fix: onnx runtime version

blender/avatar_main_output.py

@@ -79,6 +79,7 @@ class AvatarMainOutput(blender_node.ObjectOps):
             "files": [{
                 "filename": filepath.replace(f"{self.output_dir}/", ""),
                 "content_type": "model/gltf+json",
+                "type": "output"
             },],
             "SHAPE_FLOW": {SHAPE_FLOW}, 
             "auto_save": {'true' if auto_save else 'false'}, 

blender/mesh_utils.py

@@ -259,7 +259,7 @@ def get_avatar_file(output):
 
 def upload_avatar_file(output):
     file = get_avatar_file(output)
-    response = requests.get("https://labs.avatech.ai/api/share")
+    response = requests.get("https://labs.avatech.ai/api/share?version=v2")
     labData = response.json()
     modelId = labData["modelId"]
 
@@ -272,15 +272,15 @@ def upload_avatar_file(output):
     requests.put(labData["url"], headers=headers, data=file)
 
     # send notification
-    webhook_url = os.getenv("DISCORD_WEBHOOK_URL")
-    data = {
-        "username": "Avabot",
-        "avatar_url": "https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/avatechai.png",
-        "content": "[API Call] New register!",
-    }
-    headers = {
-        "Content-Type": "application/json",
-    }
-    response = requests.post(webhook_url, headers=headers, data=json.dumps(data))
+    # webhook_url = os.getenv("DISCORD_WEBHOOK_URL")
+    # data = {
+    #     "username": "Avabot",
+    #     "avatar_url": "https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/avatechai.png",
+    #     "content": "[API Call] New register!",
+    # }
+    # headers = {
+    #     "Content-Type": "application/json",
+    # }
+    # response = requests.post(webhook_url, headers=headers, data=json.dumps(data))
 
     return modelId

blender/ops_create_mesh_layer.py

@@ -41,7 +41,7 @@ class Object_CreateMeshLayer(blender_node.ObjectOps):
         bpy.ops.mesh.select_all(action='SELECT')
         bpy.ops.mesh.edge_face_add()
 
-        bpy.ops.transform.resize(value=(scale_x, scale_y, 1))
+        bpy.ops.transform.resize(value=(float(scale_x), float(scale_y), 1))
 
         bpy.context.object.vertex_groups.new(name=mesh_layer_name)
         bpy.ops.object.vertex_group_assign()

blender/ops_set_shape_key_value.py

@@ -0,0 +1,26 @@
+import blender_node
+
+
+class Mesh_SetShapeKeyValue(blender_node.ObjectOps):
+
+    CUSTOM_NAME = "Set Shape Key Value"
+
+    EXTRA_INPUT_TYPES = {
+        "shape_key_name": ("STRING", {
+            "multiline": False,
+            "default": "my_shape_key",
+        }),
+        "value": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "display": "number"}),
+    }
+
+    def blender_process(self, bpy, BPY_OBJ, shape_key_name, value):
+        # Check if the object has shape keys
+        if BPY_OBJ.data.shape_keys:
+            # Check if the specified shape key exists
+            if shape_key_name in BPY_OBJ.data.shape_keys.key_blocks:
+                BPY_OBJ.data.shape_keys.key_blocks[shape_key_name].value = float(value)
+            else:
+                print(f"The shape key {shape_key_name} does not exist on the object.")
+        else:
+            print("The object does not have any shape keys.")
+        return (BPY_OBJ,)

blender/render_image.py

@@ -0,0 +1,132 @@
+import blender_node
+import math
+import folder_paths
+import torch
+import numpy as np
+import os
+from PIL import Image, ImageOps
+
+def get_incremented_filename(folder_path, base_filename):
+    # Initialize the counter and create the full initial path
+    counter = 0
+    output_path = f"{folder_path}/{base_filename}.png"
+
+    # Check if the file exists and increment the counter until the file does not exist
+    while os.path.exists(output_path):
+        counter += 1
+        output_path = f"{folder_path}/{base_filename}_{counter}.png"
+
+    return output_path
+
+class BlenderRenderImage(blender_node.ObjectOps):
+    def __init__(self):
+        pass
+
+    EXTRA_INPUT_TYPES = {
+    }
+
+    # OUTPUT_NODE = True
+    RETURN_TYPES = ("BPY_OBJ", "IMAGE")
+
+    def add_light(self, bpy):
+        # Check if there is at least one light source in the scene
+        light_exists = any(ob for ob in bpy.data.objects if ob.type == 'LIGHT')
+        if not light_exists:
+            # Create a new Area light datablock for ambient light
+            light_data = bpy.data.lights.new(name='AmbientLight', type='AREA')
+            light_object = bpy.data.objects.new(name='AmbientLight', object_data=light_data)
+            bpy.context.collection.objects.link(light_object)
+            # Position the light in the scene
+            light_object.location = (0, 0, 10)
+            # Set light size for soft shadows and ambient effect
+            light_data.size = 10
+            light_data.energy = 1000
+            print("Added an ambient light source to the scene.")
+    
+    def add_camera(self, bpy):
+        # Check if there is a camera in the scene
+        if bpy.context.scene.camera:
+            return bpy.context.scene.camera 
+
+        # If not, create a new camera
+        cam_data = bpy.data.cameras.new(name='Camera')
+        cam = bpy.data.objects.new(name='Camera', object_data=cam_data)
+        bpy.context.collection.objects.link(cam)
+        # Set the new camera to the active camera
+        bpy.context.scene.camera = cam
+        # Position the camera to a default view
+        cam.location = (0, 0, 10)
+        return cam
+    
+    def get_texture_size(self, obj):
+        # Get the first material slot
+        mat = obj.data.materials[0]
+        
+        # Check if the material has a node tree
+        if mat.node_tree:
+            nodes = mat.node_tree.nodes
+            # Find an image texture node in the node tree
+            for node in nodes:
+                if node.type == 'TEX_IMAGE':
+                    texture = node.image
+                    if texture:
+                        return texture.size
+            print("No image texture node found in the material's node tree.")
+        else:
+            print("Material has no node tree.")
+
+
+    def blender_process(self, bpy, BPY_OBJ=None):
+        cam = self.add_camera(bpy)
+
+        plane = BPY_OBJ
+        if plane:
+            tex_width, tex_height = self.get_texture_size(plane)
+            # Calculate the aspect ratio of the plane
+            aspect_ratio_plane = tex_width / tex_height
+
+            # Set the render resolution to match the plane's aspect ratio
+            # Choose an arbitrary resolution for the longer side of the plane
+            base_resolution = 512
+
+            if aspect_ratio_plane > 1:
+                # Plane is wider than it is tall
+                bpy.context.scene.render.resolution_x = base_resolution
+                bpy.context.scene.render.resolution_y = int(base_resolution / aspect_ratio_plane)
+            else:
+                # Plane is taller than it is wide
+                bpy.context.scene.render.resolution_x = int(base_resolution * aspect_ratio_plane)
+                bpy.context.scene.render.resolution_y = base_resolution
+            bpy.context.scene.render.resolution_percentage = 100
+
+
+        ortho_scale = max(plane.dimensions.x, plane.dimensions.y)
+        cam.data.type = 'ORTHO'
+        cam.data.ortho_scale = ortho_scale
+
+        self.add_light(bpy)
+
+        # Update the scene to reflect changes
+        bpy.context.view_layer.update()
+
+        # Set render engine (e.g., 'BLENDER_EEVEE', 'CYCLES', 'BLENDER_WORKBENCH')
+        bpy.context.scene.render.engine = "BLENDER_EEVEE"
+
+        # Specify the render output path
+        
+        output_path = get_incremented_filename(folder_paths.get_output_directory(), "render")
+        bpy.context.scene.render.filepath = output_path
+
+        # Render the image
+        bpy.ops.render.render(write_still=True)
+
+        # Load the image
+        i = Image.open(output_path)
+        i = ImageOps.exif_transpose(i)
+        image = i.convert("RGB")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+
+        # print(image.shape)
+
+        return (BPY_OBJ, image)

