fix: onnx runtime version

# Conflicts:
#	blender/avatar_main_output.py

.gitignore

@@ -1,7 +1,9 @@
 # Created by https://www.toptal.com/developers/gitignore/api/node,python,react
 # Edit at https://www.toptal.com/developers/gitignore?templates=node,python,react
 
+workflow_templates/
 js/output.css
+*.task
 
 ### Node ###
 # Logs

README.md

@@ -1,44 +1,170 @@
 # avatar-graph-comfyui
 
-This introduce a custom workflow on top of comfyui built for avatar-graph.
+![image](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270574817-201a005b-7e00-4671-85a1-54937bf0704e.png)
+
+Wanna animate or got a question? Join our [Discord](https://discord.gg/Xp6mZ4Ez5P)
+
+A custom nodes module for **creating real-time interactive avatars** powered by blender bpy mesh api + Avatech Shape Flow runtime.
+
+> **WARNING**  
+> We are still making changes to the nodes and demo templates, please stay tuned.
+
+# Demo
+
+| <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/12e2bfc6-438e-4d16-bead-9957ced3bae1" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=cce15b92-6d1c-4966-91b9-362d7833cb5d) | <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/0c497025-7ed5-4e25-b4d1-5a257e1ba814" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=42a8182f-b140-48c0-a556-35cddf0f76f7) | <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/a2bf71e3-0d9c-4ddd-957f-a6b0cb7e622a" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=7c23b8d6-d1a5-41c7-a084-250461dbef22) | <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/ad808c42-5297-4e61-8be8-d5cb7729d2ff" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=268b32c4-f9b9-4db8-a27c-a7e974f0f0ac) |
+| :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/1d1ad8f9-31a6-48ec-bad2-ce972ee3b12f" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=f97fc5bb-93b0-4b02-bbc0-327dd41d0fc5) | <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/06958585-f780-4b38-8f5d-bddabd7da78a" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=4d50aa03-26e4-47e7-97b6-c3fe9d8fc96e) | <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/3d0e6b54-d45f-45ac-90bd-d8b149880f98" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=791014cb-7836-4641-afdb-ac331064b682) | <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/1d1ad8f9-31a6-48ec-bad2-ce972ee3b12f" width="180"/><br>[Interact 👆](https://editor.avatech.ai/viewer?avatarId=f97fc5bb-93b0-4b02-bbc0-327dd41d0fc5) |
+
+# How to?
+
+- [Basic Rigging Workflow Template](#basic-rigging-workflow-template)
+- [Best Practices for image input](#best-practices-for-image-input)
+- [Custom Nodes List](#custom-nodes)
+- [Shape Flow](#shape-flow)
+- [Installation](#installation)
+- [Development](#development)
+- [Join Discord 💬](https://discord.gg/WNtBYksDwF)
+
+# Basic Rigging Workflow Template
+
+### 1. Creating an eye blink and lipsync avatar
+
+![ComfyUI_00668_](https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/72a0abe8-2482-4a0d-8436-8eb231fd2f6d)
+
+For optimal results, please input a character image with an open mouth and a minimum resolution of 768x768. This higher resolution will enable the tool to accurately recognize and work with facial features.
+
+Download: Save the image, and drag into Comfyui or [Simple Shape Flow](https://github.com/avatechai/avatar-graph-comfyui/blob/main/workflow_templates/SimpleEye+MouthMovement.json)
+
+### 2. Creating an eye blink and lipsync emoji avatar
+
+| ![ComfyUI_00045_](https://github.com/avatechai/avatar-graph-comfyui/assets/18395202/b4787166-85df-43c6-9fe9-252989f68d18) | ![emoji_480p30_high](https://github.com/avatechai/avatar-graph-comfyui/assets/18395202/7d8b2b0a-e979-421d-8055-b4acac50a0c1) | 
+| :--: | :--: |
+
+Download: Save the image, and drag into Comfyui
+
+| ![ComfyUI_09609_](https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/caa98eec-4fb2-449d-9558-5d4a45e07580) | ![dog_480p15_high](https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/9fb701c9-f25c-408f-b96c-749773a53bd2) | 
+| :--: | :--: |
+
+Download: Save the image, and drag into Comfyui or [Dog Workflow](https://github.com/avatechai/avatar-graph-comfyui/blob/main/workflow_templates/Dog_workflow.json)
+
+# Best practices for image input
+
+### 1. Generate a new character image
+
+![image](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270609114-acea9933-359b-4398-8d2a-582bf02bef99.png)
+
+We need a character image with an open mouth and enable the tool to easily recognize facial features, so please add to the prompt:
+`looking at viewer, detailed face, open mouth, [smile], solo,eye-level angle`
+
+Download: [Character Gen Template](https://github.com/avatechai/avatar-graph-comfyui/blob/main/workflow_templates/SimpleCharacterGen.json)
+
+### 2. Make existing character image mouth open (Inpaint)
+
+![inpaint_workflow](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270589181-d11d840b-7ea6-4b47-bc26-a2af7c8c27a5.png)
+
+To maintain consistency with the base image, it is recommended to utilize a checkpoint model that aligns with its style.
+
+Download: [Mouth Open Inpaint Template](<https://github.com/avatechai/avatar-graph-comfyui/blob/main/workflow_templates/MouthOpen_(inpaint).json>)
+
+<details>
+<summary> Inpaint Demonstration </summary>
+
+<video src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/e3b77295-a1bf-4d96-9551-7cc423a4af73"/>
+
+</details>
+
+### 3. Pose Constraints (ControlNet)
+
+![image](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270943267-c3cae113-2df4-45f2-a19c-885cbee75450.png)
+
+Place normal and openpose image with reference to images.
+
+Download: [ControlNet Gen](https://github.com/avatechai/avatar-graph-comfyui/tree/main/workflow_templates/TemplateGen01)
+
+
+# Recommend Checkpoint Model List
+
+##### Anime Style SD1.5
+
+- https://civitai.com/models/35960/flat-2d-animerge
+- https://civitai.com/models/24149/mistoonanime
+
+##### Realistic Style SD1.5
+
+- https://civitai.com/models/4201/realistic-vision-v51
+- https://civitai.com/models/49463/am-i-real
+- https://civitai.com/models/43331/majicmix-realistic
+
+# Custom Nodes
+
+Expand to see all the available nodes description.
+
+<details>
+<summary> All Custom Nodes </summary>
+
+| Name                 | Description                                                                                    | Preview                                                                                                                                                              |
+| -------------------- | ---------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `Segmentation (SAM)` | Integrative SAM node allowing you to directly select and create multiple image segment output. | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270576351-8aabeba8-5450-4d39-8203-e91f9ab47190.png" width="300"> |
+
+
+| Name                         | Description                                                                                     | Preview                                                                                                                                                              |
+| ---------------------------- | ----------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `Create Mesh Layer`          | Create a mesh object from the input images (usually a segmented part of the entire image)       | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270576646-40740d25-9411-4cd3-a6c0-8b9008bca41c.png" width="300"> |
+| `Join Meshes`                | Combine multiple meshes into a single mesh object                                               | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270577004-ba7afbc5-9cd5-4f97-9614-f71133f5783e.png" width="300"> |
+| `Match Texture Aspect Ratio` | Since the mesh is created in 1:1 aspect ratio, a re-scale is needed at the end of the operation | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270602782-cb7155be-fb31-49f8-a24a-d001a1484ea7.png" width="300"> |
+| `Plane Texture Unwrap`       | Will perform mesh face fill and UV Cube project on the target plane mesh, scaled to bounds.     | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270603006-4b9c0cf5-0497-47bf-8e06-5a3370084c11.png" width="300"> |
+
+| Name                    | Description                                                                                        | Preview                                                                                                                                                              |
+| ----------------------- | -------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `Mesh Modify Shape Key` | Given shape key name & target vertex_group, modify the vertex / all vertex’s transform             | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270577944-ab4f259c-89a7-4f51-bc54-fd179e252073.png" width="300"> |
+| `Create Shape Flow`     | Create runtime shape flow graph, allowing interactive inputs affecting shape keys value in runtime | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270592752-abfdd801-0387-4c5d-9c11-6c23337ff1dd.png" width="300"> |
+
+| Name                 | Description                                                                                           | Preview                                                                                                                                                              |
+| -------------------- | ----------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `Avatar Main Output` | The primary output of the .ava file. The embedded Avatar View will auto update with this node's output | <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270592519-6a9a8bb4-05ec-4a2e-98bf-194b6af3a62a.png" width="300"> |
+
+</details>
+
+# Shape Flow
+
+![image](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270618471-a834e535-4f87-4b77-81a6-435e3a67ca4a.png)
 
 # Installation
 
+## Method 1 - Windows
+
+1. Download Python environment from [here](https://avatech-avatar-dev1.nyc3.digitaloceanspaces.com/comfyui/ComfyUI_3.10.7z)
+
+2. Unzip it to ComfyUI directory
+
+3. Run the `run_cpu_3.10.bat` or `run_nvidia_gpu_3.10.bat`
+
+4. Install avatar-graph-comfyui from [ComfyUI Manager](https://github.com/ltdrdata/ComfyUI-Manager)
+
+## Method 2 - macOS/Linux
+
+Make sure your Python environment is `3.10.x` as required by the [bpy](https://pypi.org/project/bpy/) package. Then go to the [ComfyUI](https://github.com/comfyanonymous/ComfyUI) directory and run:
+
+> Suggest using conda for your comfyui python environment
+> 
+> `conda create --name comfyui python=3.10`
+> 
+> `conda activate comfyui`
+> 
+> `pip install -r requirements.txt`
+
 1. `cd custom_nodes`
 
 2. `git clone https://github.com/avatechgg/avatar-graph-comfyui.git`
 
-3. Install deps `cd avatar-graph-comfyui && python -m pip install -r requirements.txt`
+3. `cd avatar-graph-comfyui && python -m pip install -r requirements.txt`
 
-4. Navigate to in your comfyui installation folder, open `comfyui/main.py` and add the following lines:
+4. Restart ComfyUI with enable-cors-header `python main.py --enable-cors-header` or (for mac) `python main.py --force-fp16 --enable-cors-header`
-```py
-def prompt_worker(q, server):
-    e = execution.PromptExecutor(server)
-
-    # This is a workaround since using blender as a python module, the bpy module has to be imported after in the custom thread, otherwise it will cause a segfault if imported in the custom nodes.
-
-    # Add next line
-    import global_bpy
-
-    while True:
-        item, item_id = q.get()
-        # Add next line
-        global_bpy.reset_bpy()
-
-if __name__ == "__main__":
-    # Add next two lines 
-    import sys
-    sys.path.append(os.path.join(os.path.dirname(__file__), "custom_nodes", "avatar-graph-comfyui"))
-
-
-```
-
-5. Restart comfyui
-
-6. Run comfyui with enable-cors-header `python main.py --enable-cors-header` or (mac)`python main.py --force-fp16 --enable-cors-header`
 
 # Development
 
+If you are interested in contributing
+
 For comfyui frontend extension, frontend js located at `avatar-graph-comfyui/js`
 
 Web stack used: [vanjs](https://github.com/vanjs-org/van) [tailwindcss](https://github.com/tailwindlabs/tailwindcss)
@@ -57,12 +183,31 @@ pnpm dev
 
 For each changes, simply refresh the comfyui page to see the changes.
 
