `git clone https://github.com/rubi-du/ComfyUI-BiRefNet-Super.git`
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3. `cd ComfyUI-BiRefNet-Super`
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4. `pip install -r requirements.txt`
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5. 重启ComfyUI
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@@ -35,11 +35,13 @@
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直接下载节点源码包，然后解压到custom_nodes目录下，最后重启ComfyUI
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#### 方法3：
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通过ComfyUI-Manager安装，搜索“ComfyUI-BiRefNet-lite”进行安装
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通过ComfyUI-Manager安装，搜索“ComfyUI-BiRefNet-Super”进行安装
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## 使用
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示例工作流放置在`ComfyUI-BiRefNet-lite/workflow`中<br/>
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### BiRefNet_Super | BiRefNet_Lite
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示例工作流放置在`ComfyUI-BiRefNet-Super/workflow`中<br/>
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加载模型有两种方式可选：一是自动下载并加载远程模型，二是加载本地模型（此时需将`load_local_model`设为true，并将`local_model_path`设置为指向models/birefnet目录下的本地模型路径，例如BiRefNet文件夹）<br/>
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@@ -49,7 +51,7 @@ ___
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模型下载地址：<br/>
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#### 模型下载地址：<br/>
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BiRefNet: https://huggingface.co/ZhengPeng7/BiRefNet/tree/main<br/>
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BiRefNet_lite-2K: https://huggingface.co/ZhengPeng7/BiRefNet_lite-2K/tree/main<br/>
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BiRefNet-portrait: https://huggingface.co/ZhengPeng7/BiRefNet-portrait/tree/main<br/>
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@@ -65,8 +67,38 @@ ___
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工作流video_workflow.json的使用<br/>
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[workflow地址](./workflow/video_workflow.json)
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### BiRefNet_onnx
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示例工作流放置在 [workflow](./workflow/workflow-onnx.json)中<br/>
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模型下载完放置在 `ComfyUI/models/birefnet` 目录下.<br/>
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模型名称如下:
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- birefnet-general.onnx
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- birefnet-general-lite.onnx
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- birefnet-portrait.onnx
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- birefnet_massive.onnx
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- birefnet-hrsod.onnx
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- birefnet-dis.onnx
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- birefnet-cod.onnx
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#### 模型下载地址：<br/>
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birefnet-genernal: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-epoch_244.onnx<br/>
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birefnet-genernal-lite: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-bb_swin_v1_tiny-epoch_232.onnx<br/>
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birefnet-portrait: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-portrait-epoch_150.onnx <br/>
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birefnet_massive: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-massive-TR_DIS5K_TR_TEs-epoch_420.onnx<br/>
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birefnet-hrsod: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-HRSOD_DHU-epoch_115.onnx <br/>
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birefnet-dis: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-DIS-epoch_590.onnx <br/>
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birefnet-cod: https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-COD-epoch_125.onnx <br/>
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## 感谢
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感谢BiRefNet仓库的所有作者 [ZhengPeng7/BiRefNet](https://github.com/zhengpeng7/birefnet)
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部分代码参考了 [MoonHugo/ComfyUI-BiRefNet-Hugo](https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo) 感谢！
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部分代码参考了 [MoonHugo/ComfyUI-BiRefNet-Hugo](https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo) 感谢！
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部分代码参考了 [danielgatis/rembg](https://github.com/danielgatis/rembg). 感谢！
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@@ -10,6 +10,7 @@ from scipy.ndimage import binary_erosion
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from pymatting.alpha.estimate_alpha_cf import estimate_alpha_cf
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from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
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from pymatting.util.util import stack_images
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from .rmbg.session_factory import new_session
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from folder_paths import models_dir, add_model_folder_path, get_folder_paths
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torch.set_float32_matmul_precision(["high", "highest"][0])
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@@ -154,7 +155,112 @@ def get_device_by_name(device):
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print("\033[93mUse Device(使用设备):", device, "\033[0m")
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return device
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def get_device_provider(device):
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providers = []
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cuda_provider_options = {
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"gpu_mem_limit": 9 * 1024 * 1024 * 1024, # 9GB.