js/AvatarPreview.js

@@ -706,7 +706,7 @@ export function AvatarPreview() {
         console.log(showPreview);
 
         return (
-          (showPreview.val ? "" : "hidden ") +
+          (showPreview.val && !showEditor.val ? "" : "hidden ") +
           "absolute w-[360px] h-[360px] rounded-xl overflow-hidden right-0 top-0 z-[99] pointer-events-auto flex border-none bg-transparent"
         );
       },

js/CombinePointsDialog.js

@@ -0,0 +1,146 @@
+import { combinePointsNode, samPrompts } from "./state.js";
+import { van } from "./van.js";
+const { div, dialog, form, button, h3, input, span } = van.tags;
+
+van.derive(() => {
+  if (
+    combinePointsNode.val != undefined &&
+    combinePointsNode.val.type === "Combine Points"
+  ) {
+    const inputNames = combinePointsNode.val.inputs?.map((x) => x.name) || [];
+    const record = Object.keys(samPrompts.val);
+
+    const diff = inputNames.filter((x) => !record.includes(x));
+    const missingDiff = record.filter((x) => !inputNames.includes(x));
+
+    if (diff.length > 0) {
+      diff.forEach((x) => {
+        combinePointsNode.val.removeInput(
+          combinePointsNode.val.findInputSlot(x)
+        );
+      });
+      combinePointsNode.val.graph.change();
+    }
+
+    if (missingDiff.length > 0) {
+      missingDiff.forEach((x) => {
+        combinePointsNode.val.addInput(x, "POINTS");
+      });
+      combinePointsNode.val.graph.change();
+    }
+  }
+});
+
+export function CombinePointsDialog() {
+  const showAddLayer = van.state(false);
+
+  return div(
+    {
+      class: () =>
+        "absolute z-[100] top-0 left-0 flex justify-center w-full h-full ",
+    },
+    () =>
+      dialog(
+        { id: "combine_points_dialog", class: "modal" },
+        div(
+          { class: "modal-box text-base-content" },
+          form(
+            {
+              class: "gap-2 flex flex-col",
+              method: "dialog",
+              onsubmit: (e) => {
+                e.preventDefault();
+                combine_points_dialog.close();
+              },
+            },
+            button(
+              {
+                type: "button",
+                class: "btn btn-sm btn-circle btn-ghost absolute right-2 top-2",
+                onclick: (e) => {
+                  e.stopPropagation();
+                  combine_points_dialog.close();
+                },
+              },
+              "✕"
+            ),
+            h3({ class: "font-bold text-lg text-base-content" }, "Edit points"),
+            () =>
+              div(
+                { class: "flex flex-col gap-2 mb-2" },
+                ...Object.keys(samPrompts.val).map((key) => {
+                  return span(key);
+                })
+              ),
+
+            () =>
+              showAddLayer.val
+                ? div(
+                    {
+                      class:
+                        "flex flex-row justify-center items-center border rounded-md pr-2",
+                    },
+                    input({
+                      type: "text",
+                      placeholder: "Type here",
+                      id: "layerName",
+                      class:
+                        "input input-ghost w-full focus:ring-0 focus:border-none focus:outline-none",
+                      autofocus: true,
+                    }),
+                    button(
+                      {
+                        onclick: (e) => {
+                          e.stopPropagation();
+                          e.preventDefault();
+                          showAddLayer.val = false;
+                        },
+                      },
+                      span({
+                        class: "iconify text-2xl",
+                        "data-icon": "iconoir:cancel",
+                      })
+                    ),
+                    button(
+                      {
+                        onclick: (e) => {
+                          e.stopPropagation();
+                          e.preventDefault();
+                          showAddLayer.val = false;
+                          const inputText =
+                            document.getElementById("layerName").value;
+                          samPrompts.val = {
+                            ...samPrompts.val,
+                            [inputText]: [],
+                          };
+                        },
+                      },
+                      span({
+                        class: "iconify text-2xl",
+                        "data-icon": "mdi:tick",
+                      })
+                    )
+                  )
+                : button(
+                    {
+                      class: "btn btn-outline btn",
+                      onclick: (e) => {
+                        e.stopPropagation();
+                        e.preventDefault();
+                        showAddLayer.val = true;
+                      },
+                    },
+                    "Add new layer"
+                  ),
+            button(
+              {
+                type: "submit",
+                class: "btn btn-sm  btn-ghost place-self-end",
+              },
+              "Confirm"
+            )
+          )
+        )
+      )
+  );
+}