-p.s. For tailwind autocomplete, add the following to your vscode settings.json.
+<details>
-
+<summary>p.s. For tailwind autocomplete, add the following to your vscode settings.json.</summary>
+    
 ```json
 {
     "tailwindCSS.experimental.classRegex": [
         ["class\\s?:\\s?([\\s\\S]*)", "(?:\"|')([^\"']*)(?:\"|')"]
     ]
 }
-```
+```
+</details>
+
+## Update blender node types
+
+To update blender operations input and output types (stored in `blender/input_types.txt`), run:
+
+```bash
+python generate_blender_types.py
+```
+
+# FAQ
+
+## What is `--enable-cors-header` used for?
+It is used to enable communication between ComfyUI and our editor (https://editor.avatech.ai), which is in charge of animating static characters. The only messages exchanged between them are the character data like the meshes of eyes and mouth, and the JSON format of our editor graph.
+
+When you execute the ComfyUI graph, it sends the character data and the JSON graph to our editor for animating. When you modify and save the graph in our editor, it sends the modified graph back to ComfyUI. To validate it, you can open the `js/index.js`, and log the message in `window.addEventListener("message", ...)` and `postMessage(message)`.
+
+You can also run ComfyUI *without* the `--enable-cors-header`: execute the ComfyUI workflow, then download the .GLB or .GLTF format by right clicking the Avatar Main Output node and Save File option. Yet, this will disable the real-time character preview in the top-right corner of ComfyUI. Feel free to view it in other software like Blender.

__init__.py

@@ -4,14 +4,20 @@
 @nickname: Avatar Graph
 @description: Include nodes for sam + bpy operation, that allows workflow creations for generative 2d character rig.
 """
-import routes
-
-import inspect
 import os
 import sys
+
+sys.path.append(os.path.join(os.path.dirname(__file__)))
+
+import routes
+import inspect
+import sys
 import importlib
 import subprocess
-from folder_paths import add_model_folder_path
+import requests
+import folder_paths
+from folder_paths import add_model_folder_path, get_filename_list, get_folder_paths
+from tqdm import tqdm
 
 ag_path = os.path.join(os.path.dirname(__file__))
 
@@ -30,7 +36,7 @@ ag_path = os.path.join(os.path.dirname(__file__))
 
 
 def get_python_files(path):
-    return [f[:-3] for f in os.listdir(path) if f.endswith('.py')]
+    return [f[:-3] for f in os.listdir(path) if f.endswith(".py")]
 
 
 def append_to_sys_path(path):
@@ -38,16 +44,67 @@ def append_to_sys_path(path):
         sys.path.append(path)
 
 
-def create_sam_model_dir():
+folder_paths.folder_names_and_paths["sams"] = (
-    model_dir = os.path.join(ag_path, "../../models/sam")
+    [os.path.join(folder_paths.models_dir, "sams")],
+    folder_paths.supported_pt_extensions,
+)
+
+
+def download_model(url, save_path):
+    response = requests.get(url, stream=True)
+    response.raise_for_status()
+    file_size = int(response.headers.get("Content-Length", 0))
+    chunk_size = 1024
+    num_bars = int(file_size / chunk_size)
+
+    with open(save_path, "wb") as f:
+        for chunk in tqdm(
+            response.iter_content(chunk_size=chunk_size),
+            total=num_bars,
+            unit="KB",
+            desc=url.split("/")[-1],
+        ):
+            f.write(chunk)
+
+
+def download_sam_model():
+    model_dir = get_folder_paths("sams")[0]
     if not os.path.isdir(model_dir):
         os.makedirs(model_dir)
-    add_model_folder_path('sam', model_dir)
+
+    add_model_folder_path("sams", model_dir)
+
+    files = get_filename_list("sams")
+    if "sam_vit_h_4b8939.pth" not in files:
+        print("Downloading sam model...")
+        url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
+        download_model(url, f"{model_dir}/sam_vit_h_4b8939.pth")
 
 
-create_sam_model_dir()
+download_sam_model()
 
-paths = ['blender', 'sam', 'common']
+
+def download_face_and_pose_landmarker():
+    model_dir = os.path.join(ag_path, "mediapipe_models")
+    if not os.path.isdir(model_dir):
+        os.makedirs(model_dir)
+
+    model_path = os.path.join(model_dir, "face_landmarker.task")
+    if not os.path.isfile(model_path):
+        print("Downloading face landmarker model...")
+        url = "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/latest/face_landmarker.task"
+        download_model(url, model_path)
+
+    model_path = os.path.join(model_dir, "pose_landmarker_full.task")
+    if not os.path.isfile(model_path):
+        print("Downloading pose landmarker model...")
+        url = "https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_full/float16/latest/pose_landmarker_full.task"
+        download_model(url, model_path)
+
+
+download_face_and_pose_landmarker()
+
+paths = ["blender", "sam"]
 files = []
 
 for path in paths:
@@ -59,6 +116,7 @@ NODE_CLASS_MAPPINGS = {}
 NODE_DISPLAY_NAME_MAPPINGS = {}
 
 import blender_node
+
 base_class = blender_node.ObjectOps
 
 # Import all the modules and append their mappings
@@ -69,22 +127,20 @@ for file in files:
         if inspect.isclass(obj):
             if issubclass(obj, base_class) and obj != base_class:
                 NODE_CLASS_MAPPINGS.update(obj.NODE_CLASS_MAPPINGS())
-                NODE_DISPLAY_NAME_MAPPINGS.update(
+                NODE_DISPLAY_NAME_MAPPINGS.update(obj.NODE_DISPLAY_NAME_MAPPINGS())
-                    obj.NODE_DISPLAY_NAME_MAPPINGS())
 
-    if (hasattr(module, 'BLENDER_NODES')):
+    if hasattr(module, "BLENDER_NODES"):
         for node in module.BLENDER_NODES:
             # print(node)
             # NODE_CLASS_MAPPINGS.update({node: module.BLENDER_NODES[node]})
             # NODE_DISPLAY_NAME_MAPPINGS.update({node: node})
             NODE_CLASS_MAPPINGS.update(node.NODE_CLASS_MAPPINGS())
-            NODE_DISPLAY_NAME_MAPPINGS.update(
+            NODE_DISPLAY_NAME_MAPPINGS.update(node.NODE_DISPLAY_NAME_MAPPINGS())
-                node.NODE_DISPLAY_NAME_MAPPINGS())
 
-    if hasattr(module, 'NODE_CLASS_MAPPINGS'):
+    if hasattr(module, "NODE_CLASS_MAPPINGS"):
         NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
-    if hasattr(module, 'NODE_DISPLAY_NAME_MAPPINGS'):
+    if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS"):
         NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
 
 WEB_DIRECTORY = "js"
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
+__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]

blender/avatar_main_output.py

@@ -1,6 +1,6 @@
 import platform
 import blender_node
-from mesh_utils import assign_texture, open_in_blender as open_blender, export_gltf
+from mesh_utils import upload_avatar_file, open_in_blender as open_blender, export_gltf
 import folder_paths
 
 global_blender_path = ''
@@ -33,6 +33,9 @@ class AvatarMainOutput(blender_node.ObjectOps):
         "open_in_blender": ("BOOLEAN", {
             "default": False
         }),
+        "auto_save": ("BOOLEAN", {
+            "default": False
+        }),
         "blender_path_override": ("STRING", {
             "multiline": False,
             "default": ''
@@ -42,16 +45,18 @@ class AvatarMainOutput(blender_node.ObjectOps):
             "multiline": False,
             "default": "out"
         }),
-        "model_type": (["GLB", "GLTF_EMBEDDED"],),
+        "model_type": (["AVA","GLB", "GLTF_EMBEDDED"],),
         "write_mode": (["Overwrite", "Increment"],),
-
+        "upload_to_cloud": ("BOOLEAN", {
-        "blendshapes": ("blendshapes",),
+            "default": False
+        }),
+        "SHAPE_FLOW": ("SHAPE_FLOW",),
     }
 
     OUTPUT_NODE = True
     RETURN_TYPES = ()
 
-    def blender_process(self, bpy, BPY_OBJ, BPY_OBJS=None, open_in_blender=False, blender_path_override='', filename='', model_type='', write_mode='', blendshapes=''):
+    def blender_process(self, bpy, BPY_OBJ=None, BPY_OBJS=None, open_in_blender=False, auto_save=False, blender_path_override='', filename='', model_type='', write_mode='', upload_to_cloud=False, SHAPE_FLOW=''):
         if open_in_blender:
             p = blender_path_override if blender_path_override else global_blender_path
             output_file = self.output_dir + '/tmp.blend'
@@ -63,7 +68,30 @@ class AvatarMainOutput(blender_node.ObjectOps):
         objs = BPY_OBJS if BPY_OBJS else [BPY_OBJ]
 
         filepath = export_gltf(
-            self.output_dir, objs, filename, model_type, write_mode
+            self.output_dir, objs, filename, model_type, write_mode, SHAPE_FLOW
         )
 
-        return {"ui": {"gltfFilename": {filepath.replace(f"{self.output_dir}/", "")}, "blendshapes": {blendshapes}}}
+        import global_bpy
+        global_bpy.set_should_reset_scene(True)
+
+        outputs = {
+            "gltfFilename": [filepath.replace(f"{self.output_dir}/", "")],
+            "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'}, 
+        }
+        if upload_to_cloud:
+            avatarId = upload_avatar_file(outputs)
+            return {
+                "ui": {
+                    **outputs,
+                    "avatarId": [avatarId],
+                },
+            }
+        return {
+            "ui": outputs
+        }

blender/b_vector3d.py

@@ -5,17 +5,17 @@ class VECTOR3D:
             "required": {
                 "x": ("FLOAT", {
                     "default": 0,
-                    "step": 0.1,
+                    "step": 0.01,
                     "display": "number"
                 }),
                 "y": ("FLOAT", {
                     "default": 0,
-                    "step": 0.1,
+                    "step": 0.01,
                     "display": "number"
                 }),
                 "z": ("FLOAT", {
                     "default": 0,
-                    "step": 0.1,
+                    "step": 0.01,
                     "display": "number"
                 }),
             },

blender/b_vector4d.py

@@ -5,17 +5,17 @@ class VECTOR4D:
             "required": {
                 "x": ("FLOAT", {
                     "default": 0,
-                    "step": 0.1,
+                    "step":  0.01,
                     "display": "number"
                 }),
                 "y": ("FLOAT", {
                     "default": 0,
-                    "step": 0.1,
+                    "step":  0.01,
                     "display": "number"
                 }),
                 "z": ("FLOAT", {
                     "default": 0,
-                    "step": 0.1,
+                    "step":  0.01,
                     "display": "number"
                 }),
                 "u": ("FLOAT", {

blender/blender_node.py

@@ -1,5 +1,8 @@
 import inspect
 import re
+import json
+import os
+
 BPY_OBJS = "BPY_OBJS"
 BPY_OBJ = "BPY_OBJ"
 
@@ -7,6 +10,14 @@ BPY_OBJS_TYPE = {
     BPY_OBJS: (BPY_OBJS,),
 }
 
+node_input_types = {}
+with open(f"{os.path.dirname(__file__)}/input_types.txt") as f:
+    input_types = f.readlines()
+    for input_type in input_types:
+        node_cls, node_types = input_type.split("|")
+        node_input_types[node_cls] = json.loads(node_types)
+
+type_generation = os.getenv('TYPE_GENERATION', 0)
 
 class ObjectOps:
     @classmethod
@@ -38,16 +49,29 @@ class ObjectOps:
 
     @classmethod
     def INPUT_TYPES(cls):
-        import global_bpy
+        if type_generation:
-        bpy = global_bpy.get_bpy()
+            import global_bpy
-        result = {
+
-            "required": {},
+            bpy = global_bpy.get_bpy()
-            "optional": {
+            result = {
-                **cls.get_base_input_types(bpy),
+                "required": {},
-                **cls.get_extra_input_types(bpy)
+                "optional": {
+                    **cls.get_base_input_types(bpy),
+                    **cls.get_extra_input_types(bpy),
+                },
+            }
+
+            return result
+        elif cls.__name__ in node_input_types:
+            return node_input_types[cls.__name__]
+        else:
+            return {
+                "required": {},
+                "optional": {
+                    **cls.get_base_input_types(None),
+                    **cls.get_extra_input_types(None),
+                },
             }
-        }
-        return result
 
     @classmethod
     def NODE_CLASS_MAPPINGS(cls):
@@ -76,14 +100,14 @@ class ObjectOps:
         import global_bpy
         bpy = global_bpy.get_bpy()
 
-        if props.get("BPY_OBJ") != None:
+        if props.get("BPY_OBJ") is not None:
             bpy.context.view_layer.objects.active = props["BPY_OBJ"]
 
         results = self.blender_process(bpy, **props)
 
         if results is None:
-            print(results)
+            # print(results)
-            if props.get("BPY_OBJ") != None:
+            if props.get("BPY_OBJ") is not None:
                 return (props["BPY_OBJ"], )
             else:
                 return (bpy.context.view_layer.objects.active, )
@@ -190,12 +214,12 @@ def print_blender_functions(path):
     if ag_path not in sys.path:
         sys.path.append(ag_path)
 
-    import global_bpy
+    # import global_bpy
-    bpy = global_bpy.get_bpy()
+    # bpy = global_bpy.get_bpy()
 
     # add a an empty object
 
-    print(dir(get_nested_attr(bpy, path)), type(get_nested_attr(bpy, path)))
+    # print(dir(get_nested_attr(bpy, path)), type(get_nested_attr(bpy, path)))
 
 
 def create_ops_class(cls, path, name=None, name_prefix=''):
@@ -236,7 +260,8 @@ def create_primitive_shape_class(cls, path, name=None, name_prefix=''):
 
 
 def assign_and_return(BPY_OBJ, name, value):
-    BPY_OBJ[name] = value
+    setattr(BPY_OBJ, name, value)
+    # BPY_OBJ[name] = value
     # print(BPY_OBJ,name, BPY_OBJ[name])
     return None
 

blender/combine_mesh.py

@@ -1,35 +0,0 @@
-class CombineMesh:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "obj_1": ("BPY_OBJS",),
-                "obj_2": ("BPY_OBJS",),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, obj_1, obj_2):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        override = bpy.context.copy()
-        override["active_object"] = obj_1[0]
-        override["selected_editable_objects"] = list([*obj_1, *obj_2])
-        bpy.ops.object.join(override)
-        joined_object = override["active_object"]
-        return ([joined_object], )
-
-NODE_CLASS_MAPPINGS = {
-    "CombineMesh": CombineMesh
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "CombineMesh": "Combine Mesh"
-}

blender/create_shape_flow.py

@@ -4,21 +4,20 @@ class CreateShapeFlow():
     def INPUT_TYPES(s):
         return {
             "required": {
-                "blendshapes": ("STRING", {
+                "shape_flow": ("STRING", {
                     "multiline": False,
-                    "default": "{node: ''}"
+                    "default": '{"nodes": []}'
                 }),
             },
         }
 
-    RETURN_TYPES = ("blendshapes",)
+    RETURN_TYPES = ("SHAPE_FLOW",)
-    RETURN_NAMES = ("blendshapes",)
     FUNCTION = "process"
 
     CATEGORY = "mesh"
 
-    def process(self, blendshapes):
+    def process(self, shape_flow):
-        return (blendshapes,)
+        return (shape_flow,)
 
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique

blender/create_vertex_bounds.py

@@ -1,63 +0,0 @@
-class CreateVertexBounds():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-                "name": ("STRING", {
-                    "multiline": False,
-                    "default": "Group"
-                }),
-                "extrude": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target, name, extrude):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        if len(bpy_objs_target) == 0:
-            # throw error
-            return
-
-        target_object = bpy_objs_target[0]
-
-        # deselect all objects
-        bpy.ops.object.select_all(action='DESELECT')
-        # select only the target object
-        bpy.context.view_layer.objects.active = target_object
-        # enter enter edit mode and select all faces of the object to fill
-        bpy.ops.object.mode_set(mode='EDIT')
-        bpy.ops.mesh.select_all(action='SELECT')
-
-        bpy.ops.mesh.extrude_region_move(MESH_OT_extrude_region={
-                                         "use_normal_flip": False, "use_dissolve_ortho_edges": False, "mirror": False})
-
-        bpy.ops.transform.resize(
-            value=(extrude, extrude, 1), constraint_axis=(False, False, False))
-
-        # remove the extruded vertex from the vertex group "name"
-
-        bpy.ops.object.vertex_group_remove_from()
-
-        # bpy.ops.mesh.delete(type='FACE')
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        return ([target_object],)
-
-    
-NODE_CLASS_MAPPINGS = {
-    "CreateVertexBounds": CreateVertexBounds
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "CreateVertexBounds": "Vertex Bounds"
-}

blender/create_vertex_group.py

@@ -1,56 +0,0 @@
-class CreateVertexGroup():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-                "name": ("STRING", {
-                    "multiline": False,
-                    "default": "Group"
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target, name):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        if len(bpy_objs_target) == 0:
-            # throw error
-            return
-            
-        target_object = bpy_objs_target[0]
-
-        # deselect all objects
-        bpy.ops.object.select_all(action='DESELECT')
-        # select only the target object
-        bpy.context.view_layer.objects.active = target_object
-        # enter enter edit mode and select all faces of the object to fill
-        bpy.ops.object.mode_set(mode='EDIT')
-        bpy.ops.mesh.select_all(action='SELECT')
-
-        # create vertex group and assign all selected faces to it (the name of the vertex group is the same as the name of the object)
-        bpy.context.object.vertex_groups.new(name=name)
-        bpy.ops.object.vertex_group_assign()
-
-        # bpy.ops.mesh.delete(type='FACE')
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        return ([target_object],)
-
-
-NODE_CLASS_MAPPINGS = {
-    "CreateVertexGroup": CreateVertexGroup
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "CreateVertexGroup": "Vertex Group (Old)"
-}

blender/disolve_faces.py

@@ -1,61 +0,0 @@
-class DissolveFaces():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-                 "target_vertex_group": ("STRING", {
-                    "multiline": False,
-                    "default": ""
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target, target_vertex_group):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        target_object = bpy_objs_target[0]
-
-        # deselect all objects
-        bpy.ops.object.select_all(action='DESELECT')
-        # select only the target object
-        bpy.context.view_layer.objects.active = target_object
-        # enter enter edit mode and select all faces of the object to fill
-        bpy.ops.object.mode_set(mode='EDIT')
-
-        bpy.ops.mesh.select_all(action='DESELECT')
-
-        # if we have vertex group, select that instead
-        if target_vertex_group and target_vertex_group.strip() != "":
-            group = target_object.vertex_groups.get(target_vertex_group)
-            if group:
-                bpy.ops.object.vertex_group_set_active(group=group.name)
-                bpy.ops.object.vertex_group_select()
-        else:
-            bpy.ops.mesh.select_all(action='SELECT')
-
-        # dissolve the faces
-        # bpy.ops.mesh.dissolve_faces()
-        bpy.ops.mesh.delete(type='ONLY_FACE')
-        # bpy.ops.mesh.delete(type='FACE')
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        return ([target_object],)
-
-
-NODE_CLASS_MAPPINGS = {
-    "DissolveFaces": DissolveFaces
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "DissolveFaces": "Dissolve Faces (Old)"
-}

blender/fill_face.py

@@ -1,46 +0,0 @@
-class FillFace:
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        target_object = bpy_objs_target[0]
-
-        # deselect all objects
-        bpy.ops.object.select_all(action='DESELECT')
-        # select only the target object
-        bpy.context.view_layer.objects.active = target_object
-        # enter enter edit mode and select all faces of the object to fill
-        bpy.ops.object.mode_set(mode='EDIT')
-        bpy.ops.mesh.select_all(action='SELECT')
-
-        # fill the faces
-        bpy.ops.mesh.fill()
-
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        return ([target_object],)
-
-
-NODE_CLASS_MAPPINGS = {
-    "FillFace": FillFace
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "FillFace": "Fill Face (Old)"
-}

blender/group_object.py

@@ -1,30 +0,0 @@
-class GroupObject():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-                "bpy_objs_target_2": ("BPY_OBJS",),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target, bpy_objs_target_2):
-        combined_list = bpy_objs_target + bpy_objs_target_2
-        return (combined_list,)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GroupObject": GroupObject
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GroupObject": "Group Object"
-}

blender/group_vertex_inside.py

@@ -1,108 +0,0 @@
-class GroupVertexInside():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy": ("BPY",),
-                "bpy_objs_target": ("BPY_OBJS",),
-                "name": ("STRING", {
-                    "multiline": False,
-                    "default": "Group"
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target, name):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        target_object = bpy_objs_target[0]
-
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        # deselect all objects
-        bpy.ops.object.select_all(action='DESELECT')
-        # select only the target object
-        bpy.context.view_layer.objects.active = target_object
-        # enter enter edit mode and select all faces of the object to fill
-        bpy.ops.object.mode_set(mode='EDIT')
-        bpy.ops.mesh.select_all(action='DESELECT')
-
-        # select the vetex group's vertices
-        bpy.ops.object.vertex_group_select()
-
-        # get vertex group
-        vertex_group = target_object.vertex_groups[name]
-
-        verts = target_object.data.vertices
-
-        # all_vertices = [(v.co.x, v.co.y, i) for i, v in target_object.data.vertices]
-
-        # get the verts in the group
-        verts_in_group = [v for v in verts if vertex_group.index in [vg.group for vg in v.groups]]
-
-        # get all selected vertices's xy
-        # get the vertices of the vertex group
-        vertex_group_vertices = [(v.co.x, v.co.y) for v in verts_in_group]
-
-        # get all vertices
-        # all_vertices = [(v.co.x, v.co.y) for v in target_object.data.vertices]
-        all_vertices_without_vertex_group = [(v.co.x, v.co.y, i) for i, v in enumerate(target_object.data.vertices) if v not in verts_in_group]
-
-        from scipy.spatial import ConvexHull, Delaunay
-
-        # create a convex hull of the vertex group's vertices
-        hull = ConvexHull(vertex_group_vertices)
-
-        # create a Delaunay triangulation of the convex hull
-        tri = Delaunay(hull.points[hull.vertices])
-
-        # get the indices of the vertices that are inside the convex hull
-        inside_vertices_indices = [i for (x,y,i) in all_vertices_without_vertex_group if tri.find_simplex((x,y))>=0]
-        
-        print(inside_vertices_indices)
-        print(len(vertex_group_vertices))
-
-        bpy.ops.mesh.select_all(action='DESELECT')
-
-        # select the vertices that are inside the convex hull
-        # for i in inside_vertices_indices:
-        #     target_object.data.vertices[i].select = True 
-
-        # # select vertex from 1 to 10
-        # for i in range(1, 100):
-        #     target_object.data.vertices[i].select = True
-
-        # create a new vertex group
-        # bpy.context.object.vertex_groups.new(name="fuck")
-        # bpy.ops.object.vertex_group_assign()
-        bpy.ops.object.mode_set(mode='OBJECT')
-        # create a new vertex group
-        # new_group = bpy.context.object.vertex_groups.new(name="new_group")
-        vertex_group.add(inside_vertices_indices, weight=1.0, type='REPLACE')
-
-        # assign the selected vertices to the new vertex group
-        # new_group.add([v.index for v in target_object.data.vertices if v.select], weight=1.0, type='REPLACE')
-        # new_group.add([20, 100], weight=1.0, type='REPLACE')
-        # assign the selected vertices to the active vertex group
-        bpy.ops.object.mode_set(mode='OBJECT')  
-
-
-        return ([target_object],)
-
-
-NODE_CLASS_MAPPINGS = {
-    "GroupVertexInside": GroupVertexInside
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "GroupVertexInside": "Group Vertex Inside"
-}

blender/image_alpha_mask_merge.py

@@ -0,0 +1,32 @@
+import folder_paths
+import os
+from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
+import numpy as np
+import torch
+import json
+
+class ImageAlphaMaskMerge:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"image": ("IMAGE",) ,
+                    "mask": ("MASK",) },
+                }
+
+    CATEGORY = "image"
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_image"
+    def load_image(self, image, mask):
+        if image.shape[1] == mask.shape[0] and image.shape[2] == mask.shape[1]:
+            image = torch.cat((image, 1 - mask.unsqueeze(0).unsqueeze(3)), dim=3)
+        return (image, )
+
+NODE_CLASS_MAPPINGS = {
+     "Image Alpha Mask Merge": ImageAlphaMaskMerge,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Image Alpha Mask Merge": "Image Alpha Mask Merge"
+}

blender/input_types.txt

@@ -0,0 +1,597 @@
+Mesh_AttributeSet|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value_float": ["FLOAT", {"min": -3.4028234663852886e+38, "max": 3.4028234663852886e+38, "default": 0.0}], "value_float_vector_2d": ["B_VECTOR2", {}], "value_float_vector_3d": ["B_VECTOR3", {}], "value_int": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "value_int_vector_2d": ["B_VECTOR2", {}], "value_color": ["B_VECTOR4", {}], "value_bool": ["BOOLEAN", {"default": false}]}}
+Mesh_AverageNormals|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "average_type": [["CUSTOM_NORMAL", "FACE_AREA", "CORNER_ANGLE"]], "weight": ["INT", {"min": 1, "max": 100, "default": 50}], "threshold": ["FLOAT", {"min": 0.0, "max": 10.0, "default": 0.009999999776482582}]}}
+Mesh_BeautifyFill|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "angle_limit": ["FLOAT", {"min": 0.0, "max": 3.1415927410125732, "default": 3.1415927410125732}]}}
+Mesh_Bevel|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "offset_type": [["OFFSET", "WIDTH", "DEPTH", "PERCENT", "ABSOLUTE"]], "offset": ["FLOAT", {"min": 0.0, "max": 1000000.0, "default": 0.0}], "profile_type": [["SUPERELLIPSE", "CUSTOM"]], "offset_pct": ["FLOAT", {"min": 0.0, "max": 100.0, "default": 0.0}], "segments": ["INT", {"min": 1, "max": 1000, "default": 1}], "profile": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.5}], "affect": [["VERTICES", "EDGES"]], "clamp_overlap": ["BOOLEAN", {"default": false}], "loop_slide": ["BOOLEAN", {"default": true}], "mark_seam": ["BOOLEAN", {"default": false}], "mark_sharp": ["BOOLEAN", {"default": false}], "material": ["INT", {"min": -1, "max": 2147483647, "default": -1}], "harden_normals": ["BOOLEAN", {"default": false}], "face_strength_mode": [["NONE", "NEW", "AFFECTED", "ALL"]], "miter_outer": [["SHARP", "PATCH", "ARC"]], "miter_inner": [["SHARP", "ARC"]], "spread": ["FLOAT", {"min": 0.0, "max": 1000000.0, "default": 0.10000000149011612}], "vmesh_method": [["ADJ", "CUTOFF"]], "release_confirm": ["BOOLEAN", {"default": false}]}}
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+Mesh_PrimitiveUvSphereAdd|{"required": {}, "optional": {"seed": ["INT", {"default": 0, "min": 0, "max": 18446744073709551615}], "segments": ["INT", {"min": 3, "max": 100000, "default": 32}], "ring_count": ["INT", {"min": 3, "max": 100000, "default": 16}], "radius": ["FLOAT", {"min": 0.0, "max": 999999995904.0, "default": 1.0}], "calc_uvs": ["BOOLEAN", {"default": true}], "enter_editmode": ["BOOLEAN", {"default": false}], "align": [["WORLD", "VIEW", "CURSOR"]], "location": ["B_VECTOR3", {}], "rotation": ["B_VECTOR3", {}], "scale": ["B_VECTOR3", {}]}}
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+Transform_Bend|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["B_VECTOR1", {}], "mirror": ["BOOLEAN", {"default": false}], "use_proportional_edit": ["BOOLEAN", {"default": false}], "proportional_edit_falloff": [["SMOOTH", "SPHERE", "ROOT", "INVERSE_SQUARE", "SHARP", "LINEAR", "CONSTANT", "RANDOM"]], "proportional_size": ["FLOAT", {"min": 9.999999974752427e-07, "max": 999999995904.0, "default": 1.0}], "use_proportional_connected": ["BOOLEAN", {"default": false}], "use_proportional_projected": ["BOOLEAN", {"default": false}], "snap": ["BOOLEAN", {"default": false}], "gpencil_strokes": ["BOOLEAN", {"default": false}], "center_override": ["B_VECTOR3", {}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
+Transform_CreateOrientation|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "name": ["STRING", {"default": "", "multiline": false}], "use_view": ["BOOLEAN", {"default": false}], "use": ["BOOLEAN", {"default": false}], "overwrite": ["BOOLEAN", {"default": false}]}}
+Transform_DeleteOrientation|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+Transform_EdgeBevelweight|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["FLOAT", {"min": -1.0, "max": 1.0, "default": 0.0}], "snap": ["BOOLEAN", {"default": false}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
+Transform_EdgeCrease|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["FLOAT", {"min": -1.0, "max": 1.0, "default": 0.0}], "snap": ["BOOLEAN", {"default": false}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
+Transform_EdgeSlide|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["FLOAT", {"min": -10.0, "max": 10.0, "default": 0.0}], "single_side": ["BOOLEAN", {"default": false}], "use_even": ["BOOLEAN", {"default": false}], "flipped": ["BOOLEAN", {"default": false}], "use_clamp": ["BOOLEAN", {"default": true}], "mirror": ["BOOLEAN", {"default": false}], "snap": ["BOOLEAN", {"default": false}], "snap_elements": ["B_ENUM_SET", {}], "use_snap_project": ["BOOLEAN", {"default": false}], "snap_target": [["CLOSEST", "CENTER", "MEDIAN", "ACTIVE"]], "use_snap_self": ["BOOLEAN", {"default": true}], "use_snap_edit": ["BOOLEAN", {"default": true}], "use_snap_nonedit": ["BOOLEAN", {"default": true}], "use_snap_selectable": ["BOOLEAN", {"default": false}], "snap_point": ["B_VECTOR3", {}], "correct_uv": ["BOOLEAN", {"default": true}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
+Transform_FromGizmo|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+Transform_Mirror|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "orient_type": ["B_ENUM", {}], "orient_matrix": ["B_VECTOR9", {}], "orient_matrix_type": ["B_ENUM", {}], "constraint_axis": ["B_BOOLEAN3", {}], "gpencil_strokes": ["BOOLEAN", {"default": false}], "center_override": ["B_VECTOR3", {}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
+Transform_PushPull|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["FLOAT", {"min": -3.4028234663852886e+38, "max": 3.4028234663852886e+38, "default": 0.0}], "mirror": ["BOOLEAN", {"default": false}], "use_proportional_edit": ["BOOLEAN", {"default": false}], "proportional_edit_falloff": [["SMOOTH", "SPHERE", "ROOT", "INVERSE_SQUARE", "SHARP", "LINEAR", "CONSTANT", "RANDOM"]], "proportional_size": ["FLOAT", {"min": 9.999999974752427e-07, "max": 999999995904.0, "default": 1.0}], "use_proportional_connected": ["BOOLEAN", {"default": false}], "use_proportional_projected": ["BOOLEAN", {"default": false}], "snap": ["BOOLEAN", {"default": false}], "center_override": ["B_VECTOR3", {}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
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+Transform_SelectOrientation|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "orientation": ["B_ENUM", {}]}}
+Transform_SeqSlide|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["B_VECTOR2", {}], "snap": ["BOOLEAN", {"default": false}], "view2d_edge_pan": ["BOOLEAN", {"default": false}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
