From 37a20e516cf80261e6f16e4b231b48980d8238ab Mon Sep 17 00:00:00 2001
From: yueliang <2567830152@qq.com>
Date: Thu, 5 Sep 2024 11:21:41 +0800
Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E8=AE=BE=E5=A4=87=E9=80=89?=
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 =?UTF-8?q?=E6=8B=A9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 BiRefNet_node_config.py   |   2 +-
 __init__.py               |   7 --
 assets/Colors_Presets.png | Bin 22399 -> 0 bytes
 nodes.py                  | 132 ++++++--------------------------------
 4 files changed, 19 insertions(+), 122 deletions(-)
 delete mode 100644 assets/Colors_Presets.png

diff --git a/BiRefNet_node_config.py b/BiRefNet_node_config.py
index 6d6dc09..4b4f600 100644
--- a/BiRefNet_node_config.py
+++ b/BiRefNet_node_config.py
@@ -2,7 +2,7 @@
 
 import os
 import math
-# from folder_paths import models_dir
+
 os.environ['HOME'] = os.path.expanduser("~")
 class Config():
     def __init__(self) -> None:
diff --git a/__init__.py b/__init__.py
index 604cbbc..d721463 100644
--- a/__init__.py
+++ b/__init__.py
@@ -1,10 +1,3 @@
-import os
-import folder_paths
-
-rembg_path = os.path.join(folder_paths.models_dir, 'rembg')
-if not os.path.exists(rembg_path):
-    os.makedirs(rembg_path)
-
 from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
 
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
\ No newline at end of file
diff --git a/assets/Colors_Presets.png b/assets/Colors_Presets.png
deleted file mode 100644
index aef379968d7c96d447556ce471049c22e151be99..0000000000000000000000000000000000000000
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diff --git a/nodes.py b/nodes.py
index a0122d7..36fc804 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2,15 +2,20 @@ from transformers import AutoModelForImageSegmentation
 import torch
 from torchvision import transforms
 import numpy as np
-from PIL import Image, ImageColor
+from PIL import Image
 import torch.nn.functional as F
 from .BiRefNet_node_config import Config
-import folder_paths
-import os
 import comfy.model_management as mm
-from huggingface_hub import snapshot_download
 
-comfyui_models_dir = folder_paths.models_dir
+Config()
+
+torch.set_float32_matmul_precision(["high", "highest"][0])
+
+birefnet = AutoModelForImageSegmentation.from_pretrained(
+    "ZhengPeng7/BiRefNet", trust_remote_code=True
+)
+
+
 
 transform_image = transforms.Compose(
     [
@@ -43,84 +48,28 @@ def get_device_by_name(device):
             device = "cpu"
             if torch.cuda.is_available():
                 device = "cuda"
-                # device = torch.device("cuda")
             elif torch.backends.mps.is_available():
                 device = "mps"
-                # device = torch.device("mps")
             elif torch.xpu.is_available():
                 device = "xpu"
-                # device = torch.device("xpu")
-            # device = mm.get_torch_device()
         except:
                 raise AttributeError("What's your device(到底用什么设备跑的)？")
-    # elif device == 'cuda':
-    #     device = torch.device("cuda")
-    # elif device == "mps":
-    #     device = torch.device("mps")
-    # elif device == "xpu":
-    #     device = torch.device("xpu")
     print("\033[93mUse Device(使用设备):", device, "\033[0m")
     return device
 
-def get_dtype_by_name(dtype):
-    """
-    "dtype": (["auto","fp16","bf16","fp32", "fp8_e4m3fn", "fp8_e4m3fnuz", "fp8_e5m2", "fp8_e5m2fnuz"],{"default":"auto"}),
-    """
-    if dtype == 'auto':
-        try:
-            if mm.should_use_fp16():
-                dtype = torch.float16
-            elif mm.should_use_bf16():
-                dtype = torch.bfloat16
-            else:
-                dtype = torch.float32
-        except:
-                raise AttributeError("ComfyUI version too old, can't autodetect properly. Set your dtypes manually.")
-    elif dtype== "fp16":
-         dtype = torch.float16
-    elif dtype == "bf16":
-        dtype = torch.bfloat16
-    elif dtype == "fp32":
-        dtype = torch.float32
-    elif dtype == "fp8_e4m3fn":
-        dtype = torch.float8_e4m3fn
-    elif dtype == "fp8_e4m3fnuz":
-        dtype = torch.float8_e4m3fnuz
-    elif dtype == "fp8_e5m2":
-        dtype = torch.float8_e5m2
-    elif dtype == "fp8_e5m2fnuz":
-        dtype = torch.float8_e5m2fnuz
-    print("\033[93mModel Precision(模型精度):", dtype, "\033[0m")
-    return dtype
-
 
 class BiRefNet_Hugo:
     def __init__(self):
-        self.model = None
-        self.loaded_model_name = None
+        pass
 
