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custom_nodes/ActionSelectorNode/.ipynb_checkpoints/__init__-checkpoint.py

@@ -0,0 +1,174 @@
+import datetime
+
+ACTION_MAPPING = {
+    # ================= 原有动作 =================
+    "[口部] 强力跪姿深喉": {
+        "high_noise_lora": "口H", "low_noise_lora": "口H", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7, 
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n女人裸体 乳房巨大丰满 被男人暴力使劲推倒在床上，女人表情害怕，欧美男人暴力的走在女人头部位置跪下 把粗大又长阴茎暴力插入女人嘴巴 暴力深喉，手按住头 整根插入喉咙深处，每次都带着口水唾液拉成丝 镜头拉近女人面部 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[体位] 经典传教士": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "镜头拉远，女孩皮肤细节清晰自然，视频全程少女脸部特征与第一帧保持一致。少女快速脱掉衣服完全赤裸，裤子同时消失，，上半身向前倾倒坐在一个男人的阴茎上，眼睛保持看向镜头，镜头正对少女面部。这个场景是从下往上看的，少女完全裸体，丰满的乳房和乳头和的乳晕可见，双腿张开，阴道阴唇可见，少女表情害羞。在画面的底部，可以部分看到一个中国男人，他用手拿着他的阴茎，阴茎很短龟头很大，并将他的阴茎插入女人的阴道，阴茎在阴道快速的反复拔出和插入。 男人头部不出现，"
+    },
+    "[体位] 仰卧后入式": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n女人裸体 乳房巨大丰满 被男人使劲一推，身体倒在床上，男人勃起的粗大又长阴茎，女人大开双腿 男人扶着阴茎插入 两人身上都是爱的汗水 镜头缓慢往上升移动 超深视角俯视视角两人性交画面完全展示 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[互动] 乳交交互准备": {
+        "high_noise_lora": "ru交H", "low_noise_lora": "ru交L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n画面中的所有人事物场景不变，镜头拉近到女人整个脸部位置，(女人脸部占用整个视频画面可以清晰的看见女人面部皮肤纹理细节：1.3)镜头缓慢拉远女人脱掉全部衣物全身裸体一丝不挂，巨大丰满乳房(乳房巨大丰满：1.3)往后仰躺整个身体平躺桌面，一个裸体年轻男子第一视角俯视视角俯视女人全部身体走上床上坐在女人身上上扶着勃起粗大又长的阴茎放在女人乳房中间(阴茎青筋暴起真实皮肤：1.3)镜头缓慢拉近整体画面增强视觉冲击感 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[互动] 贴身亲昵互动": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "欧美女人面部五官完全保持与第一帧的女人五官一模一样完全一致。\n第一视角，不改变女人五官与第一帧女人五官保持一致，男人俯视女人全身身体，超低视角，女人流畅的整个身体转身全身脱掉所有衣物裸体一丝不挂撅起臀部把屁股翘得很高胯宽臀部翘背对男人，男人勃起粗大又长的阴精俯视视角后入女人，每次插入撞击臀部回弹(臀翘胯宽:1.3)。男人手抓着女人双手后拉，女人面部看微微转头看向后面阿合高吐出舌头，女人回头的时候哪边身体也微微往后，从背面能看到晃动的巨大丰满乳房(巨大丰满乳房：1.3)，镜头缓慢拉远拉高。细腻的皮肤，流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[特写] 私密部位舔舐": {
+        "high_noise_lora": "口H", "low_noise_lora": "口L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名欧美女人保持与第一帧的时候完全保持的��模一样。\n画面中的所有人事物场景不变女人缓慢往后仰躺双腿大开，镜头缓慢移动到女人阴道处，整个画面被女人漂亮完美阴道占用阴道的皮肤纹理细节清晰可见能看见阴道里的褶皱细节(尿道，阴蒂，阴唇：1.3)，旁边一个欧美年轻男人趴在女人双腿处头部靠近女人阴道伸出舌头舔舐阴道女人双手抱紧男人头部阴道流出透明液体，镜头整体拉近视觉冲击，女人面部做出随机的淫荡表情(阴道上有大量的透明液体：1.3)禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[场景] 窗前趴式后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "欧美女人面部五官完全保持与第一帧的女人五官一模一样完全一致。\n保持原图像中的所有东西不要改变，相机镜头移动拉近女人五官，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)场景转场，女人全身裸体一丝不挂的的身体露出巨大丰满乳房(乳房巨大丰满：1.3)狗趴式性交女人双手趴在酒店窗户玻璃上丰满乳房随着每次插入都会贴到窗户玻璃上后面的欧美裸体年迈老男人从后面阴茎插入女人的Vaginal / anal(阴茎和阴道润滑液流出：1.3) (女人表情淫荡魅惑看向镜头：1.3)镜头拉近整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[体位] 女位正面互动": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "欧美女人面部五官完全保持与第一帧的女人五官一模一样完全一致。\n保持原图像中的所有东西不要改变，场景转场男人平躺第一视角仰视视角仰视，女人面对镜头整个身体面对观众男人身上双腿大开面对观众女上位，男人正面插入女人Vaginal / anal(阴茎粗大又长青筋暴起真实皮肤纹理：1.3)(女人胯宽：1.3)(阴茎和阴道交合透明液体拉成丝混着润滑液流出：1.3) 镜头拉远整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[场景] 扶桌站立后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "欧美女人面部五官完全保持与第一帧的女人五官一模一样完全一致。\n保持原图像中的所有东西不要改变，(镜头一直跟随女人:1.3)女人转身反背身手扶桌子对着男人，男人走到女人臀部后面手扶女人下面裸体一丝不挂露出勃起粗大又长的阴茎插入女人Vaginal / anal(阴茎和阴道交合透明液体拉成丝混着润滑液流出：1.3) 镜头拉远整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[体位] 俯卧背向后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n女人转身狗趴式背对男人双腿大开，男人第一视角俯视视角俯视女人整个背对身体(超低视角：1.3)男人站立勃起粗大又长的阴茎俯视视角插入阴道或肛门女人面部看向侧面(阴茎上有大量的润滑液：1.3) 禁忌氛围画面 超级画质 流畅 8K"
+    },
+    "[互动] 背后撕衣吻胸": {
+        "high_noise_lora": "被揉胸H", "low_noise_lora": "被揉胸L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "镜头拉远，一位女生，保留原本的形象、场景环境不变，整体氛围轻松自然。视频全程女生的脸部特征与第一帧保持一致，视频全程女生的全部脸部出现在画面中。一个男子从女孩背后出现，女孩的衣服被男人双手粗暴的抓住快速的从中间撕开，女孩表情很害羞，女孩完全赤裸，乳房剧烈跳动着，乳房和乳头可见，男子用手搓揉女孩乳房，男人用嘴吮吸女孩乳头，女孩很害羞，女孩身体微微前倾，可以清楚看清女人的表情。 男人一直揉搓女孩乳房，并且男人吻女孩��嘴，女孩陷入了困境。\n(欧美女人保持所有与第一帧完全一模一样一直持续到视频结束：1.3)"
+    },
+    "[体位] 经典趴式后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n镜头拉近到女人面部面部皮肤纹理细节完美展现，女人转身狗趴式双腿背对男人大开第一视角俯视视角俯视女人整个身体(超低视角：1.3)男人战立勃起粗大又长的阴茎俯视视角插入阴道或者肛门女人看向侧面女人面部随机做出阿合和表情(阴茎上有大量的润滑液：1.3) 禁忌氛围画面 超级画质 流畅 8K"
+    },
+    "[口部] 极限全深喉": {
+        "high_noise_lora": "终极深喉H", "low_noise_lora": "终极深喉L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "a cinematic scene with a woman in the frame, a naked man enters from the left side, only his lower body is visible, side view of his hips, thighs and legs, with a gigantic erected penis with testicles appears and she starts engaging in a deepthroat blowjob with that penis. She swallows the entire penis, her nose smashes agains the man's hips. She moves her head back and fort swallowing the penis, realistic proportions, natural lighting, cinematic composition"
+    },
+    "[口部] 跪姿互动特写": {
+        "high_noise_lora": "口H", "low_noise_lora": "口L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n镜头拉到女人面部完全看清女人面部皮肤纹理细节，女人双腿大开缓慢流畅的下跪地面一位男人站立完全第一视角俯视视角(超低视角：1.3)俯视整个女人露出勃起粗大又长的阴茎(阴茎青筋暴起真实皮肤：1.3)女人抬头眼神看向镜头手握着阴茎张嘴口含整根阴茎在嘴里来回滑动女人随机做淫荡的面部表情(阴茎上有大量的润滑液：1.3) 禁忌氛围画面 超级画质 流畅 8K"
+    },
+    "[口部] 深喉终焉释放": {
+        "high_noise_lora": "口H", "low_noise_lora": "口L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名欧美女人保持与第一帧的时候完全保持的一模一样。\n欧美女人裸体 乳房巨大丰满 被男人暴力使劲推倒在床上，欧美女人表情害怕，欧美男人暴力的走在女人头部位置跪下 把粗大又长阴茎暴力插入女人嘴巴 暴力深喉，手按住头 整根插入喉咙深处，每次都带着口水唾液拉成丝 女人面部汗水 镜头拉近女人面部 男人射精乳白色浓稠精液在嘴里大量精液在嘴里流出来 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+
+    # ================= 新增动作 =================
+    "[特写] 阴道扣弄": {
+        "high_noise_lora": "女自慰H", "low_noise_lora": "女自慰L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "完美阴户", "aux_low_lora": "完美阴户", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n保持原图像中的所有东西不要改变，镜头拉近到女人整个脸部位置，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节展示在画面中：1.3)(女人表情淫荡魅惑看向镜头漏出邪魅笑容：1.3)女人缓慢往后仰躺双腿大开，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)，\n镜头缓慢移动到女人阴道处，女人双手扣弄自己涂满精油的阴道，聚焦女人阴道，镜头缓慢推进直到进入阴道和肛门占满整个画面，阴道的皮肤纹理细节清晰可见能看见阴道里的褶皱细节(尿道，阴蒂，阴唇：1.3)，(阴道上有大量的透明液体：1.3)禁忌氛围画面，堪称杰作，肌肤纹理极其细腻逼真，流畅动作， 超级画质，电影打光，极致细节 8K"
+    },
+    "[互动] 揉胸舐乳": {
+        "high_noise_lora": "吸乳头H", "low_noise_lora": "吸乳头L", "high_model_strength": 0.25, "low_model_strength": 0.8,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "n1ppl3suck1ng，\n一名女人保持与第一帧的时候完全保持的一模一样。\n保持原图像中的所有东西不要改变，镜头拉近到女人整个脸部位置，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)镜头缓慢拉远\n女人脱掉衣服，露出丰满的乳房，她的乳房又大又挺。(女人表情淫荡魅惑看向镜头：1.3)，她用双手揉搓乳房，用手头舔舐着左边乳头，专注于乳晕和乳头区域。咬着并拉伸着。她的表情既愉悦又专注(女人表情淫荡魅惑看向镜头：1.3)镜头拉近整个视频画面。\n整个场景私密、感性，并着重展现了动作的细节。堪称杰作，肌肤纹理极其细腻逼真，流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[口部] 舔舐阴部": {
+        "high_noise_lora": "口阴H", "low_noise_lora": "口阴L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "cunn1l1ngu5，一名女人保持与第一帧的时候完全保持的一模一样。\n保持原图像中的所有东西不要改变，女人缓慢往后仰躺双腿大开，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)，镜头缓慢移动到女人阴道处，整个画面被女人漂亮完美阴道占用阴道的皮肤纹理细节清晰可见能看见阴道里的褶皱细节(尿道，阴蒂，阴唇：1.3)，旁边一个男人趴在女人双腿处头部靠近女人阴道伸出舌头舔舐阴道，他的舌头有节奏地轻舔吮吸着她的阴蒂。接受刺激的女性臀部不由自主地微微扭动，颤抖着，这是快感的体现。她的阴道变得湿润闪亮，充满了唾液，并从男人的口中滴落。女人双手抱紧男人头部，镜头整体拉近视觉冲击，女人面部做出随机的淫荡表情，整个场景私密、感性，并着重展现了动作的细节。堪称杰作，肌肤纹理极其细腻逼真， 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[特写] 满脸喷精": {
+        "high_noise_lora": "射精H", "low_noise_lora": "射精L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "f4c3spl4sh, the scenes starts with the camera zooming out revealing a cinematic scene with a woman in the frame with a man entering from the left side, only his lower body is visible, side view of his hips, thighs and legs, with a gigantic erected penis with testicles. She starts recieving a MASSIVE cumshot on her face from the penis. The MASSIVE stream of semen is directed towards her mouth. The force of the ejaculation is strong, and the trajectory of the semen is aimed directly at her open mouth. The quantity of semen is MASSIVE, covering a significant portion of her face and neck area. The woman's eyes are open, and her mouth is slightly open as she receives the semen. The camera is positioned close to the woman's face and chest, capturing the moment of male ejaculation directly onto her face and chest. The semen is seen forcefully erupting from the penis and landing on her skin, creating a visible pool of semen on her chest."
+    },
+    "[体位] 兔女郎骑乘": {
+        "high_noise_lora": "女上ITH", "low_noise_lora": "女上ITL", "high_model_strength": 0.25, "low_model_strength": 0.8,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "图中女人面部五官完全保持与第一帧的女人五官一模一样完全一致。\n保持原图像中的所有东西不要改变，相机镜头移动拉近聚焦女人五官，女人面部占满镜头，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)(女人表情淫荡魅惑看向镜头漏出邪魅笑容：1.3)场景转场，女人全身赤裸不穿任何衣服，头上戴着兔内多头上，男人全身赤裸躺在地上，女人全身裸体露出巨大丰满乳房(乳房巨大丰满：1.3)女人骑马式坐在男人阴茎上，阴茎插入女人阴部，女人双手撑在男人胸上，女人下体不断抬起坐下，阴部因为阴茎的抽插滴落白色透明液体Vaginal / anal(阴茎和阴道润滑液流出：1.3) (女人表情淫荡魅惑看向镜头：1.3)镜头拉近整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    }
+}
+
+class ActionSelectorNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "action": (list(ACTION_MAPPING.keys()),),
+                "seconds": ("INT", {"default": 6, "min": 1, "max": 120, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("*", "*", "*", "*", "FLOAT", "FLOAT", "FLOAT", "FLOAT", "STRING", "INT", "STRING")
+    RETURN_NAMES = ("高噪 LoRA", "底噪 LoRA", "辅高 LoRA*", "辅低 LoRA*", "高噪模型强度", "低噪模型强度", "辅高模型强度*", "辅低模型强度*", "提示词 (Prompt)", "帧数", "储存文件名")
+    FUNCTION = "select_action"
+    CATEGORY = "Custom"
+
+    def select_action(self, action, seconds):
+        data = ACTION_MAPPING[action]
+        
+        # 封装一个内部函数处理 LoRA 后缀扩展名
+        def format_lora(lora_name):
+            if not lora_name or lora_name == "None":
+                return "None"
+            return lora_name if lora_name.endswith(".safetensors") else f"{lora_name}.safetensors"
+
+        # 主 LoRA 获取
+        high_noise = format_lora(data.get("high_noise_lora", "None"))
+        low_noise = format_lora(data.get("low_noise_lora", "None"))
+        
+        # =========== 核心修复逻辑 ===========
+        # 辅高 LoRA：如果为空，强制复用主高噪LoRA名称，但输出强度设为0.0
+        aux_high_raw = data.get("aux_high_lora", "None")
+        if aux_high_raw == "None" or not aux_high_raw:
+            aux_high = high_noise  
+            aux_high_strength = 0.0 # 强制 0.0 强度，相当于跳过加载
+        else:
+            aux_high = format_lora(aux_high_raw)
+            aux_high_strength = float(data.get("aux_high_strength", 0.7))
+            
+        # 辅低 LoRA：如果为空，强制复用主底噪LoRA名称，但输出强度设为0.0
+        aux_low_raw = data.get("aux_low_lora", "None")
+        if aux_low_raw == "None" or not aux_low_raw:
+            aux_low = low_noise   
+            aux_low_strength = 0.0 # 强制 0.0 强度，相当于跳过加载
+        else:
+            aux_low = format_lora(aux_low_raw)
+            aux_low_strength = float(data.get("aux_low_strength", 0.7))
+        # ====================================
+        
+        high_strength = float(data.get("high_model_strength", 0.7))
+        low_strength = float(data.get("low_model_strength", 0.7))
+        
+        prompt = data["prompt"]
+        frame_count = int(seconds * 16 + 1)
+        current_time = datetime.datetime.now().strftime("%m%d%H%M")
+        
+        action_name_cn = action.split("]")[-1].strip() if "]" in action else action
+        action_time_name = f"{action_name_cn}_{current_time}"
+        
+        return (
+            high_noise, low_noise, aux_high, aux_low, 
+            high_strength, low_strength, aux_high_strength, aux_low_strength, 
+            prompt, frame_count, action_time_name
+        )
+
+NODE_CLASS_MAPPINGS = {"ActionSelectorNode": ActionSelectorNode}
+NODE_DISPLAY_NAME_MAPPINGS = {"ActionSelectorNode": "🎬 Action Selector (动作选择器)"}