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"arena_extend_strategy": "kSameAsRequested",
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"cudnn_conv_use_max_workspace": "0"
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}
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if device == 'cuda' and torch.cuda.is_available():
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providers.append(("CUDAExecutionProvider", cuda_provider_options))
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if len(providers) == 0:
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providers.append(("CPUExecutionProvider", {}))
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return providers
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_birefnet_model = None
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_birefnet_model_name = ""
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_birefnet_onnx_model = None
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_birefnet_model_name_onnx = ""
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class BiRefNet_onnx:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"image": ("IMAGE",),
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"device": (["cpu", "cuda"],{"default": "cpu"}),
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"model_name": ([
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"birefnet-general",
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"birefnet-general-lite",
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"birefnet-portrait",
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"birefnet-cod",
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"birefnet-dis",
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"birefnet-hrsod",
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"birefnet-massive"
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], {"default": "birefnet-general"}),
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"cutout_func": (["putalpha", "naive", "alpha_matting"],{"default": "putalpha"}),
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# "cached": ("BOOLEAN", {"default": True}),
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"alpha_matting_foreground_threshold": ("INT", {"default": 240}),
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"alpha_matting_background_threshold": ("INT", {"default": 10}),
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"alpha_matting_erode_size": ("INT", {"default": 10}),
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},
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask",)
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FUNCTION = "background_remove"
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CATEGORY = "🔥BiRefNet"
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def background_remove(self,
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image,
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device,
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model_name,
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cutout_func,
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# cached,
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alpha_matting_foreground_threshold,
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alpha_matting_background_threshold,
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alpha_matting_erode_size,
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):
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providers = get_device_provider(device)
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processed_images = []
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processed_masks = []
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global _birefnet_onnx_model, _birefnet_model_name_onnx
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## TODO: cache model
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if _birefnet_onnx_model is not None and _birefnet_model_name_onnx == model_name:
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session = _birefnet_onnx_model
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print("Loading from cache...")
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else:
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session = new_session(model_name, providers=providers)
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if _birefnet_onnx_model is not None:
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_birefnet_onnx_model = None
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_birefnet_model_name_onnx = ''
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clear_memory()
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# if cached:
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# _birefnet_onnx_model = session
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# _birefnet_model_name_onnx = model_name
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for image in image:
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orig_image = tensor2pil(image)
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masks = session.predict(orig_image)
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pil_im = masks[0]
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if cutout_func == 'putalpha':
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new_im = putalpha_cutout(orig_image, pil_im)
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elif cutout_func == 'naive':
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new_im = naive_cutout(orig_image, pil_im)
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elif cutout_func == 'alpha_matting':
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new_im = alpha_matting_cutout(
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orig_image,
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pil_im,
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foreground_threshold=alpha_matting_foreground_threshold,
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background_threshold=alpha_matting_background_threshold,
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erode_structure_size=alpha_matting_erode_size
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)
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new_im_tensor = pil2tensor(new_im)
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pil_im_tensor = pil2tensor(pil_im)
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processed_images.append(new_im_tensor)
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processed_masks.append(pil_im_tensor)
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clear_memory()
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new_ims = torch.cat(processed_images, dim=0)
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new_masks = torch.cat(processed_masks, dim=0)
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return new_ims, new_masks
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class BiRefNet_Lite:
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def __init__(self):
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@@ -205,15 +311,18 @@ class BiRefNet_Lite:
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device = get_device_by_name(device)
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birefnet = None
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global _birefnet_model
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if cached and _birefnet_model is not None:
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global _birefnet_model_name
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local_model_path = kwargs.get("local_model_path", model_path)
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if cached and _birefnet_model is not None and _birefnet_model_name == local_model_path:
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birefnet = _birefnet_model
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else:
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if _birefnet_model is not None:
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_birefnet_model = None
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_birefnet_model_name = None
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clear_memory()
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if load_local_model:
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local_model_path = kwargs.get("local_model_path", model_path)
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local_model_path = os.path.join(get_folder_paths('birefnet')[-1], local_model_path)
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# 判断是否开启双卡支持
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# 获取不同显卡的内存可用量
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@@ -244,6 +353,9 @@ class BiRefNet_Lite:
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print('No need to delete:', e)
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birefnet = AutoModelForImageSegmentation.from_pretrained(local_model_path,trust_remote_code=True, **spare_params)
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if cached:
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_birefnet_model = birefnet
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_birefnet_model_name = local_model_path
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else:
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birefnet = AutoModelForImageSegmentation.from_pretrained(
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"ZhengPeng7/BiRefNet", trust_remote_code=True
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@@ -297,10 +409,14 @@ class BiRefNet_Lite:
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NODE_CLASS_MAPPINGS = {
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"BiRefNet_Lite": BiRefNet_Lite
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"BiRefNet_Super": BiRefNet_Lite,
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"BiRefNet_Lite": BiRefNet_Lite,
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"BiRefNet_onnx": BiRefNet_onnx
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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NODE_DISPLAY_NAME_MAPPINGS = {
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"BiRefNet_Lite": "🔥BiRefNet_Lite"
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"BiRefNet_Lite": "🔥BiRefNet_Lite",
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"BiRefNet_Super": "🔥BiRefNet_Super",
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"BiRefNet_onnx": "🔥BiRefNet_onnx"
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}
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+4
-4
@@ -1,16 +1,16 @@
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[project]
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name = "comfyui-birefnet-lite"
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name = "comfyui-BiRefNet-Super"
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description = "This repository packages the latest BiRefNet model as a ComfyUI node for use, supporting chunked loading on both CPU and GPU, as well as model caching features."
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version = "1.0.5"
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version = "1.0.6"
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license = {file = "LICENSE"}
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[project.urls]
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Repository = "https://github.com/rubi-du/ComfyUI-BiRefNet-lite"
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Repository = "https://github.com/rubi-du/ComfyUI-BiRefNet-Super"
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# Used by Comfy Registry https://comfyregistry.org
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dependencies = ["numpy", "timm", "pymatting", "scikit-image","scipy"]
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[tool.comfy]
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PublisherId = "duhaifeng"
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DisplayName = "ComfyUI-BiRefNet-lite"
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DisplayName = "ComfyUI-BiRefNet-Super"
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Icon = ""
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||||
|
||||
@@ -0,0 +1,52 @@
|
||||
import os
|
||||
|
||||
import pooch
|
||||
|
||||
from .birefnet_general import BiRefNetSessionGeneral
|
||||
|
||||
|
||||
class BiRefNetSessionCOD(BiRefNetSessionGeneral):
|
||||
"""
|
||||
This class represents a BiRefNet-COD session, which is a subclass of BiRefNetSessionGeneral.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""
|
||||
Downloads the BiRefNet-COD model file from a specific URL and saves it.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The path to the downloaded model file.
|
||||
"""
|
||||
fname = f"{cls.name(*args, **kwargs)}.onnx"
|
||||
pooch.retrieve(
|
||||
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-COD-epoch_125.onnx",
|
||||
(
|
||||
None
|
||||
if cls.checksum_disabled(*args, **kwargs)
|
||||
else "md5:f6d0d21ca89d287f17e7afe9f5fd3b45"
|
||||
),
|
||||
fname=fname,
|
||||
path=cls.u2net_home(*args, **kwargs),
|
||||
progressbar=True,
|
||||
)
|
||||
|
||||
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""
|
||||
Returns the name of the BiRefNet-COD session.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The name of the session.
|
||||
"""
|
||||
return "birefnet-cod"
|
||||
@@ -0,0 +1,52 @@
|
||||
import os
|
||||
|
||||
import pooch
|
||||
|
||||
from .birefnet_general import BiRefNetSessionGeneral
|
||||
|
||||
|
||||
class BiRefNetSessionDIS(BiRefNetSessionGeneral):
|
||||
"""
|
||||
This class represents a BiRefNet-DIS session, which is a subclass of BiRefNetSessionGeneral.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""
|
||||
Downloads the BiRefNet-DIS model file from a specific URL and saves it.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The path to the downloaded model file.
