Land #2212 on main

annotator/anime_face_segment/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Miaomiao Li
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

annotator/anime_face_segment/__init__.py

@@ -0,0 +1,174 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+import fnmatch
+import cv2
+
+import sys
+
+import numpy as np
+from modules import devices
+from einops import rearrange
+from annotator.annotator_path import models_path
+
+import torchvision
+from torchvision.models import MobileNet_V2_Weights
+from torchvision import transforms
+
+COLOR_BACKGROUND = (255,255,0)
+COLOR_HAIR = (0,0,255)
+COLOR_EYE = (255,0,0)
+COLOR_MOUTH = (255,255,255)
+COLOR_FACE = (0,255,0)
+COLOR_SKIN = (0,255,255)
+COLOR_CLOTHES = (255,0,255)
+PALETTE = [COLOR_BACKGROUND,COLOR_HAIR,COLOR_EYE,COLOR_MOUTH,COLOR_FACE,COLOR_SKIN,COLOR_CLOTHES]
+
+class UNet(nn.Module):
+    def __init__(self):
+        super(UNet, self).__init__()
+        self.NUM_SEG_CLASSES = 7 # Background, hair, face, eye, mouth, skin, clothes
+
+        mobilenet_v2 = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.IMAGENET1K_V1)
+        mob_blocks = mobilenet_v2.features
+
+        # Encoder
+        self.en_block0 = nn.Sequential(    # in_ch=3 out_ch=16
+            mob_blocks[0],
+            mob_blocks[1]
+        )
+        self.en_block1 = nn.Sequential(    # in_ch=16 out_ch=24
+            mob_blocks[2],
+            mob_blocks[3],
+        )
+        self.en_block2 = nn.Sequential(    # in_ch=24 out_ch=32
+            mob_blocks[4],
+            mob_blocks[5],
+            mob_blocks[6],
+        )
+        self.en_block3 = nn.Sequential(    # in_ch=32 out_ch=96
+            mob_blocks[7],
+            mob_blocks[8],
+            mob_blocks[9],
+            mob_blocks[10],
+            mob_blocks[11],
+            mob_blocks[12],
+            mob_blocks[13],
+        )
+        self.en_block4 = nn.Sequential(    # in_ch=96 out_ch=160
+            mob_blocks[14],
+            mob_blocks[15],
+            mob_blocks[16],
+        )
+
+        # Decoder
+        self.de_block4 = nn.Sequential(     # in_ch=160 out_ch=96
+            nn.UpsamplingNearest2d(scale_factor=2),
+            nn.Conv2d(160, 96, kernel_size=3, padding=1),
+            nn.InstanceNorm2d(96),
+            nn.LeakyReLU(0.1),
+            nn.Dropout(p=0.2)
+        )
+        self.de_block3 = nn.Sequential(     # in_ch=96x2 out_ch=32
+            nn.UpsamplingNearest2d(scale_factor=2),
+            nn.Conv2d(96*2, 32, kernel_size=3, padding=1),
+            nn.InstanceNorm2d(32),
+            nn.LeakyReLU(0.1),
+            nn.Dropout(p=0.2)
+        )
+        self.de_block2 = nn.Sequential(     # in_ch=32x2 out_ch=24
+            nn.UpsamplingNearest2d(scale_factor=2),
+            nn.Conv2d(32*2, 24, kernel_size=3, padding=1),
+            nn.InstanceNorm2d(24),
+            nn.LeakyReLU(0.1),
+            nn.Dropout(p=0.2)
+        )
+        self.de_block1 = nn.Sequential(     # in_ch=24x2 out_ch=16
+            nn.UpsamplingNearest2d(scale_factor=2),
+            nn.Conv2d(24*2, 16, kernel_size=3, padding=1),
+            nn.InstanceNorm2d(16),
+            nn.LeakyReLU(0.1),
+            nn.Dropout(p=0.2)
+        )
+
+        self.de_block0 = nn.Sequential(     # in_ch=16x2 out_ch=7
+            nn.UpsamplingNearest2d(scale_factor=2),
+            nn.Conv2d(16*2, self.NUM_SEG_CLASSES, kernel_size=3, padding=1),
+            nn.Softmax2d()
+        )
+
+    def forward(self, x):
+        e0 = self.en_block0(x)
+        e1 = self.en_block1(e0)
+        e2 = self.en_block2(e1)
+        e3 = self.en_block3(e2)
+        e4 = self.en_block4(e3)
+
+        d4 = self.de_block4(e4)
+        d4 = F.interpolate(d4, size=e3.size()[2:], mode='bilinear', align_corners=True)
+        c4 = torch.cat((d4,e3),1)
+
+        d3 = self.de_block3(c4)
+        d3 = F.interpolate(d3, size=e2.size()[2:], mode='bilinear', align_corners=True)
+        c3 = torch.cat((d3,e2),1)
+
+        d2 = self.de_block2(c3)
+        d2 = F.interpolate(d2, size=e1.size()[2:], mode='bilinear', align_corners=True)
+        c2 =torch.cat((d2,e1),1)
+
+        d1 = self.de_block1(c2)
+        d1 = F.interpolate(d1, size=e0.size()[2:], mode='bilinear', align_corners=True)
+        c1 = torch.cat((d1,e0),1)
+        y = self.de_block0(c1)
+
+        return y
+
+
+class AnimeFaceSegment:
+
+    model_dir = os.path.join(models_path, "anime_face_segment")
+
+    def __init__(self):
+        self.model = None
+        self.device = devices.get_device_for("controlnet")
+
+    def load_model(self):
+        remote_model_path = "https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/Annotators/UNet.pth"
+        modelpath = os.path.join(self.model_dir, "Unet.pth")
+        if not os.path.exists(modelpath):
+            from basicsr.utils.download_util import load_file_from_url
+            load_file_from_url(remote_model_path, model_dir=self.model_dir)
+        net = UNet()
+        ckpt = torch.load(modelpath)
+        for key in list(ckpt.keys()):
+            if 'module.' in key:
+                ckpt[key.replace('module.', '')] = ckpt[key]
+                del ckpt[key]
+        net.load_state_dict(ckpt)
+        net.eval()
+        self.model = net.to(self.device)
+
+    def unload_model(self):
+        if self.model is not None:
+            self.model.cpu()
+
+    def __call__(self, input_image):
+
+        if self.model is None:
+            self.load_model()
+        self.model.to(self.device)
+        transform = transforms.Compose([  
+            transforms.Resize(512,interpolation=transforms.InterpolationMode.BICUBIC),  
+            transforms.ToTensor(),])
+        img = Image.fromarray(input_image)
+        with torch.no_grad():
+            img = transform(img).unsqueeze(dim=0).to(self.device)
+            seg = self.model(img).squeeze(dim=0)
+            seg = seg.cpu().detach().numpy()
+            img = rearrange(seg,'h w c -> w c h')
+            img = [[PALETTE[np.argmax(val)] for val in buf]for buf in img]
+            return np.array(img).astype(np.uint8)
+
+