custom_nodes/ActionSelectorNode/__init__.py

@@ -0,0 +1,186 @@
+import datetime
+
+ACTION_MAPPING = {
+    # ================= 原有动作 =================
+    "[口部] 强力跪姿深喉": {
+        "high_noise_lora": "口H", "low_noise_lora": "口H", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7, 
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n女人裸体 乳房巨大丰满 被男人暴力使劲推倒在床上，女人表情害怕，男人暴力的走在女人头部位置跪下 把粗大又长阴茎暴力插入女人嘴巴 暴力深喉，手按住头 整根插入喉咙深处，每次都带着口水唾液拉成丝 镜头拉近女人面部 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[体位] 经典传教士": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "镜头拉远，女人皮肤细节清晰自然，视频全程少女脸部特征与第一帧保持一致(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。女人快速脱掉衣服完全赤裸，裤子同时消失，，上半身向前倾倒坐在一个男人的阴茎上，眼睛保持看向镜头，镜头正对少女面部。这个场景是从下往上看的，少女完全裸体，丰满的乳房和乳头和的乳晕可见，双腿张开，阴道阴唇可见，少女表情害羞。在画面的底部，可以部分看到一个中国男人，他用手拿着他的阴茎，阴茎很短龟头很大，并将他的阴茎插入女人的阴道，阴茎在阴道快速的反复拔出和插入。 男人头部不出现，"
+    },
+    "[体位] 仰卧后入式": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n女人裸体 乳房巨大丰满 被男人使劲一推，身体倒在床上，男人勃起的粗大又长阴茎，女人大开双腿 男人扶着阴茎插入 两人身上都是爱的汗水 镜头缓慢往上升移动 超深视角俯视视角两人性交画面完全展示 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[互动] 乳交交互准备": {
+        "high_noise_lora": "ru交H", "low_noise_lora": "ru交L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n画面中的所有人事物场景不变，镜头拉近到女人整个脸部位置，(女人脸部占用整个视频画面可以清晰的看见女人面部皮肤纹理细节：1.3)镜头缓慢拉远女人脱掉全部衣物全身裸体一丝不挂，巨大丰满乳房(乳房巨大丰满：1.3)往后仰躺整个身体平躺桌面，一个裸体年轻男子第一视角俯视视角俯视女人全部身体走上床上坐在女人身上上扶着勃起粗大又长的阴茎放在女人乳房中间(阴茎青筋暴起真实皮肤：1.3)镜头缓慢拉近整体画面增强视觉冲击感 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[互动] 贴身亲昵互动": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "欧美女人面部五官完全保持与第一帧的女人五官一模一样完全一致(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n第一视角，不改变女人五官与第一帧女人五官保持一致，男人俯视女人全身身体，超低视角，女人流畅的整个身体转身全身脱掉所有衣物裸体一丝不挂撅起臀部把屁股翘得很高胯宽臀部翘背对男人，男人勃起粗大又长的阴精俯视视角后入女人，每次插入撞击臀部回弹(臀翘胯宽:1.3)。男人手抓着女人双手后拉，女人面部看微微转头看向后面阿合高吐出舌头，女人回头的时候哪边身体���微微往后，从背面能看到晃动的巨大丰满乳房(巨大丰满乳房：1.3)，镜头缓慢拉远拉高。细腻的皮肤，流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[场景] 窗前趴式后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "欧美女人面部五官完全保持与第一帧的女人五官一模一样完全一致(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n保持原图像中的所有东西不要改变，相机镜头移动拉近女人五官，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)场景转场，女人全身裸体一丝不挂的的身体露出巨大丰满乳房(乳房巨大丰满：1.3)狗趴式性交女人双手趴在酒店窗户玻璃上丰满乳房随着每次插入都会贴到窗户玻璃上后面的欧美裸体年迈老男人从后面阴茎插入女人的Vaginal / anal(阴茎和阴道润滑液流出：1.3) (女人表情淫荡魅惑看向镜头：1.3)镜头拉近整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[体位] 女位正面互动": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "女人面部五官完全保持与第一帧的女人五官一模一样完全一致(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n保持原图像中的所有东西不要改变，场景转场男人平躺第一视角仰视视角仰视，女人面对镜头整个身体面对观众男人身上双腿大开面对观众女上位，男人正面插入女人Vaginal / anal(阴茎粗大又长青筋暴起真实皮肤纹理：1.3)(女人胯宽：1.3)(阴茎和阴道交合透明液体拉成丝混着润滑液流出：1.3) 镜头拉远整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[场景] 扶桌站立后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "女人面部五官完全保持与第一帧的女人五官一模一样完全一致。\n保持原图像中的所有东西不要改变，(镜头一直跟随女人:1.3)(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)女人转身反背身手扶桌子对着男人，男人走到女人臀部后面手扶女人下面裸体一丝不挂露出勃起粗大又长的阴茎插入女人Vaginal / anal(阴茎和阴道交合透明液体拉成丝混着润滑液流出：1.3) 镜头拉远整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[体位] 俯卧背向后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n女人转身狗趴式背对男人双腿大开，男人第一视角俯视视角俯视女人整个背对身体(超低视角：1.3)男人站立勃起粗大又长的阴茎俯视视角插入阴道或肛门女人面部看向侧面(阴茎上有大量的润滑液：1.3) 禁忌氛围画面 超级画质 流畅 8K"
+    },
+    "[互动] 背后撕衣吻胸": {
+        "high_noise_lora": "被揉胸H", "low_noise_lora": "被揉胸L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "镜头拉远，一位女生，保留原本的形象、场景环境不变(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。视频全程女生的脸部特征与第一帧保持一致，视频全程女生的全部脸部出现在画面中。一个男子走到女人身后，女孩的衣服被男人双手粗暴的抓住快速的从中间撕开，女孩表情很害羞，女孩完全赤裸，乳���剧烈跳动着，乳房和乳头可见，男子用手搓揉女孩乳房，男人用嘴吮吸女孩乳头，女孩很害羞，女孩身体微微前倾，可以清楚看清女人的表情。 男人一直揉搓女孩乳房，并且男人吻女孩的嘴，女孩陷入了困境。\n(女人保持所有与第一帧完全一模一样一直持续到视频结束：1.3)"
+    },
+    "[体位] 经典趴式后入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n镜头拉近到女人面部面部皮肤纹理细节完美展现，女人转身狗趴式双腿背对男人大开第一视角俯视视角俯视女人整个身体(超低视角：1.3)男人战立勃起粗大又长的阴茎俯视视角插入阴道或者肛门女人看向侧面女人面部随机做出阿合和表情(阴茎上有大量的润滑液：1.3) 禁忌氛围画面 超级画质 流畅 8K"
+    },
+    "[口部] 极限全深喉": {
+        "high_noise_lora": "终极深喉H", "low_noise_lora": "终极深喉L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "a cinematic scene with a woman in the frame, a naked man enters from the left side, only his lower body is visible, side view of his hips, thighs and legs, with a gigantic erected penis with testicles appears and she starts engaging in a deepthroat blowjob with that penis. She swallows the entire penis, her nose smashes agains the man's hips. She moves her head back and fort swallowing the penis, realistic proportions, natural lighting, cinematic composition"
+    },
+    "[口部] 跪姿互动特写": {
+        "high_noise_lora": "口H", "low_noise_lora": "口L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n镜头拉到女人面部完全看清女人面部皮肤纹理细节(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)，女人双腿大开缓慢流畅的下跪地面一位男人站立完全第一视角俯视视角(超低视角：1.3)俯视整个女人露出勃起粗大又长的阴茎(阴茎青筋暴起真实皮肤：1.3)女人抬头眼神看向镜头手握着阴茎张嘴口含整根阴茎在嘴里来回滑动女人随机做淫荡的面部表情(阴茎上有大量的润滑液：1.3) 禁忌氛围画面 超级画质 流畅 8K"
+    },
+    "[口部] 深喉终焉释放": {
+        "high_noise_lora": "口H", "low_noise_lora": "口L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名欧美女人保持与第一帧的时候完全保持的一模一样(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)。\n女人裸体 乳房巨大丰满 被男人暴力使劲推倒在床上，欧美女人表情害怕，欧美男人暴力的走在女人头部位置跪下 把粗大又长阴茎暴力插入女人嘴巴 暴力深喉，手按住头 整根插入喉咙深处，每次都带着口水唾液拉成丝 女人面部汗水 镜头拉近女人面部 男人射精乳白色浓稠精液在嘴里大量精液在嘴里流出来 禁忌氛围画面 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+
+    # ================= 特写/互动动作 =================
+    "[特写] 阴道扣弄": {
+        "high_noise_lora": "女自慰H", "low_noise_lora": "女自慰L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "完美阴户", "aux_low_lora": "完美阴户", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一名女人保持与第一帧的时候完全保持的一模一样。\n保持原图像中的所有东西不要改变，镜头拉近到女人整个脸部位置，(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)(女人表情淫荡魅惑看向镜头漏出邪魅笑容：1.3)女人缓慢往后仰躺双腿大开，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)，\n镜头缓慢移动到女人阴道处，女人双手扣弄自己涂满精油的阴道，聚焦女人阴道，镜头缓慢推进直到进入阴道和肛门占满整个画面，阴道的皮肤纹理细节清晰可见能看见阴道里的褶皱细节(尿道，阴蒂，阴唇：1.3)，(阴道上有大量的透明液体：1.3)禁忌氛围画面，堪称杰作，肌肤纹理极其细腻逼真，流畅动作， 超级画质，电影打光，极致细节 8K"
+    },
+    "[互动] 揉胸舐乳": {
+        "high_noise_lora": "吸乳头H", "low_noise_lora": "吸乳头L", "high_model_strength": 0.25, "low_model_strength": 0.8,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "n1ppl3suck1ng，\n一名女人保持与第一帧的时候完全保持的一模一样。\n保持原图像中的所有东西不要改变，镜头拉近到女人整个脸部位置，(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)镜头缓慢拉远\n女人脱掉衣服，露出丰满的乳房，她的乳房又大又挺。(女人表情淫荡魅惑看向镜头：1.3)，她用双手揉搓乳房，用手头舔舐着左边乳头，专注于乳晕和乳头区域。咬着并拉伸着。她的表情既愉悦又专注(女人表情淫荡魅惑看向镜头：1.3)镜头拉近整个视频画面。\n整个场景私密、感性，并着重展现了动作的细节。堪称杰作，肌肤纹理极其细腻逼真，流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[口部] 舔舐阴部": {