js/Container.js

@@ -1,18 +1,20 @@
-import { LayerEditor } from './LayerEditor.js';
-import { ShapeFlowEditor } from './ShapeFlowEditor.js';
-import { van } from './van.js';
-import { AvatarPreview } from './AvatarPreview.js';
-import { Loading } from './Loading.js';
-import { Alert } from './Alert.js';
-import { AppHeader } from './AppHeader.js';
+import { LayerEditor } from "./LayerEditor.js";
+import { ShapeFlowEditor } from "./ShapeFlowEditor.js";
+import { van } from "./van.js";
+import { AvatarPreview } from "./AvatarPreview.js";
+import { Loading } from "./Loading.js";
+import { Alert } from "./Alert.js";
+import { AppHeader } from "./AppHeader.js";
+import { CombinePointsDialog } from "./CombinePointsDialog.js";
 const { button, iframe, div, img } = van.tags;
 
 export function Container() {
   return div(
     {
-      class: 'fixed left-0 top-0 w-full h-full z-[1000] pointer-events-none',
-      id: 'avatech-editor',
+      class: "fixed left-0 top-0 w-full h-full z-[1000] pointer-events-none",
+      id: "avatech-editor",
     },
+    CombinePointsDialog(),
     ShapeFlowEditor(),
     LayerEditor(),
     AvatarPreview(),

js/SideBar.js

@@ -6,6 +6,7 @@ import {
   targetNode,
   showImageEditor,
   allImagePrompts,
+  samPrompts,
 } from "./state.js";
 import { van } from "./van.js";
 const {
@@ -24,6 +25,33 @@ const {
   span,
 } = van.tags;
 
+export const updateOutputs = () => {
+  const outputNames = targetNode.val.outputs.map((x) => x.name).slice(1);
+  const record = Object.keys(imagePromptsMulti.val);
+
+  const diff = outputNames.filter((x) => !record.includes(x));
+  const missingDiff = record.filter((x) => !outputNames.includes(x));
+
+  if (diff.length > 0) {
+    console.log("Cleaning up missing output slots", diff);
+    diff.forEach((x) => {
+      targetNode.val.removeOutput(targetNode.val.findOutputSlot(x));
+    });
+    targetNode.val.graph.change();
+  }
+
+  if (missingDiff.length > 0) {
+    console.log("Adding missing output slots", diff);
+    missingDiff.forEach((x) => {
+      targetNode.val.addOutput(
+        x,
+        targetNode.val.type === "SAM MultiLayer" ? "IMAGE" : "SAM_PROMPT"
+      );
+    });
+    targetNode.val.graph.change();
+  }
+};
+
 van.derive(() => {
   if (
     showImageEditor.val &&
@@ -31,30 +59,7 @@ van.derive(() => {
     targetNode.val.outputs &&
     targetNode.val.type === "SAM MultiLayer"
   ) {
-    const outputNames = targetNode.val.outputs.map((x) => x.name).slice(1);
-    const record = Object.keys(imagePromptsMulti.val);
-
-    const diff = outputNames.filter((x) => !record.includes(x));
-    const missingDiff = record.filter((x) => !outputNames.includes(x));
-
-    if (diff.length > 0) {
-      console.log("Cleaning up missing output slots", diff);
-      diff.forEach((x) => {
-        targetNode.val.removeOutput(targetNode.val.findOutputSlot(x));
-      });
-      targetNode.val.graph.change();
-    }
-
-    if (missingDiff.length > 0) {
-      console.log("Adding missing output slots", diff);
-      missingDiff.forEach((x) => {
-        targetNode.val.addOutput(
-          x,
-          targetNode.val.type === "SAM MultiLayer" ? "IMAGE" : "SAM_PROMPT"
-        );
-      });
-      targetNode.val.graph.change();
-    }
+    updateOutputs();
   }
 });
 

js/index.js

@@ -5,6 +5,7 @@ import {
   imageUrl,
   imagePrompts,
   targetNode,
+  combinePointsNode,
   fileName,
   embeddings,
   imagePromptsMulti,
@@ -17,6 +18,7 @@ import {
   previewModelId,
   embeddingID,
   enableAutoSegment,
+  samPrompts,
 } from "./state.js";
 import { van } from "./van.js";
 import { app } from "./app.js";
@@ -33,6 +35,7 @@ import {
 import { infoDialog } from "./dialog.js";
 import { sharedAvatarLink } from "./AvatarPreview.js";
 import { updateImagePrompts } from "./LayerEditor.js";
+import { updateOutputs } from "./SideBar.js";
 
 export const generatedImages = {};
 
