update sim

.gitignore

@@ -1,2 +1,3 @@
 __pycache__
 .idea
+*.DS_Store

README.md

@@ -1,6 +1,6 @@
 # [Portrait-Maker](https://github.com/THtianhao/ComfyUI-Portrait-Maker)
 This project is an adaptation of [EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto), which breaks down the process of [EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto) and will add a series of operations on human portraits in the future.
-![](./images/easyphoto.png)
+![](./images/easyphoto.jpg)
 
 English | [简体中文](./README_zh-CN.md)
 
@@ -11,13 +11,21 @@ If you have any questions or suggestions, you can reach us through:
 - Email: tototianhao@gmail.com
 - telegram: https://t.me/+JoFE2vqHU4phZjg1
 - QQ Group: 10419777
-- WeChat Group: <img src="./images/wechat.jpg" width="200">
+- WeChat Group: <img src="./images/wechat.jpg" width="300">
+
+## V1.2.0 Update
+1. Add PM_SuperColorTransfer node to simplify the color transfer process
+2. Add PM_SuperMakeUpTransfer node to simplify the process of makeup transfer 
+3. Add v1.2.0 workflow
+
 
 ## V1.1.0 Update
 1. faceskin adds blur option
-2. Add PM_FaceShapMatch node. See node introduction for details.
-3. Add PM_MakeUpTransfer node. See node introduction for details.
-3. Add a super-resolution model to the PM_PortraitEnhancement node. This super-resolution model can not highlight faces.
+2. Add PM_FaceShapMatch node. same as easyphot faceshap match
+3. Add PM_MakeUpTransfer node. same as easyphoto makeup transfer.
+4. Add a super-resolution model to the PM_PortraitEnhancement node. This super-resolution model can not highlight faces.
+5. Add v1.1.0 workflow
+6. RetinaFace supports face selection
 
 ## V1.0.0 Update
 1. Added log for model downloads.
@@ -67,6 +75,7 @@ Click "Load" in the right panel of ComfyUI and select the ./workflow/easyphoto_w
 * RetainFace PM: Perform matting using models from Model Scope. [Link](https://www.modelscope.cn/models/damo/cv_resnet50_face-detection_retinaface/summary)
   * image: Input image
   * multi_user_facecrop_ratio: Multiplicative factor for extracting the head region.
+  * face_index : Choose which face
 
 * FaceFusion PM: Merge faces from two images.
   * image: Input image
@@ -123,6 +132,8 @@ Click "Load" in the right panel of ComfyUI and select the ./workflow/easyphoto_w
 * Face Shape Match PM: Apply a certain level of fusion between the diffused image and the original image to reduce differences around the face.
 
 * Makeup Transfer PM: Use a GAN network model to perform makeup transfer.
+* SuperMakeUpTransfer PM:(Multi-node integration) makeup by merging two pictures
+* SuperColorTransfer PM：(Multi-node integration) transfer the colors of two pictures
 ## Contribution
 
 If you find any issues or have suggestions for improvement, feel free to contribute. Follow these steps:

README_zh-CN.md

@@ -2,7 +2,7 @@
 
 这个项目改编于[EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto)，对于[EasyPhoto](https://github.com/aigc-apps/sd-webui-EasyPhoto)进行了流程上的拆解，后续会加入其他项目处理人物头像上的系列操作。
 
-![](./images/easyphoto.png)
+![](./images/easyphoto.jpg)
 
 English | [简体中文](./README_zh-CN.md)
 
@@ -13,13 +13,20 @@ English | [简体中文](./README_zh-CN.md)
 - 电子邮件：tototianhao@gmail.com
 - telegram: https://t.me/+JoFE2vqHU4phZjg1
 - QQ 群：10419777
-- 微信群： <img src="./images/wechat.jpg" width="200">
+- 微信群： <img src="./images/wechat.jpg" width="300">
+
+## V1.2.0 Update
+1. 增加PM_SuperColorTransfer 节点，简化了颜色迁移的流程
+2. 增加PM_SuperMakeUpTransfer 节点，简化了进行装扮迁移的流程
+3. 增加v1.2.0 workflow 
 