+        "high_noise_lora": "口阴H", "low_noise_lora": "口阴L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "cunn1l1ngu5，一名女人保持与第一帧的时候完全保持的一模一样。\n保持原图像中的所有东西不要改变，女人缓慢往后仰躺双腿大开，(女人五官皮肤细节纹理保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节清晰展示在画面中：1.3)，镜头缓慢移动到女人阴道处，整个画面被女人阴道占用阴道的皮肤纹理细节清晰可见能看见阴道里的褶皱细节(尿道，阴蒂，阴唇：1.3)，旁边一个男人趴在女人双腿处头部靠近女人阴道伸出舌头舔舐阴道，他的舌头有节奏地轻舔吮吸着她的阴蒂。接受刺激的女性臀部不由自主地微微扭动，颤抖着，这是快感的体现。她的阴道变得湿润闪亮，充满了唾液，并从男人的口中滴落。女人双手抱紧男人头部，镜头整体拉近视觉冲击，女人面部做出随机的淫荡表情，整个场景私密、感性，并着重展现了动作的细节。堪称杰作，肌肤纹理极其细腻逼真， 超级画质 流畅 增加超高清，完美细节 电影打光，极致细节 8K"
+    },
+    "[特写] 满脸喷精": {
+        "high_noise_lora": "射精H", "low_noise_lora": "射精L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "f4c3spl4sh, the scenes starts with the camera zooming out revealing a cinematic scene with a woman in the frame with a man entering from the left side, only his lower body is visible, side view of his hips, thighs and legs, with a gigantic erected penis with testicles. She starts recieving a MASSIVE cumshot on her face from the penis. The MASSIVE stream of semen is directed towards her mouth. The force of the ejaculation is strong, and the trajectory of the semen is aimed directly at her open mouth. The quantity of semen is MASSIVE, covering a significant portion of her face and neck area. The woman's eyes are open, and her mouth is slightly open as she receives the semen. The camera is positioned close to the woman's face and chest, capturing the moment of male ejaculation directly onto her face and chest. The semen is seen forcefully erupting from the penis and landing on her skin, creating a visible pool of semen on her chest."
+    },
+    "[体位] 兔女郎骑乘": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.25, "low_model_strength": 0.8,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "图中女人面部五官完全保持与第一帧的女人五官一模一样完全一致。\n保持原图像中的所有东西不要改变，相机镜头移动拉近聚焦女人五官，女人面部占满镜头，(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节和精致妆容完美展示在画面中：1.3)(女人表情淫荡魅惑看向镜头漏出邪魅笑容：1.3)场景转场，女人全身赤裸不穿任何衣服，头上戴着兔内多头上，男人全身赤裸躺在地上，女人全身裸体露出巨大丰满乳房(乳房巨大丰满：1.3)女人骑马式坐在男人阴茎上，阴茎插入女人阴部，女人双手撑在男人胸上，女人下体不断抬起坐下，阴部因为阴茎的抽插滴落白色透明液体Vaginal / anal(阴茎和阴道润滑液流出：1.3) (女人表情淫荡魅惑看向镜头：1.3)镜头拉近整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    
+    # ================= 新增动作 =================
+    "[体位] 恶魔躺插": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "图中女人面部五官、发型、肤色、面部皮肤纹理、面部皱纹、面部轮廓完全保持与第一帧的女人面部五官、发型、肤色、面部皮肤纹理、面部皱纹、面部轮廓一模一样完全一致。\n保持原图像中的所有东西不要改变，相机镜头移动拉近聚焦女人五官，女人面部占满镜头(女人五官保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节与第一帧保持一致展示在画面中：1.3)(女人面部轮廓与第一帧保持一致展示在画面中：1.3)(女人眼神迷离表情淫荡看向镜头：1.3)，场景转场，女人全身赤裸没有任何衣服，只有腿上穿着暗红色宽边的黑丝，头上戴着小恶魔角，男人全身赤裸，女人全身裸体露出乳房(乳房巨大丰满：1.3)女人向后躺下，男人将阴茎插入女人阴部，女人双手揉捏自己的乳房，阴部因为阴茎的抽插滴落白色透明液体Vaginal / anal(阴茎和阴道润滑液流出：1.3) (女人表情淫荡魅惑看向镜头：1.3)镜头拉近整个视频画面。\n流畅动作 超清画质 8K 增强超清 完美光线 电影打光，极致细节"
+    },
+    "[口部] 口加手": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "图中女人面部五官发型完全保持与第一帧的女人五官发型一模一样完全一致。\n保持原图像中的所有东西不要改变，女人先抬手遮挡住全部镜头，相机镜头移动拉近聚焦女人手掌心，之后再场景转场到落地窗前，(女人五官发型保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节展示在画面中：1.3)(女人眼神迷离表情魅惑看向镜头：1.3)，镜头视角展露女人全身，女人全身赤裸没有任何衣服，跪在落地窗前地毯上，露出她的乳房(乳房:1.3)，女人眼神饥渴注视镜头前方，一个裸体欧美男人的粗大勃起阴茎直指她的嘴巴，龟头射出精液到女人嘴里，她伸出舌头用嘴紧紧含住阴茎，双手握住阴茎深喉吞入，动作渐快她手淫阴茎更快喉咙深吞更猛，喉咙鼓起每次抽插带出黏稠唾液丝线(口水拉丝滴落:1.3)，乳房随着节奏剧烈晃动乳头硬挺。\n超逼真，8K高细节逼真物理，逼真逼真，品质卓越 NSFW动作逼真，24fps动画流畅，面部表情细致，嘴唇与动作同步 。(视频中的女人从视频到结束一直保持第一帧的五官发型保持一致。)"
+    },
+    "[体位] 捆绑插入": {
+        "high_noise_lora": "传教士H", "low_noise_lora": "传教士L", "high_model_strength": 0.7, "low_model_strength": 0.7,
+        "aux_high_lora": "None", "aux_low_lora": "None", "aux_high_strength": 0.7, "aux_low_strength": 0.7,
+        "prompt": "一个女人五官所有装饰发型不变保持与第一帧的时候一模一样。\n保持原图像中的所有东西不要改变，女人先拿出手机，手机上是家庭后院花园中女人全身赤裸双腿分别被两边情趣带捆住大腿吊在半空露出阴部和乳房的照片(乳房阴部:1.3)，相机镜头移动拉近聚焦女人手机，手机占据整个镜头，之后场景转场到家庭后院花园中，(女人五官发型保持与第一帧女人一模一样不改变：1.2)(女人面部皮肤纹理细节展示在画面中：1.3)(女人眼神迷离表情魅惑看向镜头：1.3)随后拉远，女人全身赤裸没有任何衣服，只有腿上穿着吊带丝袜，女人双腿分别被两边的情趣捆绑绳捆住大腿吊起在半空，女人双手抓住两边的捆绑绳，乳房阴部暴露(乳房阴部:1.3)，欧美男人从侧面出现将阴茎插入女人阴部，阴部因为阴茎的抽插滴落点滴的白色透明液体Vaginal / anal(阴道液体滴落：1.3)(女人表情淫荡魅惑看向镜头：1.3)女人魅惑的看着镜头\n超逼真，8K高细节逼真物理，逼真逼真，品质卓越 NSFW动作逼真，24fps动画流畅，面部表情细致，嘴唇与动作同步 。(视频中的女人从视频到结束一直保持第一帧的五官保持一致。)"
+    }
+}
+
+class ActionSelectorNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "action": (list(ACTION_MAPPING.keys()),),
+                "seconds": ("INT", {"default": 6, "min": 1, "max": 120, "step": 1}),
+            }
+        }
+
+    RETURN_TYPES = ("*", "*", "*", "*", "FLOAT", "FLOAT", "FLOAT", "FLOAT", "STRING", "INT", "STRING")
+    RETURN_NAMES = ("高噪 LoRA", "底噪 LoRA", "辅高 LoRA*", "辅低 LoRA*", "高噪模型强度", "低噪模型强度", "辅高模型强度*", "辅低模型强度*", "提示词 (Prompt)", "帧数", "储存文件名")
+    FUNCTION = "select_action"
+    CATEGORY = "Custom"
+
+    def select_action(self, action, seconds):
+        data = ACTION_MAPPING[action]
+        
+        # 封装一个内部函数处理 LoRA 后缀扩展名
+        def format_lora(lora_name):
+            if not lora_name or lora_name == "None":
+                return "None"
+            return lora_name if lora_name.endswith(".safetensors") else f"{lora_name}.safetensors"
+
+        # 主 LoRA 获取
+        high_noise = format_lora(data.get("high_noise_lora", "None"))
+        low_noise = format_lora(data.get("low_noise_lora", "None"))
+        
+        # =========== 核心修复逻辑 ===========
+        # 辅高 LoRA：如果为空，强制复用主高噪LoRA名称，但输出强度设为0.0
+        aux_high_raw = data.get("aux_high_lora", "None")
+        if aux_high_raw == "None" or not aux_high_raw:
+            aux_high = high_noise  
+            aux_high_strength = 0.0 # 强制 0.0 强度，相当于跳过加载
+        else:
+            aux_high = format_lora(aux_high_raw)
+            aux_high_strength = float(data.get("aux_high_strength", 0.7))
+            
+        # 辅低 LoRA：如果为空，强制复用主底噪LoRA名称，但输出强度设为0.0
+        aux_low_raw = data.get("aux_low_lora", "None")
+        if aux_low_raw == "None" or not aux_low_raw:
+            aux_low = low_noise   
+            aux_low_strength = 0.0 # 强制 0.0 强度，相当于跳过加载
+        else:
+            aux_low = format_lora(aux_low_raw)
+            aux_low_strength = float(data.get("aux_low_strength", 0.7))
+        # ====================================
+        
+        high_strength = float(data.get("high_model_strength", 0.7))
+        low_strength = float(data.get("low_model_strength", 0.7))
+        
+        prompt = data["prompt"]
+        frame_count = int(seconds * 16 + 1)
+        current_time = datetime.datetime.now().strftime("%m%d%H%M")
+        
+        action_name_cn = action.split("]")[-1].strip() if "]" in action else action
+        action_time_name = f"{action_name_cn}_{current_time}"
+        
+        return (
+            high_noise, low_noise, aux_high, aux_low, 
+            high_strength, low_strength, aux_high_strength, aux_low_strength, 
+            prompt, frame_count, action_time_name
+        )
+
+NODE_CLASS_MAPPINGS = {"ActionSelectorNode": ActionSelectorNode}
+NODE_DISPLAY_NAME_MAPPINGS = {"ActionSelectorNode": "🎬 Action Selector (动作选择器)"}