|
||||
"""
|
||||
fname = f"{cls.name(*args, **kwargs)}.onnx"
|
||||
pooch.retrieve(
|
||||
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-DIS-epoch_590.onnx",
|
||||
(
|
||||
None
|
||||
if cls.checksum_disabled(*args, **kwargs)
|
||||
else "md5:2d4d44102b446f33a4ebb2e56c051f2b"
|
||||
),
|
||||
fname=fname,
|
||||
path=cls.u2net_home(*args, **kwargs),
|
||||
progressbar=True,
|
||||
)
|
||||
|
||||
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""
|
||||
Returns the name of the BiRefNet-DIS session.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The name of the session.
|
||||
"""
|
||||
return "birefnet-dis"
|
||||
@@ -0,0 +1,93 @@
|
||||
""" BiRefNet General Session """
|
||||
import os
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import pooch
|
||||
from PIL import Image
|
||||
from PIL.Image import Image as PILImage
|
||||
|
||||
from .sessions.base import BaseSession
|
||||
|
||||
|
||||
class BiRefNetSessionGeneral(BaseSession):
|
||||
"""
|
||||
This class represents a BiRefNet-General session, which is a subclass of BaseSession.
|
||||
"""
|
||||
|
||||
def sigmoid(self, mat):
|
||||
""" Sigmoidal function applied on an array 'mat' """
|
||||
return 1 / (1 + np.exp(-mat))
|
||||
|
||||
def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]:
|
||||
"""
|
||||
Predicts the output masks for the input image using the inner session.
|
||||
|
||||
Parameters:
|
||||
img (PILImage): The input image.
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
List[PILImage]: The list of output masks.
|
||||
"""
|
||||
ort_outs = self.inner_session.run(
|
||||
None,
|
||||
self.normalize(
|
||||
img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (1024, 1024)
|
||||
),
|
||||
)
|
||||
|
||||
pred = self.sigmoid(ort_outs[0][:, 0, :, :])
|
||||
|
||||
ma = np.max(pred)
|
||||
mi = np.min(pred)
|
||||
|
||||
pred = (pred - mi) / (ma - mi)
|
||||
pred = np.squeeze(pred)
|
||||
|
||||
mask = Image.fromarray((pred * 255).astype("uint8"), mode="L")
|
||||
mask = mask.resize(img.size, Image.Resampling.LANCZOS)
|
||||
|
||||
return [mask]
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""
|
||||
Downloads the BiRefNet-General model file from a specific URL and saves it.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The path to the downloaded model file.
|
||||
"""
|
||||
fname = f"{cls.name(*args, **kwargs)}.onnx"
|
||||
pooch.retrieve(
|
||||
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-epoch_244.onnx",
|
||||
(
|
||||
None
|
||||
if cls.checksum_disabled(*args, **kwargs)
|
||||
else "md5:7a35a0141cbbc80de11d9c9a28f52697"
|
||||
),
|
||||
fname=fname,
|
||||
path=cls.u2net_home(*args, **kwargs),
|
||||
progressbar=True,
|
||||
)
|
||||
|
||||
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""
|
||||
Returns the name of the BiRefNet-General session.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The name of the session.
|
||||
"""
|
||||
return "birefnet-general"
|
||||
@@ -0,0 +1,52 @@
|
||||
import os
|
||||
|
||||
import pooch
|
||||
|
||||
from .birefnet_general import BiRefNetSessionGeneral
|
||||
|
||||
|
||||
class BiRefNetSessionGeneralLite(BiRefNetSessionGeneral):
|
||||
"""
|
||||
This class represents a BiRefNet-General-Lite session, which is a subclass of BiRefNetSessionGeneral.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""
|
||||
Downloads the BiRefNet-General-Lite model file from a specific URL and saves it.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The path to the downloaded model file.
|
||||
"""
|
||||
fname = f"{cls.name(*args, **kwargs)}.onnx"
|
||||
pooch.retrieve(
|
||||
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-bb_swin_v1_tiny-epoch_232.onnx",
|
||||
(
|
||||
None
|
||||
if cls.checksum_disabled(*args, **kwargs)
|
||||
else "md5:4fab47adc4ff364be1713e97b7e66334"
|
||||
),
|
||||
fname=fname,
|
||||
path=cls.u2net_home(*args, **kwargs),
|
||||
progressbar=True,
|
||||
)
|
||||
|
||||
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""
|
||||
Returns the name of the BiRefNet-General-Lite session.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The name of the session.