 ## v1.1.0 更新
 1. faceskin 增加模糊选项
-2. 增加 PM_FaceShapMatch节点 详情查看节点介绍
-3. 增加 PM_MakeUpTransfer节点 详情查看节点介绍
-3. PM_PortraitEnhancement节点增加一种超分模型，此超分模型可以对人脸不做高光
+2. 增加 PM_FaceShapMatch节点 与easyphoto的FaceshapMatch一致
+3. 增加 PM_MakeUpTransfer节点 与easyphoto的MakeupTransfer一致
+4. PM_PortraitEnhancement节点增加一种超分模型，此超分模型可以对人脸不做高光
+5. 增加v1.1.0 workflow
+6. RetinaFace 支持选择人脸
 
 ## v1.0.0 更新
 
@@ -70,6 +77,7 @@ Easyphoto工作位置: [./workflow/easyphoto.json](./workflows/easyphoto.json )
 * RetainFace PM:使用Model Scope中的模型进行抠图 [链接](https://www.modelscope.cn/models/damo/cv_resnet50_face-detection_retinaface/summary)
 	* image：输入图像
 	* multi_user_facecrop_ratio：提取头像区域的倍数
+    * face_index : 选择第几个人脸
 * FaceFusion PM:将两张图像的人脸进行融合
 	* image：输入图像
 	* user_image：要融合的头像
@@ -110,6 +118,8 @@ Easyphoto工作位置: [./workflow/easyphoto.json](./workflows/easyphoto.json )
 * GetImageInfo PM: 提取图片的宽高
 * FaceShapMatchPM: 扩散后的图片和原图片进行一定的融合，减少脸旁边的差异
 * MakeUpTransferPM: 使用gan网络模型对妆容进行一定的迁移
+* SuperMakeUpTransferPM:(多节点的整合)融合两张图片的装扮
+* SuperColorTransferPM：(多节点的整合)迁移两张图片的颜色
 
 ## 贡献
 

__init__.py

@@ -1,12 +1,9 @@
-import sys
-import os
+import os, sys
 
 main_path = os.path.dirname(__file__)
 sys.path.append(main_path)
 
-import subprocess
-import threading
-
+import portrait.install
 import requests
 from tqdm import tqdm
 from portrait.nodes import *
@@ -14,30 +11,6 @@ from portrait.nodes import *
 # import pydevd_pycharm
 # pydevd_pycharm.settrace('49.7.62.197', port=10090, stdoutToServer=True, stderrToServer=True)
 
-
-def handle_stream(stream, prefix):
-    for line in stream:
-        print(prefix, line, end="")
-
-def run_script(cmd, cwd='.'):
-    process = subprocess.Popen(cmd, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1)
-
-    stdout_thread = threading.Thread(target=handle_stream, args=(process.stdout, ""))
-    stderr_thread = threading.Thread(target=handle_stream, args=(process.stderr, "[!]"))
-
-    stdout_thread.start()
-    stderr_thread.start()
-
-    stdout_thread.join()
-    stderr_thread.join()
-
-    return process.wait()
-
-print("##  installing dependencies")
-
-requirements_path = os.path.join(main_path, "requirements.txt")
-run_script([sys.executable, '-s', '-m', 'pip', 'install', '-q', '-r', requirements_path])
-
 def urldownload_progressbar(url, file_path):
     response = requests.get(url, stream=True)
     total_size = int(response.headers.get('content-length', 0))
@@ -77,6 +50,9 @@ NODE_CLASS_MAPPINGS = {
     "PM_GetImageInfo": GetImageInfoPM,
     "PM_MakeUpTransfer": MakeUpTransferPM,
     "PM_FaceShapMatch": FaceShapMatchPM,
+    "PM_SuperColorTransfer": SuperColorTransferPM,
+    "PM_SuperMakeUpTransfer": SuperMakeUpTransferPM,
+    "PM_Similarity": SimilarityPM,
 }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "PM_RetinaFace": "RetinaFace PM",
@@ -95,7 +71,11 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "PM_ImageResizeTarget": "ImageResizeTarget PM",
     "PM_GetImageInfo": "GetImageInfo PM",
     "PM_MakeUpTransfer": "MakeUpTransfer PM",
-    "PM_FaceShapMatch":"FaceShapMatch PM"
+    "PM_FaceShapMatch": "FaceShapMatch PM",
+    "PM_SuperColorTransfer": "SuperColorTransfer PM",
+    "PM_SuperMakeUpTransfer": "SuperMakeUpTransfer PM",
+    "PM_Similarity": "Similarity PM",
+
 }
 