+Transform_Shear|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["FLOAT", {"min": -3.4028234663852886e+38, "max": 3.4028234663852886e+38, "default": 0.0}], "orient_axis": [["X", "Y", "Z"]], "orient_axis_ortho": [["X", "Y", "Z"]], "orient_type": ["B_ENUM", {}], "orient_matrix": ["B_VECTOR9", {}], "orient_matrix_type": ["B_ENUM", {}], "mirror": ["BOOLEAN", {"default": false}], "use_proportional_edit": ["BOOLEAN", {"default": false}], "proportional_edit_falloff": [["SMOOTH", "SPHERE", "ROOT", "INVERSE_SQUARE", "SHARP", "LINEAR", "CONSTANT", "RANDOM"]], "proportional_size": ["FLOAT", {"min": 9.999999974752427e-07, "max": 999999995904.0, "default": 1.0}], "use_proportional_connected": ["BOOLEAN", {"default": false}], "use_proportional_projected": ["BOOLEAN", {"default": false}], "snap": ["BOOLEAN", {"default": false}], "gpencil_strokes": ["BOOLEAN", {"default": false}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
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+Transform_SkinResize|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "value": ["B_VECTOR3", {}], "orient_type": ["B_ENUM", {}], "orient_matrix": ["B_VECTOR9", {}], "orient_matrix_type": ["B_ENUM", {}], "constraint_axis": ["B_BOOLEAN3", {}], "mirror": ["BOOLEAN", {"default": false}], "use_proportional_edit": ["BOOLEAN", {"default": false}], "proportional_edit_falloff": [["SMOOTH", "SPHERE", "ROOT", "INVERSE_SQUARE", "SHARP", "LINEAR", "CONSTANT", "RANDOM"]], "proportional_size": ["FLOAT", {"min": 9.999999974752427e-07, "max": 999999995904.0, "default": 1.0}], "use_proportional_connected": ["BOOLEAN", {"default": false}], "use_proportional_projected": ["BOOLEAN", {"default": false}], "snap": ["BOOLEAN", {"default": false}], "snap_elements": ["B_ENUM_SET", {}], "use_snap_project": ["BOOLEAN", {"default": false}], "snap_target": [["CLOSEST", "CENTER", "MEDIAN", "ACTIVE"]], "use_snap_self": ["BOOLEAN", {"default": true}], "use_snap_edit": ["BOOLEAN", {"default": true}], "use_snap_nonedit": ["BOOLEAN", {"default": true}], "use_snap_selectable": ["BOOLEAN", {"default": false}], "snap_point": ["B_VECTOR3", {}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}]}}
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+ObjectCall_UserOfId|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "id": ["id", {"default": null}]}}
+ObjectCall_UserRemap|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "new_id": ["new_id", {"default": null}]}}
+ObjectCall_VisibleGet|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+ObjectCall_VisibleInViewportGet|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "viewport": ["viewport", {"default": null}]}}
+UV_Align|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "axis": [["ALIGN_S", "ALIGN_T", "ALIGN_U", "ALIGN_AUTO", "ALIGN_X", "ALIGN_Y"]]}}
+UV_AlignRotation|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "method": [["AUTO", "EDGE", "GEOMETRY"]], "axis": [["X", "Y", "Z"]]}}
+UV_AverageIslandsScale|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "scale_uv": ["BOOLEAN", {"default": false}], "shear": ["BOOLEAN", {"default": false}]}}
+UV_Copy|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_CubeProject|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "cube_size": ["FLOAT", {"min": 0.0, "max": 3.4028234663852886e+38, "default": 1.0}], "correct_aspect": ["BOOLEAN", {"default": true}], "clip_to_bounds": ["BOOLEAN", {"default": false}], "scale_to_bounds": ["BOOLEAN", {"default": false}]}}
+UV_CursorSet|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "location": ["B_VECTOR2", {}]}}
+UV_CylinderProject|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "direction": [["VIEW_ON_EQUATOR", "VIEW_ON_POLES", "ALIGN_TO_OBJECT"]], "align": [["POLAR_ZX", "POLAR_ZY"]], "pole": [["PINCH", "FAN"]], "seam": ["BOOLEAN", {"default": false}], "radius": ["FLOAT", {"min": 0.0, "max": 3.4028234663852886e+38, "default": 1.0}], "correct_aspect": ["BOOLEAN", {"default": true}], "clip_to_bounds": ["BOOLEAN", {"default": false}], "scale_to_bounds": ["BOOLEAN", {"default": false}]}}
+UV_ExportLayout|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "filepath": ["STRING", {"default": "", "multiline": false}], "export_all": ["BOOLEAN", {"default": false}], "modified": ["BOOLEAN", {"default": false}], "mode": [["SVG", "EPS", "PNG"]], "size": ["B_VECTOR2", {}], "opacity": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.25}], "check_existing": ["BOOLEAN", {"default": true}]}}
+UV_FollowActiveQuads|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "mode": [["EVEN", "LENGTH", "LENGTH_AVERAGE"]]}}
+UV_Hide|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "unselected": ["BOOLEAN", {"default": false}]}}
+UV_LightmapPack|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "PREF_CONTEXT": [["SEL_FACES", "ALL_FACES"]], "PREF_PACK_IN_ONE": ["BOOLEAN", {"default": true}], "PREF_NEW_UVLAYER": ["BOOLEAN", {"default": false}], "PREF_BOX_DIV": ["INT", {"min": 1, "max": 48, "default": 12}], "PREF_MARGIN_DIV": ["FLOAT", {"min": 0.0010000000474974513, "max": 1.0, "default": 0.10000000149011612}]}}
+UV_MarkSeam|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "clear": ["BOOLEAN", {"default": false}]}}
+UV_MinimizeStretch|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "fill_holes": ["BOOLEAN", {"default": true}], "blend": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0}], "iterations": ["INT", {"min": 0, "max": 2147483647, "default": 0}]}}
+UV_PackIslands|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "udim_source": [["CLOSEST_UDIM", "ACTIVE_UDIM", "ORIGINAL_AABB"]], "rotate": ["BOOLEAN", {"default": true}], "rotate_method": [["AXIS_ALIGNED", "CARDINAL", "ANY"]], "scale": ["BOOLEAN", {"default": true}], "merge_overlap": ["BOOLEAN", {"default": false}], "margin_method": [["SCALED", "ADD", "FRACTION"]], "margin": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0010000000474974513}], "pin": ["BOOLEAN", {"default": false}], "pin_method": [["SCALE", "ROTATION", "ROTATION_SCALE", "LOCKED"]], "shape_method": [["CONCAVE", "CONVEX", "AABB"]]}}
+UV_Paste|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_Pin|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "clear": ["BOOLEAN", {"default": false}]}}
+UV_ProjectFromView|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "orthographic": ["BOOLEAN", {"default": false}], "camera_bounds": ["BOOLEAN", {"default": true}], "correct_aspect": ["BOOLEAN", {"default": true}], "clip_to_bounds": ["BOOLEAN", {"default": false}], "scale_to_bounds": ["BOOLEAN", {"default": false}]}}
+UV_RandomizeUvTransform|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "random_seed": ["INT", {"min": 0, "max": 10000, "default": 0}], "use_loc": ["BOOLEAN", {"default": true}], "loc": ["B_VECTOR2", {}], "use_rot": ["BOOLEAN", {"default": true}], "rot": ["FLOAT", {"min": -6.2831854820251465, "max": 6.2831854820251465, "default": 0.0}], "use_scale": ["BOOLEAN", {"default": true}], "scale_even": ["BOOLEAN", {"default": false}], "scale": ["B_VECTOR2", {}]}}
+UV_RemoveDoubles|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "threshold": ["FLOAT", {"min": 0.0, "max": 10.0, "default": 0.019999999552965164}], "use_unselected": ["BOOLEAN", {"default": false}]}}
+UV_Reset|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_Reveal|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "select": ["BOOLEAN", {"default": true}]}}
+UV_Rip|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "mirror": ["BOOLEAN", {"default": false}], "release_confirm": ["BOOLEAN", {"default": false}], "use_accurate": ["BOOLEAN", {"default": false}], "location": ["B_VECTOR2", {}]}}
+UV_RipMove|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_SeamsFromIslands|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "mark_seams": ["BOOLEAN", {"default": true}], "mark_sharp": ["BOOLEAN", {"default": false}]}}
+UV_Select|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "extend": ["BOOLEAN", {"default": false}], "deselect": ["BOOLEAN", {"default": false}], "toggle": ["BOOLEAN", {"default": false}], "deselect_all": ["BOOLEAN", {"default": false}], "select_passthrough": ["BOOLEAN", {"default": false}], "location": ["B_VECTOR2", {}]}}
+UV_SelectAll|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "action": [["TOGGLE", "SELECT", "DESELECT", "INVERT"]]}}
+UV_SelectBox|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "pinned": ["BOOLEAN", {"default": false}], "xmin": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "xmax": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "ymin": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "ymax": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "wait_for_input": ["BOOLEAN", {"default": true}], "mode": [["SET", "ADD", "SUB"]]}}
+UV_SelectCircle|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "x": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "y": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "radius": ["INT", {"min": 1, "max": 2147483647, "default": 25}], "wait_for_input": ["BOOLEAN", {"default": true}], "mode": [["SET", "ADD", "SUB"]]}}
+UV_SelectEdgeRing|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "extend": ["BOOLEAN", {"default": false}], "location": ["B_VECTOR2", {}]}}
+UV_SelectLasso|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "mode": [["SET", "ADD", "SUB"]]}}
+UV_SelectLess|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_SelectLinked|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_SelectLinkedPick|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "extend": ["BOOLEAN", {"default": false}], "deselect": ["BOOLEAN", {"default": false}], "location": ["B_VECTOR2", {}]}}
+UV_SelectLoop|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "extend": ["BOOLEAN", {"default": false}], "location": ["B_VECTOR2", {}]}}
+UV_SelectMode|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "type": [["VERTEX", "EDGE", "FACE", "ISLAND"]]}}
+UV_SelectMore|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_SelectOverlap|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "extend": ["BOOLEAN", {"default": false}]}}
+UV_SelectPinned|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_SelectSimilar|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "type": [["PIN", "LENGTH", "LENGTH_3D", "AREA", "AREA_3D", "MATERIAL", "OBJECT", "SIDES", "WINDING", "FACE"]], "compare": [["EQUAL", "GREATER", "LESS"]], "threshold": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0}]}}
+UV_SelectSplit|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+UV_ShortestPathPick|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "use_face_step": ["BOOLEAN", {"default": false}], "use_topology_distance": ["BOOLEAN", {"default": false}], "use_fill": ["BOOLEAN", {"default": false}], "skip": ["INT", {"min": 0, "max": 2147483647, "default": 0}], "nth": ["INT", {"min": 1, "max": 2147483647, "default": 1}], "offset": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}], "object_index": ["INT", {"min": -1, "max": 2147483647, "default": -1}], "index": ["INT", {"min": -1, "max": 2147483647, "default": -1}]}}
+UV_ShortestPathSelect|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "use_face_step": ["BOOLEAN", {"default": false}], "use_topology_distance": ["BOOLEAN", {"default": false}], "use_fill": ["BOOLEAN", {"default": false}], "skip": ["INT", {"min": 0, "max": 2147483647, "default": 0}], "nth": ["INT", {"min": 1, "max": 2147483647, "default": 1}], "offset": ["INT", {"min": -2147483648, "max": 2147483647, "default": 0}]}}
+UV_SmartProject|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "angle_limit": ["FLOAT", {"min": 0.0, "max": 1.5707963705062866, "default": 1.1519173383712769}], "margin_method": [["SCALED", "ADD", "FRACTION"]], "island_margin": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0}], "area_weight": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0}], "correct_aspect": ["BOOLEAN", {"default": true}], "scale_to_bounds": ["BOOLEAN", {"default": false}]}}
+UV_SnapCursor|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "target": [["PIXELS", "SELECTED", "ORIGIN"]]}}
+UV_SnapSelected|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "target": [["PIXELS", "CURSOR", "CURSOR_OFFSET", "ADJACENT_UNSELECTED"]]}}
+UV_SphereProject|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "direction": [["VIEW_ON_EQUATOR", "VIEW_ON_POLES", "ALIGN_TO_OBJECT"]], "align": [["POLAR_ZX", "POLAR_ZY"]], "pole": [["PINCH", "FAN"]], "seam": ["BOOLEAN", {"default": false}], "correct_aspect": ["BOOLEAN", {"default": true}], "clip_to_bounds": ["BOOLEAN", {"default": false}], "scale_to_bounds": ["BOOLEAN", {"default": false}]}}
+UV_Stitch|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "use_limit": ["BOOLEAN", {"default": false}], "snap_islands": ["BOOLEAN", {"default": true}], "limit": ["FLOAT", {"min": 0.0, "max": 3.4028234663852886e+38, "default": 0.009999999776482582}], "static_island": ["INT", {"min": 0, "max": 2147483647, "default": 0}], "active_object_index": ["INT", {"min": 0, "max": 2147483647, "default": 0}], "midpoint_snap": ["BOOLEAN", {"default": false}], "clear_seams": ["BOOLEAN", {"default": true}], "mode": [["VERTEX", "EDGE"]], "stored_mode": [["VERTEX", "EDGE"]], "objects_selection_count": ["B_VECTOR6", {}]}}
+UV_Unwrap|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "method": [["ANGLE_BASED", "CONFORMAL"]], "fill_holes": ["BOOLEAN", {"default": true}], "correct_aspect": ["BOOLEAN", {"default": true}], "use_subsurf_data": ["BOOLEAN", {"default": false}], "margin_method": [["SCALED", "ADD", "FRACTION"]], "margin": ["FLOAT", {"min": 0.0, "max": 1.0, "default": 0.0010000000474974513}]}}
+UV_Weld|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}

blender/knift_project.py

@@ -1,121 +0,0 @@
-class KnifeProjection:
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_knife": ("BPY_OBJS",),
-                "bpy_objs_target": ("BPY_OBJS",),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_knife, bpy_objs_target):
-        import mathutils
-        import math
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        knife_object = bpy_objs_knife[0]
-        target_object = bpy_objs_target[0]
-
-        bpy.ops.object.select_all(action='DESELECT')
-        bpy.context.view_layer.objects.active = target_object
-        # knife_object.select_set(True)
-        # target_object.select_set(True)
-
-        # bpy.ops.object.mode_set(mode='EDIT')
-
-        area = next(
-            area for area in bpy.context.screen.areas if area.type == 'VIEW_3D')
-        space = next(space for space in area.spaces if space.type == 'VIEW_3D')
-        region = next(
-            region for region in area.regions if region.type == 'WINDOW')
-
-        # space.region_3d.view_perspective = 'ORTHO'
-        # space.region_3d.view_rotation = mathutils.Euler(
-        #     (0, 0, 0)).to_quaternion()
-
-        # set blender to orthographic view without using numpad
-
-        # bpy.ops.view3d.view_axis(type='TOP')
-
-        override = bpy.context.copy()
-
-        # override["active_object"] = target_object
-        # override["edit_object"] = target_object
-        # override["selected_objects"] = [knife_object, target_object]
-        # override["selected_editable_objects"] = [knife_object, target_object]
-
-        override["region"] = region
-        override["area"] = area
-        override["space"] = space
-
-        override["active_object"] = target_object
-        override["edit_object"] = target_object
-        override["selected_objects"] = [knife_object, target_object]
-        override["selected_editable_objects"] = [knife_object, target_object]
-
-        knife_object.select_set(False)
-        target_object.select_set(True)
-        # move the kinfe object up a bit
-        bpy.ops.transform.translate(value=(0, 0, -0.1))
-        bpy.ops.object.mode_set(mode='EDIT')
-        knife_object.select_set(False)
-
-        # Get the viewport rotation
-        viewport_rotation = space.region_3d.view_rotation
-
-        with bpy.context.temp_override(**override):
-            # # Create a rotation matrix representing a top-down view
-            # rotation_matrix = mathutils.Matrix.Rotation(math.radians(90.0), 4, 'X')
-
-            # # Create a rotation matrix to make the object face the user
-            # rotation_matrix_face_user = mathutils.Matrix.Rotation(math.radians(180.0), 4, 'Z')
-
-            # # Create a rotation matrix for the viewport rotation
-            # rotation_matrix_viewport = viewport_rotation.to_matrix().to_4x4()
-
-            # # Remember the original locations
-            # original_location_knife = knife_object.location.copy()
-            # original_location_target = target_object.location.copy()
-
-            # # Move the objects to the origin
-            # knife_object.location = mathutils.Vector((0, 0, 0))
-            # target_object.location = mathutils.Vector((0, 0, 0))
-
-            # # Rotate the objects
-            # knife_object.matrix_world = rotation_matrix @ rotation_matrix_face_user @ rotation_matrix_viewport @ knife_object.matrix_world
-            # target_object.matrix_world = rotation_matrix @ rotation_matrix_face_user @ rotation_matrix_viewport @ target_object.matrix_world
-
-            bpy.ops.view3d.view_axis(type='TOP', align_active=False) 
-            bpy.ops.view3d.view_persportho()
-            bpy.ops.wm.redraw_timer(type='DRAW_WIN_SWAP', iterations=1)
-            bpy.ops.mesh.knife_project()
-
-            # # Rotate the objects back
-            # knife_object.matrix_world = rotation_matrix.inverted() @ knife_object.matrix_world
-            # target_object.matrix_world = rotation_matrix.inverted() @ target_object.matrix_world
-
-            # # Move the objects back to their original locations
-            # knife_object.location = original_location_knife
-            # target_object.location = original_location_target
-
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        return ([target_object],)
-
-
-NODE_CLASS_MAPPINGS = {
-    "KnifeProjection": KnifeProjection
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "KnifeProjection": "Knife Projection"
-}

blender/load_image_with_alpha.py

@@ -0,0 +1,55 @@
+import folder_paths
+import os
+from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
+import numpy as np
+import torch
+import json
+
+class LoadImageWithAlpha:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":
+                    {"image": (sorted(files), {"image_upload": True})},
+                }
+
+    CATEGORY = "image"
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_image"
+    def load_image(self, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
+        image = i.convert("RGBA")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+
+        print(image.shape)
+
+        return (image, )
+
+    @classmethod
+    def IS_CHANGED(s, image):
+        image_path = folder_paths.get_annotated_filepath(image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, image):
+        if not folder_paths.exists_annotated_filepath(image):
+            return "Invalid image file: {}".format(image)
+
+        return True
+
+NODE_CLASS_MAPPINGS = {
+     "LoadImageWithAlpha": LoadImageWithAlpha,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LoadImageWithAlpha": "LoadImageWithAlpha"
+}

blender/mesh_from_texture.py

@@ -1,104 +0,0 @@
-class MeshFromTexture:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                # For disabling cache
-                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE", "BPY_OBJS")
-    RETURN_NAMES = ("image", "bpy_objs")
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, image, seed):
-        import torch
-        import cv2
-        import numpy as np
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        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)
-
-        # 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]
-        contours = [largest_contour]
-
-        def normalize_vertices(vertices, max_value):
-            return vertices / float(max_value) * 2 - 1
-
-        # Get the image width and height
-        height, width = image.shape[:2]
-
-        # Normalize the vertices
-        normalized_contours = []
-        for contour in contours:
-            normalized_contour = []
-            for vertex in contour:
-                normalized_vertex = [normalize_vertices(
-                    vertex[0][0], width), normalize_vertices(vertex[0][1], height) * -1]
-                normalized_contour.append(normalized_vertex)
-            normalized_contours.append(
-                np.array(normalized_contour, dtype=np.float32))
-
-        meshes = []
-        # print(len(normalized_contours))
-        for i, contour in enumerate(normalized_contours):
-            # Create a new mesh for each contour
-            mesh = bpy.data.meshes.new(name=f"NewMesh{i}")
-            # Create a new object for each mesh
-            obj = bpy.data.objects.new(f"NewObject{i}", mesh)
-            # Link the object to the current collection
-            bpy.context.collection.objects.link(obj)
-
-            # Add a z coordinate to each vertex
-            ordered_vertices = [(*vertex, 0) for vertex in contour]
-            # Create a face from the vertices
-            face = list(range(len(ordered_vertices)))
-
-            # Create the mesh from the vertices and face
-            mesh.from_pydata(ordered_vertices, [], [face])
-
-            # Create a default shape key for the mesh
-            sk_basis = obj.shape_key_add(name='Basis')
-
-            meshes.append(obj)  # Add the object to the list of meshes
-
-        # Draw contours on the original image
-        if not image.flags['C_CONTIGUOUS']:
-            image = np.ascontiguousarray(image)
-        cv2.drawContours(image, contours, -1, (0, 255, 0), 3)
-
-        # Convert image back to tensor
-        image = [torch.from_numpy(image)]
-        return (image, meshes)
-
-
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "MeshFromTexture": MeshFromTexture
-}
-
-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "MeshFromTexture": "Mesh from texture"
-}

blender/mesh_utils.py

@@ -1,6 +1,9 @@
 import atexit
 import subprocess
 import os
+import folder_paths
+import requests
+import json
 
 def genreate_mesh_from_texture(bpy, image):
     import torch
@@ -14,6 +17,9 @@ def genreate_mesh_from_texture(bpy, image):
     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]
 
@@ -67,7 +73,7 @@ def genreate_mesh_from_texture(bpy, image):
     cv2.drawContours(image, contours, -1, (0, 255, 0), 3)
 
     # Convert image back to tensor
-    image = [torch.from_numpy(image)]
+    image = torch.from_numpy(np.expand_dims(image, axis=0))
     return (image, meshes[0])
 
 
@@ -86,7 +92,7 @@ def assign_texture(bpy, BPY_OBJ, texture, texture_name):
 
     # Create an image with the required dimensions
     img = bpy.data.images.new(
-        texture_name, width=texture.shape[1], height=texture.shape[0])
+        texture_name, width=texture.shape[1], height=texture.shape[0], alpha = True)
 
     # If there is no alpha channel, append one full of 1's
     if texture.shape[2] == 3:
@@ -118,6 +124,7 @@ def assign_texture(bpy, BPY_OBJ, texture, texture_name):
     # Create a material
     mat = bpy.data.materials.new("MaterialName")
     mat.use_nodes = True
+    mat.blend_method = 'BLEND'
     nodes = mat.node_tree.nodes
     for node in nodes:
         nodes.remove(node)
@@ -138,6 +145,8 @@ def assign_texture(bpy, BPY_OBJ, texture, texture_name):
               texture_node.outputs['Color'])
     links.new(output_node.inputs['Surface'], bsdf_node.outputs['BSDF'])
 
+    links.new(bsdf_node.inputs['Alpha'], texture_node.outputs['Alpha'])
+
     # Assign the material to the active object
     if obj.data.materials:
         obj.data.materials[0] = mat
@@ -196,7 +205,7 @@ def kill_blender_process():
         process.kill()
 
 
-def export_gltf(output_dir, bpy_objects, filename, model_type, write_mode):
+def export_gltf(output_dir, bpy_objects, filename, model_type, write_mode, metadata):
     import global_bpy
     bpy = global_bpy.get_bpy()
     # print(bpy, bpy_objects)
@@ -206,21 +215,72 @@ def export_gltf(output_dir, bpy_objects, filename, model_type, write_mode):
     override["selected_objects"] = list(bpy_objects)
     override["active_object"] = list(bpy_objects)[0]
 
+    bpy_objects[0]["metadata"] = metadata
     for obj in bpy_objects:
         obj.select_set(True)
 
-    filepath = output_dir + "/" + filename + '.' + ("glb" if model_type == "GLB" else "gltf")
+    def get_file_extension(model_type):
+        if model_type == "GLB":
+            return ".glb"
+        elif model_type == "GLTF_EMBEDDED":
+            return ".gltf"
+        elif model_type == "GLTF_SEPARATE":
+            return ".gltf"
+        elif model_type == "AVA":
+            return ".ava"
+
+    ext = get_file_extension(model_type)
+    filepath = output_dir + "/" + filename + ext + (".glb" if model_type == "AVA" else "")
 
     if write_mode == "Increment":
         count = 0
         # while file exists, increment count
-        while os.path.exists(output_dir + "/" + filename + '_' + str(count) + '.' + ("glb" if model_type == "GLB" else "gltf")):
+        while os.path.exists(output_dir + "/" + filename + '_' + str(count) + ext):
             count += 1
 
-        filepath = output_dir + "/" + filename + '_' + str(count) + '.' + ("glb" if model_type == "GLB" else "gltf")
+        filepath = output_dir + "/" + filename + '_' + str(count) + ext + (".glb" if model_type == "AVA" else "")
     
     with bpy.context.temp_override(**override):
-        bpy.ops.export_scene.gltf(filepath=filepath, export_format=model_type, use_selection=True)
+        bpy.ops.export_scene.gltf(filepath=filepath, export_format="GLB" if model_type == "AVA" else model_type, use_selection=True, export_extras=True)
         # print(filepath)
+        if filepath.endswith('.ava.glb'):
+            new_filepath = filepath.replace('.ava.glb', '.ava')
+            os.replace(filepath, new_filepath)
+            filepath = new_filepath
 
-    return filepath
+    return filepath
+
+def get_avatar_file(output):
+    avatar_filename = output["gltfFilename"][0]
+    with open(
+        f"{folder_paths.get_output_directory()}/{avatar_filename}", "rb"
+    ) as f:
+        return f.read()
+
+def upload_avatar_file(output):
+    file = get_avatar_file(output)
+    response = requests.get("https://labs.avatech.ai/api/share?version=v2")
+    labData = response.json()
+    modelId = labData["modelId"]
+
+    # upload model
+    headers = {
+        "x-amz-acl": "public-read",
+        "Content-Type": "model/gltf-binary",
+        "Content-Length": str(len(file)),
+    }
+    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))
+
+    return modelId

blender/open_in_blender.py

@@ -1,48 +0,0 @@
-import subprocess
-import folder_paths
-import os
-from mesh_utils import open_in_blender
-
-
-class OpenInBlender:
-    def __init__(self):
-        self.my_blender_process = None
-        self.output_dir = folder_paths.get_output_directory()
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "bpy_objs": ("BPY_OBJS",),
-                "blender_path": ("STRING", {
-                    "multiline": False,
-                    "default": "blender"
-                }),
-                "shading": (["Material", "Solid", "Rendered", "Wireframe"],),
-                "camera_location": ("VECTOR3D",),
-                "camera_rotation": ("VECTOR3D",),
-            },
-        }
-
-    RETURN_TYPES = ()
-    FUNCTION = "process"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs, blender_path, shading, camera_location, camera_rotation):
-        output_file = self.output_dir + '/tmp.blend'
-        p = open_in_blender(self.my_blender_process, blender_path=blender_path, output_file=output_file, camera_location=camera_location,
-                        camera_rotation=camera_rotation, shading=shading)
-        self.my_blender_process = p
-        return {"ui": {}}
-
-
-NODE_CLASS_MAPPINGS = {
-    "OpenInBlender": OpenInBlender
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "OpenInBlender": "Open in Blender"
-}

blender/ops_create_mesh_layer.py

@@ -9,9 +9,12 @@ class Object_CreateMeshLayer(blender_node.ObjectOps):
 
     EXTRA_INPUT_TYPES = {
         "image": ("IMAGE",),
-        "face_threshold": ("FLOAT", {"display": "number", "default": 0.7}),
+        "convex_hull": ("BOOLEAN", {"default": True}),
+        # "face_threshold": ("FLOAT", {"display": "number", "default": 0.7}),
         "shape_threshold": ("FLOAT", {"display": "number", "default": 0.7}),
         "mesh_layer_name": ("STRING", {"default": "mesh_layer"}),
+        "scale_x": ("FLOAT", {"display": "number", "default": 1}),
+        "scale_y": ("FLOAT", {"display": "number", "default": 1}),
         "extrude_x": ("FLOAT", {"display": "number", "default": 0}),
         "extrude_y": ("FLOAT", {"display": "number", "default": 0}),
         "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
@@ -19,22 +22,27 @@ class Object_CreateMeshLayer(blender_node.ObjectOps):
 
     RETURN_TYPES = ("BPY_OBJ", "IMAGE")
 
-    def blender_process(self, bpy, image, face_threshold, shape_threshold, mesh_layer_name, extrude_x, extrude_y, seed):
+    def blender_process(self, bpy, image, convex_hull, shape_threshold, mesh_layer_name, scale_x,scale_y , extrude_x, extrude_y, seed):
         image, BPY_OBJ = genreate_mesh_from_texture(bpy, image)
 
         bpy.context.view_layer.objects.active = BPY_OBJ
 
         self.edit_mode(bpy)
-        bpy.ops.mesh.convex_hull(
+
-            delete_unused=True, use_existing_faces=True,
+        if convex_hull:
-            face_threshold=face_threshold,
+            bpy.ops.mesh.convex_hull(
-            shape_threshold=shape_threshold,
+                delete_unused=True, use_existing_faces=True,
-        )
+                shape_threshold=shape_threshold,
+                # face_threshold=face_threshold
+                face_threshold=0.7
+            )
 
         bpy.ops.mesh.delete(type='EDGE_FACE')
         bpy.ops.mesh.select_all(action='SELECT')
         bpy.ops.mesh.edge_face_add()
 
+        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_create_mesh_layer_advanced.py

@@ -0,0 +1,62 @@
+import blender_node
+from mesh_utils import genreate_mesh_from_texture
+
+class Object_CreateMeshLayer_Advanced(blender_node.ObjectOps):
+
+    BASE_INPUT_TYPES = {}
+    
+    CUSTOM_NAME = "Create Mesh Layer (Advanced)"
+
+    EXTRA_INPUT_TYPES = {
+        "image": ("IMAGE",),
+        "convex_hull": ("BOOLEAN", {"default": True}),
+        # "face_threshold": ("FLOAT", {"display": "number", "default": 0.7}),
+        "shape_threshold": ("FLOAT", {"display": "number", "default": 0.7}),
+        "mesh_layer_name": ("STRING", {"default": "mesh_layer"}),
+        "scale_x": ("FLOAT", {"display": "number", "default": 1}),
+        "scale_y": ("FLOAT", {"display": "number", "default": 1}),
+        "extrude_x": ("FLOAT", {"display": "number", "default": 0}),
+        "extrude_y": ("FLOAT", {"display": "number", "default": 0}),
+        "inner_translate_x": ("FLOAT", {"display": "number", "default": 0, "step": 0.01}),
+        "inner_translate_y": ("FLOAT", {"display": "number", "default": 0, "step": 0.01}),
+        "outer_translate_x": ("FLOAT", {"display": "number", "default": 0, "step": 0.01}),
+        "outer_translate_y": ("FLOAT", {"display": "number", "default": 0, "step": 0.01}),
+        "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+    }
+
+    RETURN_TYPES = ("BPY_OBJ", "IMAGE")
+
+    def blender_process(self, bpy, image, convex_hull, shape_threshold, mesh_layer_name, scale_x,scale_y , extrude_x, extrude_y, inner_translate_x, inner_translate_y, outer_translate_x, outer_translate_y, seed):
+        image, BPY_OBJ = genreate_mesh_from_texture(bpy, image)
+
+        bpy.context.view_layer.objects.active = BPY_OBJ
+
+        self.edit_mode(bpy)
+
+        if convex_hull:
+            bpy.ops.mesh.convex_hull(
+                delete_unused=True, use_existing_faces=True,
+                shape_threshold=shape_threshold,
+                # face_threshold=face_threshold
+                face_threshold=0.7
+            )
+
+        bpy.ops.mesh.delete(type='EDGE_FACE')
+        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.translate(value=(inner_translate_x, inner_translate_y, 0))
+
+        bpy.context.object.vertex_groups.new(name=mesh_layer_name)
+        bpy.ops.object.vertex_group_assign()
+
+        if extrude_x != 0 or extrude_y != 0:
+            bpy.ops.mesh.extrude_region_move()
+            bpy.ops.object.vertex_group_remove_from()
+            bpy.ops.transform.resize(value=(extrude_x, extrude_y, 0))
+            bpy.ops.transform.translate(value=(outer_translate_x, outer_translate_y, 0))
+            bpy.ops.mesh.delete(type='ONLY_FACE')
+