scripts/global_state.py

@@ -103,6 +103,7 @@ def unified_preprocessor(preprocessor_name: str, *args, **kwargs):
     "recolor_luminance": recolor_luminance,
     "recolor_intensity": recolor_intensity,
     "blur_gaussian": blur_gaussian,
+    "anime_face_segment": anime_face_segment,
 }
 
 cn_preprocessor_unloadable = {
@@ -133,7 +134,8 @@ def unified_preprocessor(preprocessor_name: str, *args, **kwargs):
     "lineart_coarse": unload_lineart_coarse,
     "lineart_anime": unload_lineart_anime,
     "lineart_anime_denoise": unload_lineart_anime_denoise,
-    "inpaint_only+lama": unload_lama_inpaint
+    "inpaint_only+lama": unload_lama_inpaint,
+    "anime_face_segment": unload_anime_face_segment,
 }
 
 preprocessor_aliases = {
@@ -154,6 +156,7 @@ def unified_preprocessor(preprocessor_name: str, *args, **kwargs):
     "oneformer_ade20k": "seg_ofade20k",
     "pidinet_scribble": "scribble_pidinet",
     "inpaint": "inpaint_global_harmonious",
+    "anime_face_segment": "seg_anime_face",
 }
 
 ui_preprocessor_keys = ['none', preprocessor_aliases['invert']]

scripts/processor.py

@@ -620,6 +620,26 @@ def blur_gaussian(img, res=512, thr_a=1.0, **kwargs):
     return result, True
 
 
+model_anime_face_segment = None
+
+
+def anime_face_segment(img, res=512, **kwargs):
+    img, remove_pad = resize_image_with_pad(img, res)
+    global model_anime_face_segment
+    if model_anime_face_segment is None:
+        from annotator.anime_face_segment import AnimeFaceSegment
+        model_anime_face_segment = AnimeFaceSegment()
+
+    result = model_anime_face_segment(img)
+    return remove_pad(result), True
+
+
+def unload_anime_face_segment():
+    global model_anime_face_segment
+    if model_anime_face_segment is not None:
+        model_anime_face_segment.unload_model()
+
+
 model_free_preprocessors = [
     "reference_only",
     "reference_adain",
@@ -986,6 +1006,14 @@ def blur_gaussian(img, res=512, thr_a=1.0, **kwargs):
             "step": 0.001
         }
     ],
+    "anime_face_segment": [
+        {
+            "name": flag_preprocessor_resolution,
+            "value": 512,
+            "min": 64,
+            "max": 2048
+        }
+    ],
 }
 
 preprocessor_filters = {