 __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

portrait/config.py

@@ -6,7 +6,7 @@ utils_path = os.path.join(root_path, "utils")
 models_path = os.path.join(root_path, "models")
 # save_dirs
 urls = [
-    "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/webui/ChilloutMix-ni-fp16.safetensors",
+    # "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/webui/ChilloutMix-ni-fp16.safetensors",
     "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/webui/control_v11p_sd15_openpose.pth",
     "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/webui/control_v11p_sd15_canny.pth",
     "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/webui/control_v11f1e_sd15_tile.pth",
@@ -23,7 +23,7 @@ urls = [
 
 ]
 filenames = [
-    os.path.join(folder_names_and_paths['checkpoints'][0][0], "Chilloutmix-Ni-pruned-fp16-fix.safetensors"),
+    # os.path.join(folder_names_and_paths['checkpoints'][0][0], "Chilloutmix-Ni-pruned-fp16-fix.safetensors"),
     os.path.join(folder_names_and_paths['controlnet'][0][0], "control_v11p_sd15_openpose.pth"),
     os.path.join(folder_names_and_paths['controlnet'][0][0], "control_v11p_sd15_canny.pth"),
     os.path.join(folder_names_and_paths['controlnet'][0][0], "control_v11f1e_sd15_tile.pth"),

portrait/install.py

@@ -0,0 +1,107 @@
+import sys
+import subprocess
+import threading
+import locale
+import traceback
+import re
+from portrait.config import *
+
+windows_not_install = ['mmcv_full\n']
+
+def log(msg, end=None, file=None):
+    print('Portrait Maker ==============', msg, end=end, file=file)
+
+def handle_stream(stream, is_stdout):
+    stream.reconfigure(encoding=locale.getpreferredencoding(), errors='replace')
+
+    for msg in stream:
+        if is_stdout:
+            log(msg, end="", file=sys.stdout)
+        else:
+            log(msg, end="", file=sys.stderr)
+
+def process_wrap(cmd_str, cwd=None, handler=None):
+    log(f"[Portrait Maker] EXECUTE: {cmd_str} in '{cwd}'")
+    process = subprocess.Popen(cmd_str, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1)
+
+    if handler is None:
+        handler = handle_stream
+
+    stdout_thread = threading.Thread(target=handler, args=(process.stdout, True))
+    stderr_thread = threading.Thread(target=handler, args=(process.stderr, False))
+
+    stdout_thread.start()
+    stderr_thread.start()
+
+    stdout_thread.join()
+    stderr_thread.join()
+
+    return process.wait()
+
+# ---
+pip_list = None
+
+def get_installed_packages():
+    global pip_list
+    if pip_list is None:
+        try:
+            result = subprocess.check_output([sys.executable, '-m', 'pip', 'list'], universal_newlines=True)
+            pip_list = set([line.split()[0].lower() for line in result.split('\n') if line.strip()])
+        except subprocess.CalledProcessError as e:
+            log(f"[ComfyUI-Manager] Failed to retrieve the information of installed pip packages.")
+            return set()
+
+    return pip_list
+
+def is_installed(name):
+    name = name.strip()
+    pattern = r'([^<>!=]+)([<>!=]=?)'
+    match = re.search(pattern, name)
+
+    if match:
+        name = match.group(1)
+
+    result = name.lower() in get_installed_packages()
+    return result
+
+def check_and_install_requirements(file_path):
+    log(file_path)
+    version = sys.version_info[:2]
+    if os.path.exists(file_path):
+        with open(file_path, 'r') as file:
+            lines = file.readlines()
+            for line in lines:
+                log(line)
+                if not is_installed(line):
+                    if platform.system() == "Windows" and version[1] == 11 and 'insightface' in line:
+                        process_wrap(pip_install + ['insightface-0.7.3-cp311-cp311-win_amd64.whl'], cwd=root_path)
+                        continue
+                    if platform.system() == "Windows" and line in windows_not_install:
+                        log(f"windows skip {line}")
+                        continue
+                    log(f"install {line}")
+                    process_wrap(pip_install + [line], cwd=root_path)
+            return False
+    return True
+
+try:
+    import platform
+
+    log("### ComfyUI-Portrait-Maker: Check dependencies")
+    if "python_embed" in sys.executable or "python_embedded" in sys.executable:
+        pip_install = [sys.executable, '-s', '-m', 'pip', 'install', '-q']
+        mim_install = [sys.executable, '-s', '-m', 'mim', 'install']
+    else:
+        pip_install = [sys.executable, '-m', 'pip', 'install', '-q']
+        mim_install = [sys.executable, '-m', 'mim', 'install']
+
+    subpack_req = os.path.join(root_path, "requirements.txt")
+    check_and_install_requirements(subpack_req)
+    if platform.system() != "Windows" :
+        process_wrap(pip_install + ['mmcv_full'], cwd=root_path)
+    if sys.argv[0] == 'install.py':
+        sys.path.append('.')  # for portable version
+
+except Exception as e:
+    log("[ERROR] ComfyUI-Impact-Pack: Dependency installation has failed. Please install manually.")
+    traceback.print_exc()