|
||||
"""
|
||||
return "birefnet-general-lite"
|
||||
@@ -0,0 +1,52 @@
|
||||
import os
|
||||
|
||||
import pooch
|
||||
|
||||
from .birefnet_general import BiRefNetSessionGeneral
|
||||
|
||||
|
||||
class BiRefNetSessionHRSOD(BiRefNetSessionGeneral):
|
||||
"""
|
||||
This class represents a BiRefNet-HRSOD session, which is a subclass of BiRefNetSessionGeneral.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""
|
||||
Downloads the BiRefNet-HRSOD model file from a specific URL and saves it.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The path to the downloaded model file.
|
||||
"""
|
||||
fname = f"{cls.name(*args, **kwargs)}.onnx"
|
||||
pooch.retrieve(
|
||||
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-HRSOD_DHU-epoch_115.onnx",
|
||||
(
|
||||
None
|
||||
if cls.checksum_disabled(*args, **kwargs)
|
||||
else "md5:c017ade5de8a50ff0fd74d790d268dda"
|
||||
),
|
||||
fname=fname,
|
||||
path=cls.u2net_home(*args, **kwargs),
|
||||
progressbar=True,
|
||||
)
|
||||
|
||||
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""
|
||||
Returns the name of the BiRefNet-HRSOD session.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The name of the session.
|
||||
"""
|
||||
return "birefnet-hrsod"
|
||||
@@ -0,0 +1,52 @@
|
||||
import os
|
||||
|
||||
import pooch
|
||||
|
||||
from .birefnet_general import BiRefNetSessionGeneral
|
||||
|
||||
|
||||
class BiRefNetSessionMassive(BiRefNetSessionGeneral):
|
||||
"""
|
||||
This class represents a BiRefNet-Massive session, which is a subclass of BiRefNetSessionGeneral.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""
|
||||
Downloads the BiRefNet-Massive model file from a specific URL and saves it.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The path to the downloaded model file.
|
||||
"""
|
||||
fname = f"{cls.name(*args, **kwargs)}.onnx"
|
||||
pooch.retrieve(
|
||||
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-massive-TR_DIS5K_TR_TEs-epoch_420.onnx",
|
||||
(
|
||||
None
|
||||
if cls.checksum_disabled(*args, **kwargs)
|
||||
else "md5:33e726a2136a3d59eb0fdf613e31e3e9"
|
||||
),
|
||||
fname=fname,
|
||||
path=cls.u2net_home(*args, **kwargs),
|
||||
progressbar=True,
|
||||
)
|
||||
|
||||
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""
|
||||
Returns the name of the BiRefNet-Massive session.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The name of the session.
|
||||
"""
|
||||
return "birefnet-massive"
|
||||
@@ -0,0 +1,53 @@
|
||||
""" birefnet_portrait """
|
||||
import os
|
||||
|
||||
import pooch
|
||||
|
||||
from .birefnet_general import BiRefNetSessionGeneral
|
||||
|
||||
|
||||
class BiRefNetSessionPortrait(BiRefNetSessionGeneral):
|
||||
"""
|
||||
This class represents a BiRefNet-Portrait session, which is a subclass of BiRefNetSessionGeneral.
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""
|
||||
Downloads the BiRefNet-Portrait model file from a specific URL and saves it.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The path to the downloaded model file.
|
||||
"""
|
||||
fname = f"{cls.name(*args, **kwargs)}.onnx"
|
||||
pooch.retrieve(
|
||||
"https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-portrait-epoch_150.onnx",
|
||||
(
|
||||
None
|
||||
if cls.checksum_disabled(*args, **kwargs)
|
||||
else "md5:c3a64a6abf20250d090cd055f12a3b67"
|
||||
),
|
||||
fname=fname,
|
||||
path=cls.u2net_home(*args, **kwargs),
|
||||
progressbar=True,
|
||||
)
|
||||
|
||||
return os.path.join(cls.u2net_home(*args, **kwargs), fname)
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""
|
||||
Returns the name of the BiRefNet-Portrait session.