+        self.object_mode(bpy)
+        return (BPY_OBJ, image)

blender/ops_group.py

@@ -8,5 +8,6 @@ class GroupOps(blender_node.ObjectOps):
     RETURN_TYPES = (blender_node.BPY_OBJS,)
 
     def blender_process(self, bpy, BPY_OBJ, BPY_OBJ2, **props):
-        return ([BPY_OBJ, BPY_OBJ2],)
+        prop_values = props.values()
+        return ([BPY_OBJ, BPY_OBJ2, *prop_values],)
 

blender/ops_join.py

@@ -9,13 +9,13 @@ class Mesh_JoinMesh(blender_node.ObjectOps):
     CUSTOM_NAME = "Join Meshes"
 
     def blender_process(self, bpy, BPY_OBJ, **props):
-        
+        prop_values = props.values()        
-        prop_values = props.values()
+        for obj in list(prop_values) + [BPY_OBJ]:
-        
+            if obj is not None:
-        override = bpy.context.copy()
+                obj.select_set(True)
-        override["active_object"] = BPY_OBJ
+        if bpy.context.view_layer.objects is not None:
-        override["selected_editable_objects"] = list(prop_values) + [BPY_OBJ]
+            bpy.context.view_layer.objects.active = BPY_OBJ
-        bpy.ops.object.join(override)
+        bpy.ops.object.join()
 
         return (BPY_OBJ,)
 

blender/ops_match_texture_aspect_ratio.py

@@ -6,7 +6,7 @@ class Object_MatchTextureAspectRatio(blender_node.EditOps):
 
     EXTRA_INPUT_TYPES = {
         "image": ("IMAGE",),
-        "scale": ('FLOAT', {'default': 0.001, })
+        "scale": ('FLOAT', {'default': 0.001, "display": "number" , "step": 0.001})
     }
 
     CUSTOM_NAME = "Match Texture Aspect Ratio"

blender/ops_modify_shape_key.py

@@ -51,8 +51,8 @@ class Mesh_ModifyShapeKey(blender_node.ObjectOps):
         # Create a new array of verts with sk as co and verts' groups
         new_verts = []
         for i, vert in enumerate(verts):
-            print(i)
+            # print(i)
-            print(sk.data[i].co)
+            # print(sk.data[i].co)
             new_vert = {
                 'co': sk.data[i].co,
                 'groups': vert.groups
@@ -129,6 +129,6 @@ class Mesh_ModifyShapeKey(blender_node.ObjectOps):
             new_pos = center + vec
 
             # Apply new position
-            print(i, vert['co'], new_pos, weight)
+            # print(i, vert['co'], new_pos, weight)
 
             sk.data[i].co = new_pos

blender/ops_modify_uv.py

@@ -0,0 +1,24 @@
+import blender_node
+from mesh_utils import genreate_mesh_from_texture, assign_texture
+
+class Object_UV_Modifier(blender_node.EditOps):
+
+    EXTRA_INPUT_TYPES = {
+        "scale": ('FLOAT', {'default': 1, "display": "number", "step": 0.01}),
+        "texture_name": ('STRING', {'default': 'Texture', })
+    }
+
+    CUSTOM_NAME = "UV Modifier"
+
+    def blender_process(self, bpy, BPY_OBJ, scale, texture_name):
+        import bmesh
+
+        bm = bmesh.from_edit_mesh(BPY_OBJ.data)
+
+        uv_layer = bm.loops.layers.uv.verify()
+        for f in bm.faces:
+            # move all of the UVs in this face up one UDIM tile
+            for l in f.loops:
+                l[uv_layer].uv = (l[uv_layer].uv[0], 0.998 if l[uv_layer].uv[1] == 1 else l[uv_layer].uv[1])
+
+        bmesh.update_edit_mesh(BPY_OBJ.data)

blender/ops_plane_texture_unwrap.py

@@ -5,7 +5,7 @@ class Object_PlaneTextureUnwrap(blender_node.EditOps):
 
     EXTRA_INPUT_TYPES = {
         "image": ("IMAGE",),
-        "scale": ('FLOAT', {'default': 1, }),
+        "scale": ('FLOAT', {'default': 1, "display": "number", "step": 0.01}),
         "texture_name": ('STRING', {'default': 'Texture', })
     }
 

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/plane.py

@@ -1,74 +0,0 @@
-class Plane:
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "division": ("INT", {
-                    "default": 0, 
-                    "min": 0,
-                    "max": 256,
-                    "step": 1,
-                    "display": "number" 
-                }),
-                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), # For disabling cache
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, division, seed):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        # Define coordinates for the plane to object mode 
-        if bpy.context.active_object is not None:
-            bpy.ops.object.mode_set(mode='OBJECT')
-        coords = [(-1, -1, 0), (1, -1, 0), (-1, 1, 0), (1, 1, 0)]
-
-        # Create a mesh object
-        mesh = bpy.data.meshes.new(name="PlaneMesh")
-        mesh.from_pydata(coords,[],[(0,1,3,2)])
-
-        # Create a new object with the mesh
-        object_a = bpy.data.objects.new(name="PlaneObject", object_data=mesh)
-        bpy.ops.object.select_all(action='DESELECT')
-        bpy.context.collection.objects.link(object_a)
-        bpy.context.view_layer.objects.active = object_a
-        object_a.select_set(True)
-
-        # Invert the Y axis of the UV to fix the vertical inversion
-        bpy.ops.transform.resize(value=(-1, -1, 1))
-        
-        # Create a new UV map
-        uv_map = mesh.uv_layers.new(name="newUV")
-        mesh.uv_layers.active = uv_map
-
-        # Unwrap the mesh
-        bpy.ops.object.mode_set(mode='EDIT')
-        bpy.ops.uv.unwrap()
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        # Subdivide the plane
-        if division > 0:
-            original_mode = bpy.context.object.mode
-            bpy.ops.object.mode_set(mode='EDIT')
-            bpy.ops.mesh.subdivide(number_cuts=division)
-            bpy.ops.object.mode_set(mode=original_mode)
-
-        sk_basis = object_a.shape_key_add(name='Basis')
-
-        return ([object_a],)
-
-NODE_CLASS_MAPPINGS = {
-    "Plane": Plane
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Plane": "Plane (Old)"
-}

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)

blender/save_image_exclude.py

@@ -0,0 +1,82 @@
+import folder_paths
+import os
+from PIL import Image, ImageOps
+from PIL.PngImagePlugin import PngInfo
+import numpy as np
+import torch
+import json
+
+class SaveImageWithWorkflow:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required": 
+                   {"image": (sorted(files), {"image_upload": True}),
+                    "filename_prefix": ("STRING", {"default": "ComfyUI"})},
+               "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+               }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "image"
+
+    def save_images(self, image, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        image_path = folder_paths.get_annotated_filepath(image)
+        i = Image.open(image_path)
+        i = ImageOps.exif_transpose(i)
+        image = i.convert("RGBA")
+        image = np.array(image).astype(np.float32) / 255.0
+        image = torch.from_numpy(image)[None,]
+        images=(image)
+        
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+        results = list()
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            metadata = None
+            metadata = PngInfo()
+            if prompt is not None:
+                # prompt = json.loads(prompt)
+                prompt = {k: v for k, v in prompt.items() if v['class_type'] != 'Save Image With Workflow'}
+                metadata.add_text("prompt", json.dumps(prompt))
+
+            print(extra_pnginfo)
+
+            # if prompt is not None:
+            #     metadata.add_text("prompt", json.dumps(prompt))
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    if (x == 'workflow'):
+                        extra_pnginfo[x]["nodes"] = [node for node in extra_pnginfo[x]["nodes"] if node['type'] != 'Save Image With Workflow']
+
+                    metadata.add_text(x, json.dumps(extra_pnginfo[x]))
+
+            file = f"{filename}_{counter:05}_.png"
+            img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=4)
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        return { "ui": { "images": results } }
+
+NODE_CLASS_MAPPINGS = {
+     "Save Image With Workflow": SaveImageWithWorkflow,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Save Image With Workflow": "Save Image With Workflow"
+}

blender/scale_vertex.py

@@ -1,55 +0,0 @@
-class ScaleVertex():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-                "extrude": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target, extrude):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-        
-        if len(bpy_objs_target) == 0:
-            # throw error
-            return
-
-        target_object = bpy_objs_target[0]
-
-        # deselect all objects
-        bpy.ops.object.select_all(action='DESELECT')
-        # select only the target object
-        bpy.context.view_layer.objects.active = target_object
-        # enter enter edit mode and select all faces of the object to fill
-        bpy.ops.object.mode_set(mode='EDIT')
-        bpy.ops.mesh.select_all(action='SELECT')
-
-        bpy.ops.transform.resize(
-            value=(extrude, extrude, 1), constraint_axis=(False, False, False))
-
-        # remove the extruded vertex from the vertex group "name"
-        # bpy.ops.object.vertex_group_remove_from()
-
-        # bpy.ops.mesh.delete(type='FACE')
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-        return ([target_object],)
-
-    
-NODE_CLASS_MAPPINGS = {
-    "ScaleVertex": ScaleVertex
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "ScaleVertex": "Scale Vertex"
-}

blender/texture.py

@@ -1,92 +0,0 @@
-class Texture:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "bpy_objs": ("BPY_OBJS",),
-                "texture": ("IMAGE",),
-                "texture_name": ("STRING", {
-                    "multiline": False,
-                    "default": "my_image"
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    # OUTPUT_NODE = False
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs, texture, texture_name):
-        import numpy as np
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        # Convert image to numpy
-        texture = texture[0].numpy()
-
-        # Flip the image vertically
-        texture = np.flipud(texture)
-        
-        # Create an image with the required dimensions
-        img = bpy.data.images.new(texture_name, width=texture.shape[1], height=texture.shape[0])
-
-        # If there is no alpha channel, append one full of 1's
-        if texture.shape[2] == 3:
-            alpha_channel = np.ones((*texture.shape[:2], 1))
-            texture = np.concatenate((texture, alpha_channel), axis=2)
-
-        # Flatten image data and rearrange color channels for blender
-        img.pixels = texture.ravel()
-
-        # Pack image to store it within .blend file
-        img.pack()
-
-        # Save image to a file
-        # img.filepath_raw = 'test.png'
-        # img.file_format = 'PNG'
-        # img.save()
-
-        # Get the active object
-        obj = bpy_objs[0]
-
-        # Create a material
-        mat = bpy.data.materials.new("MaterialName")
-        mat.use_nodes = True
-        nodes = mat.node_tree.nodes
-        for node in nodes:
-            nodes.remove(node)
-
-        # Add a new texture node
-        texture_node = nodes.new(type='ShaderNodeTexImage')
-        texture_node.image = img
-    
-        # Add a new BSDF node
-        bsdf_node = nodes.new(type='ShaderNodeBsdfPrincipled')
-
-        # Add a new output node
-        output_node = nodes.new(type='ShaderNodeOutputMaterial')
-
-        # Link nodes together
-        links = mat.node_tree.links
-        links.new(bsdf_node.inputs['Base Color'], texture_node.outputs['Color'])
-        links.new(output_node.inputs['Surface'], bsdf_node.outputs['BSDF'])
-
-        # Assign the material to the active object
-        if obj.data.materials:
-            obj.data.materials[0] = mat
-        else:
-            obj.data.materials.append(mat)
-        return (bpy_objs,)
-
-NODE_CLASS_MAPPINGS = {
-    "Texture": Texture
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Texture": "Texture"
-}

blender/transform_object.py

@@ -1,45 +0,0 @@
-class TransfromObject():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-                "x": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}),
-                "y": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}),
-                "z": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}),
-
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target, x, y, z):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        target_object = bpy_objs_target[0]
-
-        bpy.ops.object.select_all(action='DESELECT')
-        bpy.context.view_layer.objects.active = target_object
-
-        # move the object in level transformation
-        target_object.location.x += x
-        target_object.location.y += y
-        target_object.location.z += z
-
-        return ([target_object],)
-
-
-NODE_CLASS_MAPPINGS = {
-    "TransfromObject": TransfromObject
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "TransfromObject": "Transfrom Object"
-}

blender/uv_unwrap.py

@@ -1,50 +0,0 @@
-class UV_Unwrap():
-    @classmethod
-    def INPUT_TYPES(s):
-
-        return {
-            "required": {
-                "bpy_objs_target": ("BPY_OBJS",),
-            },
-        }
-
-    RETURN_TYPES = ("BPY_OBJS",)
-    RETURN_NAMES = ("bpy_objs",)
-
-    FUNCTION = "process"
-
-    CATEGORY = "mesh"
-
-    def process(self, bpy_objs_target):
-        import global_bpy
-        bpy = global_bpy.get_bpy()
-
-        target_object = bpy_objs_target[0]
-
-        bpy.context.view_layer.objects.active = target_object
-        bpy.ops.object.mode_set(mode='OBJECT')
-        # deselect all objects
-        bpy.ops.object.select_all(action='DESELECT')
-        # select only the target object
-        bpy.context.view_layer.objects.active = target_object
-        # enter enter edit mode and select all faces of the object to fill
-        bpy.ops.object.mode_set(mode='EDIT')
-        bpy.ops.mesh.select_all(action='SELECT')
-
-        # perform a cube projection unwrap
-        bpy.ops.uv.cube_project(cube_size=1.0, correct_aspect=True, clip_to_bounds=False, scale_to_bounds=True)
-
-        # bpy.ops.mesh.delete(type='FACE')
-        bpy.ops.object.mode_set(mode='OBJECT')
-
-
-        return ([target_object],)
-
-
-NODE_CLASS_MAPPINGS = {
-    "UV_Unwrap": UV_Unwrap
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "UV_Unwrap": "UV Unwrap"
-}

common/point_visualizer.py

@@ -1,52 +0,0 @@
-import torch
-from einops import rearrange
-from nodes import PreviewImage
-
-class PointVisualizer(PreviewImage):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-                "point": ("VECTOR3D",),
-                "point_size": ("INT", {
-                    "default": 1, 
-                    "min": 10,
-                    "max": 20,
-                    "step": 1,
-                    "display": "number"
-                }),
-            },
-        }
-
-    FUNCTION = "process"
-
-    CATEGORY = "image"
-
-    def process(self, images, point, point_size):
-        x, y, z = point
-        h, w, c = images[0].shape
-        point_image = torch.zeros(images[0].shape)
-
-        center_x, center_y, _ = point
-        top_left_x = center_x - point_size // 2
-        top_left_y = center_y - point_size // 2
-
-        # Make sure the square is within the image boundaries
-        top_left_x = max(0, min(w - point_size, top_left_x))
-        top_left_y = max(0, min(h - point_size, top_left_y))
-
-        point_image[top_left_y:top_left_y+point_size, top_left_x:top_left_x+point_size] = 1
-        point_image = rearrange(point_image, 'h w c -> 1 h w c')
-
-        # Blending
-        images = images * 0.2 + point_image * 0.8
-        return self.save_images(images)
-
-NODE_CLASS_MAPPINGS = {
-    "PointVisualizer": PointVisualizer
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PointVisualizer": "Point Visualizer"
-}

common/vector3d.py

@@ -1,45 +0,0 @@
-class VECTOR3D:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "x": ("INT", {
-                    "default": 0, 
-                    "min": -1024,
-                    "max": 1024,
-                    "step": 1,
-                    "display": "number"
-                }),
-                "y": ("INT", {
-                    "default": 0, 
-                    "min": -1024,
-                    "max": 1024,
-                    "step": 1,
-                    "display": "number"
-                }),
-                "z": ("INT", {
-                    "default": 0, 
-                    "min": -1024,
-                    "max": 1024,
-                    "step": 1,
-                    "display": "number"
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("VECTOR3D",)
-
-    FUNCTION = "run"
-
-    CATEGORY = "utils"
-
-    def run(self, x, y, z):
-        return ([x, y, z],)
-
-NODE_CLASS_MAPPINGS = {
-    "VECTOR3D": VECTOR3D
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "VECTOR3D": "Vector 3D"
-}

generate_blender_types.py

@@ -0,0 +1,17 @@
+import os
+import sys
+
+os.environ["TYPE_GENERATION"] = "1"
+
+current_dir = os.path.dirname(__file__)
+blender_dir = os.path.join(current_dir, "blender")
+sys.path.append(blender_dir)
+
+import json
+from blender.ops_mesh import BLENDER_NODES
+
+
+with open(f"{blender_dir}/input_types.txt", "w") as f:
+    for node in BLENDER_NODES:
+        results = node.INPUT_TYPES()
+        f.write(node.__name__ + "|" + json.dumps(results) + "\n")

global_bpy.py

@@ -1,37 +1,49 @@
 import bpy
-global_bpy = bpy
 
-def get_bpy(): 
+global_bpy = bpy
-    global global_bpy
+should_reset_scene = True
+
+
+def set_should_reset_scene(value):
+    global should_reset_scene
+    should_reset_scene = value
+
+
+def get_bpy():
+    global global_bpy, should_reset_scene
+    if should_reset_scene:
+        reset_bpy()
+        should_reset_scene = False
     return global_bpy
 
+
 def reset_bpy():
     global global_bpy
     bpy = global_bpy
     # not using this cause this cause seg fault frequently
-    # global_bpy.ops.wm.read_factory_settings(use_empty=True)
+    bpy.ops.wm.read_factory_settings(use_empty=True)
 
-    # check if there's no object, return
+    # # check if there's no object, return
-    if len(bpy.data.objects) == 0:
+    # if len(bpy.data.objects) == 0:
-        return
+    #     return
 
-    # enter object mode
+    # # enter object mode
-    if bpy.context.mode != 'OBJECT':
+    # if bpy.context.mode != 'OBJECT':
-        bpy.ops.object.mode_set(mode='OBJECT')
+    #     bpy.ops.object.mode_set(mode='OBJECT')
 
-    # Delete all objects
+    # # Delete all objects
-    bpy.ops.object.select_all(action='DESELECT')
+    # bpy.ops.object.select_all(action='DESELECT')
-    bpy.ops.object.select_all(action='SELECT')
+    # bpy.ops.object.select_all(action='SELECT')
-    bpy.ops.object.delete()
+    # bpy.ops.object.delete()
 
-    # Delete all meshes
+    # # Delete all meshes
-    for mesh in bpy.data.meshes:
+    # for mesh in bpy.data.meshes:
-        bpy.data.meshes.remove(mesh)
+    #     bpy.data.meshes.remove(mesh)
 
-    # Delete all materials
+    # # Delete all materials
-    for material in bpy.data.materials:
+    # for material in bpy.data.materials:
-        bpy.data.materials.remove(material)
+    #     bpy.data.materials.remove(material)
 
-    # Delete all textures
+    # # Delete all textures
-    for texture in bpy.data.textures:
+    # for texture in bpy.data.textures:
-        bpy.data.textures.remove(texture)
+    #     bpy.data.textures.remove(texture)

js/Alert.js

@@ -0,0 +1,44 @@
+import { alertDialog } from "./state.js";
+import { van } from "./van.js";
+const { div, span } = van.tags;
+
+export function Alert() {
+  const color = van.state("bg-orange-100 text-orange-700 border-orange-500");
+
+  van.derive(() => {
+    if (alertDialog.val.time > 0) {
+      switch (alertDialog.val.type) {
+        case "error":
+          color.val = "bg-red-100 text-red-700 border-red-500";
+          break;
+        case "success":
+          color.val = "bg-green-100 text-green-700 border-green-500";
+          break;
+        case "info":
+          color.val = "bg-blue-100 text-blue-700 border-blue-500";
+          break;
+        case "warning":
+        default:
+          color.val = "bg-orange-100 text-orange-700 border-orange-500";
+          break;
+      }
+      setTimeout(() => {
+        alertDialog.val = { text: "", time: 0 };
+      }, alertDialog.val.time);
+    }
+  });
+
+  return div(
+    {
+      class: () =>
+        "absolute z-[100] bottom-8 flex justify-center w-full " +
+        (alertDialog.val.text ? "" : "hidden"),
+    },
+    div(
+      {
+        class: () => `${color.val} border-t-4 rounded-sm p-2`,
+      },
+      () => span(alertDialog.val.text)
+    )
+  );
+}

js/AppHeader.js

@@ -0,0 +1,28 @@
+import { van } from "./van.js";
+const { div, span } = van.tags;
+
+export function AppHeader() {
+  return div(
+    {
+      class: () => "absolute flex justify-between top-0 w-full text-white p-4",
+    },
+    div(
+      {},
+      span(
+        {
+          class:
+            "block bg-gradient-to-b from-gray-500 to-white text-transparent bg-clip-text text-2xl",
+        },
+        "Avatech v1"
+      ),
+      span(
+        {
+          class:
+            "bg-gradient-to-b from-gray-500 to-white text-transparent bg-clip-text text-lg",
+        },
+        "Get your DALLE3 AI Personal Clone"
+      )
+    ),
+    span({ class: "text-gray-300" }, "Twitter")
+  );
+}

js/AvatarEditor.js

@@ -1,32 +0,0 @@
-import { iframeSrc, showEditor } from './state.js';
-import { van } from './van.js';
-const { button, iframe, div, img } = van.tags;
-
-export function AvatarEditor() {
-  return div(
-    {
-      class: 'w-full h-full',
-    },
-    button(
-      {
-        class: () =>
-          'px-4 py-2 rounded-md absolute left-0 top-0 z-[100] bg-gray-800 pointer-events-auto ' +
-          (showEditor.val ? '' : 'hidden'),
-        onclick: () => {
-          console.log('close');
-          showEditor.val = false;
-        },
-      },
-      'back',
-    ),
-    iframe({
-      id: 'avatech-editor-iframe',
-      title: 'avatech-editor-iframe',
-      name: 'avatech-editor-iframe',
-      allow: "cross-origin-isolated",
-      class: () =>
-        'w-full h-full pointer-events-auto ' + (showEditor.val ? '' : 'hidden'),
-      src: iframeSrc,
-    }),
-  );
-}

js/AvatarPreview.js

@@ -0,0 +1,774 @@
+import { van } from "./van.js";
+import {
+  imageUrl,
+  showPreview,
+  previewUrl,
+  showEditor,
+  previewImg,
+  previewImgLoading,
+  alertDialog,
+  isGenerateFlow,
+  enableAutoSegment
+} from "./state.js";
+const { button, iframe, div, img, input, label, span, textarea, ul, li } =
+  van.tags;
+import { app } from "./app.js";
+import { uploadPreview } from "./index.js";
+import { api } from "./api.js";
+import { segmented, uploadSegments } from "./LayerEditor.js";
+import { initModel } from "./onnx.js";
+// import { uploadSegments } from "./LayerEditor.js";
+
+const workflowList = [
+  "idle_avatar_(trigger)",
+  "Auto_segment_workflow",
+  "BronyaZaychik_(ChinaDress)",
+  "BronyaZaychik_(Default_Silverwing)",
+  "BronyaZaychik_(Non-official_office_ladysuit)",
+  "BronyaZaychik_(Official_office_ladysuit)",
+  "BronyaZaychikLora_withhand",
+  "SilverWolf_(Default)",
+  "SilverWolf_(Maid)",
+  "SilverWolfLora_withhand",
+];
+
+function editSegment(stage) {
+  /** @type {import('../../../web/types/litegraph.js').LGraph}*/
+  const graph = app.graph;
+  const imageNodes = graph.findNodesByType("LoadImage");
+  if (!imageNodes[0].imgs) return;
+
+  const nodes = graph.findNodesByType("SAM MultiLayer");
+
+  /** @type {any[]}*/
+  const widgets = nodes[0].widgets;
+  console.log(nodes[0]);
+  console.log(nodes[0].widgets);
+  widgets.find((x) => x.type == "button").callback();
+  stage.val = 2;
+}
+
+// const workflowList = ["Auto_segment_workflow"];
+/**
+ * Load JSON workflow
+ * @param {string} name - The name of the workflow to load
+ */
+async function loadJSONWorkflow(name) {
+  if (name === 'default' || name.toLowerCase().startsWith("auto_segment")) {
+    enableAutoSegment.val = true
+  } else {
+    enableAutoSegment.val = false
+  }
+  const json = await (await fetch(`./get_workflow?name=${name}`)).json();
+  app.loadGraphData(json);
+  console.log(json);
+}
+
+async function updatePositivePrompt(app, prompt) {
+  const positivePrompt = app.graph
+    .findNodesByType("CLIPTextEncode")
+    .find((x) => x.color == "#232");
+  if (!positivePrompt) {
+    alertDialog.val = {
+      text: "Cannot find the CLIPTextEncode node. Please make sure the workflow is correct.",
+      time: 5000,
+    };
+    return;
+  }
+
+  positivePrompt.widgets[0].inputEl.value = prompt;
+}
+
+async function updateSeedValue(app, seed) {
+  const kSampler = app.graph.findNodesByType("KSampler")[0];
+  if (!kSampler) {
+    alertDialog.val = {
+      text: "Cannot find the KSampler node. Please make sure the workflow is correct.",
+      time: 5000,
+    };
+    return;
+  }
+  kSampler.widgets[0].value = seed;
+  kSampler.widgets[1].value = "fixed";
+}
+
+async function uploadImage() {
+  /** @type {import('../../../web/types/litegraph.js').LGraph}*/
+  const graph = app.graph;
+  const nodes = graph.findNodesByType("LoadImage");
+  previewImgLoading.val = true;
+  console.log(previewImgLoading.val);
+
+  /** @type {any[]}*/
+  const widgets = nodes[0].widgets;
+  console.log(nodes[0]);
+  widgets.find((x) => x.type == "button").callback();
+  while (true) {
+    await new Promise((resolve) => setTimeout(resolve, 1000));
+    if (nodes[0]?.imgs) {
+      if (previewImg.val != "" && previewImg.val == nodes[0].imgs[0].currentSrc)
+        continue;
+      previewImgLoading.val = false;
+      return nodes[0].imgs[0].currentSrc;
+    }
+  }
+}
+
+const jsonWorkflowLoading = van.state(true);
+export const sharedAvatarLink = van.state("");
+
+async function prepareImageFromUrlRedirect(stage) {
+  await new Promise((resolve) => setTimeout(resolve, 2000));
+  const queue_id = new URLSearchParams(window.location.search).get("queue-id");
+  if (queue_id && queue_id != "") {
+    console.log(queue_id);
+    stage.val = 1;
+    const graph = app.graph;
+    const node = graph.findNodesByType("LoadImage");
+    const imageName = queue_id + ".png";
+    console.log(node[0]);
+    node[0].widgets_values[0] = imageName;
+    node[0].widgets[0].value = imageName;
+    node[0].widgets[0]._value = imageName;
+    graph.change();
+    previewImg.val = api.apiURL(
+      `/view?filename=${encodeURIComponent(
+        imageName
+      )}&type=input&subfolder=create_avatar_endpoint${app.getPreviewFormatParam()}`
+    );
+    console.log(previewImg);
+  }
+  const dragndrop = document.getElementById("dnd");
+  dragndrop.addEventListener("dragenter", (evt) => {
+    evt.preventDefault();
+    dragndrop.className =
+      "h-96 w-full border-2 border-purple-500 text-purple-500 border-dashed rounded-lg flex justify-center items-center";
+  });
+  dragndrop.addEventListener("dragleave", (evt) => {
+    evt.preventDefault();
+    dragndrop.className =
+      "h-96 w-full border-2 border-black border-dashed items-center rounded-lg flex justify-center";
+  });
+  dragndrop.addEventListener("dragover", (evt) => {
+    evt.preventDefault();
+  });
+  dragndrop.addEventListener("drop", async (evt) => {
+    evt.preventDefault();
+    dragndrop.className =
+      "h-96 w-full border-2 border-black border-dashed items-center rounded-lg flex justify-center";
+    if (evt.dataTransfer.files.length > 1) return;
+    if (
+      evt.dataTransfer.files[0].type != "image/jpeg" &&
+      evt.dataTransfer.files[0].type != "image/png" &&
+      evt.dataTransfer.files[0].type != "image/webp"
+    )
+      return;
+    stage.val = 1;
+    previewImg.val = URL.createObjectURL(evt.dataTransfer.files[0]);
+    if (Object.entries(evt.dataTransfer.files).length) {
+      await uploadFile(evt.dataTransfer.files[0], true);
+    }
+  });
+}
+
+export function AvatarPreview() {
+  console.log("getting workflow json now");
+
+  const loading = van.state(false);
+  const shareLoading = van.state("share"); // share, loading, shared
+
+  api.addEventListener("execution_start", (evt) => {
+    loading.val = true;
+  });
+
+  api.addEventListener("executed", (evt) => {
+    const nodeId = evt.detail.node;
+    const targetNode = graph._nodes_by_id[nodeId];
+    if (targetNode.type === "AvatarMainOutput") {
+      loading.val = false;
+    }
+  });
+
+  const email = van.state("");
+  const stage = van.state(0); // 0: upload image, 1: edit segment, 2: generate
+
+  // This will wait 2 seconds until the everything is loaded
+  prepareImageFromUrlRedirect(stage);
+
+  const renderSteps = () => {
+    return div(
+      {
+        class: () =>
+          "flex flex-col bg-white justify-center w-[32rem] max-w-[100%]",
+      },
+      div(
+        {
+          class: () =>
+            " bg-gradient-to-b from-black via-[#5F5F5F] via-60% to-white text-transparent bg-clip-text font-gabarito text-4xl",
+        },
+        "Avatech v1"
+      ),
+      div(
+        {
+          class: () =>
+            " bg-gradient-to-b from-black via-[#5F5F5F] via-50% to-white text-transparent bg-clip-text font-gabarito text-2xl",
+        },
+        "Get your DALLE3 AI Personal Clone"
+      ),
+      div(
+        {
+          class: () =>
+            " w-full flex flex-col justify-center items-center gap-4",
+        },
+        !isGenerateFlow.val
+          ? div(
+            {
+              class: () =>
+                "flex flex-col justify-center items-center gap-4 w-full",
+            },
+            div(
+              { class: () => "w-full flex mt-2" },
+              button(
+                {
+                  class: () => `btn w-full normal-case`,
+                  onclick: async () => {
+                    // previewImg.val = await uploadImage();
+                    // stage.val = 1;
+                    var input = document.createElement("input");
+                    input.type = "file";
+
+                    document.body.appendChild(input);
+
+                    // when the input content changes, do something
+                    input.onchange = async function (e) {
+                      stage.val = 1;
+                      if (Object.entries(e.target.files).length) {
+                        await uploadFile(e.target.files[0], true);
+                      }
+                      previewImg.val = URL.createObjectURL(e.target.files[0]);
+                      // upload files
+                      document.body.removeChild(input);
+                    };
+
+                    // Trigger file browser
+                    input.click();
+                  },
+                },
+                div({ class: "badge badge-neutral" }, "1"),
+                div("Upload your image"),
+                span({
+                  class: "iconify text-lg",
+                  "data-icon": "material-symbols:drive-folder-upload",
+                  "data-inline": "false",
+                }),
+                () =>
+                  previewImgLoading.val
+                    ? span({
+                      class: "loading loading-spinner loading-md",
+                    })
+                    : "",
+              ),
+            ),
+            () => {
+              const dnd = div(
+                {
+                  id: "dnd",
+                  class: () =>
+                    "h-96 w-full border-2 border-black border-dashed items-center rounded-lg flex justify-center text-black",
+                },
+                "or drag and drop the image here",
+              );
+              const image = img({
+                class: () => "z-[10] object-contain w-full h-[394px] border",
+                src: previewImg,
+                onload: () => {
+                  segmented.val = false;
+                }
+              });
+
+              if (isMobileDevice()) {
+                return previewImg.val !== "" ? image : "";
+              } else {
+                return previewImg.val === "" ? dnd : image;
+              }
+            },
+            button(
+              {
+                class: () =>
+                  "btn w-full normal-case " +
+                  (stage.val < 1 ? "btn-disabled" : ""),
+                onclick: () => {
+                  enableAutoSegment.val = true;
+                  editSegment(stage)
+                },
+              },
+              div({ class: "badge badge-neutral" }, "2"),
+              "Edit Segment",
+            ),
+            button(
+              {
+                class: () =>
+                  "btn w-full normal-case " +