portrait/model_holder.py

@@ -16,6 +16,7 @@ skin_retouching = None
 portrait_enhancement = None
 psgan_interface = None
 real_gan_sr = None
+face_recognition = None
 
 def get_retinaface_detection():
     global retinaface_detection
@@ -72,3 +73,10 @@ def get_pagan_interface():
         makeup_transfer_model_path = os.path.join(models_path, "makeup_transfer.pth")
         psgan_interface = PSGAN_Inference("cuda", makeup_transfer_model_path, get_retinaface_detection(), get_face_skin(), face_landmarks_model_path)
     return psgan_interface
+
+def get_face_recognition():
+    global face_recognition
+    if face_recognition is None:
+        face_recognition = pipeline("face_recognition", model="bubbliiiing/cv_retinafce_recognition", model_revision="v1.0.3")
+    return face_recognition
+

portrait/nodes.py

@@ -7,13 +7,15 @@ from .utils.img_utils import img_to_tensor, tensor_to_img, tensor_to_np, np_to_t
 from .model_holder import *
 
 # import pydevd_pycharm
+#
 # pydevd_pycharm.settrace('49.7.62.197', port=10090, stdoutToServer=True, stderrToServer=True)
 
 class RetinaFacePM:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"image": ("IMAGE",),
-                             "multi_user_facecrop_ratio": ("FLOAT", {"default": 1, "min": 0, "max": 10, "step": 0.01})
+                             "multi_user_facecrop_ratio": ("FLOAT", {"default": 1, "min": 0, "max": 10, "step": 0.01}),
+                             "face_index": ("INT", {"default": 0, "min": 0, "max": 10, "step": 1})
                              }}
 
     RETURN_TYPES = ("IMAGE", "MASK", "BOX")
@@ -21,12 +23,14 @@ class RetinaFacePM:
     FUNCTION = "retain_face"
     CATEGORY = "protrait/model"
 
-    def retain_face(self, image, multi_user_facecrop_ratio):
+    def retain_face(self, image, multi_user_facecrop_ratio, face_index):
         np_image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
         image = Image.fromarray(np_image)
-        retinaface_boxes, retinaface_keypoints, retinaface_masks, retinaface_tensor = call_face_crop(get_retinaface_detection(), image, multi_user_facecrop_ratio)
-        crop_image = image.crop(retinaface_boxes[0])
-        return (img_to_tensor(crop_image), retinaface_tensor, retinaface_boxes[0])
+        retinaface_boxes, retinaface_keypoints, retinaface_masks, retinaface_mask_nps = call_face_crop(get_retinaface_detection(), image, multi_user_facecrop_ratio)
+        crop_image = image.crop(retinaface_boxes[face_index])
+        retinaface_mask = np_to_mask(retinaface_mask_nps[face_index])
+        retinaface_boxe = retinaface_boxes[face_index]
+        return (img_to_tensor(crop_image), retinaface_mask, retinaface_boxe)
 
 class FaceFusionPM:
 