     @classmethod
     def INPUT_TYPES(cls):
-        rembg_list = os.listdir(os.path.join(comfyui_models_dir, "rembg"))
-        rembg_list.insert(0, "Auto_DownLoad-ZhengPeng7/BiRefNet")
-        rembg_list.insert(1, "Auto_DownLoad-ZhengPeng7/BiRefNet-DIS5K-TR_TEs")
-        rembg_list.insert(2, "Auto_DownLoad-ZhengPeng7/BiRefNet-COD")
-        rembg_list.insert(3, "Auto_DownLoad-ZhengPeng7/BiRefNet-HRSOD")
-        rembg_list.insert(4, "Auto_DownLoad-ZhengPeng7/BiRefNet-portrait")
+        
         return {
             "required": {
-                "model": (rembg_list, ),
-                # "model": (["Auto_Download"] + os.listdir(os.path.join(comfyui_models_dir, "rembg"))), 
                 "image": ("IMAGE",),
-                # "background_color_name": (["transparency", "green", "white", "red", "yellow", "blue", "black", "pink", "purple", "brown"],{"default": "transparency"}), 
                 "background_color_name": (colors,{"default": "transparency"}), 
-                "background_color_code": ("STRING",{"default": "00ffdd"}), 
-                "background_color_mode": ("BOOLEAN", {"default": True, "label_on": "color_name", "label_off": "color_code"}), 
-                "device": (["auto", "cuda", "cpu", "mps", "xpu", "meta"],{"default": "auto"}), 
-                "dtype": (["auto","fp16","bf16","fp32", "fp8_e4m3fn", "fp8_e4m3fnuz", "fp8_e5m2", "fp8_e5m2fnuz"],{"default":"fp32"}),
-                "cpu_offload": ("BOOLEAN", {"default": False, "label_on": "model_to_cpu", "label_off": "unload_model"}),
-                "Auto_Download_Path": ("BOOLEAN", {"default": True, "label_on": "rembg_local本地", "label_off": ".cache缓存"}),
-                "Show_Colors_In_Ternimal": ("BOOLEAN", {"default": False, "label_on": "yes", "label_off": "no"}),
+                "device": (["auto", "cuda", "cpu", "mps", "xpu", "meta"],{"default": "auto"})
             }
         }
 
@@ -131,73 +80,34 @@ class BiRefNet_Hugo:
   
     def background_remove(self, 
                           image, 
-                          model, 
                           device, 
-                          dtype, 
-                          cpu_offload,
                           background_color_name, 
-                          background_color_code,
-                          background_color_mode,
-                          Auto_Download_Path, 
-                          Show_Colors_In_Ternimal, 
                           ):
-        if Show_Colors_In_Ternimal:
-            for name, code in ImageColor.colormap.items():
-                print( f'{name:30} : {code}' )
-        Config()
-        torch.set_float32_matmul_precision(["high", "highest"][1])
         processed_images = []
         processed_masks = []
-        model_name = model.replace("Auto_DownLoad-", "")
-        if 'Auto_DownLoad-' not in model:
-            model_path = os.path.join(comfyui_models_dir, "rembg", model)
-        elif ('Auto_DownLoad-' in model) and (Auto_Download_Path == True):
-            model_path = os.path.join(comfyui_models_dir, "rembg", ("models--" + str(model_name).replace("/", "--")))
-            if not os.path.exists(os.path.join(model_path, "model.safetensors")):
-                snapshot_download(model_name, 
-                                local_dir=model_path, 
-                                local_dir_use_symlinks=False
-                                )
-        elif ('Auto_DownLoad-' in model) and (Auto_Download_Path == False):
-            model_path = model_name
-            
+       
         device = get_device_by_name(device)
-        dtype = get_dtype_by_name(dtype)
-            
-        if self.loaded_model_name != model:
-            del self.model
-            self.model = None
-        if self.model == None:
-            self. model = AutoModelForImageSegmentation.from_pretrained(
-                model_path, 
-                trust_remote_code=True,
-            ).to(device, dtype)
-        else:
-            self.model.to(device)
-        self.model.eval()
+        birefnet.to(device)
         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)
-            im_tensor=im_tensor.to(device, dtype)
+            im_tensor=im_tensor.to(device)
             with torch.no_grad():
-                result = self.model(im_tensor)[-1].sigmoid().cpu()
+                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))
-            if background_color_name == 'transparency' and background_color_mode == True:
+            if background_color_name == 'transparency':
                 color = (0,0,0,0)
                 mode = "RGBA"
             else:
                 color = background_color_name
                 mode = "RGB"
-                if not background_color_mode:
-                    color = "#" + str(background_color_code).replace("#", "").replace(":", "").replace(" ", "") # 颜色码-绿色：#00FF00
-            # new_im = Image.new("RGBA", pil_im.size, (0,0,0,0))
             new_im = Image.new(mode, pil_im.size, color)
             new_im.paste(orig_image, mask=pil_im)
             new_im_tensor = pil2tensor(new_im)
@@ -207,12 +117,6 @@ class BiRefNet_Hugo:
 
         new_ims = torch.cat(processed_images, dim=0)
         new_masks = torch.cat(processed_masks, dim=0)
-        if cpu_offload == True:
-            self.model.to("cpu")
-            self.loaded_model_name = model
-        else:
-            del self.model
-            self.model = None
 
         return new_ims, new_masks