@@ -357,7 +360,6 @@ function showMyImageEditor(node) {
           drawSegment(getClicks());
           updateImagePrompts();
         });
-        targetNode.val = node;
       })
       .catch((err) => {
         console.log(err);
@@ -385,6 +387,25 @@ const ext = {
           showMyImageEditor(node);
           btn.serialize = false;
         });
+
+        targetNode.val = node;
+        node.onConnectInput = (node, slot, targetSlot) => {
+          if (targetSlot.name === "SAM_PROMPTS") {
+            imagePromptsMulti.val = samPrompts.val;
+            updateOutputs();
+          }
+        };
+
+        return {
+          widget: btn,
+        };
+      },
+      COMBINE_POINTS(node, inputName, inputData, app) {
+        const btn = node.addWidget("button", "Edit points", "", () => {
+          console.log("Edit points");
+          combine_points_dialog.showModal();
+          combinePointsNode.val = node;
+        });
         return {
           widget: btn,
         };
@@ -675,6 +696,18 @@ const ext = {
         nodeData.input.required.sam = ["SAM_PROMPTS"];
         // nodeData.input.required.upload = ['IMAGEUPLOAD'];
         // nodeData.input.required.prompts_points = ["IMAGEUPLOAD"];
+        addMenuHandler(nodeType, function (_, options) {
+          options.unshift({
+            content: "Open In Points Editor (Local)",
+            callback: () => {
+              showMyImageEditor(this);
+            },
+          });
+        });
+        break;
+      case "Combine Points":
+        nodeData.input.required.sam = ["COMBINE_POINTS"];
+
         addMenuHandler(nodeType, function (_, options) {
           options.unshift({
             content: "Open In Points Editor (Local)",
@@ -746,11 +779,19 @@ function injectUIComponentToComfyuimenu() {
     if (!filename.toLowerCase().endsWith(".json")) {
       filename += ".json";
     }
-    app.graphToPrompt().then(p=>{
-      console.log('fkfk');
+    app.graphToPrompt().then((p) => {
       let json = JSON.stringify(p.output, null, 2); // convert the data to a JSON string
-      json = json.replace(/"seed": (\d+)/g, `"seed": "SEED"`).replace(/"image": "(?!.*mask.*\.png).*"/g, '"image": "reference_image_avatech"').replace(/"embedding_id": ".*"/g, '"embedding_id": "embedding_id_avatech"');
-      const blob = new Blob([json], {type: "application/json"});
+      json = json
+        .replace(/"seed": (\d+)/g, `"seed": "SEED"`)
+        .replace(
+          /"image": "(?!.*mask.*\.png).*"/g,
+          '"image": "reference_image_avatech"'
+        )
+        .replace(
+          /"embedding_id": ".*"/g,
+          '"embedding_id": "embedding_id_avatech"'
+        );
+      const blob = new Blob([json], { type: "application/json" });
       const url = URL.createObjectURL(blob);
       a.href = url;
       a.download = filename;

js/onnx.js

@@ -1,16 +1,18 @@
-import "https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/ort.min.js";
+import "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.16.3/dist/ort.min.js";
 import npyjs from "https://esm.sh/npyjs";
 import { imageSize } from "./state.js";
 import { modelData, onnxMaskToImage } from "./onnx_helper.js";
 
-ort.env.wasm.wasmPaths = "https://cdn.jsdelivr.net/npm/onnxruntime-web/dist/";
+ort.env.wasm.wasmPaths = "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.16.3/dist/";
 
 export let model = null;
+let modelType = null;
 
 // Initialize the ONNX model
-export const initModel = async (modelType) => {
+export const initModel = async (type) => {
   try {
-    if (!model) {
+    if (!model || modelType !== type) {
+      modelType = type;
       model = await ort.InferenceSession.create(
         `${location.protocol}//${location.host}/sam_model?type=${modelType}`
       );

js/state.js

@@ -19,11 +19,13 @@
  */
 
 import { van } from "./van.js";
-
 export const iframeSrc = van.state("https://editor.avatech.ai?comfyui=true");
 export const showEditor = van.state(false);
 // localStorage.getItem("showPreview") == 'true'
-export const showPreview = van.state(true);
+console.log(localStorage.getItem("showPreview"));
+if (localStorage.getItem("showPreview") == null)
+  localStorage.setItem("showPreview", 'true')
+export const showPreview = van.state(localStorage.getItem("showPreview") == 'true');
 export const previewUrl = van.state(
   "https://editor.avatech.ai/viewer?avatarId=default&debug=true&width=350&height=350&hideTrigger=true&voiceSelection=true&hideUI=true"
 );
@@ -63,7 +65,6 @@ export const imagePrompts = van.state([]);
 
 export const allImagePrompts = van.state([{}]);
 
-
 /** @type {State<Record<string, Point[]>>} */
 export const imagePromptsMulti = van.state({});
 
@@ -76,3 +77,7 @@ export const targetNode = van.state();
 export const imageSize = van.state({ width: 0, height: 0, samScale: 0 });
 export const embeddings = van.state();
 export const embeddingID = van.state("Test");
+
+/** @type {State<LGraphNode>} */
+export const combinePointsNode = van.state();
+export const samPrompts = van.state({});

js/tw-styles.css

@@ -1,7 +1,7 @@
 @import url('https://fonts.googleapis.com/css2?family=Gabarito&display=swap');
 
 /*
-! tailwindcss v3.4.0 | MIT License | https://tailwindcss.com
+! tailwindcss v3.3.5 | MIT License | https://tailwindcss.com
 */
 
 /*
@@ -34,11 +34,9 @@
 4. Use the user's configured `sans` font-family by default.
 5. Use the user's configured `sans` font-feature-settings by default.
 6. Use the user's configured `sans` font-variation-settings by default.
-7. Disable tap highlights on iOS
 */
 
-html,
-:host {
+html {
   line-height: 1.5;
   /* 1 */
   -webkit-text-size-adjust: 100%;
@@ -48,14 +46,12 @@ html,
   -o-tab-size: 4;
      tab-size: 4;
   /* 3 */
-  font-family: ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
+  font-family: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
   /* 4 */
   font-feature-settings: normal;
   /* 5 */
   font-variation-settings: normal;
   /* 6 */
-  -webkit-tap-highlight-color: transparent;
-  /* 7 */
 }
 