custom_nodes/ComfyUI-AutoCropFaces/.gitignore

@@ -0,0 +1,163 @@
+outputs/
+**/Resnet50_Final.pth
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

custom_nodes/ComfyUI-AutoCropFaces/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Liu Sida
+
+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.

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/data/__init__.py

@@ -0,0 +1 @@
+from .config import *

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/data/config.py

@@ -0,0 +1,42 @@
+# config.py
+
+cfg_mnet = {
+    'name': 'mobilenet0.25',
+    'min_sizes': [[16, 32], [64, 128], [256, 512]],
+    'steps': [8, 16, 32],
+    'variance': [0.1, 0.2],
+    'clip': False,
+    'loc_weight': 2.0,
+    'gpu_train': True,
+    'batch_size': 32,
+    'ngpu': 1,
+    'epoch': 250,
+    'decay1': 190,
+    'decay2': 220,
+    'image_size': 640,
+    'pretrain': True,
+    'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
+    'in_channel': 32,
+    'out_channel': 64
+}
+
+cfg_re50 = {
+    'name': 'Resnet50',
+    'min_sizes': [[16, 32], [64, 128], [256, 512]],
+    'steps': [8, 16, 32],
+    'variance': [0.1, 0.2],
+    'clip': False,
+    'loc_weight': 2.0,
+    'gpu_train': True,
+    'batch_size': 24,
+    'ngpu': 4,
+    'epoch': 100,
+    'decay1': 70,
+    'decay2': 90,
+    'image_size': 840,
+    'pretrain': True,
+    'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3},
+    'in_channel': 256,
+    'out_channel': 256
+}
+

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/layers/__init__.py

@@ -0,0 +1 @@
+from .functions import *

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/layers/functions/prior_box.py

@@ -0,0 +1,34 @@
+import torch
+from itertools import product as product
+import numpy as np
+from math import ceil
+
+
+class PriorBox(object):
+    def __init__(self, cfg, image_size=None, phase='train'):
+        super(PriorBox, self).__init__()
+        self.min_sizes = cfg['min_sizes']
+        self.steps = cfg['steps']
+        self.clip = cfg['clip']
+        self.image_size = image_size
+        self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
+        self.name = "s"
+
+    def forward(self):
+        anchors = []
+        for k, f in enumerate(self.feature_maps):
+            min_sizes = self.min_sizes[k]
+            for i, j in product(range(f[0]), range(f[1])):
+                for min_size in min_sizes:
+                    s_kx = min_size / self.image_size[1]
+                    s_ky = min_size / self.image_size[0]
+                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
+                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
+                    for cy, cx in product(dense_cy, dense_cx):
+                        anchors += [cx, cy, s_kx, s_ky]
+
+        # back to torch land
+        output = torch.Tensor(anchors).view(-1, 4)
+        if self.clip:
+            output.clamp_(max=1, min=0)
+        return output

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/models/net.py

@@ -0,0 +1,137 @@
+import time
+import torch
+import torch.nn as nn
+import torchvision.models._utils as _utils
+import torchvision.models as models
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+def conv_bn(inp, oup, stride = 1, leaky = 0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True)
+    )
+
+def conv_bn_no_relu(inp, oup, stride):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        nn.BatchNorm2d(oup),
+    )
+
+def conv_bn1X1(inp, oup, stride, leaky=0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True)
+    )
+
+def conv_dw(inp, oup, stride, leaky=0.1):
+    return nn.Sequential(
+        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
+        nn.BatchNorm2d(inp),
+        nn.LeakyReLU(negative_slope= leaky,inplace=True),
+
+        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope= leaky,inplace=True),
+    )
+
+class SSH(nn.Module):
+    def __init__(self, in_channel, out_channel):
+        super(SSH, self).__init__()
+        assert out_channel % 4 == 0
+        leaky = 0
+        if (out_channel <= 64):
+            leaky = 0.1
+        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel//2, stride=1)
+
+        self.conv5X5_1 = conv_bn(in_channel, out_channel//4, stride=1, leaky = leaky)
+        self.conv5X5_2 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)
+
+        self.conv7X7_2 = conv_bn(out_channel//4, out_channel//4, stride=1, leaky = leaky)
+        self.conv7x7_3 = conv_bn_no_relu(out_channel//4, out_channel//4, stride=1)
+
+    def forward(self, input):
+        conv3X3 = self.conv3X3(input)
+
+        conv5X5_1 = self.conv5X5_1(input)
+        conv5X5 = self.conv5X5_2(conv5X5_1)
+
+        conv7X7_2 = self.conv7X7_2(conv5X5_1)
+        conv7X7 = self.conv7x7_3(conv7X7_2)
+
+        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
+        out = F.relu(out)
+        return out
+
+class FPN(nn.Module):
+    def __init__(self,in_channels_list,out_channels):
+        super(FPN,self).__init__()
+        leaky = 0
+        if (out_channels <= 64):
+            leaky = 0.1
+        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride = 1, leaky = leaky)
+        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride = 1, leaky = leaky)
+        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride = 1, leaky = leaky)
+
+        self.merge1 = conv_bn(out_channels, out_channels, leaky = leaky)
+        self.merge2 = conv_bn(out_channels, out_channels, leaky = leaky)
+
+    def forward(self, input):
+        # names = list(input.keys())
+        input = list(input.values())
+
+        output1 = self.output1(input[0])
+        output2 = self.output2(input[1])
+        output3 = self.output3(input[2])
+
+        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode="nearest")
+        output2 = output2 + up3
+        output2 = self.merge2(output2)
+
+        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode="nearest")
+        output1 = output1 + up2
+        output1 = self.merge1(output1)
+
+        out = [output1, output2, output3]
+        return out
+
+
+
+class MobileNetV1(nn.Module):
+    def __init__(self):
+        super(MobileNetV1, self).__init__()
+        self.stage1 = nn.Sequential(
+            conv_bn(3, 8, 2, leaky = 0.1),    # 3
+            conv_dw(8, 16, 1),   # 7
+            conv_dw(16, 32, 2),  # 11
+            conv_dw(32, 32, 1),  # 19
+            conv_dw(32, 64, 2),  # 27
+            conv_dw(64, 64, 1),  # 43
+        )
+        self.stage2 = nn.Sequential(
+            conv_dw(64, 128, 2),  # 43 + 16 = 59
+            conv_dw(128, 128, 1), # 59 + 32 = 91
+            conv_dw(128, 128, 1), # 91 + 32 = 123
+            conv_dw(128, 128, 1), # 123 + 32 = 155
+            conv_dw(128, 128, 1), # 155 + 32 = 187
+            conv_dw(128, 128, 1), # 187 + 32 = 219
+        )
+        self.stage3 = nn.Sequential(
+            conv_dw(128, 256, 2), # 219 +3 2 = 241
+            conv_dw(256, 256, 1), # 241 + 64 = 301
+        )
+        self.avg = nn.AdaptiveAvgPool2d((1,1))
+        self.fc = nn.Linear(256, 1000)
+
+    def forward(self, x):
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.avg(x)
+        # x = self.model(x)
+        x = x.view(-1, 256)
+        x = self.fc(x)
+        return x
+