@@ -42,6 +46,14 @@ class FaceFusionPM:
 
     CATEGORY = "protrait/model"
 
+    def resize(self, tensor):
+        image = tensor_to_img(tensor)
+        short_side = max(image.width, image.height)
+        resize = float(short_side / 640)
+        new_size = (int(image.width // resize), int(image.height // resize))
+        resize_image = image.resize(new_size, Image.Resampling.LANCZOS)
+        return img_to_np(resize_image)
+
     def img_face_fusion(self, source_image, swap_image, mode):
         if mode == "ali":
             source_image = tensor_to_img(source_image)
@@ -51,12 +63,24 @@ class FaceFusionPM:
             result_image = Image.fromarray(cv2.cvtColor(fusion_image, cv2.COLOR_BGR2RGB))
             return (img_to_tensor(result_image),)
         else:
+            width, height = source_image.shape[2], source_image.shape[1]
+            need_resize = False
+            source_np = tensor_to_np(source_image)
+            swap_np = tensor_to_np(swap_image)
+            if source_image.shape[2] > 640 or source_image.shape[1] > 640:
+                source_np = self.resize(source_image)
+                need_resize = True
+            if swap_image.shape[2] > 640 or swap_image.shape[1] > 640:
+                swap_np = self.resize(swap_image)
             get_face_analysis().prepare(ctx_id=0, det_size=(640, 640))
-            source_image = tensor_to_np(source_image)
-            faces = get_face_analysis().get(source_image)
-            swap_image = tensor_to_np(swap_image)
-            swap_face = get_face_analysis().get(swap_image)
-            result_image = get_roop().get(source_image, faces[0], swap_face[0], paste_back=True)
+            faces = get_face_analysis().get(source_np)
+            swap_faces = get_face_analysis().get(swap_np)
+            result_image = get_roop().get(source_np, faces[0], swap_faces[0], paste_back=True)
+            if need_resize:
+                image = Image.fromarray(result_image)
+                new_size = width, height
+                result_image = image.resize(new_size, Image.Resampling.LANCZOS)
+                result_image = img_to_np(result_image)
             return (np_to_tensor(result_image),)
 
 class RatioMerge2ImagePM:
@@ -73,7 +97,7 @@ class RatioMerge2ImagePM:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "image_ratio_merge"
 
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def image_ratio_merge(self, image1, image2, fusion_rate):
         rate_fusion_image = image1 * (1 - fusion_rate) + image2 * fusion_rate
@@ -132,7 +156,7 @@ class ExpandMaskFaceWidthPM:
     RETURN_TYPES = ("MASK", "BOX")
     FUNCTION = "expand_mask_face_width"
 
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def expand_mask_face_width(self, mask, box, expand_width):
         h, w = mask.shape[1], mask.shape[2]
@@ -160,7 +184,7 @@ class BoxCropImagePM:
     RETURN_TYPES = ("IMAGE",)
     RETURN_NAMES = ("crop_image",)
     FUNCTION = "box_crop_image"
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def box_crop_image(self, image, box):
         image = image[:, box[1]:box[3], box[0]:box[2], :]
@@ -178,7 +202,7 @@ class ColorTransferPM:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "color_transfer"
 
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def color_transfer(self, transfer_from, transfer_to):
         transfer_result = color_transfer(tensor_to_np(transfer_from), tensor_to_np(transfer_to))  # 进行颜色迁移
@@ -219,7 +243,7 @@ class MaskDilateErodePM:
     RETURN_TYPES = ("MASK",)
     FUNCTION = "mask_dilate_erode"
 
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def mask_dilate_erode(self, mask):
         out_mask = Image.fromarray(np.uint8(cv2.dilate(tensor_to_np(mask), np.ones((96, 96), np.uint8), iterations=1) - cv2.erode(tensor_to_np(mask), np.ones((48, 48), np.uint8), iterations=1)))
@@ -271,13 +295,13 @@ class ImageScaleShortPM:
         return {"required": {
             "image": ("IMAGE",),
             "size": ("INT", {"default": 512, "min": 0, "max": 2048, "step": 1}),
-            "crop_face": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+            "crop_face": ("BOOLEAN", {"default": False}),
         }}
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "image_scale_short"
 