 /*
@@ -127,10 +123,8 @@ strong {
 }
 
 /*
-1. Use the user's configured `mono` font-family by default.
-2. Use the user's configured `mono` font-feature-settings by default.
-3. Use the user's configured `mono` font-variation-settings by default.
-4. Correct the odd `em` font sizing in all browsers.
+1. Use the user's configured `mono` font family by default.
+2. Correct the odd `em` font sizing in all browsers.
 */
 
 code,
@@ -139,12 +133,8 @@ samp,
 pre {
   font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
   /* 1 */
-  font-feature-settings: normal;
-  /* 2 */
-  font-variation-settings: normal;
-  /* 3 */
   font-size: 1em;
-  /* 4 */
+  /* 2 */
 }
 
 /*
@@ -1011,6 +1001,99 @@ html{
     }
   }
 
+  .btn-outline:hover{
+    --tw-border-opacity: 1;
+    border-color: var(--fallback-bc,oklch(var(--bc)/var(--tw-border-opacity)));
+    --tw-bg-opacity: 1;
+    background-color: var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)));
+    --tw-text-opacity: 1;
+    color: var(--fallback-b1,oklch(var(--b1)/var(--tw-text-opacity)));
+  }
+
+  .btn-outline.btn-primary:hover{
+    --tw-text-opacity: 1;
+    color: var(--fallback-pc,oklch(var(--pc)/var(--tw-text-opacity)));
+  }
+
+  @supports (color: color-mix(in oklab, black, black)){
+    .btn-outline.btn-primary:hover{
+      background-color: color-mix(in oklab, var(--fallback-p,oklch(var(--p)/1)) 90%, black);
+      border-color: color-mix(in oklab, var(--fallback-p,oklch(var(--p)/1)) 90%, black);
+    }
+  }
+
+  .btn-outline.btn-secondary:hover{
+    --tw-text-opacity: 1;
+    color: var(--fallback-sc,oklch(var(--sc)/var(--tw-text-opacity)));
+  }
+
+  @supports (color: color-mix(in oklab, black, black)){
+    .btn-outline.btn-secondary:hover{
+      background-color: color-mix(in oklab, var(--fallback-s,oklch(var(--s)/1)) 90%, black);
+      border-color: color-mix(in oklab, var(--fallback-s,oklch(var(--s)/1)) 90%, black);
+    }
+  }
+
+  .btn-outline.btn-accent:hover{
+    --tw-text-opacity: 1;
+    color: var(--fallback-ac,oklch(var(--ac)/var(--tw-text-opacity)));
+  }
+
+  @supports (color: color-mix(in oklab, black, black)){
+    .btn-outline.btn-accent:hover{
+      background-color: color-mix(in oklab, var(--fallback-a,oklch(var(--a)/1)) 90%, black);
+      border-color: color-mix(in oklab, var(--fallback-a,oklch(var(--a)/1)) 90%, black);
+    }
+  }
+
+  .btn-outline.btn-success:hover{
+    --tw-text-opacity: 1;
+    color: var(--fallback-suc,oklch(var(--suc)/var(--tw-text-opacity)));
+  }
+
+  @supports (color: color-mix(in oklab, black, black)){
+    .btn-outline.btn-success:hover{
+      background-color: color-mix(in oklab, var(--fallback-su,oklch(var(--su)/1)) 90%, black);
+      border-color: color-mix(in oklab, var(--fallback-su,oklch(var(--su)/1)) 90%, black);
+    }
+  }
+
+  .btn-outline.btn-info:hover{
+    --tw-text-opacity: 1;
+    color: var(--fallback-inc,oklch(var(--inc)/var(--tw-text-opacity)));
+  }
+
+  @supports (color: color-mix(in oklab, black, black)){
+    .btn-outline.btn-info:hover{
+      background-color: color-mix(in oklab, var(--fallback-in,oklch(var(--in)/1)) 90%, black);
+      border-color: color-mix(in oklab, var(--fallback-in,oklch(var(--in)/1)) 90%, black);
+    }
+  }
+
+  .btn-outline.btn-warning:hover{
+    --tw-text-opacity: 1;
+    color: var(--fallback-wac,oklch(var(--wac)/var(--tw-text-opacity)));
+  }
+
+  @supports (color: color-mix(in oklab, black, black)){
+    .btn-outline.btn-warning:hover{
+      background-color: color-mix(in oklab, var(--fallback-wa,oklch(var(--wa)/1)) 90%, black);
+      border-color: color-mix(in oklab, var(--fallback-wa,oklch(var(--wa)/1)) 90%, black);
+    }
+  }
+
+  .btn-outline.btn-error:hover{
+    --tw-text-opacity: 1;
+    color: var(--fallback-erc,oklch(var(--erc)/var(--tw-text-opacity)));
+  }
+
+  @supports (color: color-mix(in oklab, black, black)){
+    .btn-outline.btn-error:hover{
+      background-color: color-mix(in oklab, var(--fallback-er,oklch(var(--er)/1)) 90%, black);
+      border-color: color-mix(in oklab, var(--fallback-er,oklch(var(--er)/1)) 90%, black);
+    }
+  }
+
   .btn-disabled:hover,
     .btn[disabled]:hover,
     .btn:disabled:hover{
@@ -1353,6 +1436,43 @@ html{
   }
 }
 