+                  (stage.val < 2 ? "btn-disabled" : ""),
+                onclick: async () => {
+                  // const uploaded = await uploadSegments();
+                  // if (!uploaded) return;
+
+                  const graph = app.graph;
+                  const imageNodes = graph.findNodesByType("LoadImage");
+                  if (!imageNodes[0].imgs) return;
+                  document.getElementById("queue-button").click();
+                },
+              },
+              div({ class: "badge badge-neutral" }, "3"),
+              () =>
+                loading.val
+                  ? span({
+                    class: "loading loading-spinner loading-md",
+                  })
+                  : "Make It Alive!",
+            ),
+          )
+          : div(
+            {
+              class:
+                "flex flex-col justify-center items-center gap-4 w-full text-black",
+            },
+            div(
+              {
+                class:
+                  "w-full mt-2 flex flex-col rounded-md left-0 top-0",
+              },
+              textarea({
+                class:
+                  "textarea textarea-bordered border-gray-300 border-b-0 focus:outline-none resize-none rounded-t-md rounded-b-none text-md h-36",
+                placeholder: "Enter your prompt",
+                defaultValue:
+                  "1girl, looking at viewer, open mouth, simple background, white background, smile",
+                id: "positivePromptProxy",
+              }),
+              div(
+                {
+                  class:
+                    "flex flex-row gap-2 border border-gray-300 rounded-b-md text-md items-center",
+                },
+                span({ class: "ml-4" }, "Seed"),
+                div({ class: "divider divider-horizontal m-0" }),
+                input({
+                  type: "text",
+                  class: "input border-none focus:outline-none w-full p-0",
+                  placeholder: "Seed",
+                  defaultValue: "1234",
+                  id: "seedProxy",
+                }),
+                div(
+                  {
+                    onclick: () => {
+                      const random4Digits =
+                        Math.floor(Math.random() * 9000) + 1000;
+                      console.log(
+                        random4Digits,
+                        document.getElementById("seedProxy").value,
+                      );
+                      document.getElementById("seedProxy").value =
+                        random4Digits.toString();
+                    },
+                  },
+                  span({
+                    class: "iconify text-2xl mr-4 hover:cursor-pointer",
+                    "data-icon": "fad:random-1dice",
+                    "data-inline": "false",
+                  }),
+                ),
+              ),
+            ),
+            button(
+              {
+                class: "btn w-full normal-case ",
+                onclick: async () => {
+                  loading.val = true;
+
+                  updatePositivePrompt(
+                    app,
+                    document.getElementById("positivePromptProxy").value,
+                  );
+                  updateSeedValue(
+                    app,
+                    document.getElementById("seedProxy").value,
+                  );
+
+                  const sam = app.graph.findNodesByType("SAM MultiLayer")[0];
+                  if (!sam) {
+                    alertDialog.val = {
+                      text: "Cannot find the SAM node. Please make sure the workflow is correct.",
+                      time: 5000,
+                    };
+                    return;
+                  }
+                  const ckpt = sam.widgets[0].value;
+                  const modelType = ckpt.match(/vit_[lbh]/)?.[0];
+                  await initModel(modelType);
+                  await uploadSegments();
+
+                  document.getElementById("queue-button").click();
+                },
+              },
+              div({ class: "badge badge-neutral" }, "1"),
+              () =>
+                loading.val
+                  ? span({ class: "loading loading-spinner loading-md" })
+                  : "Make It Alive!",
+            ),
+            button(
+              {
+                class: () =>
+                  "btn w-full normal-case ",
+                onclick: () => {
+                  enableAutoSegment.val = false;
+                  editSegment(stage)
+                },
+              },
+              div({ class: "badge badge-neutral" }, "2"),
+              "Edit Segment",
+            ),
+            // button(
+            //   {
+            //     class: "btn w-full normal-case",
+            //     onclick: () => {
+            //       /** @type {import('../../../web/types/litegraph.js').LGraph}*/
+            //       const graph = app.graph;
+            //       const nodes = graph.findNodesByType("SAM MultiLayer");
+
+            //       /** @type {any[]}*/
+            //       const widgets = nodes[0].widgets;
+            //       console.log(nodes[0]);
+            //       console.log(nodes[0].widgets);
+            //       widgets.find((x) => x.type == "button").callback();
+            //     },
+            //   },
+            //   div({ class: "badge badge-neutral" }, "2"),
+            //   "(Optional) Edit Segment",
+            // ),
+          ),
+      ),
+    );
+  };
+
+  const renderIFrame = () => {
+    return iframe({
+      id: "avatech-viewer-iframe",
+      title: "avatech-viewer-iframe",
+      name: "avatech-viewer-iframe",
+      allow: "cross-origin-isolated",
+      class: () =>
+        "w-full h-full min-w-[350px] min-h-[350px] z-[100] pointer-events-auto flex border-none overflow-hidden bg-transparent" +
+        (showPreview.val ? "" : "hidden"),
+      // src: "https://labs.avatech.ai/viewer/default",
+      // src: "http://localhost:3000/viewer/default",
+      src: previewUrl,
+    });
+  };
+
+  const renderShareLink = () => {
+    return div(
+      {
+        class: () =>
+          "w-full flex flex-col gap-2 justify-center items-center mt-8",
+      },
+      div(
+        {
+          class: () =>
+            "w-full flex justify-center font-bold italic text-gray-500",
+        },
+        span("We are launching OpenAI Assistant API integration soon!")
+      ),
+      div(
+        { class: () => "w-[24rem] flex justify-center items-center" },
+        input({
+          type: "text",
+          class: () =>
+            "w-full input input-bordered text-black rounded rounded-l-md rounded-r-none !outline-none",
+          onchange: (e) => {
+            email.val = e.target.value;
+          },
+          placeholder: "Enter your email",
+        }),
+        button(
+          {
+            class: () =>
+              "btn rounded rounded-l-none rounded-r-md no-animation bg-neutral-800 hover:bg-neutral-950 text-white border-none normal-case",
+            onclick: async () => {
+              if (shareLoading.val === "share") {
+                shareLoading.val = "loading";
+                const url = await (await fetch("./get_webhook")).json();
+                await uploadPreview();
+                await fetch(url, {
+                  method: "POST",
+                  headers: {
+                    "Content-Type": "application/json",
+                  },
+                  body: JSON.stringify({
+                    username: "Avabot",
+                    avatar_url:
+                      "https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/avatechai.png",
+                    content: "New register! \n" + email.val,
+                  }),
+                });
+                shareLoading.val = "shared";
+              }
+              if (sharedAvatarLink.val) {
+                await navigator.clipboard.writeText(sharedAvatarLink.val);
+                alertDialog.val = {
+                  text: "Avatar link copied to clipboard!",
+                  type: "success",
+                  time: 5000,
+                };
+              }
+            },
+          },
+          () => {
+            switch (shareLoading.val) {
+              case "share":
+                return "Get Avatar Link";
+              case "loading":
+                return span({
+                  class: "loading loading-spinner loading-md",
+                });
+              case "shared":
+                return span({
+                  class: "iconify text-xl",
+                  "data-icon": "lucide:copy-check",
+                });
+            }
+          }
+        )
+      )
+    );
+  };
+
+  const renderCloseButton = () => {
+    return button(
+      {
+        class: () =>
+          "btn flex flex-row btn-ghost text-black normal-case rounded-md left-0 top-0 z-[200] pointer-events-auto sm:btn-md btn-sm ",
+        onclick: () => {
+          showPreview.val = false;
+        },
+      },
+      span({
+        class: "iconify text-lg",
+        "data-icon": "ic:round-close",
+        "data-inline": "false",
+      })
+    );
+  };
+
+  const renderRestartButton = () => {
+    return button(
+      {
+        class: () =>
+          "btn flex flex-row btn-ghost text-black normal-case rounded-md left-0 top-0 z-[200] pointer-events-auto sm:btn-md btn-sm ",
+        onclick: () => {
+          fetch("https://7a49f4ad27be4dcf.ngrok.app/restart");
+        },
+      },
+      span({
+        class: "iconify text-lg",
+        "data-icon": "mdi:restart",
+        "data-inline": "false",
+      })
+    );
+  };
+
+  const renderChangeWorkflowButton = () => {
+    return div(
+      {
+        class: () =>
+          "dropdown dropdown-hover dropdown-bottom z-[200] pointer-events-auto text-black ",
+      },
+      label(
+        {
+          class: () =>
+            "btn flex flex-row btn-ghost normal-case rounded-md sm:btn-md btn-sm",
+          tabIndex: () => 0,
+        },
+        span({
+          class: "iconify text-lg",
+          "data-icon": "ic:round-swap-vert",
+          "data-inline": "false",
+        }),
+        span({ class: "sm:flex hidden" }, () =>
+          jsonWorkflowLoading.val ? "Loading" : "Change workflow"
+        )
+      ),
+      ul(
+        {
+          class: () =>
+            "dropdown-content -left-[100px] z-[200] menu p-2 shadow rounded-box w-96 bg-white",
+          tabIndex: () => 0,
+        },
+        workflowList.map((val, index) => {
+          return li(
+            {
+              class: () => "p-4 btn btn-ghost items-start",
+              onclick: async (e) => {
+                e.preventDefault();
+                document.activeElement.blur()
+                await loadJSONWorkflow(val);
+                await new Promise((resolve) => setTimeout(resolve, 200));
+                const kSampler = app.graph.findNodesByType("KSampler")[0];
+                if (!kSampler) isGenerateFlow.val = false;
+                else isGenerateFlow.val = true;
+              },
+            },
+            () => val
+          );
+        }),
+        div({ class: () => "divider !my-0" }),
+        li(
+          {
+            class: () => "p-4 btn btn-ghost items-start",
+            onclick: (e) => {
+              let input = document.createElement("input");
+              input.type = "file";
+              document.body.appendChild(input);
+              input.accept = ".json,image/png,.latent,.safetensors";
+              input.onchange = async function (e) {
+                if (Object.entries(e.target.files).length) {
+                  await app.handleFile(e.target.files[0]);
+                }
+                await new Promise((resolve) => setTimeout(resolve, 200));
+                const kSampler = app.graph.findNodesByType("KSampler")[0];
+                if (!kSampler) isGenerateFlow.val = false;
+                else isGenerateFlow.val = true;
+                document.body.removeChild(input);
+              };
+              input.click();
+              // document.getElementById("comfy-load-button").click();
+            },
+          },
+          "Import..."
+        )
+      )
+    );
+    // return button(
+    //   {
+    //     class: () =>
+    //       "btn text-black flex flex-row btn-ghost normal-case rounded-md left-0 top-0 z-[200] pointer-events-auto sm:btn-md btn-sm ",
+    //     onclick: () => {
+    //   let input = document.createElement("input");
+    //   input.type = "file";
+    //   document.body.appendChild(input);
+    //   input.accept = ".json,image/png,.latent,.safetensors";
+    //   input.onchange = async function (e) {
+    //     if (Object.entries(e.target.files).length) {
+    //       await app.handleFile(e.target.files[0]);
+    //     }
+    //     await new Promise((resolve) => setTimeout(resolve, 200));
+    //     const kSampler = app.graph.findNodesByType("KSampler")[0];
+    //     if (!kSampler) isGenerateFlow.val = false;
+    //     else isGenerateFlow.val = true;
+    //     document.body.removeChild(input);
+    //   };
+    //   input.click();
+    //   // document.getElementById("comfy-load-button").click();
+    // },
+    //   },
+    //   span({
+    //     class: "iconify text-lg",
+    //     "data-icon": "ic:round-swap-vert",
+    //     "data-inline": "false",
+    //   }),
+    // span({ class: "sm:flex hidden" }, () =>
+    //   jsonWorkflowLoading.val ? "Loading" : "Change workflow",
+    // ),
+    // );
+  };
+
+  const renderTwitter = () => {
+    return button(
+      {
+        class: () =>
+          "absolute top-4 right-4 btn sm:w-32 w-20 text-black btn-ghost text-xs z-[200] !px-0 normal-case sm:btn-md btn-sm",
+        onclick: () => window.open("https://twitter.com/avatech_gg", "_blank"),
+      },
+      "Twitter"
+    );
+  };
+
+  const isMobileDevice = () => {
+    return window.screen.width < 768;
+  };
+
+  return div(
+    {
+      class: () => {
+        console.log(showPreview);
+
+        return (
+          (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"
+        );
+      },
+    },
+    renderIFrame(),
+  );
+}
+
+function showImage(name) {
+  const graph = app.graph;
+  const node = graph.findNodesByType("LoadImage");
+  const img = new Image();
+  img.onload = () => {
+    node[0].imgs = [img];
+    app.graph.setDirtyCanvas(true);
+  };
+  let folder_separator = name.lastIndexOf("/");
+  let subfolder = "";
+  if (folder_separator > -1) {
+    subfolder = name.substring(0, folder_separator);
+    name = name.substring(folder_separator + 1);
+  }
+  img.src = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      name
+    )}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}`
+  );
+  node.setSizeForImage?.();
+}
+
+async function uploadFile(file, updateNode, pasted = false) {
+  try {
+    // Wrap file in formdata so it includes filename
+    const graph = app.graph;
+    const nodes = graph.findNodesByType("LoadImage");
+    const widgets = nodes[0].widgets.find((w) => w.name === "image");
+    const body = new FormData();
+    body.append("image", file);
+    if (pasted) body.append("subfolder", "pasted");
+    const resp = await api.fetchApi("/upload/image", {
+      method: "POST",
+      body,
+    });
+
+    if (resp.status === 200) {
+      const data = await resp.json();
+      // Add the file to the dropdown list and update the widget value
+      let path = data.name;
+      if (data.subfolder) path = data.subfolder + "/" + path;
+
+      if (!widgets.options.values.includes(path)) {
+        widgets.options.values.push(path);
+      }
+
+      if (updateNode) {
+        showImage(path);
+        widgets.value = path;
+      }
+    } else {
+      alert(resp.status + " - " + resp.statusText);
+    }
+  } catch (error) {
+    alert(error);
+  }
+}

js/AvatarViewer.js

@@ -1,24 +0,0 @@
-import { van } from "./van.js";
-import { showEditor } from "./state.js";
-const { button, iframe, div, img } = van.tags;
-
-export function AvatarViewer() {
-  return div(
-    {
-      class: () =>
-        "w-[320px] h-[370px] absolute right-0 top-0 z-[100] pointer-events-auto mt-4 mr-4 " +
-        (!showEditor.val ? "" : "hidden"),
-    },
-    iframe({
-      id: "avatech-viewer-iframe",
-      title: "avatech-viewer-iframe",
-      name: "avatech-viewer-iframe",
-      allow: "cross-origin-isolated",
-      class: () =>
-        "w-full h-full flex pointer-events-auto rounded-2xl border-none " +
-        (!showEditor.val ? "" : "hidden"),
-      src: "https://labs.avatech.ai/viewer/default",
-      // src: 'http://localhost:3000/viewer/default',
-    })
-  );
-}

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

@@ -1,19 +1,24 @@
-import { ImageEditor } from './ImageEditor.js';
+import { LayerEditor } from "./LayerEditor.js";
-import { AvatarEditor } from './AvatarEditor.js';
+import { ShapeFlowEditor } from "./ShapeFlowEditor.js";
-import { van } from './van.js';
+import { van } from "./van.js";
-import { AvatarViewer } from './AvatarViewer.js';
+import { AvatarPreview } from "./AvatarPreview.js";
-import { Loading } from './Loading.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-[200] pointer-events-none',
+      class: "fixed left-0 top-0 w-full h-full z-[1000] pointer-events-none",
-      id: 'avatech-editor',
+      id: "avatech-editor",
     },
-    AvatarEditor(),
+    CombinePointsDialog(),
-    ImageEditor(),
+    ShapeFlowEditor(),
-    AvatarViewer(),
+    LayerEditor(),
-    Loading()
+    AvatarPreview(),
+    Loading(),
+    Alert()
   );
 }

js/GetShareLink.js

@@ -0,0 +1,29 @@
+import { van } from "./van.js";
+const { button, div, span, input } = van.tags;
+
+export function GetShareLink() {
+  return div(
+    {
+      class: () =>
+        "absolute flex flex-col justify-center items-center top-0 left-0 bg-gray-900 bg-opacity-50 pointer-events-auto w-full h-full gap-2",
+    },
+    span("We're launching OpenAI Assistant API integration soon!"),
+    div(
+      {
+        class: "w-[24rem] flex justify-center items-center",
+      },
+      input({
+        class:
+          "w-full input input-bordered text-black rounded rounded-l-md rounded-r-none",
+        placeholder: "Email",
+      }),
+      button(
+        {
+          class:
+            "btn rounded rounded-l-none rounded-r-md no-animation bg-neutral hover:bg-neutral-focus text-white border-none normal-case",
+        },
+        "Get Avatar Link"
+      )
+    )
+  );
+}

js/ImageEditor.js

@@ -1,265 +0,0 @@
-import { SideBar } from "./SideBar.js";
-import { initModel, runONNX } from "./onnx.js";
-import {
-  showImageEditor,
-  point_label,
-  imageUrl,
-  imageContainerSize,
-  imagePrompts,
-  targetNode,
-  imageSize,
-  selectedLayer,
-  imagePromptsMulti,
-  embeddings,
-} from "./state.js";
-import { van } from "./van.js";
-const { button, div, img, canvas, span } = van.tags;
-
-let throttle = false;
-
-export function updateImagePrompts() {
-  if (selectedLayer.val !== "" && selectedLayer.val !== undefined) {
-    imagePromptsMulti.val = {
-      ...imagePromptsMulti.val,
-      [selectedLayer.val]: imagePrompts.val,
-    };
-
-    targetNode.val.widgets.find((x) => x.name === "image_prompts_json").value =
-      JSON.stringify(imagePromptsMulti.val);
-  } else {
-    targetNode.val.widgets.find((x) => x.name === "image_prompts_json").value =
-      JSON.stringify(imagePrompts.val);
-  }
-  targetNode.val.graph.change();
-}
-
-function handleClick(e) {
-  const rect = e.target.getBoundingClientRect();
-  const x = e.clientX - rect.left;
-  const y = e.clientY - rect.top;
-  const relativeX = Math.trunc(
-    ((x / e.target.offsetWidth) * imageSize.val.width) / imageSize.val.imgScale
-  );
-  const relativeY = Math.trunc(
-    ((y / e.target.offsetHeight) * imageSize.val.height) /
-      imageSize.val.imgScale
-  );
-
-  imagePrompts.val = [
-    ...imagePrompts.val,
-    { x: relativeX, y: relativeY, label: e.isRight ? 0 : 1 },
-  ];
-
-  updateImagePrompts();
-  drawSegment(getClicks());
-}
-
-function handlePointClick(e, point) {
-  e.preventDefault();
-  imagePrompts.val = imagePrompts.val.filter(
-    (x) => !(x.x === point.x && x.y === point.y)
-  );
-  updateImagePrompts();
-  drawSegment(getClicks());
-}
-
-function handleImageSize(image) {
-  // Input images to SAM must be resized so the longest side is 1024
-  const documentHeight = document.documentElement.clientHeight;
-  const LONG_SIDE_LENGTH = 1024;
-  let w = image.naturalWidth;
-  let h = image.naturalHeight;
-  const samScale = LONG_SIDE_LENGTH / Math.max(h, w);
-  const imgScale = documentHeight / Math.max(h, w);
-  return { height: h, width: w, samScale, imgScale };
-}
-
-export function getClicks() {
-  return imagePrompts.val.map((point) => ({
-    x: point.x,
-    y: point.y,
-    clickType: point.label,
-  }));
-}
-
-export function drawSegment(clicks) {
-  const canvas = document.getElementById("mask-canvas");
-  const ctx = canvas.getContext("2d");
-  if (clicks.length === 0) {
-    ctx.clearRect(0, 0, canvas.width, canvas.height);
-    return;
-  }
-  if (embeddings.val) {
-    runONNX(clicks, embeddings.val).then((mask) => {
-      if (mask) {
-        ctx.clearRect(0, 0, canvas.width, canvas.height);
-        ctx.drawImage(mask, 0, 0);
-      }
-    });
-  }
-}
-
-initModel();
-
-export function ImageEditor() {
-  let realTimeSegment = true;
-  document.addEventListener("keydown", (e) => {
-    if (showImageEditor.val && e.code === "Tab") {
-      e.preventDefault();
-      realTimeSegment = !realTimeSegment;
-      if (!realTimeSegment) {
-        drawSegment(getClicks());
-      }
-    }
-  });
-
-  return div(
-    {
-      class: () =>
-        "absolute flex bg-gray-900 bg-opacity-50 top-0 w-full h-full pointer-events-auto " +
-        (showImageEditor.val ? "" : "hidden"),
-    },
-    button(
-      {
-        class: () =>
-          "btn btn-circle flex flex-row btn-ghost normal-case absolute p-0 rounded-md left-2 top-0 z-[200] w-fit",
-        onclick: () => {
-          console.log("close");
-          showImageEditor.val = false;
-        },
-      },
-      span({
-        class: "iconify text-lg",
-        "data-icon": "ic:baseline-arrow-back",
-        "data-inline": "false",
-      }),
-      div("Back")
-    ),
-    div(
-      {
-        class:
-          "hidden w-full flex justify-center absolute top-0 left-0 right-0 items-center",
-      },
-      button(
-        {
-          class: () => " px-4 py-2 rounded-md left-0 top-0 z-[200] ",
-          onclick: () => {
-            point_label.val = 1;
-          },
-        },
-        "Positive"
-      ),
-      button(
-        {
-          class: () => " px-4 py-2 rounded-md left-0 top-0 z-[200] ",
-          onclick: () => {
-            point_label.val = 0;
-          },
-        },
-        "Negative"
-      )
-    ),
-    div(
-      {
-        class: "flex items-center justify-center w-full h-full",
-        id: "image-container",
-      },
-      img({
-        class:
-          "fixed top-1/2 left-1/2 transform -translate-x-1/2 -translate-y-1/2",
-        src: imageUrl,
-        onload: (e) => {
-          imageSize.val = handleImageSize(e.target);
-
-          document.getElementById("image-container").style.scale =
-            imageSize.val.imgScale;
-
-          imageContainerSize.val = {
-            width: e.target.offsetWidth,
-            height: e.target.offsetHeight,
-          };
-
-          const canvas = document.getElementById("mask-canvas");
-          canvas.width = e.target.naturalWidth;
-          canvas.height = e.target.naturalHeight;
-        },
-        oncontextmenu: (e) => {
-          e.preventDefault();
-          e.isRight = true;
-          handleClick(e);
-        },
-        onclick: (e) => {
-          handleClick(e);
-        },
-        onmouseleave: (e) => {
-          drawSegment(getClicks());
-        },
-        onmousemove: (e) => {
-          if (!throttle) {
-            throttle = true;
-            setTimeout(() => {
-              throttle = false;
-
-              if (embeddings.val && realTimeSegment) {
-                const rect = e.target.getBoundingClientRect();
-                const x = e.clientX - rect.left;
-                const y = e.clientY - rect.top;
-                const relativeX = Math.trunc(
-                  ((x / e.target.offsetWidth) * imageSize.val.width) /
-                    imageSize.val.imgScale
-                );
-                const relativeY = Math.trunc(
-                  ((y / e.target.offsetHeight) * imageSize.val.height) /
-                    imageSize.val.imgScale
-                );
-
-                const clicks = [
-                  ...getClicks(),
-                  { x: relativeX, y: relativeY, clickType: 1 },
-                ];
-                drawSegment(clicks);
-              }
-            }, 10);
-          }
-        },
-      }),
-      canvas({
-        class:
-          "pointer-events-none fixed top-1/2 left-1/2 transform -translate-x-1/2 -translate-y-1/2 opacity-80",
-        id: "mask-canvas",
-      }),
-      () => {
-        return div(
-          {
-            class: "absolute w-full h-full pointer-events-none",
-            style: () =>
-              `width: ${imageContainerSize.val.width}px; height: ${imageContainerSize.val.height}px;`,
-          },
-          ...imagePrompts.val?.map((point) => {
-            return button({
-              style: () =>
-                `left: ${
-                  (point.x / imageSize.val.width) * imageContainerSize.val.width
-                }px; top: ${
-                  (point.y / imageSize.val.height) *
-                  imageContainerSize.val.height
-                }px; transform: translate(-50%, -50%);`,
-              class: () =>
-                `absolute w-3 h-3  p-0 rounded-full pointer-events-auto ${
-                  point.label === 1 ? "bg-green-500" : "bg-red-500"
-                }`,
-
-              oncontextmenu: (e) => {
-                handlePointClick(e, point);
-              },
-              onclick: (e) => {
-                handlePointClick(e, point);
-              },
-            });
-          })
-        );
-      }
-    ),
-    SideBar()
-  );
-}

js/LayerEditor.js

@@ -0,0 +1,696 @@
+import { SideBar } from "./SideBar.js";
+import { api } from "./api.js";
+import { app } from "./app.js";
+import { runONNX } from "./onnx.js";
+import {
+  showImageEditor,
+  point_label,
+  imageUrl,
+  imageContainerSize,
+  imagePrompts,
+  targetNode,
+  imageSize,
+  selectedLayer,
+  imagePromptsMulti,
+  embeddings,
+  embeddingID,
+  alertDialog,
+  allImagePrompts,
+  boxesMulti,
+  enableAutoSegment,
+} from "./state.js";
+import { van } from "./van.js";
+import vision from "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.3";
+const { PoseLandmarker, FaceLandmarker, FilesetResolver } = vision;
+const { button, div, img, canvas, span } = van.tags;
+
+let throttle = false;
+const positivePrompt = van.state(true);
+const enableBackgroundRemover = van.state(true);
+const isMobileDevice = () => {
+  return window.screen.width < 768;
+};
+
+// Auto segmentation
+const filesetResolver = await FilesetResolver.forVisionTasks(
+  "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-vision@0.10.3/wasm"
+);
+const faceLandmarker = await FaceLandmarker.createFromOptions(filesetResolver, {
+  baseOptions: {
+    modelAssetPath: `https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task`,
+    delegate: "GPU",
+  },
+  // outputFaceBlendshapes: true,
+  runningMode: "IMAGE",
+  numFaces: 1,
+});
+const poseLandmarker = await PoseLandmarker.createFromOptions(filesetResolver, {
+  baseOptions: {
+    modelAssetPath: `https://storage.googleapis.com/mediapipe-models/pose_landmarker/pose_landmarker_full/float16/1/pose_landmarker_full.task`,
+    delegate: "GPU",
+  },
+  runningMode: "IMAGE",
+  numPoses: 1,
+});
+const layerMapping = {
+  L_eye: {
+    useMiddle: false,
+    positiveOffsetX: 0,
+    positiveOffsetY: 0,
+    negativeOffsetX: 0,
+    negativeOffsetY: 0,
+    positiveScale: 0.25,
+    negativeScale: 0.5,
+    indices: FaceLandmarker.FACE_LANDMARKS_LEFT_EYE,
+  },
+  R_eye: {
+    useMiddle: false,
+    positiveOffsetX: 0,
+    positiveOffsetY: 0,
+    negativeOffsetX: 0,
+    negativeOffsetY: 0,
+    positiveScale: 0.25,
+    negativeScale: 0.5,
+    indices: FaceLandmarker.FACE_LANDMARKS_RIGHT_EYE,
+  },
+  L_iris: {
+    useMiddle: false,
+    positiveOffsetX: 0,
+    positiveOffsetY: 0,
+    negativeOffsetX: 0,
+    negativeOffsetY: 0,
+    positiveScale: -0.2,
+    negativeScale: 0.5,
+    indices: FaceLandmarker.FACE_LANDMARKS_LEFT_IRIS,
+  },
+  R_iris: {
+    useMiddle: false,
+    positiveOffsetX: 0,
+    positiveOffsetY: 0,
+    negativeOffsetX: 0,
+    negativeOffsetY: 0,
+    positiveScale: -0.2,
+    negativeScale: 0.5,
+    indices: FaceLandmarker.FACE_LANDMARKS_RIGHT_IRIS,
+  },
+  face: {
+    useMiddle: false,
+    positiveOffsetX: 0,
+    positiveOffsetY: 60,
+    negativeOffsetX: 0,
+    negativeOffsetY: 0,
+    positiveScale: 0.5,
+    negativeScale: 0,
+    indices: FaceLandmarker.FACE_LANDMARKS_FACE_OVAL,
+  },
+  mouth: {
+    useMiddle: false,
+    positiveOffsetX: 0,
+    positiveOffsetY: 0,
+    negativeOffsetX: 0,
+    negativeOffsetY: 0,
+    positiveScale: -0.3,
+    negativeScale: 0.3,
+    // https://stackoverflow.com/questions/66649492/how-to-get-specific-landmark-of-face-like-lips-or-eyes-using-tensorflow-js-face
+    indices: [61, 37, 270, 91, 314].map((x) => ({
+      start: x,
+      end: x,
+    })),
+  },
+  mouth_in: {
+    useMiddle: false,
+    positiveOffsetX: 0,
+    positiveOffsetY: 0,
+    negativeOffsetX: 0,
+    negativeOffsetY: 0,
+    positiveScale: -0.5,
+    negativeScale: 0.5,
+    // https://stackoverflow.com/questions/66649492/how-to-get-specific-landmark-of-face-like-lips-or-eyes-using-tensorflow-js-face
+    indices: [310, 88].map((x) => ({
+      start: x,
+      end: x,
+    })),
+  },
+};
+
+export const segmented = van.state(false);
+
+export async function autoSegment() {
+  const image = document.getElementById("image");
+  const landmarks = faceLandmarker.detect(image).faceLandmarks[0];
+
+  Object.entries(layerMapping).forEach(([key, value]) => {
+    imagePromptsMulti.val[key] = [];
+  });
+
+  Object.entries(layerMapping).forEach(([key, value]) => {
+    const positivePoints = [];
+    const middlePoints = [];
+    const negativePoints = [];
+
+    // Positive points
+    for (const { start, end } of value.indices) {
+      const startPoint = landmarks[start];
+      // const endPoint = landmarks[end];
+
+      const startX = startPoint.x * imageSize.val.width;
+      const startY = startPoint.y * imageSize.val.height;
+
+      // const endX = endPoint.x * imageSize.val.width;
+      // const endY = endPoint.y * imageSize.val.height;
+
+      if (middlePoints.length === 0) {
+        middlePoints.push({ x: startX, y: startY, label: 1, isAuto: true });
+        // middlePoints.push({ x: endX, y: endY, label: 1 });
+      } else {
+        middlePoints[0].x += startX;
+        middlePoints[0].y += startY;
+        // middlePoints[1].x += endX;
+        // middlePoints[1].y += endY;
+      }
+      positivePoints.push({ x: startX, y: startY, label: 1, isAuto: true });
+      // positivePoints.push({ x: endX, y: endY, label: 1 });
+
+      // imagePrompts.val = [...imagePrompts.val, { x, y, label: 1 }];
+    }
+
+    // Middle points
+    const len = value.indices.length;
+    middlePoints[0].x /= len;
+    middlePoints[0].y /= len;
+    // middlePoints[1].x /= len;
+    // middlePoints[1].y /= len;
+
+    if (value.useMiddle) {
+      imagePromptsMulti.val[key] = [
+        ...imagePromptsMulti.val[key],
+        ...middlePoints,
+      ];
+    } else {
+      // Negative points
+      for (const [i, { start, end }] of value.indices.entries()) {
+        const startPoint = landmarks[start];
+        // const endPoint = landmarks[end];
+
+        const startX = startPoint.x * imageSize.val.width;
+        const startY = startPoint.y * imageSize.val.height;
+
+        // const endX = endPoint.x * imageSize.val.width;
+        // const endY = endPoint.y * imageSize.val.height;
+
+        const middlePoint = middlePoints[0];
+        const directionVector = {
+          x: middlePoint.x - startX,
+          y: middlePoint.y - startY,
+        };
+        const directionVectorLength = Math.sqrt(
+          directionVector.x * directionVector.x +
+            directionVector.y * directionVector.y
+        );
+
+        if (value.negativeScale !== 0) {
+          const negativePointDistance =