|
||||
|
||||
Parameters:
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
str: The name of the session.
|
||||
"""
|
||||
return "birefnet-portrait"
|
||||
@@ -0,0 +1,46 @@
|
||||
import os
|
||||
from typing import Type
|
||||
|
||||
import onnxruntime as ort
|
||||
|
||||
from .sessions import sessions_class
|
||||
from .sessions.base import BaseSession
|
||||
|
||||
|
||||
def new_session(
|
||||
model_name:str, providers=None, *args, **kwargs
|
||||
) -> BaseSession:
|
||||
"""
|
||||
Create a new session object based on the specified model name.
|
||||
|
||||
This function searches for the session class based on the model name in the 'sessions_class' list.
|
||||
It then creates an instance of the session class with the provided arguments.
|
||||
The 'sess_opts' object is created using the 'ort.SessionOptions()' constructor.
|
||||
If the 'OMP_NUM_THREADS' environment variable is set, the 'inter_op_num_threads' option of 'sess_opts' is set to its value.
|
||||
|
||||
Parameters:
|
||||
model_name (str): The name of the model.
|
||||
providers: The providers for the session.
|
||||
*args: Additional positional arguments.
|
||||
**kwargs: Additional keyword arguments.
|
||||
|
||||
Returns:
|
||||
BaseSession: The created session object.
|
||||
"""
|
||||
session_class: Type[BaseSession]
|
||||
|
||||
for sc in sessions_class:
|
||||
if sc.name() == model_name:
|
||||
session_class = sc
|
||||
break
|
||||
|
||||
if not session_class:
|
||||
raise ValueError("Model not found")
|
||||
|
||||
sess_opts = ort.SessionOptions()
|
||||
|
||||
if "OMP_NUM_THREADS" in os.environ:
|
||||
sess_opts.inter_op_num_threads = int(os.environ["OMP_NUM_THREADS"])
|
||||
sess_opts.intra_op_num_threads = int(os.environ["OMP_NUM_THREADS"])
|
||||
|
||||
return session_class(model_name, sess_opts, providers, *args, **kwargs)
|
||||
@@ -0,0 +1,43 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import List
|
||||
|
||||
from .base import BaseSession
|
||||
|
||||
sessions_class: List[type[BaseSession]] = []
|
||||
sessions_names: List[str] = []
|
||||
|
||||
from ..birefnet_general import BiRefNetSessionGeneral
|
||||
|
||||
sessions_class.append(BiRefNetSessionGeneral)
|
||||
sessions_names.append(BiRefNetSessionGeneral.name())
|
||||
|
||||
from ..birefnet_general_lite import BiRefNetSessionGeneralLite
|
||||
|
||||
sessions_class.append(BiRefNetSessionGeneralLite)
|
||||
sessions_names.append(BiRefNetSessionGeneralLite.name())
|
||||
|
||||
from ..birefnet_portrait import BiRefNetSessionPortrait
|
||||
|
||||
sessions_class.append(BiRefNetSessionPortrait)
|
||||
sessions_names.append(BiRefNetSessionPortrait.name())
|
||||
|
||||
from ..birefnet_dis import BiRefNetSessionDIS
|
||||
|
||||
sessions_class.append(BiRefNetSessionDIS)
|
||||
sessions_names.append(BiRefNetSessionDIS.name())
|
||||
|
||||
from ..birefnet_hrsod import BiRefNetSessionHRSOD
|
||||
|
||||
sessions_class.append(BiRefNetSessionHRSOD)
|
||||
sessions_names.append(BiRefNetSessionHRSOD.name())
|
||||
|
||||
from ..birefnet_cod import BiRefNetSessionCOD
|
||||
|
||||
sessions_class.append(BiRefNetSessionCOD)
|
||||
sessions_names.append(BiRefNetSessionCOD.name())
|
||||
|
||||
from ..birefnet_massive import BiRefNetSessionMassive
|
||||
|
||||
sessions_class.append(BiRefNetSessionMassive)
|
||||
sessions_names.append(BiRefNetSessionMassive.name())
|
||||
@@ -0,0 +1,94 @@
|
||||
""" Base Session Class """
|
||||
import os
|
||||
from typing import Dict, List, Tuple
|
||||
|
||||
import numpy as np
|
||||
import onnxruntime as ort
|
||||
from PIL import Image
|
||||
from PIL.Image import Image as PILImage
|
||||
from folder_paths import models_dir
|
||||
|
||||
class BaseSession:
|
||||
"""This is a base class for managing a session with a machine learning model."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_name: str,
|
||||
sess_opts: ort.SessionOptions,
|
||||
providers=None,
|
||||
*args,
|
||||
**kwargs
|
||||
):
|
||||
"""Initialize an instance of the BaseSession class."""