+@supports (color: color-mix(in oklab, black, black)){
+  .btn-outline.btn-primary.btn-active{
+    background-color: color-mix(in oklab, var(--fallback-p,oklch(var(--p)/1)) 90%, black);
+    border-color: color-mix(in oklab, var(--fallback-p,oklch(var(--p)/1)) 90%, black);
+  }
+
+  .btn-outline.btn-secondary.btn-active{
+    background-color: color-mix(in oklab, var(--fallback-s,oklch(var(--s)/1)) 90%, black);
+    border-color: color-mix(in oklab, var(--fallback-s,oklch(var(--s)/1)) 90%, black);
+  }
+
+  .btn-outline.btn-accent.btn-active{
+    background-color: color-mix(in oklab, var(--fallback-a,oklch(var(--a)/1)) 90%, black);
+    border-color: color-mix(in oklab, var(--fallback-a,oklch(var(--a)/1)) 90%, black);
+  }
+
+  .btn-outline.btn-success.btn-active{
+    background-color: color-mix(in oklab, var(--fallback-su,oklch(var(--su)/1)) 90%, black);
+    border-color: color-mix(in oklab, var(--fallback-su,oklch(var(--su)/1)) 90%, black);
+  }
+
+  .btn-outline.btn-info.btn-active{
+    background-color: color-mix(in oklab, var(--fallback-in,oklch(var(--in)/1)) 90%, black);
+    border-color: color-mix(in oklab, var(--fallback-in,oklch(var(--in)/1)) 90%, black);
+  }
+
+  .btn-outline.btn-warning.btn-active{
+    background-color: color-mix(in oklab, var(--fallback-wa,oklch(var(--wa)/1)) 90%, black);
+    border-color: color-mix(in oklab, var(--fallback-wa,oklch(var(--wa)/1)) 90%, black);
+  }
+
+  .btn-outline.btn-error.btn-active{
+    background-color: color-mix(in oklab, var(--fallback-er,oklch(var(--er)/1)) 90%, black);
+    border-color: color-mix(in oklab, var(--fallback-er,oklch(var(--er)/1)) 90%, black);
+  }
+}
+
 .btn:focus-visible{
   outline-style: solid;
   outline-width: 2px;
@@ -1399,6 +1519,95 @@ html{
   background-color: var(--fallback-bc,oklch(var(--bc)/0.2));
 }
 
+.btn-outline{
+  border-color: currentColor;
+  background-color: transparent;
+  --tw-text-opacity: 1;
+  color: var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity)));
+  --tw-shadow: 0 0 #0000;
+  --tw-shadow-colored: 0 0 #0000;
+  box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow);
+}
+
+.btn-outline.btn-active{
+  --tw-border-opacity: 1;
+  border-color: var(--fallback-bc,oklch(var(--bc)/var(--tw-border-opacity)));
+  --tw-bg-opacity: 1;
+  background-color: var(--fallback-bc,oklch(var(--bc)/var(--tw-bg-opacity)));
+  --tw-text-opacity: 1;
+  color: var(--fallback-b1,oklch(var(--b1)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-primary{
+  --tw-text-opacity: 1;
+  color: var(--fallback-p,oklch(var(--p)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-primary.btn-active{
+  --tw-text-opacity: 1;
+  color: var(--fallback-pc,oklch(var(--pc)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-secondary{
+  --tw-text-opacity: 1;
+  color: var(--fallback-s,oklch(var(--s)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-secondary.btn-active{
+  --tw-text-opacity: 1;
+  color: var(--fallback-sc,oklch(var(--sc)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-accent{
+  --tw-text-opacity: 1;
+  color: var(--fallback-a,oklch(var(--a)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-accent.btn-active{
+  --tw-text-opacity: 1;
+  color: var(--fallback-ac,oklch(var(--ac)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-success{
+  --tw-text-opacity: 1;
+  color: var(--fallback-su,oklch(var(--su)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-success.btn-active{
+  --tw-text-opacity: 1;
+  color: var(--fallback-suc,oklch(var(--suc)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-info{
+  --tw-text-opacity: 1;
+  color: var(--fallback-in,oklch(var(--in)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-info.btn-active{
+  --tw-text-opacity: 1;
+  color: var(--fallback-inc,oklch(var(--inc)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-warning{
+  --tw-text-opacity: 1;
+  color: var(--fallback-wa,oklch(var(--wa)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-warning.btn-active{
+  --tw-text-opacity: 1;
+  color: var(--fallback-wac,oklch(var(--wac)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-error{
+  --tw-text-opacity: 1;
+  color: var(--fallback-er,oklch(var(--er)/var(--tw-text-opacity)));
+}
+
+.btn-outline.btn-error.btn-active{
+  --tw-text-opacity: 1;
+  color: var(--fallback-erc,oklch(var(--erc)/var(--tw-text-opacity)));
+}
+
 .btn.btn-disabled,
   .btn[disabled],
   .btn:disabled{
@@ -1570,6 +1779,18 @@ details.collapse summary::-webkit-details-marker{
   outline-color: var(--fallback-bc,oklch(var(--bc)/0.2));
 }
 
+.input-ghost{
+  --tw-bg-opacity: 0.05;
+}
+
+.input-ghost:focus,
+    .input-ghost:focus-within{
+  --tw-bg-opacity: 1;
+  --tw-text-opacity: 1;
+  color: var(--fallback-bc,oklch(var(--bc)/var(--tw-text-opacity)));
+  box-shadow: none;
+}
+
 .input-disabled,
   .input:disabled,
   .input[disabled]{
@@ -2248,6 +2469,10 @@ details.collapse summary::-webkit-details-marker{
   z-index: 200;
 }
 
+.z-\[999\]{
+  z-index: 999;
+}
+
 .z-\[99\]{
   z-index: 99;
 }
@@ -2314,6 +2539,10 @@ details.collapse summary::-webkit-details-marker{
   height: 24rem;
 }
 
+.h-\[360px\]{
+  height: 360px;
+}
+
 .h-\[394px\]{
   height: 394px;
 }
@@ -2322,35 +2551,6 @@ details.collapse summary::-webkit-details-marker{
   height: 100%;
 }
 