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/models/retinaface.py

@@ -0,0 +1,130 @@
+import os
+import torch
+import torch.nn as nn
+import torchvision.models.detection.backbone_utils as backbone_utils
+import torchvision.models._utils as _utils
+import torch.nn.functional as F
+from collections import OrderedDict
+
+from .net import MobileNetV1 as MobileNetV1
+from .net import FPN as FPN
+from .net import SSH as SSH
+
+
+
+class ClassHead(nn.Module):
+    def __init__(self,inchannels=512,num_anchors=3):
+        super(ClassHead,self).__init__()
+        self.num_anchors = num_anchors
+        self.conv1x1 = nn.Conv2d(inchannels,self.num_anchors*2,kernel_size=(1,1),stride=1,padding=0)
+
+    def forward(self,x):
+        out = self.conv1x1(x)
+        out = out.permute(0,2,3,1).contiguous()
+        
+        return out.view(out.shape[0], -1, 2)
+
+class BboxHead(nn.Module):
+    def __init__(self,inchannels=512,num_anchors=3):
+        super(BboxHead,self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*4,kernel_size=(1,1),stride=1,padding=0)
+
+    def forward(self,x):
+        out = self.conv1x1(x)
+        out = out.permute(0,2,3,1).contiguous()
+
+        return out.view(out.shape[0], -1, 4)
+
+class LandmarkHead(nn.Module):
+    def __init__(self,inchannels=512,num_anchors=3):
+        super(LandmarkHead,self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels,num_anchors*10,kernel_size=(1,1),stride=1,padding=0)
+
+    def forward(self,x):
+        out = self.conv1x1(x)
+        out = out.permute(0,2,3,1).contiguous()
+
+        return out.view(out.shape[0], -1, 10)
+
+class RetinaFace(nn.Module):
+    def __init__(self, cfg = None, phase = 'train', weights_path='', device="cuda"):
+        """
+        :param cfg:  Network related settings.
+        :param phase: train or test.
+        """
+        weights_path = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), weights_path)
+
+        super(RetinaFace,self).__init__()
+        self.phase = phase
+        backbone = None
+        if cfg['name'] == 'mobilenet0.25':
+            backbone = MobileNetV1()
+            if cfg['pretrain']:
+                checkpoint = torch.load(weights_path, map_location=torch.device(device))
+                from collections import OrderedDict
+                new_state_dict = OrderedDict()
+                for k, v in checkpoint['state_dict'].items():
+                    name = k[7:]  # remove module.
+                    new_state_dict[name] = v
+                # load params
+                backbone.load_state_dict(new_state_dict)
+        elif cfg['name'] == 'Resnet50':
+            import torchvision.models as models
+            backbone = models.resnet50(pretrained=cfg['pretrain'])
+
+        self.body = _utils.IntermediateLayerGetter(backbone, cfg['return_layers'])
+        in_channels_stage2 = cfg['in_channel']
+        in_channels_list = [
+            in_channels_stage2 * 2,
+            in_channels_stage2 * 4,
+            in_channels_stage2 * 8,
+        ]
+        out_channels = cfg['out_channel']
+        self.fpn = FPN(in_channels_list,out_channels)
+        self.ssh1 = SSH(out_channels, out_channels)
+        self.ssh2 = SSH(out_channels, out_channels)
+        self.ssh3 = SSH(out_channels, out_channels)
+
+        self.ClassHead = self._make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.BboxHead = self._make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.LandmarkHead = self._make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
+        
+    def _make_class_head(self,fpn_num=3,inchannels=64,anchor_num=2):
+        classhead = nn.ModuleList()
+        for i in range(fpn_num):
+            classhead.append(ClassHead(inchannels,anchor_num))
+        return classhead
+    
+    def _make_bbox_head(self,fpn_num=3,inchannels=64,anchor_num=2):
+        bboxhead = nn.ModuleList()
+        for i in range(fpn_num):
+            bboxhead.append(BboxHead(inchannels,anchor_num))
+        return bboxhead
+
+    def _make_landmark_head(self,fpn_num=3,inchannels=64,anchor_num=2):
+        landmarkhead = nn.ModuleList()
+        for i in range(fpn_num):
+            landmarkhead.append(LandmarkHead(inchannels,anchor_num))
+        return landmarkhead
+
+    def forward(self,inputs):
+        out = self.body(inputs)
+
+        # FPN
+        fpn = self.fpn(out)
+
+        # SSH
+        feature1 = self.ssh1(fpn[0])
+        feature2 = self.ssh2(fpn[1])
+        feature3 = self.ssh3(fpn[2])
+        features = [feature1, feature2, feature3]
+
+        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
+        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)],dim=1)
+        ldm_regressions = torch.cat([self.LandmarkHead[i](feature) for i, feature in enumerate(features)], dim=1)
+
+        if self.phase == 'train':
+            output = (bbox_regressions, classifications, ldm_regressions)
+        else:
+            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
+        return output

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/pytorch_retinaface.py

@@ -0,0 +1,174 @@
+import os
+import time
+import math
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+
+from .data import cfg_mnet, cfg_re50
+from .layers.functions.prior_box import PriorBox
+from .utils.nms.py_cpu_nms import py_cpu_nms
+from .models.retinaface import RetinaFace
+from .utils.box_utils import decode, decode_landm
+
+class Pytorch_RetinaFace:
+    def __init__(self, cfg="mobile0.25", pretrained_path="./weights/mobilenet0.25_Final.pth", weights_path="./weights/mobilenetV1X0.25_pretrain.tar", device="auto", vis_thres=0.6, top_k=5000, keep_top_k=750, nms_threshold=0.4, confidence_threshold=0.02):
+        self.vis_thres = vis_thres
+        self.top_k = top_k
+        self.keep_top_k = keep_top_k
+        self.nms_threshold = nms_threshold
+        self.confidence_threshold = confidence_threshold
+        self.cfg = cfg_mnet if cfg=="mobile0.25" else cfg_re50
+        
+        if device == 'auto':
+            # Automatically choose the device
+            if torch.cuda.is_available():
+                self.device = torch.device("cuda")
+            elif torch.backends.mps.is_available():
+                self.device = torch.device("mps")
+            else:
+                self.device = torch.device("cpu")
+        else:
+            # Use the specified device
+            self.device = torch.device(device)
+
+        print("Using device:", self.device)
+
+        self.net = RetinaFace(cfg=self.cfg, weights_path=weights_path, phase='test', device=self.device).to(self.device)
+        self.load_model_weights(pretrained_path)
+        self.net.eval()
+
+    def check_keys(self, model, pretrained_state_dict):
+        ckpt_keys = set(pretrained_state_dict.keys())
+        model_keys = set(model.state_dict().keys())
+        used_pretrained_keys = model_keys & ckpt_keys
+        unused_pretrained_keys = ckpt_keys - model_keys
+        missing_keys = model_keys - ckpt_keys
+        print('Missing keys:{}'.format(len(missing_keys)))
+        print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+        print('Used keys:{}'.format(len(used_pretrained_keys)))
+        assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+        return True
+
+
+    def remove_prefix(self, state_dict, prefix):
+        ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+        print('remove prefix \'{}\''.format(prefix))
+        f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+        return {f(key): value for key, value in state_dict.items()}
+
+
+    def load_model_weights(self, pretrained_path):
+        pretrained_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), pretrained_path)
+        print('Loading pretrained model from {}'.format(pretrained_path))
+        pretrained_dict = torch.load(pretrained_path, map_location=self.device)
+        if "state_dict" in pretrained_dict.keys():
+            pretrained_dict = self.remove_prefix(pretrained_dict['state_dict'], 'module.')
+        else:
+            pretrained_dict = self.remove_prefix(pretrained_dict, 'module.')
+        self.check_keys(self.net, pretrained_dict)
+        self.net.load_state_dict(pretrained_dict, strict=False)
+        self.net.to(self.device)
+        return self.net
+
+    def center_and_crop_rescale(self, image, dets, scale_factor=4, shift_factor=0.35, aspect_ratio=1.0):
+        cropped_imgs = []
+        bbox_infos = []
+        for index, bbox in enumerate(dets):
+            if bbox[4] < self.vis_thres:
+                continue
+
+            x1, y1, x2, y2 = map(int, bbox[:4])
+            face_width = x2 - x1
+            face_height = y2 - y1
+
+            default_area = face_width * face_height
+            default_area *= scale_factor
+            default_side = math.sqrt(default_area)
+
+            # New height and width based on aspect_ratio
+            new_face_width = int(default_side * math.sqrt(aspect_ratio))
+            new_face_height = int(default_side / math.sqrt(aspect_ratio))
+
+            # Center coordinates of the detected face
+            center_x = x1 + face_width // 2
+            center_y = y1 + face_height // 2 + int(new_face_height * (0.5 - shift_factor))
+
+            original_crop_x1 = center_x - new_face_width // 2
+            original_crop_x2 = center_x + new_face_width // 2
+            original_crop_y1 = center_y - new_face_height // 2
+            original_crop_y2 = center_y + new_face_height // 2
+            # Crop coordinates, adjusted to the image boundaries
+            crop_x1 = max(0, original_crop_x1)
+            crop_x2 = min(image.shape[1], original_crop_x2)
+            crop_y1 = max(0, original_crop_y1)
+            crop_y2 = min(image.shape[0], original_crop_y2)
+
+            # Crop the region and add padding to form a square
+            cropped_imgs.append(image[crop_y1:crop_y2, crop_x1:crop_x2])
+            bbox_infos.append(((original_crop_x2-original_crop_x1, original_crop_y2-original_crop_y1),(original_crop_x1, original_crop_y1, original_crop_x2, original_crop_y2)))
+        return cropped_imgs, bbox_infos
+
+    def detect_faces(self, img):
+        resize = 1
+
+        if len(img.shape) == 4 and img.shape[0] == 1:
+            img = torch.squeeze(img, 0)
+        if isinstance(img, np.ndarray):
+            img = torch.from_numpy(img)
+        img = img.to(self.device)
+
+        im_height, im_width, _ = img.shape
+        scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
+        mean_values = torch.tensor([104, 117, 123], dtype=torch.float32).to(self.device)
+        mean_values = mean_values.view(1, 1, 3)
+        img -= mean_values
+        img = img.permute(2, 0, 1)
+        img = img.unsqueeze(0)
+        scale = scale.to(self.device)
+
+        tic = time.time()
+        with torch.no_grad():
+            loc, conf, landms = self.net(img)  # forward pass
+        print('net forward time: {:.4f}'.format(time.time() - tic))
+
+        priorbox = PriorBox(self.cfg, image_size=(im_height, im_width))
+        priors = priorbox.forward()
+        priors = priors.to(self.device)
+        prior_data = priors.data
+        boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance'])
+        boxes = boxes * scale / resize
+        boxes = boxes.cpu().numpy()
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+        landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance'])
+        scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                                img.shape[3], img.shape[2], img.shape[3], img.shape[2],
+                                img.shape[3], img.shape[2]])
+        scale1 = scale1.to(self.device)
+        landms = landms * scale1 / resize
+        landms = landms.cpu().numpy()
+
+        # Ignore low scores
+        inds = np.where(scores > self.confidence_threshold)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+
+        # Keep top-K before NMS
+        order = scores.argsort()[::-1][:self.top_k]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+
+        # Perform NMS
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, self.nms_threshold)
+        dets = dets[keep, :]
+        landms = landms[keep]
+
+        # Keep top-K faster NMS
+        dets = dets[:self.keep_top_k, :]
+        landms = landms[:self.keep_top_k, :]
+
+        dets = np.concatenate((dets, landms), axis=1)
+        return dets