+            value.negativeScale * directionVectorLength;
+          const negativePoint = {
+            x:
+              startX -
+              (negativePointDistance * directionVector.x) /
+                directionVectorLength -
+              value.negativeOffsetX,
+            y:
+              startY -
+              (negativePointDistance * directionVector.y) /
+                directionVectorLength -
+              value.negativeOffsetY,
+            label: 0,
+            isAuto: true,
+          };
+          negativePoints.push(negativePoint);
+        }
+
+        const positivePointDistance =
+          value.positiveScale * directionVectorLength;
+        positivePoints[i] = {
+          x:
+            positivePoints[i].x -
+            (positivePointDistance * directionVector.x) /
+              directionVectorLength -
+            value.positiveOffsetX,
+          y:
+            positivePoints[i].y -
+            (positivePointDistance * directionVector.y) /
+              directionVectorLength -
+            value.positiveOffsetY,
+          label: 1,
+          isAuto: true,
+        };
+      }
+      imagePromptsMulti.val[key] = [
+        ...imagePromptsMulti.val[key],
+        ...positivePoints,
+        ...negativePoints,
+      ];
+    }
+
+    // Find bounding box of positive/negative points
+    const points = negativePoints.length > 0 ? negativePoints : positivePoints;
+    const box = {
+      x1: Math.min(...points.map((x) => x.x)),
+      y1: Math.min(...points.map((x) => x.y)),
+      x2: Math.max(...points.map((x) => x.x)),
+      y2: Math.max(...points.map((x) => x.y)),
+    };
+    boxesMulti.val[key] = box;
+  });
+
+  const poseLandmarks = poseLandmarker.detect(image).landmarks[0];
+  const positiveBreathX =
+    ((poseLandmarks[11].x + poseLandmarks[12].x) / 2) * imageSize.val.width;
+  const positiveBreathY =
+    ((poseLandmarks[11].y + poseLandmarks[12].y) / 2) * imageSize.val.height;
+  const negativeBreathX1 = poseLandmarks[0].x * imageSize.val.width;
+  const negativeBreathY1 = poseLandmarks[0].y * imageSize.val.height;
+  const negativeBreathX2 = poseLandmarks[9].x * imageSize.val.width;
+  const negativeBreathY2 = poseLandmarks[9].y * imageSize.val.height;
+  const negativeBreathX3 = poseLandmarks[10].x * imageSize.val.width;
+  const negativeBreathY3 = poseLandmarks[10].y * imageSize.val.height;
+
+  imagePromptsMulti.val["breath"] = [
+    { x: positiveBreathX, y: positiveBreathY, label: 1, isAuto: true },
+    { x: negativeBreathX1, y: negativeBreathY1, label: 0, isAuto: true },
+    { x: negativeBreathX2, y: negativeBreathY2, label: 0, isAuto: true },
+    { x: negativeBreathX3, y: negativeBreathY3, label: 0, isAuto: true },
+  ];
+  imagePrompts.val = imagePromptsMulti.val[selectedLayer.val];
+  segmented.val = true;
+  console.log("Done");
+}
+
+export function setRemoveBackgroundNode() {
+  const rmBgNodes = app.graph.findNodesByType(
+    "Image Rembg (Remove Background)"
+  );
+  if (!rmBgNodes?.length) {
+    alertDialog.val = {
+      text: "Remove background node not found. Please ensure the workflow is correct.",
+      time: 5000,
+    };
+    return;
+  }
+  rmBgNodes.forEach((node) => {
+    // node is bypassed if mode is 4
+    node.mode = enableBackgroundRemover.val ? 0 : 4;
+  });
+}
+
+export function updateImagePrompts() {
+  if (selectedLayer.val !== "" && selectedLayer.val !== undefined) {
+    imagePromptsMulti.val = {
+      ...imagePromptsMulti.val,
+      [selectedLayer.val]: imagePrompts.val,
+    };
+
+    targetNode.val.widgets.find((x) => x.name === "image_prompts_json").value =
+      JSON.stringify(imagePromptsMulti.val);
+
+    // const canvas = document.getElementById("mask-canvas");
+    // const base64Image = canvas.toDataURL();
+    // api.fetchApi("/segments", {
+    //   method: "POST",
+    //   body: JSON.stringify({
+    //     name: embeddingID.val,
+    //     segments: {
+    //       [selectedLayer.val]: base64Image,
+    //     },
+    //   }),
+    // });
+  } else {
+    targetNode.val.widgets.find((x) => x.name === "image_prompts_json").value =
+      JSON.stringify(imagePrompts.val);
+  }
+  targetNode.val.graph.change();
+}
+
+export async function uploadSegments() {
+  const emptyLayers = [];
+  Object.entries(imagePromptsMulti.val).forEach(([key, value]) => {
+    if (value.length === 0) {
+      emptyLayers.push(key);
+    }
+  });
+  if (emptyLayers.length > 0) {
+    alertDialog.val = {
+      text: "The following layers have no segments: " + emptyLayers.join(", "),
+      time: 5000,
+    };
+    return false;
+  }
+
+  const segments = {};
+  for (const [layer, prompts] of Object.entries(imagePromptsMulti.val)) {
+    await drawSegment(getClicks(prompts), layer, false);
+    const canvas = document.getElementById("mask-canvas");
+    const base64Image = canvas.toDataURL();
+    segments[layer] = base64Image;
+    // download image
+    // const a = document.createElement("a");
+    // a.href = base64Image;
+    // a.download = layer + ".png";
+    // a.click();
+  }
+  await api.fetchApi("/segments", {
+    method: "POST",
+    body: JSON.stringify({
+      name: embeddingID.val,
+      segments,
+    }),
+  });
+  return true;
+}
+
+async function handleClick(e) {
+  const rect = e.target.getBoundingClientRect();
+  const x = e.clientX - rect.left;
+  const y = e.clientY - rect.top;
+  const relativeX = Math.trunc(
+    ((x / e.target.offsetWidth) * imageSize.val.width) / imageSize.val.imgScale
+  );
+  const relativeY = Math.trunc(
+    ((y / e.target.offsetHeight) * imageSize.val.height) /
+      imageSize.val.imgScale
+  );
+
+  let label;
+  if (isMobileDevice()) {
+    label = positivePrompt.val ? 1 : 0;
+  } else {
+    label = e.isRight ? 0 : 1;
+  }
+
+  imagePrompts.val = [
+    ...imagePrompts.val,
+    { x: relativeX, y: relativeY, label },
+  ];
+  await drawSegment(getClicks());
+  updateImagePrompts();
+}
+
+async function handlePointClick(e, point) {
+  e.preventDefault();
+  imagePrompts.val = imagePrompts.val.filter(
+    (x) => !(x.x === point.x && x.y === point.y)
+  );
+  await drawSegment(getClicks());
+  updateImagePrompts();
+}
+
+function handleImageSize(image) {
+  // Input images to SAM must be resized so the longest side is 1024
+  const documentHeight = document.documentElement.clientHeight;
+  const LONG_SIDE_LENGTH = 1024;
+  let w = image.naturalWidth;
+  let h = image.naturalHeight;
+  const samScale = LONG_SIDE_LENGTH / Math.max(h, w);
+  const imgScale = documentHeight / Math.max(h, w);
+  return { height: h, width: w, samScale, imgScale };
+}
+
+export function getClicks(prompts) {
+  return (prompts || imagePrompts.val).map((point) => ({
+    x: point.x,
+    y: point.y,
+    clickType: point.label,
+    isAuto: point.isAuto,
+  }));
+}
+
+export async function drawSegment(clicks, layer, drawBox = true) {
+  const canvas = document.getElementById("mask-canvas");
+  const ctx = canvas.getContext("2d");
+  if (clicks.length === 0) {
+    ctx.clearRect(0, 0, canvas.width, canvas.height);
+    return;
+  }
+  if (embeddings.val) {
+    const box = enableAutoSegment.val
+      ? boxesMulti.val[layer || selectedLayer.val]
+      : null;
+    const filteredClicks = enableAutoSegment.val
+      ? clicks
+      : clicks.filter((click) => !click.isAuto);
+    if (filteredClicks.length === 0) {
+      ctx.clearRect(0, 0, canvas.width, canvas.height);
+      return;
+    }
+    const mask = await runONNX(filteredClicks, embeddings.val, box);
+    if (mask) {
+      ctx.clearRect(0, 0, canvas.width, canvas.height);
+      ctx.drawImage(mask, 0, 0);
+      if (box && drawBox) {
+        ctx.strokeStyle = "green";
+        ctx.lineWidth = 5;
+        ctx.strokeRect(box.x1, box.y1, box.x2 - box.x1, box.y2 - box.y1);
+      }
+    }
+  }
+}
+
+export function LayerEditor() {
+  let realTimeSegment = true;
+
+  const showSidebar = van.state(true);
+
+  document.addEventListener("keydown", (e) => {
+    if (showImageEditor.val && e.code === "Tab") {
+      e.preventDefault();
+      realTimeSegment = !realTimeSegment;
+      if (!realTimeSegment) {
+        drawSegment(getClicks());
+      }
+    }
+  });
+
+  return div(
+    {
+      class: () =>
+        "absolute flex bg-gray-900 bg-opacity-50 top-0 w-full h-full pointer-events-auto z-[1000] " +
+        (showImageEditor.val ? "" : "hidden"),
+    },
+    div(
+      {
+        class:
+          "absolute top-4 left-4 right-0 flex w-full gap-2 justify-start z-[200]",
+      },
+      button(
+        {
+          class: () => "btn btn-neutral flex flex-row normal-case rounded-md",
+          onclick: async () => {
+            console.log("close");
+            showImageEditor.val = false;
+            await uploadSegments();
+
+            const isEqual = allImagePrompts.val.map(
+              (x) =>
+                JSON.stringify(imagePromptsMulti.val) ===
+                JSON.stringify(x.prompt)
+            );
+            if (!isEqual.includes(true))
+              allImagePrompts.val = [
+                ...allImagePrompts.val,
+                {
+                  version: "v" + allImagePrompts.val.length,
+                  prompt: imagePromptsMulti.val,
+                },
+              ];
+
+            // api.fetchApi("/segments_order", {
+            //   method: "POST",
+            //   body: JSON.stringify({
+            //     name: embeddingID.val,
+            //     order: Object.keys(imagePromptsMulti.val),
+            //   }),
+            // });
+          },
+        },
+        span({
+          class: "iconify text-lg",
+          "data-icon": "ic:baseline-arrow-back",
+          "data-inline": "false",
+        }),
+        div("Back")
+      ),
+      button(
+        {
+          class: () => "btn btn-neutral flex flex-row normal-case rounded-md",
+          onclick: () => (showSidebar.val = !showSidebar.val),
+        },
+        div(() => (showSidebar.val ? "Hide UI" : "Show UI"))
+      ),
+      button(
+        {
+          class: () => "btn btn-neutral flex flex-row normal-case rounded-md",
+          onclick: () => {
+            enableAutoSegment.val = !enableAutoSegment.val;
+            drawSegment(getClicks());
+          },
+        },
+        () => (enableAutoSegment.val ? "Auto Segment On" : "Auto Segment Off")
+      ),
+      button(
+        {
+          class: () => "btn btn-neutral flex flex-row normal-case rounded-md",
+          onclick: () => {
+            enableBackgroundRemover.val = !enableBackgroundRemover.val;
+            setRemoveBackgroundNode();
+          },
+        },
+        () =>
+          enableBackgroundRemover.val
+            ? "Background Remover On"
+            : "Background Remover Off"
+      ),
+      button(
+        {
+          class: () =>
+            `btn btn-neutral flex flex-row normal-case rounded-md ${
+              isMobileDevice() ? "" : "hidden"
+            }`,
+          onclick: () => (positivePrompt.val = !positivePrompt.val),
+        },
+        div(() => (positivePrompt.val ? "Positive" : "Negative"))
+      )
+    ),
+    div(
+      {
+        class:
+          "hidden w-full justify-center absolute top-0 left-0 right-0 items-center",
+      },
+      button(
+        {
+          class: () => " px-4 py-2 rounded-md left-0 top-0 z-[200] ",
+          onclick: () => {
+            point_label.val = 1;
+          },
+        },
+        "Positive"
+      ),
+      button(
+        {
+          class: () => " px-4 py-2 rounded-md left-0 top-0 z-[200] ",
+          onclick: () => {
+            point_label.val = 0;
+          },
+        },
+        "Negative"
+      )
+    ),
+    div(
+      {
+        class: "flex items-center justify-center w-full h-full",
+        id: "image-container",
+      },
+      img({
+        id: "image",
+        class:
+          "fixed top-1/2 left-1/2 transform -translate-x-1/2 -translate-y-1/2",
+        src: imageUrl,
+        onload: async (e) => {
+          imageSize.val = handleImageSize(e.target);
+
+          document.getElementById("image-container").style.scale =
+            imageSize.val.imgScale;
+
+          imageContainerSize.val = {
+            width: e.target.offsetWidth,
+            height: e.target.offsetHeight,
+          };
+
+          const canvas = document.getElementById("mask-canvas");
+          canvas.width = e.target.naturalWidth;
+          canvas.height = e.target.naturalHeight;
+        },
+        oncontextmenu: async (e) => {
+          e.preventDefault();
+          e.isRight = true;
+          await handleClick(e);
+        },
+        onclick: async (e) => {
+          await handleClick(e);
+        },
+        onmouseleave: (e) => {
+          drawSegment(getClicks());
+        },
+        onmousemove: (e) => {
+          if (!throttle) {
+            throttle = true;
+            setTimeout(() => {
+              throttle = false;
+
+              if (embeddings.val && realTimeSegment) {
+                const rect = e.target.getBoundingClientRect();
+                const x = e.clientX - rect.left;
+                const y = e.clientY - rect.top;
+                const relativeX = Math.trunc(
+                  ((x / e.target.offsetWidth) * imageSize.val.width) /
+                    imageSize.val.imgScale
+                );
+                const relativeY = Math.trunc(
+                  ((y / e.target.offsetHeight) * imageSize.val.height) /
+                    imageSize.val.imgScale
+                );
+
+                const clicks = [
+                  ...getClicks(),
+                  { x: relativeX, y: relativeY, clickType: 1 },
+                ];
+                drawSegment(clicks);
+              }
+            }, 10);
+          }
+        },
+      }),
+      () =>
+        canvas({
+          class:
+            "pointer-events-none fixed top-1/2 left-1/2 transform -translate-x-1/2 -translate-y-1/2 opacity-80",
+          style: () =>
+            `width: ${imageContainerSize.val.width}px; height: ${imageContainerSize.val.height}px;`,
+          id: "mask-canvas",
+        }),
+      () =>
+        div(
+          {
+            class: "absolute w-full h-full pointer-events-none",
+            style: () =>
+              `width: ${imageContainerSize.val.width}px; height: ${imageContainerSize.val.height}px;`,
+          },
+          ...(enableAutoSegment.val
+            ? imagePrompts.val
+            : imagePrompts.val?.filter((click) => !click.isAuto)
+          ).map((point) => {
+            return button({
+              style: () =>
+                `left: ${
+                  (point.x / imageSize.val.width) * imageContainerSize.val.width
+                }px; top: ${
+                  (point.y / imageSize.val.height) *
+                  imageContainerSize.val.height
+                }px; transform: translate(-50%, -50%);`,
+              class: () =>
+                `absolute w-3 h-3  p-0 rounded-full pointer-events-auto ${
+                  point.label === 1 ? "bg-green-500" : "bg-red-500"
+                }`,
+
+              oncontextmenu: async (e) => {
+                await handlePointClick(e, point);
+              },
+              onclick: async (e) => {
+                await handlePointClick(e, point);
+              },
+            });
+          })
+        )
+    ),
+    () => (showSidebar.val ? SideBar() : div())
+  );
+}

js/Loading.js

@@ -1,17 +1,17 @@
-import { showLoading } from "./state.js";
+import { loadingCaption, showLoading } from "./state.js";
 import { van } from "./van.js";
 const { div, span } = van.tags;
 
 export function Loading() {
-  console.log("???", showLoading.val);
   return div(
     {
       class: () =>
-        "absolute flex justify-center items-center top-0 left-0 bg-gray-900 bg-opacity-50 pointer-events-auto w-full h-full " +
+        "absolute flex flex-col justify-center items-center top-0 left-0 bg-gray-900 bg-opacity-50 pointer-events-auto w-full h-full z-[1001] " +
         (showLoading.val ? "" : "hidden"),
     },
     span({
-      class: "loading loading-spinner loading-lg",
+      class: "loading loading-spinner loading-lg mb-2",
-    })
+    }),
+    () => span(loadingCaption.val)
   );
 }

js/ShapeFlowEditor.js

@@ -0,0 +1,82 @@
+import { iframeSrc, showEditor } from "./state.js";
+import { van } from "./van.js";
+const { button, iframe, div, img, span, dialog, form } = van.tags;
+
+export function ShapeFlowEditor() {
+  return div(
+    {
+      class: "w-full h-full relative",
+    },
+    button(
+      {
+        class: () =>
+          "ml-2 mt-2 btn btn-circle flex flex-row btn-ghost normal-case absolute px-4 rounded-md left-0 top-0 z-[200] w-fit pointer-events-auto bg-black " +
+          (showEditor.val ? "" : "hidden"),
+        onclick: () => {
+          console.log("close");
+          document.getElementById("alert-dialog").showModal();
+        },
+      },
+      span({
+        class: "iconify text-lg",
+        "data-icon": "ic:baseline-arrow-back",
+        "data-inline": "false",
+      }),
+      div("Back & Save")
+    ),
+    iframe({
+      id: "avatech-editor-iframe",
+      title: "avatech-editor-iframe",
+      name: "avatech-editor-iframe",
+      allow: "cross-origin-isolated; clipboard-read; clipboard-write",
+      class: () =>
+        "w-full h-full pointer-events-auto " + (showEditor.val ? "" : "hidden"),
+      src: iframeSrc,
+    }),
+    dialog(
+      {
+        id: "alert-dialog",
+        class: "modal modal-bottom sm:modal-middle",
+      },
+      div(
+        {
+          class: "modal-box",
+        },
+        div({ class: "text-black" }, div({ class: "text-xl font-bold" }, "Shape Flow Editor"), div({ class: "" }, "The shape flow will be save in CreateShapeFlow node (comfyui node)! Or discard the changes!")),
+        div(
+          { class: "modal-action" },
+          form(
+            { method: "dialog", class: "flex flex-row gap-4" },
+            button(
+              {
+                class: "btn",
+                onclick: () => {
+                  showEditor.val = false;
+                },
+              },
+              "Discard"
+            ),
+            button(
+              {
+                class: "btn",
+                onclick: () => {
+                  const editor = document.getElementById(
+                    "avatech-editor-iframe"
+                  );
+                  editor.contentWindow.postMessage(
+                    {
+                      method: "back",
+                    },
+                    "*"
+                  );
+                  showEditor.val = false;
+                },
+              },
+              "Save & Close"
+            )
+          )
+        )
+      )
+    )
+  );
+}

js/SideBar.js

@@ -1,9 +1,12 @@
-import { drawSegment, getClicks, updateImagePrompts } from "./ImageEditor.js";
+import { drawSegment, getClicks, updateImagePrompts } from "./LayerEditor.js";
 import {
   imagePrompts,
   selectedLayer,
   imagePromptsMulti,
   targetNode,
+  showImageEditor,
+  allImagePrompts,
+  samPrompts,
 } from "./state.js";
 import { van } from "./van.js";
 const {
@@ -22,201 +25,274 @@ 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 &&
+    targetNode.val != undefined &&
+    targetNode.val.outputs &&
+    targetNode.val.type === "SAM MultiLayer"
+  ) {
+    updateOutputs();
+  }
+});
+
 export function SideBar() {
   const layer_to_delete = van.state("");
 
   return div(
-    {
+    div(
-      class:
+      {
-        "ml-2 z-100 w-fit flex-col flex justify-center absolute top-0 left-0 bottom-0 items-start gap-2",
+        class:
-    },
+          "ml-2 z-100 w-fit flex-col flex justify-center absolute top-0 left-0 bottom-0 items-start gap-2",
-
+      },
-    () => {
+      () => {
-      const layers = Object.entries(imagePromptsMulti.val);
+        const layers = Object.entries(imagePromptsMulti.val);
-      return ul(
+        return ul(
-        {
+          {
-          class: "menu bg-base-200 w-56 rounded-box text-base-content ",
+            class: "menu bg-base-200 w-56 rounded-box text-base-content ",
-        },
-        layers.length === 0 ? li(a("Empty layer")) : null,
-        ...layers.map(([key, value]) => {
-          return li(
-            a(
-              {
-                class: () =>
-                  `normal-case text-start items-start flex items-center justify-between  ${
-                    selectedLayer.val === key ? "active" : ""
-                  }`,
-                onclick: () => {
-                  selectedLayer.val = key;
-                  imagePrompts.val = imagePromptsMulti.val[key];
-                  drawSegment(getClicks());
-                },
-              },
-              key,
-              div(
-                {},
-                button(
-                  {
-                    class:
-                      "btn btn-circle btn-xs btn-ghost group hover:text-red-500",
-                    onclick: (e) => {
-                      console.log("clear");
-                      e.preventDefault();
-                      e.stopPropagation();
-                      imagePrompts.val = [];
-                      imagePromptsMulti.val[key] = [];
-                      drawSegment([]);
-                      updateImagePrompts();
-                    },
-                  },
-                  span({
-                    class: "iconify",
-                    "data-icon": "ant-design:clear-outlined",
-                    "data-inline": "false",
-                  })
-                ),
-                button(
-                  {
-                    class:
-                      "btn btn-circle btn-xs btn-ghost group hover:text-red-500",
-                    onclick: (e) => {
-                      console.log("delete");
-                      e.preventDefault();
-                      e.stopPropagation();
-                      layer_to_delete.val = key;
-                      setTimeout(() => {
-                        delete_layer_dialog.showModal();
-                      }, 0);
-                    },
-                  },
-                  span({
-                    class: "iconify",
-                    "data-icon": "ic:baseline-delete",
-                    "data-inline": "false",
-                  })
-                )
-              )
-            )
-          );
-        }),
-        div({ class: "divider !py-0 my-0" }),
-        li(
-          a(
-            {
-              class: "flex items-center justify-between",
-              onclick: () => {
-                my_modal_3.showModal();
-              },
-            },
-            "New Layer",
-            span({
-              class: "iconify",
-              "data-icon": "ic:outline-plus",
-              "data-inline": "false",
-            })
-          )
-        )
-      );
-    },
-    () =>
-      ConfirmDialog(
-        {
-          id: "delete_layer_dialog",
-          title: "Delete Layer: " + layer_to_delete.val,
-          onsubmit: () => {
-            imagePromptsMulti.val = Object.fromEntries(
-              Object.entries(imagePromptsMulti.val).filter(
-                ([key, value]) => key !== layer_to_delete.val
-              )
-            );
-            console.log(imagePromptsMulti.val);
-            if (selectedLayer.val === layer_to_delete.val) {
-              // Select another layer if there is one
-              if (Object.keys(imagePromptsMulti.val).length > 0) {
-                selectedLayer.val = Object.keys(imagePromptsMulti.val)[0];
-                imagePrompts.val = imagePromptsMulti.val[selectedLayer.val];
-              } else {
-                selectedLayer.val = "";
-                imagePrompts.val = [];
-              }
-            }
-            const outputIndex = targetNode.val.findOutputSlot(
-              layer_to_delete.val
-            );
-            targetNode.val.removeOutput(outputIndex);
-            targetNode.val.graph.change();
-            updateImagePrompts();
-            delete_layer_dialog.close();
           },
-        },
+          button(
-        p("Are you sure you want to delete this layer?")
-      ),
-    () =>
-      dialog(
-        { id: "my_modal_3", class: "modal" },
-        div(
-          { class: "modal-box text-base-content" },
-          form(
             {
-              class: "gap-2 flex flex-col",
+              onclick: () => {
-              method: "dialog",
+                layers.map(([key, value]) => {
-              onsubmit: (e) => {
+                  imagePrompts.val = [];
-                console.log("add new layer");
+                  imagePromptsMulti.val[key] = [];
-                e.preventDefault();
+                });
-                const inputText = e.target.elements[1].value;
+                drawSegment([]);
-                imagePromptsMulti.val = {
-                  ...imagePromptsMulti.val,
-                  [inputText]: [],
-                };
-                console.log(inputText, imagePromptsMulti.val);
-                my_modal_3.close();
-                e.target.elements[1].value = "";
-                targetNode.val.addOutput(
-                  inputText,
-                  targetNode.val.type === "SAM" ? "IMAGE" : "SAM_PROMPT"
-                );
-                targetNode.val.graph.change();
-                if (
-                  selectedLayer.val === null ||
-                  selectedLayer.val === undefined ||
-                  selectedLayer.val === ""
-                ) {
-                  selectedLayer.val = inputText;
-                }
                 updateImagePrompts();
               },
+              class: "btn btn-ghost normal-case flex",
             },
-            button(
+            "Clear ALL"
+          ),
+          layers.length === 0 ? li(a("Empty layer")) : null,
+          ...layers.map(([key, value]) => {
+            return li(
+              a(
+                {
+                  class: () =>
+                    `normal-case text-start items-start flex items-center justify-between  ${
+                      selectedLayer.val === key ? "active" : ""
+                    }`,
+                  onclick: () => {
+                    selectedLayer.val = key;
+                    imagePrompts.val = imagePromptsMulti.val[key];
+                    drawSegment(getClicks());
+                  },
+                },
+                key,
+                div(
+                  {},
+                  button(
+                    {
+                      class:
+                        "btn btn-circle btn-xs btn-ghost group hover:text-red-500",
+                      onclick: (e) => {
+                        console.log("clear");
+                        e.preventDefault();
+                        e.stopPropagation();
+                        imagePrompts.val = [];
+                        imagePromptsMulti.val[key] = [];
+                        drawSegment([]);
+                        updateImagePrompts();
+                      },
+                    },
+                    span({
+                      class: "iconify",
+                      "data-icon": "ant-design:clear-outlined",
+                      "data-inline": "false",
+                    })
+                  ),
+                  button(
+                    {
+                      class:
+                        "btn btn-circle btn-xs btn-ghost group hover:text-red-500",
+                      onclick: (e) => {
+                        console.log("delete");
+                        e.preventDefault();
+                        e.stopPropagation();
+                        layer_to_delete.val = key;
+                        setTimeout(() => {
+                          delete_layer_dialog.showModal();
+                        }, 0);
+                      },
+                    },
+                    span({
+                      class: "iconify",
+                      "data-icon": "ic:baseline-delete",
+                      "data-inline": "false",
+                    })
+                  )
+                )
+              )
+            );
+          }),
+          div({ class: "divider !py-0 my-0" }),
+          li(
+            a(
               {
-                type: "button",
+                class: "flex items-center justify-between",
-                class: "btn btn-sm btn-circle btn-ghost absolute right-2 top-2",
+                onclick: () => {
-                onclick: (e) => {
+                  my_modal_3.showModal();
-                  e.stopPropagation();
-                  my_modal_3.close();
                 },
               },
-              "✕"
+              "New Layer",
-            ),
+              span({
-            h3(
+                class: "iconify",
-              { class: "font-bold text-lg text-base-content" },
+                "data-icon": "ic:outline-plus",
-              "Add new layer!"
+                "data-inline": "false",
-            ),
+              })
-            input({
+            )
-              type: "text",
+          )
-              placeholder: "Type here",
+        );
-              class: "input input-bordered w-full",
+      },
-              autofocus: true,
+      () =>
-            }),
+        ConfirmDialog(
-            button(
+          {
+            id: "delete_layer_dialog",
+            title: "Delete Layer: " + layer_to_delete.val,
+            onsubmit: () => {
+              imagePromptsMulti.val = Object.fromEntries(
+                Object.entries(imagePromptsMulti.val).filter(
+                  ([key, value]) => key !== layer_to_delete.val
+                )
+              );
+              console.log(imagePromptsMulti.val);
+              if (selectedLayer.val === layer_to_delete.val) {
+                // Select another layer if there is one
+                if (Object.keys(imagePromptsMulti.val).length > 0) {
+                  selectedLayer.val = Object.keys(imagePromptsMulti.val)[0];
+                  imagePrompts.val = imagePromptsMulti.val[selectedLayer.val];
+                } else {
+                  selectedLayer.val = "";
+                  imagePrompts.val = [];
+                }
+              }
+              targetNode.val.graph.change();
+              updateImagePrompts();
+              delete_layer_dialog.close();
+            },
+          },
+          p("Are you sure you want to delete this layer?")
+        ),
+      () =>
+        dialog(
+          { id: "my_modal_3", class: "modal" },
+          div(
+            { class: "modal-box text-base-content" },
+            form(
               {
-                type: "submit",
+                class: "gap-2 flex flex-col",
-                class: "btn btn-sm  btn-ghost place-self-end",
+                method: "dialog",
+                onsubmit: (e) => {
+                  console.log("add new layer");
+                  e.preventDefault();
+                  const inputText = e.target.elements[1].value;
+                  imagePromptsMulti.val = {
+                    ...imagePromptsMulti.val,
+                    [inputText]: [],
+                  };
+                  console.log(inputText, imagePromptsMulti.val);
+                  my_modal_3.close();
+                  e.target.elements[1].value = "";
+
+                  selectedLayer.val = inputText;
+                  imagePrompts.val = imagePromptsMulti.val[inputText];
+                  drawSegment(getClicks());
+                  updateImagePrompts();
+                },
               },
-              "Confirm"
+              button(
+                {
+                  type: "button",
+                  class:
+                    "btn btn-sm btn-circle btn-ghost absolute right-2 top-2",
+                  onclick: (e) => {
+                    e.stopPropagation();
+                    my_modal_3.close();
+                  },
+                },
+                "✕"
+              ),
+              h3(
+                { class: "font-bold text-lg text-base-content" },
+                "Add new layer!"
+              ),
+              input({
+                type: "text",
+                placeholder: "Type here",
+                class: "input input-bordered w-full",
+                autofocus: true,
+              }),
+              button(
+                {
+                  type: "submit",
+                  class: "btn btn-sm  btn-ghost place-self-end",
+                },
+                "Confirm"
+              )
             )
           )
         )
-      )
+    ),
+    div(
+      {
+        class:
+          "ml-2 z-100 w-fit flex-col flex justify-center absolute top-0 right-0 bottom-0 items-start gap-2 bg-transparent",