-.h-fit{
-  height: -moz-fit-content;
-  height: fit-content;
-}
-
-.h-\[400px\]{
-  height: 400px;
-}
-
-.h-\[420px\]{
-  height: 420px;
-}
-
-.h-\[410px\]{
-  height: 410px;
-}
-
-.h-\[360px\]{
-  height: 360px;
-}
-
-.min-h-\[400px\]{
-  min-height: 400px;
-}
-
-.min-h-\[380px\]{
-  min-height: 380px;
-}
-
 .min-h-\[350px\]{
   min-height: 350px;
 }
@@ -2379,6 +2579,10 @@ details.collapse summary::-webkit-details-marker{
   width: 32rem;
 }
 
+.w-\[360px\]{
+  width: 360px;
+}
+
 .w-fit{
   width: -moz-fit-content;
   width: fit-content;
@@ -2388,38 +2592,6 @@ details.collapse summary::-webkit-details-marker{
   width: 100%;
 }
 
-.w-\[\]{
-  width: ;
-}
-
-.w-\[400px\]{
-  width: 400px;
-}
-
-.w-\[420px\]{
-  width: 420px;
-}
-
-.w-\[410px\]{
-  width: 410px;
-}
-
-.w-\[360px\]{
-  width: 360px;
-}
-
-.min-w-\[400px\]{
-  min-width: 400px;
-}
-
-.min-w-\[380\]{
-  min-width: 380;
-}
-
-.min-w-\[380px\]{
-  min-width: 380px;
-}
-
 .min-w-\[350px\]{
   min-width: 350px;
 }
@@ -2514,10 +2686,6 @@ details.collapse summary::-webkit-details-marker{
   border-radius: 0.125rem;
 }
 
-.rounded-2xl{
-  border-radius: 1rem;
-}
-
 .rounded-xl{
   border-radius: 0.75rem;
 }
@@ -2739,10 +2907,6 @@ details.collapse summary::-webkit-details-marker{
   padding: 1rem;
 }
 
-.p-24{
-  padding: 6rem;
-}
-
 .\!px-0{
   padding-left: 0px !important;
   padding-right: 0px !important;
@@ -2763,9 +2927,8 @@ details.collapse summary::-webkit-details-marker{
   padding-bottom: 0.5rem;
 }
 
-.py-16{
-  padding-top: 4rem;
-  padding-bottom: 4rem;
+.pr-2{
+  padding-right: 0.5rem;
 }
 
 .text-start{
@@ -3092,11 +3255,21 @@ img[src] {
   color: rgb(239 68 68 / var(--tw-text-opacity));
 }
 
+.focus\:border-none:focus{
+  border-style: none;
+}
+
 .focus\:outline-none:focus{
   outline: 2px solid transparent;
   outline-offset: 2px;
 }
 
+.focus\:ring-0:focus{
+  --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
+  --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(0px + var(--tw-ring-offset-width)) var(--tw-ring-color);
+  box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
+}
+
 @media (min-width: 640px){
   .sm\:flex{
     display: flex;

routes.py

@@ -285,6 +285,52 @@ async def post_prompt_block(request):
                     return web.json_response({"id": modelId}, status=200)
         time.sleep(0.5)
 
+# TODO: refactor the code
+@server.PromptServer.instance.routes.post("/rendering_generation")
+async def post_data_generation(request):
+    prompt_server = server.PromptServer.instance
+    post = await request.json()
+    workflow_name = post.get("workflow_name")
+    workflow = load_workflow(workflow_name)
+    workflow = workflow.replace("SEED", str(randomSeed()))
+
+    inputs = post.get("inputs")
+
+    for key, value in inputs.items():
+        workflow = workflow.replace(f'"{key}"', f'"{str(value)}"')
+
+    res = post_prompt({"prompt": json.loads(workflow)})
+    prompt_id = json.loads(res.text)["prompt_id"]
+    while True:
+        history = prompt_server.prompt_queue.get_history(prompt_id=prompt_id)
+        if history:
+            outputs = history[prompt_id]["outputs"]
+            for node_id, output in outputs.items():
+                if "images" in output:
+                    filename = output["images"][0]["filename"]
+                    if filename.startswith("rendered"):
+                        return web.json_response({"image": filename}, status=200)
+        time.sleep(0.5)
+
+@server.PromptServer.instance.routes.post("/image_generation")
+async def post_image_generation(request):
+    prompt_server = server.PromptServer.instance
+    workflow = load_workflow("generation")
+    workflow = workflow.replace("SEED", str(randomSeed()))
+
+    res = post_prompt({"prompt": json.loads(workflow)})
+    prompt_id = json.loads(res.text)["prompt_id"]
+    while True:
+        history = prompt_server.prompt_queue.get_history(prompt_id=prompt_id)
+        if history:
+            outputs = history[prompt_id]["outputs"]
+            for node_id, output in outputs.items():
+                if "images" in output:
+                    filename = output["images"][0]["filename"]
+                    if filename.startswith("avatar"):
+                        return web.json_response({"image": filename}, status=200)
+        time.sleep(0.5)
+
 
 # @server.PromptServer.instance.routes.get("/get_default_workflow")
 # async def get_default_workflow(request):

sam/combine_points.py

@@ -0,0 +1,27 @@
+import json
+
+class CombinePoints:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+            },
+        }
+
+    RETURN_NAMES = ("SAM_PROMPTS",)
+    RETURN_TYPES = ("STRING",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+
+    # OUTPUT_NODE = True
+
+    def run(self, *args, **kwargs):
+        sam_prompts = json.dumps(kwargs, default=str)
+        return (sam_prompts,)
+
+
+NODE_CLASS_MAPPINGS = {"Combine Points": CombinePoints}
+
+NODE_DISPLAY_NAME_MAPPINGS = {"Combine Points": "Combine Points"}