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/utils/box_utils.py

@@ -0,0 +1,330 @@
+import torch
+import numpy as np
+
+
+def point_form(boxes):
+    """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
+    representation for comparison to point form ground truth data.
+    Args:
+        boxes: (tensor) center-size default boxes from priorbox layers.
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat((boxes[:, :2] - boxes[:, 2:]/2,     # xmin, ymin
+                     boxes[:, :2] + boxes[:, 2:]/2), 1)  # xmax, ymax
+
+
+def center_size(boxes):
+    """ Convert prior_boxes to (cx, cy, w, h)
+    representation for comparison to center-size form ground truth data.
+    Args:
+        boxes: (tensor) point_form boxes
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat((boxes[:, 2:] + boxes[:, :2])/2,  # cx, cy
+                     boxes[:, 2:] - boxes[:, :2], 1)  # w, h
+
+
+def intersect(box_a, box_b):
+    """ We resize both tensors to [A,B,2] without new malloc:
+    [A,2] -> [A,1,2] -> [A,B,2]
+    [B,2] -> [1,B,2] -> [A,B,2]
+    Then we compute the area of intersect between box_a and box_b.
+    Args:
+      box_a: (tensor) bounding boxes, Shape: [A,4].
+      box_b: (tensor) bounding boxes, Shape: [B,4].
+    Return:
+      (tensor) intersection area, Shape: [A,B].
+    """
+    A = box_a.size(0)
+    B = box_b.size(0)
+    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2),
+                       box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
+    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2),
+                       box_b[:, :2].unsqueeze(0).expand(A, B, 2))
+    inter = torch.clamp((max_xy - min_xy), min=0)
+    return inter[:, :, 0] * inter[:, :, 1]
+
+
+def jaccard(box_a, box_b):
+    """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
+    is simply the intersection over union of two boxes.  Here we operate on
+    ground truth boxes and default boxes.
+    E.g.:
+        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
+    Args:
+        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
+        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
+    Return:
+        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
+    """
+    inter = intersect(box_a, box_b)
+    area_a = ((box_a[:, 2]-box_a[:, 0]) *
+              (box_a[:, 3]-box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
+    area_b = ((box_b[:, 2]-box_b[:, 0]) *
+              (box_b[:, 3]-box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
+    union = area_a + area_b - inter
+    return inter / union  # [A,B]
+
+
+def matrix_iou(a, b):
+    """
+    return iou of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
+    return area_i / (area_a[:, np.newaxis] + area_b - area_i)
+
+
+def matrix_iof(a, b):
+    """
+    return iof of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    return area_i / np.maximum(area_a[:, np.newaxis], 1)
+
+
+def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
+    """Match each prior box with the ground truth box of the highest jaccard
+    overlap, encode the bounding boxes, then return the matched indices
+    corresponding to both confidence and location preds.
+    Args:
+        threshold: (float) The overlap threshold used when mathing boxes.
+        truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
+        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
+        variances: (tensor) Variances corresponding to each prior coord,
+            Shape: [num_priors, 4].
+        labels: (tensor) All the class labels for the image, Shape: [num_obj].
+        landms: (tensor) Ground truth landms, Shape [num_obj, 10].
+        loc_t: (tensor) Tensor to be filled w/ endcoded location targets.
+        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
+        landm_t: (tensor) Tensor to be filled w/ endcoded landm targets.
+        idx: (int) current batch index
+    Return:
+        The matched indices corresponding to 1)location 2)confidence 3)landm preds.
+    """
+    # jaccard index
+    overlaps = jaccard(
+        truths,
+        point_form(priors)
+    )
+    # (Bipartite Matching)
+    # [1,num_objects] best prior for each ground truth
+    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
+
+    # ignore hard gt
+    valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
+    best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
+    if best_prior_idx_filter.shape[0] <= 0:
+        loc_t[idx] = 0
+        conf_t[idx] = 0
+        return
+
+    # [1,num_priors] best ground truth for each prior
+    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
+    best_truth_idx.squeeze_(0)
+    best_truth_overlap.squeeze_(0)
+    best_prior_idx.squeeze_(1)
+    best_prior_idx_filter.squeeze_(1)
+    best_prior_overlap.squeeze_(1)
+    best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)  # ensure best prior
+    # TODO refactor: index  best_prior_idx with long tensor
+    # ensure every gt matches with its prior of max overlap
+    for j in range(best_prior_idx.size(0)):     # 判别此anchor是预测哪一个boxes
+        best_truth_idx[best_prior_idx[j]] = j
+    matches = truths[best_truth_idx]            # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
+    conf = labels[best_truth_idx]               # Shape: [num_priors]      此处为每一个anchor对应的label取出来
+    conf[best_truth_overlap < threshold] = 0    # label as background   overlap<0.35的全部作为负样本
+    loc = encode(matches, priors, variances)
+
+    matches_landm = landms[best_truth_idx]
+    landm = encode_landm(matches_landm, priors, variances)
+    loc_t[idx] = loc    # [num_priors,4] encoded offsets to learn
+    conf_t[idx] = conf  # [num_priors] top class label for each prior
+    landm_t[idx] = landm
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:])/2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+def encode_landm(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 10].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded landm (tensor), Shape: [num_priors, 10]
+    """
+
+    # dist b/t match center and prior's center
+    matched = torch.reshape(matched, (matched.size(0), 5, 2))
+    priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
+    g_cxcy = matched[:, :, :2] - priors[:, :, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, :, 2:])
+    # g_cxcy /= priors[:, :, 2:]
+    g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
+    # return target for smooth_l1_loss
+    return g_cxcy
+
+
+# Adapted from https://github.com/Hakuyume/chainer-ssd
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((
+        priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+def decode_landm(pre, priors, variances):
+    """Decode landm from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        pre (tensor): landm predictions for loc layers,
+            Shape: [num_priors,10]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded landm predictions
+    """
+    landms = torch.cat((priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
+                        priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
+                        ), dim=1)
+    return landms
+
+
+def log_sum_exp(x):
+    """Utility function for computing log_sum_exp while determining
+    This will be used to determine unaveraged confidence loss across
+    all examples in a batch.
+    Args:
+        x (Variable(tensor)): conf_preds from conf layers
+    """
+    x_max = x.data.max()
+    return torch.log(torch.sum(torch.exp(x-x_max), 1, keepdim=True)) + x_max
+
+
+# Original author: Francisco Massa:
+# https://github.com/fmassa/object-detection.torch
+# Ported to PyTorch by Max deGroot (02/01/2017)
+def nms(boxes, scores, overlap=0.5, top_k=200):
+    """Apply non-maximum suppression at test time to avoid detecting too many
+    overlapping bounding boxes for a given object.
+    Args:
+        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
+        scores: (tensor) The class predscores for the img, Shape:[num_priors].
+        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
+        top_k: (int) The Maximum number of box preds to consider.
+    Return:
+        The indices of the kept boxes with respect to num_priors.
+    """
+
+    keep = torch.Tensor(scores.size(0)).fill_(0).long()
+    if boxes.numel() == 0:
+        return keep
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    area = torch.mul(x2 - x1, y2 - y1)
+    v, idx = scores.sort(0)  # sort in ascending order
+    # I = I[v >= 0.01]
+    idx = idx[-top_k:]  # indices of the top-k largest vals
+    xx1 = boxes.new()
+    yy1 = boxes.new()
+    xx2 = boxes.new()
+    yy2 = boxes.new()
+    w = boxes.new()
+    h = boxes.new()
+
+    # keep = torch.Tensor()
+    count = 0
+    while idx.numel() > 0:
+        i = idx[-1]  # index of current largest val
+        # keep.append(i)
+        keep[count] = i
+        count += 1
+        if idx.size(0) == 1:
+            break
+        idx = idx[:-1]  # remove kept element from view
+        # load bboxes of next highest vals
+        torch.index_select(x1, 0, idx, out=xx1)
+        torch.index_select(y1, 0, idx, out=yy1)
+        torch.index_select(x2, 0, idx, out=xx2)
+        torch.index_select(y2, 0, idx, out=yy2)
+        # store element-wise max with next highest score
+        xx1 = torch.clamp(xx1, min=x1[i])
+        yy1 = torch.clamp(yy1, min=y1[i])
+        xx2 = torch.clamp(xx2, max=x2[i])
+        yy2 = torch.clamp(yy2, max=y2[i])
+        w.resize_as_(xx2)
+        h.resize_as_(yy2)
+        w = xx2 - xx1
+        h = yy2 - yy1
+        # check sizes of xx1 and xx2.. after each iteration
+        w = torch.clamp(w, min=0.0)
+        h = torch.clamp(h, min=0.0)
+        inter = w*h
+        # IoU = i / (area(a) + area(b) - i)
+        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)
+        union = (rem_areas - inter) + area[i]
+        IoU = inter/union  # store result in iou
+        # keep only elements with an IoU <= overlap
+        idx = idx[IoU.le(overlap)]
+    return keep, count
+
+

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/utils/nms/py_cpu_nms.py

@@ -0,0 +1,38 @@
+# --------------------------------------------------------
+# Fast R-CNN
+# Copyright (c) 2015 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ross Girshick
+# --------------------------------------------------------
+
+import numpy as np
+
+def py_cpu_nms(dets, thresh):
+    """Pure Python NMS baseline."""
+    x1 = dets[:, 0]
+    y1 = dets[:, 1]
+    x2 = dets[:, 2]
+    y2 = dets[:, 3]
+    scores = dets[:, 4]
+
+    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
+    order = scores.argsort()[::-1]
+
+    keep = []
+    while order.size > 0:
+        i = order[0]
+        keep.append(i)
+        xx1 = np.maximum(x1[i], x1[order[1:]])
+        yy1 = np.maximum(y1[i], y1[order[1:]])
+        xx2 = np.minimum(x2[i], x2[order[1:]])
+        yy2 = np.minimum(y2[i], y2[order[1:]])
+
+        w = np.maximum(0.0, xx2 - xx1 + 1)
+        h = np.maximum(0.0, yy2 - yy1 + 1)
+        inter = w * h
+        ovr = inter / (areas[i] + areas[order[1:]] - inter)
+
+        inds = np.where(ovr <= thresh)[0]
+        order = order[inds + 1]
+
+    return keep