|
||||
self.model_name = model_name
|
||||
|
||||
self.providers = []
|
||||
|
||||
if providers:
|
||||
if type(providers) == list:
|
||||
self.providers = providers
|
||||
else:
|
||||
self.providers.append(providers)
|
||||
|
||||
self.inner_session = ort.InferenceSession(
|
||||
str(self.__class__.download_models(*args, **kwargs)),
|
||||
providers=self.providers,
|
||||
sess_options=sess_opts,
|
||||
)
|
||||
|
||||
def normalize(
|
||||
self,
|
||||
img: PILImage,
|
||||
mean: Tuple[float, float, float],
|
||||
std: Tuple[float, float, float],
|
||||
size: Tuple[int, int],
|
||||
*args,
|
||||
**kwargs
|
||||
) -> Dict[str, np.ndarray]:
|
||||
""" Normalizes and resizes image to be used in prediction """
|
||||
im = img.convert("RGB").resize(size, Image.Resampling.LANCZOS)
|
||||
|
||||
im_ary = np.array(im)
|
||||
im_ary = im_ary / np.max(im_ary)
|
||||
|
||||
tmpImg = np.zeros((im_ary.shape[0], im_ary.shape[1], 3))
|
||||
tmpImg[:, :, 0] = (im_ary[:, :, 0] - mean[0]) / std[0]
|
||||
tmpImg[:, :, 1] = (im_ary[:, :, 1] - mean[1]) / std[1]
|
||||
tmpImg[:, :, 2] = (im_ary[:, :, 2] - mean[2]) / std[2]
|
||||
|
||||
tmpImg = tmpImg.transpose((2, 0, 1))
|
||||
|
||||
return {
|
||||
self.inner_session.get_inputs()[0]
|
||||
.name: np.expand_dims(tmpImg, 0)
|
||||
.astype(np.float32)
|
||||
}
|
||||
|
||||
def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]:
|
||||
"""Predict using the loaded model"""
|
||||
raise NotImplementedError
|
||||
|
||||
@classmethod
|
||||
def checksum_disabled(cls, *args, **kwargs):
|
||||
"""Checks whether checksums are disabled"""
|
||||
return os.getenv("MODEL_CHECKSUM_DISABLED", None) is not None
|
||||
|
||||
@classmethod
|
||||
def u2net_home(cls, *args, **kwargs):
|
||||
"""Returns U2Net home directory"""
|
||||
return os.path.join(models_dir, 'birefnet')
|
||||
# return os.path.expanduser(
|
||||
# os.getenv(
|
||||
# "U2NET_HOME", os.path.join(os.getenv("XDG_DATA_HOME", "~"), ".u2net")
|
||||
# )
|
||||
# )
|
||||
|
||||
@classmethod
|
||||
def download_models(cls, *args, **kwargs):
|
||||
"""Download models"""
|
||||
raise NotImplementedError
|
||||
|
||||
@classmethod
|
||||
def name(cls, *args, **kwargs):
|
||||
"""Model Name"""
|
||||
raise NotImplementedError
|
||||
@@ -0,0 +1,231 @@
|
||||
{
|
||||
"last_node_id": 12,
|
||||
"last_link_id": 17,
|
||||
"nodes": [
|
||||
{
|
||||
"id": 12,
|
||||
"type": "PreviewImage",
|
||||
"pos": {
|
||||
"0": 1554,
|
||||
"1": 515
|
||||
},
|
||||
"size": {
|
||||
"0": 210,
|
||||
"1": 246
|
||||
},
|
||||
"flags": {},
|
||||
"order": 4,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 17
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"Node name for S&R": "PreviewImage"
|
||||
},
|
||||
"widgets_values": []
|
||||
},
|
||||
{
|
||||
"id": 6,
|
||||
"type": "LoadImage",
|
||||
"pos": {
|
||||
"0": 526,
|
||||
"1": 30
|
||||
},
|
||||