sam/extract_boundary_points.py

@@ -0,0 +1,66 @@
+import cv2
+import numpy as np
+import torch
+
+
+class ExtractBoundaryPoints:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "n_points": ("INT", {"default": -1, "min": -1, "max": 100}),
+            },
+        }
+
+    RETURN_TYPES = ("POINTS", "IMAGE")
+
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+
+    def find_main_contour(self, image, n_points):
+        image = np.copy(image[0].numpy())
+        gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+        gray = (gray * 255).astype(np.uint8)
+        # Find contours
+        contours, _ = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        if len(contours) == 0:
+            raise Exception(
+                "No contours found. Please ensure that the image has the correct segments (e.g. when you click on the mouth, it should display a proper blue area over the mouth region)."
+            )
+
+        # Get the largest contour
+        areas = [cv2.contourArea(contour) for contour in contours]
+
+        max_area_index = areas.index(max(areas))
+        largest_contour = contours[max_area_index]
+        if n_points > 0:
+            divided_by = int(largest_contour.shape[0] / n_points)
+            divided_by = min(divided_by, largest_contour.shape[0])
+            largest_contour = largest_contour[::divided_by].astype(int)
+        contours = [largest_contour]
+
+        if not image.flags["C_CONTIGUOUS"]:
+            image = np.ascontiguousarray(image)
+        cv2.drawContours(image, contours, -1, (0, 255, 0), 3)
+
+        points = []
+        for point in largest_contour:
+            points.append(
+                {"x": point[0][0], "y": point[0][1], "label": 1, "isAuto": True}
+            )
+
+        return image, points
+
+    def run(self, image, n_points):
+        contour_image, points = self.find_main_contour(image, n_points)
+        contour_image = torch.from_numpy(np.expand_dims(contour_image, axis=0))
+        print(points)
+        return (points, contour_image)
+
+
+NODE_CLASS_MAPPINGS = {"Extract Boundary Points": ExtractBoundaryPoints}
+
+NODE_DISPLAY_NAME_MAPPINGS = {"Extract Boundary Points": "Extract Boundary Points"}

sam/load_value_from_request.py

@@ -0,0 +1,31 @@
+class LoadValueFromRequest:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "name": (
+                    "STRING",
+                    {"multiline": False, "default": "key_name"},
+                ),
+            },
+            "optional": {
+                "value": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}),
+            }
+        }
+
+    RETURN_TYPES = ("FLOAT",)
+    RETURN_NAMES = ("value",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+
+    def run(self, name, value=None):
+        if name:
+            value = name
+        return (value,)
+
+
+NODE_CLASS_MAPPINGS = {"LoadValueFromRequest": LoadValueFromRequest}
+
+NODE_DISPLAY_NAME_MAPPINGS = {"LoadValueFromRequest": "Load Value From Request"}

sam/sam_multilayer.py

@@ -4,6 +4,7 @@ import numpy as np
 import torch
 import re
 import json
+import uuid
 from segment_anything import sam_model_registry, SamPredictor
 from einops import rearrange, repeat
 from PIL import Image
@@ -126,7 +127,7 @@ class SAMMultiLayer:
         global global_predictor, face_landmarker, pose_landmarker
 
         ckpt = folder_paths.get_full_path("sams", ckpt)
-        sam = sam_model_registry[model_type](checkpoint=ckpt)  # .to("cuda")
+        sam = sam_model_registry[model_type](checkpoint=ckpt) # .to("cuda")
         global_predictor = SamPredictor(sam)
 
         face_landmarker_model_path = os.path.join(
@@ -270,16 +271,24 @@ class SAMMultiLayer:
             image_format=mp.ImageFormat.SRGB, data=(np_image * 255).astype(np.uint8)
         )
         face_landmarks = face_landmarker.detect(mp_image).face_landmarks
-        face_landmarks = face_landmarks[0] if len(face_landmarks) > 0 else None
+        if len(face_landmarks) == 0:
+            print("Warning: no face detected")
+            return None, None
+
         pose_landmarks = pose_landmarker.detect(mp_image).pose_landmarks
-        pose_landmarks = pose_landmarks[0] if len(pose_landmarks) > 0 else None
+        if len(pose_landmarks) == 0:
+            print("Warning: no pose detected")
+            return None, None
+
         imagePromptsMulti, boxesMulti = self.auto_segment(
-            np_image, face_landmarks, pose_landmarks
+            np_image, face_landmarks[0], pose_landmarks[0]
         )
 
         return imagePromptsMulti, boxesMulti
 
     def load_image(self, image, ckpt, embedding_id, image_prompts_json):
+        if 'COMFY_DEPLOY' in os.environ and os.getenv('COMFY_DEPLOY', 'FALSE') == "TRUE":
+            embedding_id = str(uuid.uuid4())
         image_prompts = json.loads(image_prompts_json.replace("'", '"'))
 
         order_file = f"{self.output_dir}/segments_{embedding_id}/order.json"
@@ -338,7 +347,7 @@ class SAMMultiLayer:
             imagePromptsMulti, boxesMulti = self.detect_face(image[0].numpy())
 
             image_prompts = json.loads(image_prompts_json.replace("'", '"'))
-            result = [image_prompts]
+            result = [image_prompts]  # use imagePromptsMulti
 
             if isinstance(image_prompts, list):
                 pass
@@ -350,7 +359,9 @@ class SAMMultiLayer:
                         continue
 
                     points = (
-                        imagePromptsMulti[key] if key in imagePromptsMulti else item
+                        imagePromptsMulti[key]
+                        if imagePromptsMulti is not None and key in imagePromptsMulti
+                        else item
                     )
                     point_coords = np.array([[p["x"], p["y"]] for p in points])
                     point_labels = np.array([p["label"] for p in points])
@@ -358,7 +369,9 @@ class SAMMultiLayer:
                     masks, _, _ = global_predictor.predict(
                         point_coords=point_coords,
                         point_labels=point_labels,
-                        box=boxesMulti[key] if key in boxesMulti else None,
+                        box=boxesMulti[key]
+                        if boxesMulti is not None and key in boxesMulti
+                        else None,
                     )
                     masks = torch.from_numpy(masks)
                     masks = rearrange(masks[0], "h w -> 1 h w")