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/weights/mobilenet0.25_Final.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2979b33ffafda5d74b6948cd7a5b9a7a62f62b949cef24e95fd15d2883a65220
+size 1789735

custom_nodes/ComfyUI-AutoCropFaces/Pytorch_Retinaface/weights/mobilenetV1X0.25_pretrain.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75c37f8d438b7c6aed2e6b310fd0540c40dabf0dd820770bd7082668dfd7c1c1
+size 3827150

custom_nodes/ComfyUI-AutoCropFaces/README.md

@@ -0,0 +1,41 @@
+# ComfyUI-AutoCropFaces
+Use RetinaFace to detect and automatically crop faces
+
+Forked and modified from [biubug6/Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface)
+
+## Custom Nodes
+
+### Auto Crop Faces
+
+Detect faces and focus on one of them.
+
+Sure, here is the updated documentation:
+
+### Auto Crop Faces
+
+Detect faces and focus on one of them.
+
+* `number_of_faces`: How many faces would you like to detect in total? (default: 5, min: 1, max: 100)
+
+* `start_index`: Which face would you like to start with? (default: 0, step: 1). The starting index of the detected faces list. If the start index is out of bounds, it wraps around in a circular fashion just like a Python list.
+
+* `scale_factor`: How much padding would you like to add? 1 for no padding. (default: 1.5, min: 0.5, max: 10, step: 0.5)
+
+* `shift_factor`: Where would you like the face to be placed in the output image? Set to 0 to place the face at the top edge, 0.5 to center it, and 1.0 to place it at the bottom edge. (default: 0.45, min: 0, max: 1, step: 0.01)
+
+* `max_faces_per_image`: The maximum number of faces to detect for each image. (default: 50, min: 1, max: 1000, step: 1)
+
+* `aspect_ratio`: The aspect ratio for cropping, specified as `width` : `height`. (default: 1:1)
+
+![Simple Usage](images/workflow-AutoCropFaces-Simple.png)
+
+![At Bottom Edge](images/workflow-AutoCropFaces-bottom.png)
+
+Recommandation:
+
+Users might upload extremely large images, so it would be a good idea to first pass through the ["Constrain Image"](https://github.com/pythongosssss/ComfyUI-Custom-Scripts#constrain-image) node.
+
+![Pass Through Constrain Image first](images/workflow-AutoCropFaces-with-Constrain.png)
+
+It now supports CROP_DATA, which is compatible with [WAS node suite](https://github.com/WASasquatch/was-node-suite-comfyui).
+![Crop and Paste](images/Crop_Data.png)

custom_nodes/ComfyUI-AutoCropFaces/__init__.py

@@ -0,0 +1,179 @@
+import torch
+import comfy.utils
+from .Pytorch_Retinaface.pytorch_retinaface import Pytorch_RetinaFace
+from comfy.model_management import get_torch_device
+
+class AutoCropFaces:
+    def __init__(self):
+        pass
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "number_of_faces": ("INT", {
+                    "default": 5, 
+                    "min": 1,
+                    "max": 100,
+                    "step": 1,
+                }),
+                "scale_factor": ("FLOAT", {
+                    "default": 1.5,
+                    "min": 0.5,
+                    "max": 10,
+                    "step": 0.5,
+                    "display": "slider"
+                }),
+                "shift_factor": ("FLOAT", {
+                    "default": 0.45,
+                    "min": 0,
+                    "max": 1,
+                    "step": 0.01,
+                    "display": "slider"
+                }),
+                "start_index": ("INT", {
+                    "default": 0,
+                    "step": 1,
+                    "display": "number"
+                }),
+                "max_faces_per_image": ("INT", {
+                    "default": 50,
+                    "min": 1,
+                    "max": 1000,
+                    "step": 1,
+                }),
+                # "aspect_ratio": ("FLOAT", {
+                #     "default": 1, 
+                #     "min": 0.2,
+                #     "max": 5,
+                #     "step": 0.1,
+                # }),
+                "aspect_ratio": (["9:16", "2:3", "3:4", "4:5", "1:1", "5:4", "4:3", "3:2", "16:9"], {
+                    "default": "1:1",
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "CROP_DATA")
+    RETURN_NAMES = ("face",)
+
+    FUNCTION = "auto_crop_faces"
+
+    CATEGORY = "Faces"
+
+    def aspect_ratio_string_to_float(self, str_aspect_ratio="1:1"):
+        a, b = map(float, str_aspect_ratio.split(':'))
+        return a / b
+
+    def auto_crop_faces_in_image (self, image, max_number_of_faces, scale_factor, shift_factor, aspect_ratio, method='lanczos'): 
+        image_255 = image * 255
+        rf = Pytorch_RetinaFace(top_k=50, keep_top_k=max_number_of_faces, device=get_torch_device())
+        dets = rf.detect_faces(image_255)
+        cropped_faces, bbox_info = rf.center_and_crop_rescale(image, dets, scale_factor=scale_factor, shift_factor=shift_factor, aspect_ratio=aspect_ratio)
+
+        # Add a batch dimension to each cropped face
+        cropped_faces_with_batch = [face.unsqueeze(0) for face in cropped_faces]
+        return cropped_faces_with_batch, bbox_info
+
+    def auto_crop_faces(self, image, number_of_faces, start_index, max_faces_per_image, scale_factor, shift_factor, aspect_ratio, method='lanczos'):
+        """ 
+        "image" - Input can be one image or a batch of images with shape (batch, width, height, channel count)
+        "number_of_faces" - This is passed into PyTorch_RetinaFace which allows you to define a maximum number of faces to look for.
+        "start_index" - The starting index of which face you select out of the set of detected faces.
+        "scale_factor" - How much crop factor or padding do you want around each detected face.
+        "shift_factor" - Pan up or down relative to the face, 0.5 should be right in the center.
+        "aspect_ratio" - When we crop, you can have it crop down at a particular aspect ratio.
+        "method" - Scaling pixel sampling interpolation method.
+        """
+        
+        # Turn aspect ratio to float value
+        aspect_ratio = self.aspect_ratio_string_to_float(aspect_ratio)
+
+        selected_faces, detected_cropped_faces = [], []
+        selected_crop_data, detected_crop_data = [], []
+        original_images = []
+
+        # Loop through the input batches. Even if there is only one input image, it's still considered a batch.
+        for i in range(image.shape[0]):
+
+            original_images.append(image[i].unsqueeze(0)) # Temporarily the image, but insure it still has the batch dimension.
+            # Detect the faces in the image, this will return multiple images and crop data for it.
+            cropped_images, infos = self.auto_crop_faces_in_image(
+                image[i],
+                max_faces_per_image,
+                scale_factor,
+                shift_factor,
+                aspect_ratio,
+                method)
+
+            detected_cropped_faces.extend(cropped_images)
+            detected_crop_data.extend(infos)
+
+        # If we haven't detected anything, just return the original images, and default crop data.
+        if not detected_cropped_faces or len(detected_cropped_faces) == 0:
+            selected_crop_data = [(0, 0, img.shape[3], img.shape[2]) for img in original_images]
+            return (image, selected_crop_data)
+
+         # Circular index calculation
+        start_index = start_index % len(detected_cropped_faces)
+
+        if number_of_faces >= len(detected_cropped_faces):
+            selected_faces = detected_cropped_faces[start_index:] + detected_cropped_faces[:start_index]
+            selected_crop_data = detected_crop_data[start_index:] + detected_crop_data[:start_index]
+        else:
+            end_index = (start_index + number_of_faces) % len(detected_cropped_faces)
+            if start_index < end_index:
+                selected_faces = detected_cropped_faces[start_index:end_index]
+                selected_crop_data = detected_crop_data[start_index:end_index]
+            else:
+                selected_faces = detected_cropped_faces[start_index:] + detected_cropped_faces[:end_index]
+                selected_crop_data = detected_crop_data[start_index:] + detected_crop_data[:end_index]
+
+        # If we haven't selected anything, then return original images.
+        if len(selected_faces) == 0: 
+            # selected_crop_data = [(0, 0, img.shape[3], img.shape[2]) for img in original_images]
+            return (image, None)
+
+        # If there is only one detected face in batch of images, just return that one.
+        elif len(selected_faces) <= 1:
+            out = selected_faces[0]
+            crop_data = selected_crop_data[0] # to be compatible with WAS
+            return (out, crop_data)
+
+        # Determine the index of the face with the maximum width
+        max_width_index = max(range(len(selected_faces)), key=lambda i: selected_faces[i].shape[1])
+
+        # Determine the maximum width
+        max_width = selected_faces[max_width_index].shape[1]
+        max_height = selected_faces[max_width_index].shape[2]
+        shape = (max_height, max_width)
+
+        out = None
+        # All images need to have the same width/height to fit into the tensor such that we can output as image batches.
+        for face_image in selected_faces:
+            if shape != face_image.shape[1:3]: # Determine whether cropped face image size matches largest cropped face image. 
+                face_image = comfy.utils.common_upscale( # This method expects (batch, channel, height, width)
+                    face_image.movedim(-1, 1), # Move channel dimension to width dimension
+                    max_height, # Height
+                    max_width, # Width
+                    method, # Pixel sampling method.
+                    "" # Only "center" is implemented right now, and we don't want to use that.
+                ).movedim(1, -1)
+            # Append the fitted image into the tensor.
+            if out is None:
+                out = face_image
+            else:
+                out = torch.cat((out, face_image), dim=0)
+
+        #TODO: WAS doesn't not support multiple faces, so this won't work with WAS.
+        return (out, selected_crop_data)
+
+NODE_CLASS_MAPPINGS = {
+    "AutoCropFaces": AutoCropFaces
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AutoCropFaces": "Auto Crop Faces"
+}

custom_nodes/ComfyUI-AutoCropFaces/test.py

@@ -0,0 +1,46 @@
+from __future__ import print_function
+import os
+import argparse
+import numpy as np
+import cv2
+from Pytorch_Retinaface.pytorch_retinaface import Pytorch_RetinaFace
+
+parser = argparse.ArgumentParser(description='Retinaface')
+
+parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
+parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
+parser.add_argument('--confidence_threshold', default=0.02, type=float, help='confidence_threshold')
+parser.add_argument('--top_k', default=5000, type=int, help='top_k')
+parser.add_argument('--nms_threshold', default=0.4, type=float, help='nms_threshold')
+parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
+parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
+parser.add_argument('--vis_thres', default=0.6, type=float, help='visualization_threshold')
+args = parser.parse_args()
+
+
+def main():
+    rf = Pytorch_RetinaFace()
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    output_dir = os.path.join(current_dir, "./Pytorch_Retinaface/outputs")
+
+    image_path = os.path.join(current_dir, "./Pytorch_Retinaface/images/test.webp")
+    if not os.path.exists(image_path):
+        raise FileNotFoundError
+    img_raw = cv2.imread(image_path, cv2.IMREAD_COLOR)
+
+    img = np.float32(img_raw)
+
+    dets = rf.detect_faces(img)
+
+    # Crop and save each detected face
+    cropped_imgs = rf.center_and_crop_rescale(img_raw, dets)
+    for index, cropped_img in enumerate(cropped_imgs[0]):
+        # Save the final image
+        os.makedirs(output_dir, exist_ok=True)
+        cv2.imwrite(os.path.join(output_dir, f"cropped_face_{index}.jpg"), cropped_img)
+        print(f"Saved: cropped_face_{index}.jpg")
+
+if __name__ == '__main__':
+    main()

custom_nodes/ComfyUI-AutoCropFaces/tests/AutoCropFaces-Testing.json

@@ -0,0 +1,1997 @@
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+        },
+        {
+          "name": "CROP_DATA",
+          "type": "CROP_DATA",
+          "links": null,
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AutoCropFaces"
+      },
+      "widgets_values": [
+        5,
+        0,
+        -1,
+        1.5,
+        0.5,
+        1
+      ]
+    },
+    {
+      "id": 91,
+      "type": "PreviewImage",
+      "pos": [
+        3410,
+        -320
+      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
+      "flags": {},
+      "order": 33,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 140
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
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+      "type": "PreviewImage",
+      "pos": [
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+        260
+      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
+      "flags": {},
+      "order": 32,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 122
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 36,
+      "type": "AutoCropFaces",
+      "pos": [
+        3120,
+        2130
+      ],
+      "size": {
+        "0": 315,
+        "1": 198
+      },
+      "flags": {},
+      "order": 28,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 81
+        }
+      ],
+      "outputs": [
+        {
+          "name": "face",
+          "type": "IMAGE",
+          "links": [
+            53
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "CROP_DATA",
+          "type": "CROP_DATA",
+          "links": null,
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "AutoCropFaces"
+      },
+      "widgets_values": [
+        50,
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+        15,
+        1.5,
+        0.5,
+        1
+      ]
+    },
+    {
+      "id": 85,
+      "type": "VAEDecode",
+      "pos": [
+        2550,
+        -330
+      ],
+      "size": {
+        "0": 210,
+        "1": 46
+      },
+      "flags": {},
+      "order": 22,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "samples",
+          "type": "LATENT",
+          "link": 128
+        },
+        {
+          "name": "vae",
+          "type": "VAE",
+          "link": 129
+        }
+      ],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            141
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VAEDecode"
+      }
+    },
+    {
+      "id": 83,
+      "type": "KSampler",
+      "pos": [
+        2200,
+        -350
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 16,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 123
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 124
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 125
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 126
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            128
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        444793366152357,
+        "fixed",
+        50,
+        8,
+        "euler",
+        "normal",
+        1
+      ]
+    }
+  ],
+  "links": [
+    [
+      53,
+      36,
+      0,
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+    ],
+    [
+      62,
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+    [
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+    [
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+    [
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+    [
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+    [
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+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.5730855330116886,
+      "offset": [
+        -309.80067971195854,
+        -137.54286569310958
+      ]
+    }
+  },
+  "version": 0.4
+}

custom_nodes/ComfyUI-Custom-Scripts/.github/workflows/publish.yml

@@ -0,0 +1,25 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+permissions:
+  issues: write
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'pythongosssss' }}
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@v1
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

custom_nodes/ComfyUI-Custom-Scripts/.gitignore

@@ -0,0 +1,5 @@
+__pycache__
+pysssss.json
+user/autocomplete.txt
+web/js/assets/favicon.user.ico
+web/js/assets/favicon-active.user.ico

custom_nodes/ComfyUI-Custom-Scripts/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 pythongosssss
+
+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.