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def image_scale_short(self, image, size, crop_face):
         input_image = tensor_to_img(image)
@@ -304,7 +328,7 @@ class ImageResizeTargetPM:
 
     FUNCTION = "image_resize_target"
 
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def image_resize_target(self, image, width, height):
         imagepi = tensor_to_img(image)
@@ -323,7 +347,7 @@ class GetImageInfoPM:
 
     FUNCTION = "get_image_info"
 
-    CATEGORY = "protrait/model"
+    CATEGORY = "protrait/other"
 
     def get_image_info(self, image):
         width = image.shape[2]
@@ -345,8 +369,8 @@ class MakeUpTransferPM:
     CATEGORY = "protrait/model"
 
     def makeup_transfer(self, source_image, makeup_image):
-        source_image = tensor_to_img(source_image).resize([256, 256])
-        makeup_image = tensor_to_img(makeup_image).resize([256, 256])
+        source_image = tensor_to_img(source_image)
+        makeup_image = tensor_to_img(makeup_image)
         result = get_pagan_interface().transfer(source_image, makeup_image)
         return (img_to_tensor(result),)
 
@@ -386,3 +410,110 @@ class FaceShapMatchPM:
         input_image_uint8 = np.array(source_image) * face_skin_mask + np.array(match_image) * (1 - face_skin_mask)
 
         return (np_to_tensor(input_image_uint8),)
+
+class SuperColorTransferPM:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return \
+            {
+                "required": {
+                    "main_image": ("IMAGE",),
+                    "transfer_image": ("IMAGE",),
+                },
+                "optional": {
+                    "avatar_box": ("BOX",),
+                },
+            }
+
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "super_color_transfer"
+    CATEGORY = "protrait/super"
+
+    def super_color_transfer(self, main_image, transfer_image, avatar_box=None):
+        origin_np = tensor_to_np(main_image)
+        result_np = None
+        if avatar_box is not None:
+            main_image = main_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+            transfer_image = transfer_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+
+        transfer_result = color_transfer(tensor_to_np(main_image), tensor_to_np(transfer_image))  # 进行颜色迁移
+
+        face_skin_img = get_face_skin()(Image.fromarray(transfer_result), get_retinaface_detection(), [[1, 2, 3, 4, 5, 10, 12, 13]])[0]
+        face_skin_np = img_to_np(face_skin_img)
+        face_skin_np = cv2.blur(face_skin_np, (32, 32)) / 255
+
+        masked_img_np = tensor_to_np(main_image) * (1 - face_skin_np) + transfer_result * face_skin_np
+        result_np = masked_img_np
+
+        if avatar_box is not None:
+            origin_np[avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :] = masked_img_np
+            result_np = origin_np
+
+        return (np_to_tensor(result_np),)
+
+class SuperMakeUpTransferPM:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return \
+            {
+                "required": {
+                    "main_image": ("IMAGE",),
+                    "makeup_image": ("IMAGE",),
+                },
+                "optional": {
+                    "avatar_box": ("BOX",),
+                },
+            }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "super_makeup_transfer"
+    CATEGORY = "protrait/super"
+
+    def super_makeup_transfer(self, main_image, makeup_image, avatar_box=None):
+        box_width, box_height = avatar_box[2] - avatar_box[0], avatar_box[3] - avatar_box[1]
+        origin_np = tensor_to_np(main_image)
+        if avatar_box is not None:
+            main_image = main_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+            makeup_image = makeup_image[:, avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :]
+        resize_source_box_image = tensor_to_img(main_image).resize([256, 256])
+        resize_makeup_box_image = tensor_to_img(makeup_image).resize([256, 256])
+        transfer_image = get_pagan_interface().transfer(resize_source_box_image, resize_makeup_box_image)
+        box_size_transfer = transfer_image.resize([box_width, box_height], Image.Resampling.LANCZOS)
+        origin_np[avatar_box[1]:avatar_box[3], avatar_box[0]:avatar_box[2], :] = img_to_np(box_size_transfer)
+        return (np_to_tensor(origin_np),)
+
+class SimilarityPM:
+    @classmethod
+    def INPUT_TYPES(s):
+        return \
+            {
+                "required": {
+                    "main_image": ("IMAGE",),
+                    "compare_image": ("IMAGE",),
+                    "model": (["sim"],),
+                    "result_prefix": ("STRING",{"default": ""}),
+                },
+            }
+
+    RETURN_TYPES = ("STRING",)
+
+    FUNCTION = "similarity_compare"
+    CATEGORY = "protrait/model"
+
+    def similarity_compare(self, main_image, compare_image, model, result_prefix):
+        main_image_copy = tensor_to_img(main_image)
+        compare_image_copy = tensor_to_img(compare_image)
+        score = None
+        result = None
+        if model == "sim":
+            root_embedding = get_face_recognition()(dict(user=Image.fromarray(np.uint8(main_image_copy))))[OutputKeys.IMG_EMBEDDING]
+            compare_embedding = get_face_recognition()(dict(user=Image.fromarray(np.uint8(compare_image_copy))))[OutputKeys.IMG_EMBEDDING]
+            score = float(np.dot(root_embedding, np.transpose(compare_embedding))[0][0])
+        if result_prefix is "":
+            result = str(round(score, 2))
+        else:
+            result = f"{result_prefix}_{round(score, 2)}"
+        return (result,)