"size": {
|
||||
"0": 513.1171875,
|
||||
"1": 501.84765625
|
||||
},
|
||||
"flags": {},
|
||||
"order": 0,
|
||||
"mode": 0,
|
||||
"inputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
14
|
||||
],
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "MASK",
|
||||
"type": "MASK",
|
||||
"links": null
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "LoadImage"
|
||||
},
|
||||
"widgets_values": [
|
||||
"测试截图.png",
|
||||
"image"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 8,
|
||||
"type": "PreviewImage",
|
||||
"pos": {
|
||||
"0": 1533,
|
||||
"1": -18
|
||||
},
|
||||
"size": {
|
||||
"0": 405.41015625,
|
||||
"1": 454.2578125
|
||||
},
|
||||
"flags": {},
|
||||
"order": 2,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "images",
|
||||
"type": "IMAGE",
|
||||
"link": 15
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"properties": {
|
||||
"Node name for S&R": "PreviewImage"
|
||||
},
|
||||
"widgets_values": []
|
||||
},
|
||||
{
|
||||
"id": 10,
|
||||
"type": "BiRefNet_onnx",
|
||||
"pos": {
|
||||
"0": 1118,
|
||||
"1": 116
|
||||
},
|
||||
"size": {
|
||||
"0": 315,
|
||||
"1": 222
|
||||
},
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 14
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
15
|
||||
],
|
||||
"slot_index": 0
|
||||
},
|
||||
{
|
||||
"name": "mask",
|
||||
"type": "MASK",
|
||||
"links": [
|
||||
16
|
||||
],
|
||||
"slot_index": 1
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "BiRefNet_onnx"
|
||||
},
|
||||
"widgets_values": [
|
||||
"cpu",
|
||||
"birefnet-general",
|
||||
"putalpha",
|
||||
240,
|
||||
10,
|
||||
10
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 11,
|
||||
"type": "MaskToImage",
|
||||
"pos": {
|
||||
"0": 1294,
|
||||
"1": 511
|
||||
},
|
||||
"size": {
|
||||
"0": 264.5999755859375,
|
||||
"1": 26
|
||||
},
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"name": "mask",
|
||||
"type": "MASK",
|
||||
"link": 16
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "IMAGE",
|
||||
"type": "IMAGE",
|
||||
"links": [
|
||||
17
|
||||
],
|
||||
"slot_index": 0
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "MaskToImage"
|
||||
},
|
||||
"widgets_values": []
|
||||
}
|
||||
],
|
||||
"links": [
|
||||
[
|
||||
14,
|
||||
6,
|
||||
0,
|
||||
10,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
15,
|
||||
10,
|
||||
0,
|
||||
8,
|
||||
0,
|
||||
"IMAGE"
|
||||
],
|
||||
[
|
||||
16,
|
||||
10,
|
||||
1,
|
||||
11,
|
||||
0,
|
||||
"MASK"
|
||||
],
|
||||
[
|
||||
17,
|
||||
11,
|
||||
0,
|
||||
12,
|
||||
0,
|
||||
"IMAGE"
|
||||
]
|
||||
],
|
||||
"groups": [],
|
||||
"config": {},
|
||||
"extra": {
|
||||
"ds": {
|
||||
"scale": 0.8390545288824016,
|
||||
"offset": [
|
||||
-524.8041373279959,
|
||||
67.63914579313717
|
||||
]
|
||||
}
|
||||
},
|
||||
"version": 0.4
|
||||
}
|
||||
Reference in New Issue
Block a user