custom_nodes/ComfyUI-Custom-Scripts/README.md

@@ -0,0 +1,160 @@
+# ComfyUI-Custom-Scripts
+
+### ⚠️ While these extensions work for the most part, i'm very busy at the moment and so unable to keep on top of everything here, thanks for your patience!
+
+# Installation
+
+1. Clone the repository:
+`git clone https://github.com/pythongosssss/ComfyUI-Custom-Scripts.git`  
+to your ComfyUI `custom_nodes` directory
+
+   The script will then automatically install all custom scripts and nodes.  
+   It will attempt to use symlinks and junctions to prevent having to copy files and keep them up to date.
+
+- For uninstallation:
+  - Delete the cloned repo in `custom_nodes`
+  - Ensure `web/extensions/pysssss/CustomScripts` has also been removed
+
+# Update
+1. Navigate to the cloned repo e.g. `custom_nodes/ComfyUI-Custom-Scripts`
+2. `git pull`
+
+# Features
+
+## Autocomplete
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b5971135-414f-4f4e-a6cf-2650dc01085f)  
+Provides embedding and custom word autocomplete. You can view embedding details by clicking on the info icon on the list.  
+Define your list of custom words via the settings.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/160ef61c-7d7e-49d0-b60f-5a1501b74c9d)  
+You can quickly default to danbooru tags using the Load button, or load/manage other custom word lists.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/cc180b35-5f45-442f-9285-3ddf3fa320d0)
+
+## Auto Arrange Graph
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/04b06081-ca6f-4c0f-8584-d0a157c36747)  
+Adds a menu option to auto arrange the graph in order of execution, this makes very wide graphs!
+
+## Always Snap to Grid
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/66f36d1f-e579-4959-9880-9a9624922e3a)  
+Adds a setting to make moving nodes always snap to grid.
+
+## [Testing] "Better" Loader Lists
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/664caa71-f25f-4a96-a04a-1466d6b2b8b4)  
+Adds custom Lora and Checkpoint loader nodes, these have the ability to show preview images, just place a png or jpg next to the file and it'll display in the list on hover (e.g. sdxl.safetensors and sdxl.png).  
+Optionally enable subfolders via the settings:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/e15b5e83-4f9d-4d57-8324-742bedf75439)   
+Adds an "examples" widget to load sample prompts, triggerwords, etc:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ad1751e4-4c85-42e7-9490-e94fb1cbc8e7)  
+These should be stored in a folder matching the name of the model, e.g. if it is `loras/add_detail.safetensors` put your files in as  `loras/add_detail/*.txt`  
+To quickly save a generated image as the preview to use for the model, you can right click on an image on a node, and select Save as Preview and choose the model to save the preview for:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9fa8e9db-27b3-45cb-85c2-0860a238fd3a)
+
+## Checkpoint/LoRA/Embedding Info
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/6b67bf40-ee17-4fa6-a0c1-7947066bafc2)
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/32405df6-b367-404f-a5df-2d4347089a9e)  
+Adds "View Info" menu option to view details about the selected LoRA or Checkpoint. To view embedding details, click the info button when using embedding autocomplete.
+
+## Constrain Image
+Adds a node for resizing an image to a max & min size optionally cropping if required.
+
+## Custom Colors
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/fa7883f3-f81c-49f6-9ab6-9526e4debab6)  
+Adds a custom color picker to nodes & groups
+
+## Favicon Status
+![image](https://user-images.githubusercontent.com/125205205/230171227-31f061a6-6324-4976-bed9-723a87500cf3.png)
+![image](https://user-images.githubusercontent.com/125205205/230171445-c7202a45-b511-4d69-87fa-945ad44c063f.png)  
+Adds a favicon and title to the window, favicon changes color while generating and the window title includes the number of prompts in the queue
+
+## Image Feed
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/caea0d48-85b9-4ca9-9771-5c795db35fbc)
+Adds a panel showing images that have been generated in the current session, you can control the direction that images are added and the position of the panel via the ComfyUI settings screen and the size of the panel and the images via the sliders at the top of the panel.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ca093d38-41a3-4647-9223-5bd0b9ee4f1e)
+
+## KSampler (Advanced) denoise helper
+Provides a simple method to set custom denoise on the advanced sampler  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/42946bd8-0078-4c7a-bfe9-7adb1382b5e2)
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7cfccb22-f155-4848-934b-a2b2a6efe16f)
+
+## Math Expression
+Allows for evaluating complex expressions using values from the graph. You can input `INT`, `FLOAT`, `IMAGE` and `LATENT` values.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/1593edde-67b8-45d8-88cb-e75f52dba039)  
+Other nodes values can be referenced via the `Node name for S&R` via the `Properties` menu item on a node, or the node title.  
+Supported operators: `+ - * /` (basic ops) `//` (floor division) `**` (power) `^` (xor) `%` (mod)  
+Supported functions `floor(num, dp?)` `floor(num)` `ceil(num)` `randomint(min,max)`  
+If using a `LATENT` or `IMAGE` you can get the dimensions using `a.width` or `a.height` where `a` is the input name.
+
+## Node Finder
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/177d2b67-acbc-4ec3-ab31-7c295a98c194)  
+Adds a menu item for following/jumping to the executing node, and a menu to quickly go to a node of a specific type.
+
+## Preset Text
+![image](https://user-images.githubusercontent.com/125205205/230173939-08459efc-785b-46da-93d1-b02f0300c6f4.png)  
+Adds a node that lets you save and use text presets (e.g. for your 'normal' negatives)
+
+## Quick Nodes
+![image](https://user-images.githubusercontent.com/125205205/230174266-5232831a-a03b-4bf7-bc8b-c45466a0bc64.png)  
+Adds various menu items to some nodes for quickly setting up common parts of graphs
+
+## Play Sound
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/9bcf9fb3-5898-4432-a974-fb1e17d3b7e8)  
+Plays a sound when the node is executed, either after each prompt or only when the queue is empty for queuing multiple prompts.  
+You can customize the sound by replacing the mp3 file `ComfyUI/custom_nodes/ComfyUI-Custom-Scripts/web/js/assets/notify.mp3`
+
+## System Notification
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/30354775/993fd783-5cd6-4779-aa97-173bc06cc405)
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/30354775/e45227fb-5714-4f45-b96b-6601902ef6e2)
+
+Sends a system notification via the browser when the node is executed, either after each prompt or only when the queue is empty for queuing multiple prompts.
+
+## [WIP] Repeater
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/ec0dac25-14e4-4d44-b975-52193656709d)
+Node allows you to either create a list of N repeats of the input node, or create N outputs from the input node.  
+You can optionally decide if you want to reuse the input node, or create a new instance each time (e.g. a Checkpoint Loader would want to be re-used, but a random number would want to be unique)
+TODO: Type safety on the wildcard outputs to require match with input
+
+## Show Text
+![image](https://user-images.githubusercontent.com/125205205/230174888-c004fd48-da78-4de9-81c2-93a866fcfcd1.png)  
+Takes input from a node that produces a string and displays it, useful for things like interrogator, prompt generators, etc.
+
+## Show Image on Menu
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/b6ab58f2-583b-448c-bcfc-f93f5cdab0fc)  
+Shows the current generating image on the menu at the bottom, you can disable this via the settings menu.
+
+## String Function
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/01107137-8a93-4765-bae0-fcc110a09091)  
+Supports appending and replacing text  
+`tidy_tags` will add commas between parts when in `append` mode.  
+`replace` mode supports regex replace by using `/your regex here/` and you can reference capturing groups using `\number` e.g. `\1`
+
+## Touch Support
+Provides basic support for touch screen devices, its not perfect but better than nothing
+
+## Widget Defaults
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/3d675032-2b19-4da8-a7d7-fa2d7c555daa)  
+Allows you to specify default values for widgets when adding new nodes, the values are configured via the settings menu  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b57a3d8-98d3-46e9-9b33-6645c0da41e7)
+
+## Workflows
+Adds options to the menu for saving + loading workflows:  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/7b5a3012-4c59-47c6-8eea-85cf534403ea)
+
+## Workflow Images
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/06453fd2-c020-46ee-a7db-2b8bf5bcba7e)  
+Adds menu options for importing/exporting the graph as SVG and PNG showing a view of the nodes
+
+## (Testing) Reroute Primitive
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/8b870eef-d572-43f9-b394-cfa7abbd2f98)  Provides a node that allows rerouting primitives.  
+The node can also be collapsed to a single point that you can drag around.  
+![image](https://github.com/pythongosssss/ComfyUI-Custom-Scripts/assets/125205205/a9bd0112-cf8f-44f3-af6d-f9a8fed152a7)  
+Warning: Don't use normal reroutes or primitives with these nodes, it isn't tested and this node replaces their functionality.
+
+<br>
+<br>
+
+
+## WD14 Tagger
+Moved to: https://github.com/pythongosssss/ComfyUI-WD14-Tagger
+
+## ~~Lock Nodes & Groups~~
+This is now a standard feature of ComfyUI  
+~~Adds a lock option to nodes & groups that prevents you from moving them until unlocked~~

custom_nodes/ComfyUI-Custom-Scripts/__init__.py

@@ -0,0 +1,25 @@
+import importlib.util
+import glob
+import os
+import sys
+from .pysssss import init, get_ext_dir
+
+NODE_CLASS_MAPPINGS = {}
+NODE_DISPLAY_NAME_MAPPINGS = {}
+
+if init():
+    py = get_ext_dir("py")
+    files = glob.glob(os.path.join(py, "*.py"), recursive=False)
+    for file in files:
+        name = os.path.splitext(file)[0]
+        spec = importlib.util.spec_from_file_location(name, file)
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[name] = module
+        spec.loader.exec_module(module)
+        if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
+            NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
+            if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
+                NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
+
+WEB_DIRECTORY = "./web"
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]