portrait/utils/face_process_utils.py

@@ -65,9 +65,8 @@ def safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, face_seg,
         retinaface_boxs = [retinaface_boxs[index] for index in argindex]
         retinaface_keypoints = [retinaface_keypoints[index] for index in argindex]
         retinaface_mask_pils = [retinaface_mask_pils[index] for index in argindex]
-        retinaface_mask_np = [retinaface_masks[index] for index in argindex]
-        mask_tensor = np_to_mask(retinaface_mask_np[0])
-        return retinaface_boxs, retinaface_keypoints, retinaface_mask_pils, mask_tensor
+        retinaface_mask_nps = [retinaface_masks[index] for index in argindex]
+        return retinaface_boxs, retinaface_keypoints, retinaface_mask_pils, retinaface_mask_nps
 
     else:
         retinaface_box = np.array([])
@@ -120,9 +119,9 @@ def call_face_crop(retinaface_detection, image, crop_ratio, prefix="tmp"):
     # retinaface detect 
     retinaface_result = retinaface_detection(image)
     # get mask and keypoints
-    retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_tensor = safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, None, "crop")
+    retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_nps = safe_get_box_mask_keypoints(image, retinaface_result, crop_ratio, None, "crop")
 
-    return retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_tensor
+    return retinaface_box, retinaface_keypoints, retinaface_mask_pil, retinaface_mask_nps
 
 def color_transfer(sc, dc):
     """

portrait/utils/psgan_utils.py

@@ -456,7 +456,7 @@ class PreProcess:
         lms = lms[:, ::-1]
 
         mask, diff = self.process(mask, lms, device=self.device)
-        image = image.resize((self.img_size, self.img_size), Image.ANTIALIAS)
+        image = image.resize((self.img_size, self.img_size), Image.Resampling.NEAREST)
         image = self.transform(image)
         real = to_var(image.unsqueeze(0))
         return [real, mask, diff], face_on_image, crop_face
@@ -830,7 +830,7 @@ class PostProcess:
 
         height, width = source.shape[:2]
         small_source = cv2.resize(source, (self.img_size, self.img_size))
-        laplacian_diff = source.astype(np.float) - cv2.resize(small_source, (width, height)).astype(np.float)
+        laplacian_diff = source.astype(np.float64) - cv2.resize(small_source, (width, height)).astype(np.float64)
         result = (cv2.resize(result, (width, height)) + laplacian_diff).round().clip(0, 255).astype(np.uint8)
         if self.denoise:
             result = cv2.fastNlMeansDenoisingColored(result)

requirements.txt

@@ -1,3 +1,4 @@
+aliyun-python-sdk-core-v3==2.13.10
 opencv-python
 tensorflow-cpu
 tensorflow
@@ -7,4 +8,5 @@ modelscope
 scikit-image
 matplotlib
 insightface
-diffusers==0.18.2
+diffusers==0.18.2
+sentencepiece