+      },
+      () => {
+        return ul(
+          {
+            class: "menu bg-base-200 w-56 rounded-box text-base-content ",
+          },
+          span("Segment History"),
+          ...allImagePrompts.val.map((e) =>
+            li(
+              a(
+                {
+                  class:
+                    "normal-case text-start flex items-center justify-between",
+                  onclick: async () => {
+                    imagePromptsMulti.val = e.prompt;
+                    imagePrompts.val = imagePromptsMulti.val[selectedLayer.val];
+                    drawSegment(getClicks());
+                    updateImagePrompts();
+                  },
+                },
+                e.version
+              )
+            )
+          )
+        );
+      }
+    )
   );
 }
 

js/dialog.js

@@ -0,0 +1,33 @@
+import { ComfyDialog, $el } from '../../scripts/ui.js';
+
+export class InfoDialog extends ComfyDialog {
+	constructor() {
+		super();
+		this.element.classList.add("comfy-normal-modal");
+	}
+	createButtons() {
+		return [
+			$el("button", {
+				type: "button",
+				textContent: "Close",
+				onclick: () => this.close(),
+			}),
+		];
+	}
+
+	close() {
+		this.element.style.display = "none";
+	}
+
+	show(html) {
+		if (typeof html === "string") {
+			this.textElement.innerHTML = html;
+		} else {
+			this.textElement.replaceChildren(html);
+		}
+		this.element.style.display = "flex";
+		this.element.style.zIndex = 1001;
+	}
+}
+
+export const infoDialog = new InfoDialog()

js/index.js

@@ -5,19 +5,45 @@ import {
   imageUrl,
   imagePrompts,
   targetNode,
+  combinePointsNode,
   fileName,
   embeddings,
   imagePromptsMulti,
   selectedLayer,
   showLoading,
+  loadingCaption,
+  alertDialog,
+  showPreview,
+  shareLoading,
+  previewModelId,
+  embeddingID,
+  enableAutoSegment,
+  samPrompts,
 } from "./state.js";
 import { van } from "./van.js";
 import { app } from "./app.js";
 import { api } from "./api.js";
 import { Container } from "./Container.js";
-import { loadNpyTensor } from "./onnx.js";
+import { initModel, loadNpyTensor } from "./onnx.js";
 import "https://code.iconify.design/3/3.1.0/iconify.min.js";
-import { drawSegment, getClicks } from "./ImageEditor.js";
+import {
+  autoSegment,
+  drawSegment,
+  getClicks,
+  segmented,
+} from "./LayerEditor.js";
+import { infoDialog } from "./dialog.js";
+import { sharedAvatarLink } from "./AvatarPreview.js";
+import { updateImagePrompts } from "./LayerEditor.js";
+import { updateOutputs } from "./SideBar.js";
+
+export const generatedImages = {};
+
+const stylesheet = document.createElement("link");
+stylesheet.setAttribute("type", "text/css");
+stylesheet.setAttribute("rel", "stylesheet");
+stylesheet.setAttribute("href", "./avatar-graph-comfyui/tw-styles.css");
+document.head.appendChild(stylesheet);
 
 /** @type {import( '../../../web/types/litegraph.js').LGraphGroup} */
 const recomputeInsideNodesOps = LGraphGroup.prototype.recomputeInsideNodes;
@@ -199,7 +225,7 @@ function openInAvatechEditor(url, fileName) {
       key: "key",
       value: fileName,
       method: "store",
-      blendshapes: targetNode.val.widgets.find((x) => x.name === "blendshapes")
+      blendshapes: targetNode.val.widgets.find((x) => x.name === "shape_flow")
         .value,
     },
     "*"
@@ -207,7 +233,7 @@ function openInAvatechEditor(url, fileName) {
 }
 
 function updateBlendshapesPrompts(value) {
-  targetNode.val.widgets.find((x) => x.name === "blendshapes").value = value;
+  targetNode.val.widgets.find((x) => x.name === "shape_flow").value = value;
   targetNode.val.graph.change();
 }
 
@@ -223,15 +249,35 @@ function getInputWidgetValue(node, inputIndex, widgetName) {
   /** @type {LGraphNode} */
   let nodea = graph._nodes_by_id[targetLink.origin_id];
 
-  while (nodea.type == "Reroute") {
+  while (nodea.type === "Reroute") {
     nodea = nodea.getInputNode(0);
   }
 
   console.log(targetLink, nodea);
-  console.log(nodea.getInputNode(0, true));
 
+  if (nodea.type === "LoadImage") {
+    /** @type {string} */
+    const isGeneratedImage = false;
+    return [
+      isGeneratedImage,
+      nodea.widgets.find((x) => x.name === widgetName).value,
+    ];
+  }
+
+  const saveImageNodeLink = nodea.outputs
+    .find((x) => x.type === "IMAGE")
+    .links.find((link) => {
+      const targetLink = graph.links[link];
+      const targetNode = graph._nodes_by_id[targetLink.target_id];
+      if (targetNode.type === "SaveImage") {
+        return true;
+      }
+    });
+  const saveImageNode =
+    graph._nodes_by_id[graph.links[saveImageNodeLink].target_id];
   /** @type {string} */
-  return nodea.widgets.find((x) => x.name === widgetName).value;
+  const isGeneratedImage = true;
+  return [isGeneratedImage, generatedImages[saveImageNode.id]];
 }
 
 /**
@@ -239,45 +285,87 @@ function getInputWidgetValue(node, inputIndex, widgetName) {
  * @param {LGraphNode} node
  */
 function showMyImageEditor(node) {
-  let connectedImageFileName = getInputWidgetValue(node, 0, "image");
+  let [isGeneratedImage, connectedImageFileName] = getInputWidgetValue(
-  const split = connectedImageFileName.split("/");
+    node,
-  if (split.length > 1) connectedImageFileName = split[1];
+    0,
-
+    "image"
-  const embeddingFilename = node.widgets.find(
-    (x) => x.name === "embedding_id"
-  ).value;
-
-  const v = JSON.parse(
-    node.widgets.find((x) => x.name === "image_prompts_json").value
   );
-
+  if (!connectedImageFileName) {
-  if (!Array.isArray(v)) {
+    alertDialog.val = {
-    // this is a multi prompt
+      text: "Please connect or generate an image first",
-    imagePromptsMulti.val = v;
+      time: 3000,
-    selectedLayer.val = Object.keys(imagePromptsMulti.val)[0];
+    };
-    imagePrompts.val = imagePromptsMulti.val[selectedLayer.val];
+    return;
-  } else {
-    // this is a single prompt
-    selectedLayer.val = "";
-    imagePromptsMulti.val = {};
-    imagePrompts.val = v;
   }
-  showImageEditor.val = true;
+
-  imageUrl.val = api.apiURL(
+  loadingCaption.val = "Loading SAM model...";
-    `/view?filename=${encodeURIComponent(
+  showLoading.val = true;
-      connectedImageFileName
+
-    )}&type=input&subfolder=${split.length > 1 ? split[0] : ""}`
+  const ckpt = node.widgets.find((x) => x.name === "ckpt").value;
-  );
+  const modelType = ckpt.match(/vit_[lbh]/)?.[0];
-  const embeedingUrl = api.apiURL(
+  initModel(modelType).then((res) => {
-    `/view?filename=${encodeURIComponent(
+    loadingCaption.val = "Computing image embedding...";
-      `${embeddingFilename}.npy`
+
-    )}&type=output&subfolder=`
+    const split = connectedImageFileName.split("/");
-  );
+    let id = connectedImageFileName;
-  loadNpyTensor(embeedingUrl).then((tensor) => {
+    if (split.length > 1) id = split[1];
-    embeddings.val = tensor;
+
-    drawSegment(getClicks());
+    node.widgets.find((x) => x.name === "embedding_id").value = id;
+    embeddingID.val = id;
+
+    api
+      .fetchApi("/sam_model", {
+        method: "POST",
+        body: JSON.stringify({
+          image: connectedImageFileName,
+          isGeneratedImage,
+          embedding_id: id,
+          ckpt,
+          // remote: true,
+        }),
+      })
+      .then(() => {
+        showLoading.val = false;
+        const v = JSON.parse(
+          node.widgets.find((x) => x.name === "image_prompts_json").value
+        );
+
+        if (!Array.isArray(v)) {
+          // this is a multi prompt
+          imagePromptsMulti.val = v;
+          selectedLayer.val = Object.keys(imagePromptsMulti.val)[0];
+          imagePrompts.val = imagePromptsMulti.val[selectedLayer.val];
+        } else {
+          // this is a single prompt
+          selectedLayer.val = "";
+          imagePromptsMulti.val = {};
+          imagePrompts.val = v;
+        }
+        showImageEditor.val = true;
+        const subfolder =
+          isGeneratedImage || split.length === 1 ? "" : split[0];
+        imageUrl.val = api.apiURL(
+          `/view?filename=${encodeURIComponent(connectedImageFileName)}&type=${
+            isGeneratedImage ? "output" : "input"
+          }&subfolder=${subfolder}`
+        );
+        const embeedingUrl = api.apiURL(
+          `/view?filename=${encodeURIComponent(
+            `${id}_${modelType}.npy`
+          )}&type=output&subfolder=`
+        );
+        loadNpyTensor(embeedingUrl).then(async (tensor) => {
+          embeddings.val = tensor;
+          if (enableAutoSegment.val && !segmented.val) await autoSegment();
+          drawSegment(getClicks());
+          updateImagePrompts();
+        });
+      })
+      .catch((err) => {
+        console.log(err);
+        showLoading.val = false;
+      });
   });
-  targetNode.val = node;
 }
 
 /** @typedef {import('../../../web/types/comfy.js').ComfyExtension} ComfyExtension*/
@@ -286,45 +374,44 @@ const ext = {
   getCustomWidgets(app) {
     return {
       SAM_PROMPTS(node, inputName, inputData, app) {
-        const btn = node.addWidget("button", "Edit prompt", "", () => {
+        const asd = document.createElement("div");
-          showLoading.val = true;
+        Object.assign(asd, {
-
+          id: "sam",
-          let connectedImageFileName = getInputWidgetValue(node, 0, "image");
+          onclick: () => {
-
+            showMyImageEditor(node);
-          const split = connectedImageFileName.split("/");
+          },
-          let id = connectedImageFileName;
-          if (split.length > 1) id = split[1];
-
-          node.widgets.find((x) => x.name === "embedding_id").value = id;
-
-          const ckpt = node.widgets.find((x) => x.name === "ckpt").value;
-          const model_type = node.widgets.find(
-            (x) => x.name === "model_type"
-          ).value;
-
-          api
-            .fetchApi("/sam_model", {
-              method: "POST",
-              body: JSON.stringify({
-                image: connectedImageFileName,
-                embedding_id: id,
-                ckpt,
-                model_type,
-              }),
-            })
-            .then(() => {
-              showLoading.val = false;
-              showMyImageEditor(node);
-            });
         });
-        btn.serialize = false;
+        document.body.append(asd);
+
+        const btn = node.addWidget("button", "Edit prompt", "", () => {
+          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,
+        };
+      },
       BLENDSHAPES_CONFIG(node, inputName, inputData, app) {
-        const btn = node.addWidget("button", "Edit blendshapes", "", () => {
+        const btn = node.addWidget("button", "Edit Shape Flow", "", () => {
           targetNode.val = node;
           openInAvatechEditor(
             "https://editor.avatech.ai?comfyui=true",
@@ -361,6 +448,24 @@ const ext = {
           widget: btn,
         };
       },
+      GROUP_OPS(node, inputName, inputData, app) {
+        const btn = node.addWidget("button", "Add OBJ", "", () => {
+          node.addInput("BPY_OBJ" + (node.inputs.length + 1), "BPY_OBJ");
+          node.graph.change();
+        });
+        return {
+          widget: btn,
+        };
+      },
+      GROUP_OPS_DELETE(node, inputName, inputData, app) {
+        const btn = node.addWidget("button", "Delete OBJ", "", () => {
+          node.removeInput(node.inputs.length - 1);
+          node.graph.change();
+        });
+        return {
+          widget: btn,
+        };
+      },
     };
   },
 
@@ -377,6 +482,7 @@ const ext = {
 
       node.computeParentGroupResize();
     };
+    injectUIComponentToComfyuimenu();
   },
 
   async setup() {
@@ -389,6 +495,10 @@ const ext = {
     });
 
     api.addEventListener("executed", (evt) => {
+      const images = evt.detail?.output.images;
+      if (images?.length > 0 && images[0].type === "output") {
+        generatedImages[evt.detail.node] = images[0].filename;
+      }
       if (evt.detail?.output.gltfFilename) {
         const viewer = document.getElementById(
           "avatech-viewer-iframe"
@@ -401,11 +511,23 @@ const ext = {
           api.api_base +
           `/view?filename=${evt.detail?.output.gltfFilename[0]}`;
 
+        if (
+          gltfFilename.endsWith(".ava") &&
+          evt.detail?.output.auto_save[0] == "true"
+        ) {
+          const link = document.createElement("a");
+          link.href = gltfFilename;
+          link.download = gltfFilename.split("/").pop();
+          document.body.appendChild(link);
+          link.click();
+          document.body.removeChild(link);
+        }
+
         fileName.val = gltfFilename;
         viewer.postMessage(
           JSON.stringify({
             avatarURL: gltfFilename,
-            blendshapes: evt.detail?.output.blendshapes[0],
+            blendshapes: evt.detail?.output.SHAPE_FLOW[0],
           }),
           "*"
         );
@@ -414,10 +536,23 @@ const ext = {
 
     window.addEventListener(
       "keydown",
-      (event) => {
+      async (event) => {
         if (event.key === "Escape") {
           event.preventDefault();
-          showImageEditor.val = false;
+          if (my_modal_3.open) {
+            my_modal_3.close();
+          } else {
+            showImageEditor.val = false;
+            showEditor.val = false;
+            await uploadSegments();
+            // api.fetchApi("/segments_order", {
+            //   method: "POST",
+            //   body: JSON.stringify({
+            //     name: embeddingID.val,
+            //     order: Object.keys(imagePromptsMulti.val),
+            //   }),
+            // });
+          }
         }
       },
       {
@@ -477,6 +612,36 @@ const ext = {
 
   async beforeRegisterNodeDef(nodeType, nodeData, app) {
     switch (nodeData.name) {
+      case "AvatarMainOutput":
+        addMenuHandler(nodeType, function (_, options) {
+          const output = app.nodeOutputs[this.id + ""];
+          if (!output || !output.gltfFilename) return;
+
+          const gltfFilename =
+            window.location.protocol +
+            "//" +
+            api.api_host +
+            api.api_base +
+            `/view?filename=${output.gltfFilename[0]}`;
+
+          options.unshift({
+            content: "Save file",
+            callback: () => {
+              const a = document.createElement("a");
+              let url = new URL(gltfFilename);
+              url.searchParams.delete("preview");
+              a.href = url;
+              a.setAttribute(
+                "download",
+                new URLSearchParams(url.search).get("filename")
+              );
+              document.body.append(a);
+              a.click();
+              requestAnimationFrame(() => a.remove());
+            },
+          });
+        });
+        break;
       case "ExportGLTF":
         addMenuHandler(nodeType, function (_, options) {
           const output = app.nodeOutputs[this.id + ""];
@@ -527,7 +692,7 @@ const ext = {
           });
         });
         break;
-      case "SAM_Prompt_Image":
+      case "SAM MultiLayer":
         nodeData.input.required.sam = ["SAM_PROMPTS"];
         // nodeData.input.required.upload = ['IMAGEUPLOAD'];
         // nodeData.input.required.prompts_points = ["IMAGEUPLOAD"];
@@ -540,10 +705,9 @@ const ext = {
           });
         });
         break;
-      case "SAM":
+      case "Combine Points":
-        nodeData.input.required.sam = ["SAM_PROMPTS"];
+        nodeData.input.required.sam = ["COMBINE_POINTS"];
-        // nodeData.input.required.upload = ['IMAGEUPLOAD'];
+
-        // nodeData.input.required.prompts_points = ["IMAGEUPLOAD"];
         addMenuHandler(nodeType, function (_, options) {
           options.unshift({
             content: "Open In Points Editor (Local)",
@@ -555,26 +719,202 @@ const ext = {
         break;
       case "CreateShapeFlow":
         nodeData.input.required.blendshape = ["BLENDSHAPES_CONFIG"];
-        addMenuHandler(nodeType, function (_, options) {
-          options.unshift({
-            content: "Open In Blendshapes Editor",
-            callback: () => {
-              openInAvatechEditor(
-                "https://editor.avatech.ai?comfyui=true",
-                gltfFilename
-              );
-            },
-          });
-        });
         break;
       case "Mesh_JoinMesh":
         nodeData.input.required.obj = ["MESH_GROUP_CONFIG"];
         nodeData.input.required.del_obj = ["MESH_GROUP_DELETE"];
         break;
+      case "GroupOps":
+        nodeData.input.required.obj = ["GROUP_OPS"];
+        nodeData.input.required.del_obj = ["GROUP_OPS_DELETE"];
       default:
         break;
     }
   },
 };
 
+export async function uploadPreview() {
+  if (fileName.val == "")
+    app.ui.dialog.show("Please create your avatar first.");
+  else {
+    const file = await fetch(fileName.val)
+      .then((e) => e.arrayBuffer())
+      .then((e) => new Uint8Array(e));
+    const labData = await fetch("https://labs.avatech.ai/api/share", {
+      method: "GET",
+    }).then((e) => e.json());
+
+    await fetch(labData.url, {
+      method: "PUT",
+      headers: {
+        "x-amz-acl": "public-read",
+        "Content-Type": "model/gltf-binary",
+        "Content-Length": file.length,
+      },
+      body: file,
+    }).catch((error) => console.error(error));
+
+    sharedAvatarLink.val = `https://editor.avatech.ai/viewer?avatarId=${labData?.modelId}`;
+    previewModelId.val = labData.modelId;
+    return labData;
+  }
+}
+
+function injectUIComponentToComfyuimenu() {
+  const menu = document.querySelector(".comfy-menu");
+  const avatarPreview = document.createElement("button");
+  avatarPreview.textContent = "Avatar Preview";
+  avatarPreview.onclick = () => {
+    showPreview.val = !showPreview.val;
+    localStorage.setItem("showPreview", showPreview.val);
+  };
+
+  const apiFormat = document.createElement("button");
+  const a = document.createElement("a");
+  apiFormat.textContent = "Save API Format (Avatech)";
+  apiFormat.onclick = () => {
+    let filename = "workflow_api.json";
+    filename = prompt("Save workflow (API) as:", filename);
+    if (!filename) return;
+    if (!filename.toLowerCase().endsWith(".json")) {
+      filename += ".json";
+    }
+    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" });
+      const url = URL.createObjectURL(blob);
+      a.href = url;
+      a.download = filename;
+      document.body.appendChild(a);
+      a.click();
+      setTimeout(function () {
+        a.remove();
+        window.URL.revokeObjectURL(url);
+      }, 0);
+    });
+  };
+
+  const dropdown = document.createElement("div");
+  dropdown.textContent = "▼";
+  dropdown.className = "dropdownbtn";
+  dropdown.onclick = (e) => {
+    e.preventDefault();
+    e.stopPropagation();
+
+    LiteGraph.closeAllContextMenus();
+    const menu = new LiteGraph.ContextMenu(
+      [
+        {
+          title: "Create new share link",
+          callback: async () => {
+            shareAvatar.textContent = "Loading...";
+            shareAvatar.append(dropdown);
+
+            await uploadPreview();
+
+            shareLoading.val = false;
+            shareAvatar.textContent = "Share Avatar";
+            shareAvatar.append(dropdown);
+          },
+        },
+        {
+          title: "Update avatar in current share link",
+          callback: async () => {
+            if (!previewModelId.val)
+              app.ui.dialog.show("Please share your avatar first.");
+            else {
+              if (shareLoading.val) return;
+
+              const file = await fetch(fileName.val)
+                .then((e) => e.arrayBuffer())
+                .then((e) => new Uint8Array(e));
+
+              const labData = await fetch(
+                "https://labs.avatech.ai/api/share?id=" + previewModelId.val,
+                {
+                  method: "GET",
+                }
+              ).then((e) => e.json());
+
+              await fetch(labData.url, {
+                method: "PUT",
+                headers: {
+                  "x-amz-acl": "public-read",
+                  "Content-Type": "model/gltf-binary",
+                  "Content-Length": file.length,
+                },
+                body: file,
+              }).catch((error) => console.error(error));
+
+              await fetch(
+                "https://labs.avatech.ai/api/purgecdn?id=" + previewModelId.val,
+                {
+                  method: "GET",
+                }
+              ).catch((error) => console.error(error));
+
+              infoDialog.show(
+                `Preview updated: <a href='https://editor.avatech.ai/viewer?avatarId=${labData.modelId}' target="_blank">https://editor.avatech.ai/viewer?avatarId=` +
+                  labData.modelId +
+                  "</a>\n Remember to hard refresh before checking out the new preview!"
+              );
+
+              shareLoading.val = false;
+              shareAvatar.textContent = "Share Avatar";
+              shareAvatar.append(dropdown);
+            }
+          },
+        },
+      ],
+
+      {
+        event: e,
+        scale: 1.3,
+      },
+      window
+    );
+    menu.root.classList.add("popup");
+  };
+
+  const shareAvatar = document.createElement("button");
+  shareAvatar.textContent = "Share Avatar";
+  shareAvatar.className = "sharebtn";
+  shareAvatar.onclick = async () => {
+    if (shareLoading.val) return;
+
+    if (!previewModelId.val) {
+      shareLoading.val = true;
+      shareAvatar.textContent = "Loading...";
+      shareAvatar.append(dropdown);
+
+      await uploadPreview();
+
+      shareLoading.val = false;
+      shareAvatar.textContent = "Share Avatar";
+      shareAvatar.append(dropdown);
+    } else {
+      infoDialog.show(
+        `Preview avatar url: <a href='https://editor.avatech.ai/viewer?avatarId=${previewModelId.val}' target="_blank">https://editor.avatech.ai/viewer?avatarId=${previewModelId.val}</a>` +
+          `\nChat url: <a href='https://labs.avatech.ai?avatarId=${previewModelId.val}' target="_blank">https://labs.avatech.ai?avatarId=${previewModelId.val}</a>`
+      );
+    }
+  };
+
+  menu.append(avatarPreview);
+  menu.append(shareAvatar);
+  menu.append(apiFormat);
+
+  shareAvatar.append(dropdown);
+}
+
 app.registerExtension(ext);

js/onnx.js

@@ -1,23 +1,22 @@
-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/";
-
-// Define image, embedding and model paths
-const IMAGE_PATH = "/assets/data/dogs.jpg";
-const IMAGE_EMBEDDING = "/assets/data/dogs_embedding.npy";
-const MODEL_DIR = "http://127.0.0.1:8188/sam_model";
 
 export let model = null;
+let modelType = null;
 
 // Initialize the ONNX model
-export const initModel = async () => {
+export const initModel = async (type) => {
   try {
-    if (MODEL_DIR === undefined) return;
+    if (!model || modelType !== type) {
-    const URL = MODEL_DIR;
+      modelType = type;
-    model = await ort.InferenceSession.create(URL);
+      model = await ort.InferenceSession.create(
+        `${location.protocol}//${location.host}/sam_model?type=${modelType}`
+      );
+    }
   } catch (e) {
     console.log(e);
   }
@@ -25,14 +24,12 @@ export const initModel = async () => {
 
 export const loadNpyTensor = async (tensorFile, dType = "float32") => {
   let npLoader = new npyjs();
-  console.log('tensorFile', tensorFile);
   const npArray = await npLoader.load(tensorFile);
-  console.log('np array', npArray);
   const tensor = new ort.Tensor(dType, npArray.data, npArray.shape);
   return tensor;
 };
 
-export const runONNX = async (clicks, tensor) => {
+export const runONNX = async (clicks, tensor, box) => {
   // console.log('tensor', tensor);
   try {
     if (
@@ -49,6 +46,7 @@ export const runONNX = async (clicks, tensor) => {
         clicks,
         tensor,
         modelScale: imageSize.val,
+        box,
       });
       if (feeds === undefined) return;
       // Run the SAM ONNX model with the feeds returned from modelData()

js/onnx_helper.js

@@ -4,7 +4,7 @@
 // This source code is licensed under the license found in the
 // LICENSE file in the root directory of this source tree.
 
-const modelData = ({ clicks, tensor, modelScale }) => {
+const modelData = ({ clicks, tensor, modelScale, box }) => {
   const imageEmbedding = tensor;
   let pointCoords;
   let pointLabels;
@@ -18,8 +18,9 @@ const modelData = ({ clicks, tensor, modelScale }) => {
     // If there is no box input, a single padding point with
     // label -1 and coordinates (0.0, 0.0) should be concatenated
     // so initialize the array to support (n + 1) points.
-    pointCoords = new Float32Array(2 * (n + 1));
+    const numPoints = box ? n + 3 : n + 1;
-    pointLabels = new Float32Array(n + 1);
+    pointCoords = new Float32Array(2 * numPoints);
+    pointLabels = new Float32Array(numPoints);
 
     // Add clicks and scale to what SAM expects
     for (let i = 0; i < n; i++) {
@@ -28,15 +29,25 @@ const modelData = ({ clicks, tensor, modelScale }) => {
       pointLabels[i] = clicks[i].clickType;
     }
 
-    // Add in the extra point/label when only clicks and no box
+    if (box) {
-    // The extra point is at (0, 0) with label -1
+      pointCoords[2 * n] = box.x1 * modelScale.samScale;
-    pointCoords[2 * n] = 0.0;
+      pointCoords[2 * n + 1] = box.y1 * modelScale.samScale;
-    pointCoords[2 * n + 1] = 0.0;
+      pointLabels[n] = 2;
-    pointLabels[n] = -1.0;
+
+      pointCoords[2 * n + 2] = box.x2 * modelScale.samScale;
+      pointCoords[2 * n + 3] = box.y2 * modelScale.samScale;
+      pointLabels[n + 1] = 3;
+    } else {
+      // Add in the extra point/label when only clicks and no box
+      // The extra point is at (0, 0) with label -1
+      pointCoords[2 * n] = 0.0;
+      pointCoords[2 * n + 1] = 0.0;
+      pointLabels[n] = -1.0;
+    }
 
     // Create the tensor
-    pointCoordsTensor = new ort.Tensor("float32", pointCoords, [1, n + 1, 2]);
+    pointCoordsTensor = new ort.Tensor("float32", pointCoords, [1, numPoints, 2]);
-    pointLabelsTensor = new ort.Tensor("float32", pointLabels, [1, n + 1]);
+    pointLabelsTensor = new ort.Tensor("float32", pointLabels, [1, numPoints]);
   }
   const imageSizeTensor = new ort.Tensor("float32", [
     modelScale.height,

js/state.js

@@ -9,26 +9,62 @@
  * @typedef {Object} Point
  * @property {number} x - The x coordinate
  * @property {number} y - The y coordinate
- * @property {number} label - The label
+ * @property {>} label - The label
+ *
+ * @typedef {Object} Box
+ * @property {number} x1
+ * @property {number} y1
+ * @property {number} x2
+ * @property {number} y2
  */
 
-import { van } from './van.js';
+import { van } from "./van.js";
-
+export const iframeSrc = van.state("https://editor.avatech.ai?comfyui=true");
-export const iframeSrc = van.state('https://editor.avatech.ai?comfyui=true');
 export const showEditor = van.state(false);
-export const fileName = van.state('');
+// localStorage.getItem("showPreview") == '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"
+);
+export const previewImg = van.state("");
+export const previewImgLoading = van.state(false);
+export const enableAutoSegment = van.state(false);
+// export const previewUrl = van.state("http://localhost:3006/viewer?avatarId=default&hideUI=true&debug=true&width=300&height=300&showAudioControl=true");
+export const isDirty = van.state(false);
+export const fileName = van.state("");
 export const showImageEditor = van.state(false);
 export const showLoading = van.state(false);
-export const imageUrl = van.state('');
+export const alertDialog = van.state({
+  text: "",
+  time: 0,
+});
+export const shareLoading = van.state(false);
+export const previewModelId = van.state("");
+
+export const isGenerateFlow = van.state(false);
+
+export const loadingCaption = van.state("");
+export const imageUrl = van.state("");
 export const point_label = van.state(1);
 export const imageContainerSize = van.state({
   width: 0,
   height: 0,
 });
 
+/** @type {State<Box>} */
+export const boxes = van.state();
+
+/** @type {State<Record<string, Box>>} */
+export const boxesMulti = van.state({});
+
 /** @type {State<Point[]>} */
 export const imagePrompts = van.state([]);
 
+export const allImagePrompts = van.state([{}]);
+
 /** @type {State<Record<string, Point[]>>} */
 export const imagePromptsMulti = van.state({});
 
@@ -40,3 +76,8 @@ 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/style.css

@@ -1,3 +0,0 @@
-@tailwind base;
-@tailwind components;
-@tailwind utilities;

js/styles.css

@@ -0,0 +1,146 @@
+@import url('https://fonts.googleapis.com/css2?family=Gabarito&display=swap');
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+.comfy-normal-modal {
+  display: none; /* Hidden by default */
+  position: fixed; /* Stay in place */
+  z-index: 100; /* Sit on top */
+  padding: 30px 30px 10px 30px;
+  background-color: var(--comfy-menu-bg); /* Modal background */
+  box-shadow: 0 0 20px #888888;
+  border-radius: 10px;
+  top: 50%;
+  left: 50%;
+  max-width: 80vw;
+  max-height: 80vh;
+  transform: translate(-50%, -50%);
+  overflow: hidden;
+  justify-content: center;
+  font-family: monospace;
+  font-size: 15px;
+  color: #ffffff;
+}
+
+.comfy-normal-modal p {
+  overflow: auto;
+  white-space: pre-line; /* This will respect line breaks */
+  margin-bottom: 20px; /* Add some margin between the text and the close button*/
+}
+
+.comfy-normal-modal a {
+  text-decoration-line: underline;
+}
+
+.comfy-normal-modal button {
+  font-size: 20px;
+  color: var(--input-text);
+  background-color: var(--comfy-input-bg);
+  border-radius: 8px;
+  border-color: var(--border-color);
+  border-style: solid;
+  margin-top: 2px;
+}
+
+.comfy-normal-modal button:hover {
+  filter: brightness(1.2);
+  cursor: pointer;
+}
+
+.sharebtn {
+  display: flex;
+  justify-content: flex-end;
+  gap: 0.25rem;
+}
+
+.popup ~ .litecontextmenu {
+  transform: scale(1.3);
+}
+
+.dropdownbtn {
+  font-size: 12px;
+  display: flex;
+  align-items: center;
+  width: 24px;
+  height: 30px;
+  justify-content: center;
+  background: rgba(255, 255, 255, 0.1);
+  border-top-right-radius: 0.375rem;
+  border-bottom-right-radius: 0.375rem;
+}
+
+.dropdownbtn:hover {
+  filter: brightness(1.6);
+  background-color: var(--comfy-menu-bg);
+}
+
+.comfy-menu > button,
+.comfy-menu-btns button,
+.comfy-menu .comfy-list button,
+.comfy-modal button {
+  color: var(--input-text);
+  background-color: var(--comfy-input-bg);
+  border-radius: 8px;
+  border-color: var(--border-color);
+  border-style: solid;
+  margin-top: 2px;
+  border-width: 2px;
+}
+
+.comfy-menu > button:hover,
+.comfy-menu-btns button:hover,
+.comfy-menu .comfy-list button:hover,
+.comfy-modal button:hover,
+.comfy-settings-btn:hover {
+  filter: brightness(1.2);
+  cursor: pointer;
+}
+
+.comfy-list {
+  color: var(--descrip-text);
+  background-color: var(--comfy-menu-bg);
+  margin-bottom: 10px;
+  border-color: var(--border-color);
+  border-style: solid;
+  border-width: 3px;
+}
+
+.comfy-list-items {
+  overflow-y: scroll;
+  max-height: 100px;
+  min-height: 25px;
+  background-color: var(--comfy-input-bg);
+  padding: 5px;
+}
+
+.comfy-list h4 {
+  min-width: 160px;
+  margin: 0;
+  padding: 3px;
+  font-weight: normal;
+}
+
+.comfy-list-items button {
+  font-size: 10px;
+}
+
+.comfy-list-actions {
+  margin: 5px;
+  display: flex;
+  gap: 5px;
+  justify-content: center;
+}
+
+.comfy-list-actions button {
+  font-size: 12px;
+}
+
+img {
+  display: none;
+}
+
+
+img[src] {
+ display: block;
+}

package.json

@@ -4,15 +4,14 @@
   "description": "",
   "main": "index.js",
   "scripts": {
-    "dev": "concurrently \"tailwindcss -i ./js/style.css -o ./js/output.css --watch -m\" \"node ./style-watcher.js\""
+    "dev": "tailwindcss -i ./js/styles.css -o ./js/tw-styles.css --watch"
   },
   "keywords": [],
   "author": "",
   "license": "ISC",
   "devDependencies": {
     "chokidar": "^3.5.3",
-    "concurrently": "^8.2.1",
+    "daisyui": "^4.0.7",
-    "daisyui": "^3.7.5",
     "tailwindcss": "^3.3.3"
   }
 }

pnpm-lock.yaml

@@ -8,12 +8,9 @@ devDependencies:
   chokidar:
     specifier: ^3.5.3
     version: 3.5.3
-  concurrently:
-    specifier: ^8.2.1
-    version: 8.2.1
   daisyui:
-    specifier: ^3.7.5
+    specifier: ^4.0.7
-    version: 3.7.5
+    version: 4.0.7(postcss@8.4.29)
   tailwindcss:
     specifier: ^3.3.3
     version: 3.3.3
@@ -25,13 +22,6 @@ packages:
     engines: {node: '>=10'}
     dev: true
 
-  /@babel/runtime@7.22.11:
-    resolution: {integrity: sha512-ee7jVNlWN09+KftVOu9n7S8gQzD/Z6hN/I8VBRXW4P1+Xe7kJGXMwu8vds4aGIMHZnNbdpSWCfZZtinytpcAvA==}
-    engines: {node: '>=6.9.0'}
-    dependencies:
-      regenerator-runtime: 0.14.0
-    dev: true
-
   /@jridgewell/gen-mapping@0.3.3:
     resolution: {integrity: sha512-HLhSWOLRi875zjjMG/r+Nv0oCW8umGb0BgEhyX3dDX3egwZtB8PqLnjz3yedt8R5StBrzcg4aBpnh8UA9D1BoQ==}
     engines: {node: '>=6.0.0'}
@@ -83,18 +73,6 @@ packages:
       fastq: 1.15.0
     dev: true
 
-  /ansi-regex@5.0.1:
-    resolution: {integrity: sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==}
-    engines: {node: '>=8'}
-    dev: true
-
-  /ansi-styles@4.3.0:
-    resolution: {integrity: sha512-zbB9rCJAT1rbjiVDb2hqKFHNYLxgtk8NURxZ3IZwD3F6NtxbXZQCnnSi1Lkx+IDohdPlFp222wVALIheZJQSEg==}
-    engines: {node: '>=8'}
-    dependencies:
-      color-convert: 2.0.1
-    dev: true
-
   /any-promise@1.3.0:
     resolution: {integrity: sha512-7UvmKalWRt1wgjL1RrGxoSJW/0QZFIegpeGvZG9kjp8vrRu55XTHbwnqq2GpXm9uLbcuhxm3IqX9OB4MZR1b2A==}
     dev: true
@@ -139,14 +117,6 @@ packages:
     engines: {node: '>= 6'}
     dev: true
 
-  /chalk@4.1.2:
-    resolution: {integrity: sha512-oKnbhFyRIXpUuez8iBMmyEa4nbj4IOQyuhc/wy9kY7/WVPcwIO9VA668Pu8RkO7+0G76SLROeyw9CpQ061i4mA==}
-    engines: {node: '>=10'}
-    dependencies:
-      ansi-styles: 4.3.0
-      supports-color: 7.2.0
-    dev: true
-
   /chokidar@3.5.3:
     resolution: {integrity: sha512-Dr3sfKRP6oTcjf2JmUmFJfeVMvXBdegxB0iVQ5eb2V10uFJUCAS8OByZdVAyVb8xXNz3GjjTgj9kLWsZTqE6kw==}
     engines: {node: '>= 8.10.0'}
@@ -162,30 +132,6 @@ packages:
       fsevents: 2.3.3
     dev: true
 
-  /cliui@8.0.1:
-    resolution: {integrity: sha512-BSeNnyus75C4//NQ9gQt1/csTXyo/8Sb+afLAkzAptFuMsod9HFokGNudZpi/oQV73hnVK+sR+5PVRMd+Dr7YQ==}
-    engines: {node: '>=12'}
-    dependencies:
-      string-width: 4.2.3
-      strip-ansi: 6.0.1
-      wrap-ansi: 7.0.0
-    dev: true
-
-  /color-convert@2.0.1:
-    resolution: {integrity: sha512-RRECPsj7iu/xb5oKYcsFHSppFNnsj/52OVTRKb4zP5onXwVF3zVmmToNcOfGC+CRDpfK/U584fMg38ZHCaElKQ==}
-    engines: {node: '>=7.0.0'}
-    dependencies:
-      color-name: 1.1.4
-    dev: true
-
-  /color-name@1.1.4:
-    resolution: {integrity: sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==}
-    dev: true
-
-  /colord@2.9.3:
-    resolution: {integrity: sha512-jeC1axXpnb0/2nn/Y1LPuLdgXBLH7aDcHu4KEKfqw3CUhX7ZpfBSlPKyqXE6btIgEzfWtrX3/tyBCaCvXvMkOw==}
-    dev: true
-
   /commander@4.1.1:
     resolution: {integrity: sha512-NOKm8xhkzAjzFx8B2v5OAHT+u5pRQc2UCa2Vq9jYL/31o2wi9mxBA7LIFs3sV5VSC49z6pEhfbMULvShKj26WA==}
     engines: {node: '>= 6'}
@@ -195,22 +141,6 @@ packages:
     resolution: {integrity: sha512-/Srv4dswyQNBfohGpz9o6Yb3Gz3SrUDqBH5rTuhGR7ahtlbYKnVxw2bCFMRljaA7EXHaXZ8wsHdodFvbkhKmqg==}
     dev: true
 
-  /concurrently@8.2.1:
-    resolution: {integrity: sha512-nVraf3aXOpIcNud5pB9M82p1tynmZkrSGQ1p6X/VY8cJ+2LMVqAgXsJxYYefACSHbTYlm92O1xuhdGTjwoEvbQ==}
-    engines: {node: ^14.13.0 || >=16.0.0}
-    hasBin: true
-    dependencies:
-      chalk: 4.1.2
-      date-fns: 2.30.0
-      lodash: 4.17.21
-      rxjs: 7.8.1
-      shell-quote: 1.8.1
-      spawn-command: 0.0.2
-      supports-color: 8.1.1
-      tree-kill: 1.2.2
-      yargs: 17.7.2
-    dev: true
-
   /css-selector-tokenizer@0.8.0:
     resolution: {integrity: sha512-Jd6Ig3/pe62/qe5SBPTN8h8LeUg/pT4lLgtavPf7updwwHpvFzxvOQBHYj2LZDMjUnBzgvIUSjRcf6oT5HzHFg==}
     dependencies:
@@ -224,24 +154,21 @@ packages:
     hasBin: true
     dev: true
 
-  /daisyui@3.7.5:
+  /culori@3.2.0:
-    resolution: {integrity: sha512-udhiBJYVvcPGXa+mL5IElke6EdddKecjbbz6m43+9IVqzK8GBwetg092Edclo42TkNboLD9nzodeesJygqZt2A==}
+    resolution: {integrity: sha512-HIEbTSP7vs1mPq/2P9In6QyFE0Tkpevh0k9a+FkjhD+cwsYm9WRSbn4uMdW9O0yXlNYC3ppxL3gWWPOcvEl57w==}
-    engines: {node: '>=16.9.0'}
+    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
-    dependencies:
-      colord: 2.9.3
-      css-selector-tokenizer: 0.8.0
-      postcss: 8.4.29
-      postcss-js: 4.0.1(postcss@8.4.29)
-      tailwindcss: 3.3.3
-    transitivePeerDependencies:
-      - ts-node
     dev: true
 
-  /date-fns@2.30.0:
+  /daisyui@4.0.7(postcss@8.4.29):
-    resolution: {integrity: sha512-fnULvOpxnC5/Vg3NCiWelDsLiUc9bRwAPs/+LfTLNvetFCtCTN+yQz15C/fs4AwX1R9K5GLtLfn8QW+dWisaAw==}
+    resolution: {integrity: sha512-D84DnNDZKcamwNsxCMrwYaddyz5kC6VO6oe30nM1x67GzCAfarfd3Ar1rpLGXCIqSsEoNZUHO8EcXvX93W2ZkA==}
-    engines: {node: '>=0.11'}
+    engines: {node: '>=16.9.0'}
     dependencies:
-      '@babel/runtime': 7.22.11
+      css-selector-tokenizer: 0.8.0
+      culori: 3.2.0
+      picocolors: 1.0.0
+      postcss-js: 4.0.1(postcss@8.4.29)
+    transitivePeerDependencies:
+      - postcss
     dev: true
 
   /didyoumean@1.2.2:
@@ -252,15 +179,6 @@ packages:
     resolution: {integrity: sha512-+HlytyjlPKnIG8XuRG8WvmBP8xs8P71y+SKKS6ZXWoEgLuePxtDoUEiH7WkdePWrQ5JBpE6aoVqfZfJUQkjXwA==}
     dev: true
 
-  /emoji-regex@8.0.0:
-    resolution: {integrity: sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==}
-    dev: true
-
-  /escalade@3.1.1:
-    resolution: {integrity: sha512-k0er2gUkLf8O0zKJiAhmkTnJlTvINGv7ygDNPbeIsX/TJjGJZHuh9B2UxbsaEkmlEo9MfhrSzmhIlhRlI2GXnw==}
-    engines: {node: '>=6'}
-    dev: true
-
   /fast-glob@3.3.1:
     resolution: {integrity: sha512-kNFPyjhh5cKjrUltxs+wFx+ZkbRaxxmZ+X0ZU31SOsxCEtP9VPgtq2teZw1DebupL5GmDaNQ6yKMMVcM41iqDg==}
     engines: {node: '>=8.6.0'}
@@ -305,11 +223,6 @@ packages:
     resolution: {integrity: sha512-yIovAzMX49sF8Yl58fSCWJ5svSLuaibPxXQJFLmBObTuCr0Mf1KiPopGM9NiFjiYBCbfaa2Fh6breQ6ANVTI0A==}
     dev: true
 
-  /get-caller-file@2.0.5:
-    resolution: {integrity: sha512-DyFP3BM/3YHTQOCUL/w0OZHR0lpKeGrxotcHWcqNEdnltqFwXVfhEBQ94eIo34AfQpo0rGki4cyIiftY06h2Fg==}
-    engines: {node: 6.* || 8.* || >= 10.*}
-    dev: true
-
   /glob-parent@5.1.2:
     resolution: {integrity: sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==}
     engines: {node: '>= 6'}
@@ -335,11 +248,6 @@ packages:
       path-is-absolute: 1.0.1
     dev: true
 
-  /has-flag@4.0.0:
-    resolution: {integrity: sha512-EykJT/Q1KjTWctppgIAgfSO0tKVuZUjhgMr17kqTumMl6Afv3EISleU7qZUzoXDFTAHTDC4NOoG/ZxU3EvlMPQ==}
-    engines: {node: '>=8'}
-    dev: true
-
   /has@1.0.3:
     resolution: {integrity: sha512-f2dvO0VU6Oej7RkWJGrehjbzMAjFp5/VKPp5tTpWIV4JHHZK1/BxbFRtf/siA2SWTe09caDmVtYYzWEIbBS4zw==}
     engines: {node: '>= 0.4.0'}
@@ -376,11 +284,6 @@ packages:
     engines: {node: '>=0.10.0'}
     dev: true
 
-  /is-fullwidth-code-point@3.0.0:
-    resolution: {integrity: sha512-zymm5+u+sCsSWyD9qNaejV3DFvhCKclKdizYaJUuHA83RLjb7nSuGnddCHGv0hk+KY7BMAlsWeK4Ueg6EV6XQg==}
-    engines: {node: '>=8'}
-    dev: true
-
   /is-glob@4.0.3:
     resolution: {integrity: sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg==}
     engines: {node: '>=0.10.0'}
@@ -407,10 +310,6 @@ packages:
     resolution: {integrity: sha512-7ylylesZQ/PV29jhEDl3Ufjo6ZX7gCqJr5F7PKrqc93v7fzSymt1BpwEU8nAUXs8qzzvqhbjhK5QZg6Mt/HkBg==}
     dev: true
 
-  /lodash@4.17.21:
-    resolution: {integrity: sha512-v2kDEe57lecTulaDIuNTPy3Ry4gLGJ6Z1O3vE1krgXZNrsQ+LFTGHVxVjcXPs17LhbZVGedAJv8XZ1tvj5FvSg==}
-    dev: true
-
   /merge2@1.4.1:
     resolution: {integrity: sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==}
     engines: {node: '>= 8'}
@@ -580,15 +479,6 @@ packages:
       picomatch: 2.3.1
     dev: true
 
-  /regenerator-runtime@0.14.0:
-    resolution: {integrity: sha512-srw17NI0TUWHuGa5CFGGmhfNIeja30WMBfbslPNhf6JrqQlLN5gcrvig1oqPxiVaXb0oW0XRKtH6Nngs5lKCIA==}
-    dev: true
-
-  /require-directory@2.1.1:
-    resolution: {integrity: sha512-fGxEI7+wsG9xrvdjsrlmL22OMTTiHRwAMroiEeMgq8gzoLC/PQr7RsRDSTLUg/bZAZtF+TVIkHc6/4RIKrui+Q==}
-    engines: {node: '>=0.10.0'}
-    dev: true
-
   /resolve@1.22.4:
     resolution: {integrity: sha512-PXNdCiPqDqeUou+w1C2eTQbNfxKSuMxqTCuvlmmMsk1NWHL5fRrhY6Pl0qEYYc6+QqGClco1Qj8XnjPego4wfg==}
     hasBin: true
@@ -609,41 +499,11 @@ packages:
       queue-microtask: 1.2.3
     dev: true
 
-  /rxjs@7.8.1:
-    resolution: {integrity: sha512-AA3TVj+0A2iuIoQkWEK/tqFjBq2j+6PO6Y0zJcvzLAFhEFIO3HL0vls9hWLncZbAAbK0mar7oZ4V079I/qPMxg==}
-    dependencies:
-      tslib: 2.6.2
-    dev: true
-
-  /shell-quote@1.8.1:
-    resolution: {integrity: sha512-6j1W9l1iAs/4xYBI1SYOVZyFcCis9b4KCLQ8fgAGG07QvzaRLVVRQvAy85yNmmZSjYjg4MWh4gNvlPujU/5LpA==}
-    dev: true
-
   /source-map-js@1.0.2:
     resolution: {integrity: sha512-R0XvVJ9WusLiqTCEiGCmICCMplcCkIwwR11mOSD9CR5u+IXYdiseeEuXCVAjS54zqwkLcPNnmU4OeJ6tUrWhDw==}
     engines: {node: '>=0.10.0'}
     dev: true
 
-  /spawn-command@0.0.2:
-    resolution: {integrity: sha512-zC8zGoGkmc8J9ndvml8Xksr1Amk9qBujgbF0JAIWO7kXr43w0h/0GJNM/Vustixu+YE8N/MTrQ7N31FvHUACxQ==}
-    dev: true
-
-  /string-width@4.2.3:
-    resolution: {integrity: sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==}
-    engines: {node: '>=8'}
-    dependencies:
-      emoji-regex: 8.0.0
-      is-fullwidth-code-point: 3.0.0
-      strip-ansi: 6.0.1
-    dev: true
-
-  /strip-ansi@6.0.1:
-    resolution: {integrity: sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A==}
-    engines: {node: '>=8'}
-    dependencies:
-      ansi-regex: 5.0.1
-    dev: true
-
   /sucrase@3.34.0:
     resolution: {integrity: sha512-70/LQEZ07TEcxiU2dz51FKaE6hCTWC6vr7FOk3Gr0U60C3shtAN+H+BFr9XlYe5xqf3RA8nrc+VIwzCfnxuXJw==}
     engines: {node: '>=8'}
@@ -658,20 +518,6 @@ packages:
       ts-interface-checker: 0.1.13
     dev: true
 
-  /supports-color@7.2.0:
-    resolution: {integrity: sha512-qpCAvRl9stuOHveKsn7HncJRvv501qIacKzQlO/+Lwxc9+0q2wLyv4Dfvt80/DPn2pqOBsJdDiogXGR9+OvwRw==}
-    engines: {node: '>=8'}
-    dependencies:
-      has-flag: 4.0.0
-    dev: true
-
-  /supports-color@8.1.1:
-    resolution: {integrity: sha512-MpUEN2OodtUzxvKQl72cUF7RQ5EiHsGvSsVG0ia9c5RbWGL2CI4C7EpPS8UTBIplnlzZiNuV56w+FuNxy3ty2Q==}
-    engines: {node: '>=10'}
-    dependencies:
-      has-flag: 4.0.0
-    dev: true
-
   /supports-preserve-symlinks-flag@1.0.0:
     resolution: {integrity: sha512-ot0WnXS9fgdkgIcePe6RHNk1WA8+muPa6cSjeR3V8K27q9BB1rTE3R1p7Hv0z1ZyAc8s6Vvv8DIyWf681MAt0w==}
     engines: {node: '>= 0.4'}
@@ -728,60 +574,19 @@ packages:
       is-number: 7.0.0
     dev: true
 
-  /tree-kill@1.2.2:
-    resolution: {integrity: sha512-L0Orpi8qGpRG//Nd+H90vFB+3iHnue1zSSGmNOOCh1GLJ7rUKVwV2HvijphGQS2UmhUZewS9VgvxYIdgr+fG1A==}
-    hasBin: true
-    dev: true
-
   /ts-interface-checker@0.1.13:
     resolution: {integrity: sha512-Y/arvbn+rrz3JCKl9C4kVNfTfSm2/mEp5FSz5EsZSANGPSlQrpRI5M4PKF+mJnE52jOO90PnPSc3Ur3bTQw0gA==}
     dev: true
 
-  /tslib@2.6.2:
-    resolution: {integrity: sha512-AEYxH93jGFPn/a2iVAwW87VuUIkR1FVUKB77NwMF7nBTDkDrrT/Hpt/IrCJ0QXhW27jTBDcf5ZY7w6RiqTMw2Q==}
-    dev: true
-
   /util-deprecate@1.0.2:
     resolution: {integrity: sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==}
     dev: true
 
-  /wrap-ansi@7.0.0:
-    resolution: {integrity: sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==}
-    engines: {node: '>=10'}
-    dependencies:
-      ansi-styles: 4.3.0
-      string-width: 4.2.3
-      strip-ansi: 6.0.1
-    dev: true
-
   /wrappy@1.0.2:
     resolution: {integrity: sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==}
     dev: true
 
-  /y18n@5.0.8:
-    resolution: {integrity: sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==}
-    engines: {node: '>=10'}
-    dev: true
-
   /yaml@2.3.2:
     resolution: {integrity: sha512-N/lyzTPaJasoDmfV7YTrYCI0G/3ivm/9wdG0aHuheKowWQwGTsK0Eoiw6utmzAnI6pkJa0DUVygvp3spqqEKXg==}
     engines: {node: '>= 14'}
     dev: true
-
-  /yargs-parser@21.1.1:
-    resolution: {integrity: sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==}
-    engines: {node: '>=12'}
-    dev: true
-
-  /yargs@17.7.2:
-    resolution: {integrity: sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==}
-    engines: {node: '>=12'}
-    dependencies:
-      cliui: 8.0.1
-      escalade: 3.1.1
-      get-caller-file: 2.0.5
-      require-directory: 2.1.1
-      string-width: 4.2.3
-      y18n: 5.0.8
-      yargs-parser: 21.1.1
-    dev: true

requirements.txt

@@ -3,6 +3,9 @@ numpy
 opencv-python
 opencv-contrib-python
 einops
-bpy
+bpy==3.6.0
 segment-anything
+tqdm
+python-dotenv
+mediapipe
 # -e git+https://github.com/facebookresearch/segment-anything.git#egg=segment_anything

routes.py

@@ -1,12 +1,24 @@
 from aiohttp import web
 from segment_anything import sam_model_registry, SamPredictor
 from PIL import Image, ImageOps
+from dotenv import load_dotenv
+from blender.mesh_utils import upload_avatar_file
 import os
 import requests
 import folder_paths
 import json
 import numpy as np
 import server
+import re
+import base64
+from PIL import Image
+import io
+import time
+import execution
+import random
+
+load_dotenv()
+
 
 # For speeding up ONNX model, see https://github.com/facebookresearch/segment-anything/tree/main/demo#onnx-multithreading-with-sharedarraybuffer
 def inject_headers(original_handler):
@@ -32,24 +44,34 @@ for item in server.PromptServer.instance.routes._items:
 server.PromptServer.instance.routes._items = routes
 
 
+@server.PromptServer.instance.routes.get("/avatar-graph-comfyui/tw-styles.css")
+async def get_web_styles(request):
+    filename = os.path.join(os.path.dirname(__file__), "js/tw-styles.css")
+    return web.FileResponse(filename)
+
+
 @server.PromptServer.instance.routes.get("/sam_model")
 async def get_sam_model(request):
-    filename = os.path.join(folder_paths.base_path, "web/models/sam.onnx")
+    model_type = request.rel_url.query.get("type", "vit_h")
-    # print(filename)
+    filename = os.path.join(folder_paths.base_path, f"web/models/sam_{model_type}.onnx")
     if not os.path.isfile(filename):
         os.makedirs(os.path.dirname(filename), exist_ok=True)
+        print(f"Downloading ONNX model to {filename}")
         response = requests.get(
-            "https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/models/sam.onnx"
+            f"https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/models/sam_{model_type}.onnx"
         )
         response.raise_for_status()
         with open(filename, "wb") as f:
             f.write(response.content)
-        print(f"ONNX model downloaded: {filename}")
+        print(f"ONNX model downloaded")
     return web.FileResponse(filename)
 
 
-def load_image(image):
+def load_image(image, is_generated_image):
-    image_path = folder_paths.get_annotated_filepath(image)
+    if is_generated_image:
+        image_path = f"{folder_paths.get_output_directory()}/{image}"
+    else:
+        image_path = folder_paths.get_annotated_filepath(image)
     i = Image.open(image_path)
     i = ImageOps.exif_transpose(i)
     image = i.convert("RGB")
@@ -60,26 +82,349 @@ def load_image(image):
 @server.PromptServer.instance.routes.post("/sam_model")
 async def post_sam_model(request):
     post = await request.json()
+    is_generated_image = post.get("isGeneratedImage")
     emb_id = post.get("embedding_id")
-    emb_filename = f"{folder_paths.get_output_directory()}/{emb_id}.npy"
+    ckpt = post.get("ckpt")
+    ckpt = folder_paths.get_full_path("sams", ckpt)
+    remote = post.get("remote")
+    model_type = re.findall(r"vit_[lbh]", ckpt)[0]
+    emb_filename = f"{folder_paths.get_output_directory()}/{emb_id}_{model_type}.npy"
+    output_json_filename = (
+        f"{folder_paths.get_output_directory()}/{emb_id}_{model_type}.json"
+    )
     if not os.path.exists(emb_filename):
-        image = load_image(post.get("image"))
+        image = load_image(post.get("image"), is_generated_image)
-        ckpt = post.get("ckpt")
+        if remote:
-        model_type = post.get("model_type")
+            # Run embed in remote server
-        ckpt = folder_paths.get_full_path("sam", ckpt)
+            image = Image.fromarray((image * 255).astype(np.uint8))
-        sam = sam_model_registry[model_type](checkpoint=ckpt)
+            buffered = io.BytesIO()
-        predictor = SamPredictor(sam)
+            image.save(buffered, format="PNG")
-
+            image = base64.b64encode(buffered.getvalue()).decode()
-        image_np = (image * 255).astype(np.uint8)
+            res = requests.post(
-        predictor.set_image(image_np)
+                "https://avatechgg--sam-embed.modal.run",
-        emb = predictor.get_image_embedding().cpu().numpy()
+                headers={
-        np.save(emb_filename, emb)
+                    "Content-type": "application/json",
-        with open(f"{folder_paths.get_output_directory()}/{emb_id}.json", "w") as f:
+                    "Accept": "application/json",
-            json.dump(
-                {
-                    "input_size": predictor.input_size,
-                    "original_size": predictor.original_size,
                 },
-                f,
+                data=json.dumps(
+                    {
+                        "image": image,
+                    }
+                ),
+            ).json()
+            emb, input_size, original_size = (
+                res["emb"],
+                res["input_size"],
+                res["original_size"],
             )
+            emb = np.array(emb).astype(np.float32)
+            np.save(emb_filename, emb)
+            with open(output_json_filename, "w") as f:
+                data = {
+                    "input_size": input_size,
+                    "original_size": original_size,
+                }
+                json.dump(data, f)
+        else:
+            sam = sam_model_registry[model_type](checkpoint=ckpt)
+            predictor = SamPredictor(sam)
+
+            image_np = (image * 255).astype(np.uint8)
+            predictor.set_image(image_np)
+            emb = predictor.get_image_embedding().cpu().numpy()
+            np.save(emb_filename, emb)
+            with open(output_json_filename, "w") as f:
+                json.dump(
+                    {
+                        "input_size": predictor.input_size,
+                        "original_size": predictor.original_size,
+                    },
+                    f,
+                )
+    print("Finished embedding")
     return web.json_response({})
+
+
+def save_image(image, save_name=None):
+    input_folder = folder_paths.get_input_directory()
+    name, extension = os.path.splitext(image.filename)
+
+    if save_name == None:
+        save_name = f"{name}{extension}"
+        i = 1
+        while os.path.exists(f"{input_folder}/{save_name}"):
+            save_name = f"{name}_{i}{extension}"
+            i += 1
+
+    with open(f"{input_folder}/{save_name}", "wb") as f:
+        f.write(image.file.read())
+
+    return save_name
+
+
+def post_prompt(json_data):
+    prompt_server = server.PromptServer.instance
+    json_data = prompt_server.trigger_on_prompt(json_data)
+
+    if "number" in json_data:
+        number = float(json_data["number"])
+    else:
+        number = prompt_server.number
+        if "front" in json_data:
+            if json_data["front"]:
+                number = -number
+
+        prompt_server.number += 1
+
+    if "prompt" in json_data:
+        prompt = json_data["prompt"]
+        valid = execution.validate_prompt(prompt)
+        extra_data = {}
+        if "extra_data" in json_data:
+            extra_data = json_data["extra_data"]
+
+        if "client_id" in json_data:
+            extra_data["client_id"] = json_data["client_id"]
+        if valid[0]:
+            prompt_id = str(uuid.uuid4())
+            outputs_to_execute = valid[2]
+            prompt_server.prompt_queue.put(
+                (number, prompt_id, prompt, extra_data, outputs_to_execute)
+            )
+            response = {
+                "prompt_id": prompt_id,
+                "number": number,
+                "node_errors": valid[3],
+            }
+            return web.json_response(response)
+        else:
+            print("invalid prompt:", valid[1])
+            return web.json_response(
+                {"error": valid[1], "node_errors": valid[3]}, status=400
+            )
+    else:
+        return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
+
+
+def randomSeed(num_digits=15):
+    range_start = 10 ** (num_digits - 1)
+    range_end = (10**num_digits) - 1
+    return random.randint(range_start, range_end)
+
+
+def load_workflow(workflow_name):
+    with open(
+        os.path.join(
+            os.path.dirname(__file__),
+            f"workflow_templates/api/{workflow_name}.json",
+        )
+    ) as f:
+        return "\n".join(f.readlines())
+
+
+
+@server.PromptServer.instance.routes.post("/avatar_generation")
+async def post_prompt_block(request):
+    prompt_server = server.PromptServer.instance
+    post = await request.post()
+    uploaded_workflow = post.get("workflow")
+    workflow_name = post.get("workflow_name")
+    if uploaded_workflow is not None:
+        workflow = uploaded_workflow
+    elif workflow_name is not None:
+        workflow = load_workflow(workflow_name)
+
+    ref_image = post.get("ref_image")
+    base_image = post.get("base_image")
+    if ref_image is not None:
+        image_path = save_image(ref_image)
+        image_name, image_ext = os.path.splitext(image_path)
+        workflow = workflow.replace("reference_image_avatech", image_path)
+    elif base_image is not None:
+        image_path = save_image(base_image)
+        image_name, image_ext = os.path.splitext(image_path)
+        workflow = workflow.replace("base_image", image_path)
+        workflow = workflow.replace("reference_image_avatech", image_path)  # TMP
+
+    for key, value in post.items():
+        if key.startswith("mask_"):
+            mask_name = image_name + "_" + key.replace("mask_", "") + image_ext
+            mask_path = save_image(value, save_name=mask_name)
+            workflow = workflow.replace(f'"{key}"', f'"{mask_path}"')
+
+    workflow = workflow.replace("embedding_id_avatech", image_path)
+    workflow = workflow.replace("SEED", str(randomSeed()))
+    api_prompt = json.loads(workflow)
+
+    # skip generation part if base_image is provided
+    if base_image is not None:
+        for value in api_prompt.values():
+            if (
+                value["class_type"] == "LoadImageFromRequest"
+                and value["inputs"]["name"] == image_path
+            ):
+                del value["inputs"]["image"]
+            elif (
+                value["class_type"] == "PreviewImage"
+                or value["class_type"] == "SaveImage"
+            ):
+                value["inputs"] = {}
+
+    res = post_prompt({"prompt": api_prompt})
+    prompt_id = json.loads(res.text)["prompt_id"]
+    while True:
+        history = prompt_server.prompt_queue.get_history(prompt_id=prompt_id)
+        if history:
+            # file = get_avatar_file(history[prompt_id]["outputs"])
+            # return web.Response(body=file)
+            outputs = history[prompt_id]["outputs"]
+            for node_id, output in outputs.items():
+                if "gltfFilename" in output:
+                    modelId = upload_avatar_file(output)
+                    print("model id", modelId)
+                    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):
+#     # json_link = "https://cdn.discordapp.com/attachments/1119102674437156984/1172255632586448987/workflow_boy_2_1.json?ex=655fa722&is=654d3222&hm=463fa6a3c6ea60f7471196ff45382c729d3b856e86282f905d37a0398711860e&" # YP workflow
+#     # json_link = "https://cdn.discordapp.com/attachments/729003657483518063/1172504658812608572/workflow_15.json?ex=65608f0e&is=654e1a0e&hm=f707d887b9294c1e9b26e54856b1e516d1725a1b25d044b46229cea6e5c804a1&" # Benny workflow
+#     # json_link = 'https://cdn.discordapp.com/attachments/1110859802701221898/1173536418337914970/newstyle.json?ex=65644ff5&is=6551daf5&hm=f129838fae10197351bd27c69c7ff5eb4edf2c7d6ed74e6db8b55ddaa3c77dee&' # Deepwoo workflow
+#     json_link = 'https://cdn.discordapp.com/attachments/729003657483518063/1174045115757633596/girl1114.json?ex=656629b8&is=6553b4b8&hm=df3d7798b887e2b3b6b06ea438f1bc4ba041dd0f9daf54ea48101845ec7f4243&'
+#     response = requests.get(json_link)
+#     response.raise_for_status()
+#     return web.json_response(response.json())
+
+
+@server.PromptServer.instance.routes.get("/get_workflow")
+async def get_workflow(request):
+    name = request.rel_url.query.get("name", "default")
+    # if name == "default":
+    #     json_link = 'https://cdn.discordapp.com/attachments/729003657483518063/1174045115757633596/girl1114.json?ex=656629b8&is=6553b4b8&hm=df3d7798b887e2b3b6b06ea438f1bc4ba041dd0f9daf54ea48101845ec7f4243&'
+    #     response = requests.get(json_link)
+    #     response.raise_for_status()
+    #     workflow = response.json()
+    # else:
+    if name == "default":
+        name = "Auto_segment_workflow"
+
+    workflows_path = os.path.join(os.path.dirname(__file__), "workflow_templates")
+    workflow = json.load(open(f"{workflows_path}/{name}.json"))
+    return web.json_response(workflow)
+
+
+@server.PromptServer.instance.routes.post("/segments")
+async def post_segments(request):
+    post = await request.json()
+    name = post.get("name")
+    segments = post.get("segments")
+    output_dir = os.path.join(folder_paths.base_path, f"output/segments_{name}")
+    os.makedirs(output_dir, exist_ok=True)
+    for key, value in segments.items():
+        filename = os.path.join(output_dir, f"{key}.png")
+        with open(filename, "wb") as f:
+            f.write(base64.b64decode(value.split(",")[1]))
+
+    order = list(segments.keys())
+    with open(os.path.join(output_dir, "order.json"), "w") as f:
+        json.dump(order, f)
+    return web.json_response({})
+
+
+# @server.PromptServer.instance.routes.post("/segments_order")
+# async def post_segments(request):
+#     post = await request.json()
+#     name = post.get("name")
+#     order = post.get("order")
+#     output_dir = os.path.join(folder_paths.base_path, f"output/{name}")
+#     os.makedirs(output_dir, exist_ok=True)
+#     with open(os.path.join(output_dir, "order.json") , "w") as f:
+#         json.dump(order, f)
+#     return web.json_response({})
+
+
+@server.PromptServer.instance.routes.get("/get_webhook")
+async def get_webhook(request):
+    url = os.getenv("DISCORD_WEBHOOK_URL")
+    return web.json_response(url)
+
+
+import uuid
+
+
+@server.PromptServer.instance.routes.post("/create_avatar_from_image")
+async def post_input_file(request):
+    post = await request.read()
+
+    # Doesn't seems working when file isnt png / or nothing is uploaded
+    if not post:
+        raise web.HTTPBadRequest(reason="No image data received")
+
+    try:
+        queue_id = uuid.uuid4()
+
+        output_dir = os.path.join(
+            folder_paths.base_path, "input", "create_avatar_endpoint"
+        )
+        os.makedirs(output_dir, exist_ok=True)
+
+        filename = os.path.join(output_dir, str(queue_id) + ".png")
+        with open(filename, "wb") as f:
+            f.write(post)
+
+        return web.json_response(
+            {
+                "redirect_url": "https://ai-assistant.avatech.ai?queue-id="
+                + str(queue_id)
+            }
+        )
+    except Exception as e:
+        print(e)
+        return web.json_response({"error": e})

sam/avatar_segmentation.py

@@ -1,67 +0,0 @@
-import torch
-from einops import rearrange, repeat
-from torchvision import transforms
-from PIL import Image
-import numpy as np
-import cv2
-import base64
-import requests
-import json
-import io
-
-class AvatarSegmentation:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE", "IMAGE")
-    RETURN_NAMES = ("back_hair", "front_hair","eyes", "eyelashes", "mouth")
-
-    FUNCTION = "segment"
-
-    CATEGORY = "image"
-
-    def segment(self, image):
-        # Convert tensor to PIL image
-        image = image[0]
-        image = rearrange(image, 'h w c -> c h w')
-        image = transforms.ToPILImage()(image)
-        
-        # Save image to in-memory file
-        image_buff = io.BytesIO()
-        image.save(image_buff, format="PNG")
-        image_string = base64.b64encode(image_buff.getvalue()).decode("utf-8") 
-
-        # Send request to API
-        url = 'https://q41iq6s6t8.execute-api.ap-southeast-1.amazonaws.com/seg-cpu'
-        headers = {
-            'content-type': 'application/json'
-        }
-        data = {
-            'img_str': image_string
-        }
-        response = requests.post(url, headers=headers, data=json.dumps(data))
-
-        # Convert response to tensors
-        outs = []
-        masks = response.json()
-        for segment_name, img_str in masks.items():
-            mask = base64.b64decode(img_str.encode('utf-8'))
-            mask = Image.open(io.BytesIO(mask))
-            mask = np.array(mask)
-            mask = torch.from_numpy(mask) # shape: H, W, 3
-            mask = rearrange(mask, 'h w c -> 1 h w c')
-            outs.append(mask)
-        return outs
-
-NODE_CLASS_MAPPINGS = {
-    "AvatarSegmentation": AvatarSegmentation
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "AvatarSegmentation": "Avatar Segmentation"
-}

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_image_from_request.py

@@ -0,0 +1,43 @@
+import folder_paths
+from PIL import Image, ImageOps
+import numpy as np
+import torch
+
+class LoadImageFromRequest:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "name": (
+                    "STRING",
+                    {"multiline": False, "default": "face.png"},
+                ),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "run"
+
+    CATEGORY = "image"
+
+    def run(self, name, image=None):
+        try:
+            image_path = folder_paths.get_annotated_filepath(name)
+            image = Image.open(image_path)
+            image = ImageOps.exif_transpose(image)
+            # image = image.convert("RGB")
+            image = np.array(image).astype(np.float32) / 255.0
+            image = torch.from_numpy(image)[None,]
+            return [image]
+        except:
+            return [image]
+
+
+NODE_CLASS_MAPPINGS = {"LoadImageFromRequest": LoadImageFromRequest}
+
+NODE_DISPLAY_NAME_MAPPINGS = {"LoadImageFromRequest": "Load Image From Request"}

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.py

@@ -1,100 +0,0 @@
-import folder_paths
-import os
-import numpy as np
-import torch
-from segment_anything import sam_model_registry, SamPredictor
-from einops import rearrange, repeat
-
-
-global_predictor = None
-
-class SAM:
-    def __init__(self):
-        self.predictor = None
-        self.output_dir = folder_paths.get_output_directory()
-
-    @classmethod
-    def INPUT_TYPES(s):
-        input_dir = folder_paths.get_input_directory()
-        files = [
-            f
-            for f in os.listdir(input_dir)
-            if os.path.isfile(os.path.join(input_dir, f))
-        ]
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "model_type": (["vit_h", "vit_l", "vit_b"],),
-                "ckpt": (folder_paths.get_filename_list("sam"),),
-                "embedding_id": (
-                    "STRING",
-                    {"multiline": False, "default": "embedding"},
-                ),
-                # "image": (sorted(files), ),
-                "image_prompts_json": ("STRING", {"multiline": False, "default": "[]"}),
-            },
-        }
-
-    CATEGORY = "image"
-
-    RETURN_TYPES = ("SAM_PROMPT",)
-    FUNCTION = "load_image"
-
-    def load_image(self, image, model_type, ckpt, embedding_id, image_prompts_json):
-        import json
-
-        global global_predictor
-
-        if global_predictor is None:
-            ckpt = folder_paths.get_full_path("sam", ckpt)
-            sam = sam_model_registry[model_type](checkpoint=ckpt)
-            predictor = SamPredictor(sam)
-            global_predictor = predictor
-        
-        predictor = global_predictor
-        
-        emb_filename = f"{self.output_dir}/{embedding_id}.npy"
-        if not os.path.exists(emb_filename):
-            image_np = (image[0].numpy() * 255).astype(np.uint8)
-            predictor.set_image(image_np)
-            emb = predictor.get_image_embedding().cpu().numpy()
-            np.save(emb_filename, emb)
-        else:
-            emb = np.load(emb_filename)
-
-            with open(f"{self.output_dir}/{embedding_id}.json") as f:
-                data = json.load(f)
-                predictor.input_size = data["input_size"]
-                predictor.features = torch.from_numpy(emb)
-                predictor.is_image_set = True
-                predictor.original_size = data["original_size"]
-
-        image_prompts = json.loads(image_prompts_json)
-
-        result = [image_prompts]
-
-        if isinstance(image_prompts, list):
-            pass
-        elif all(isinstance(item, list) for item in image_prompts.values()):
-            for item in image_prompts.values():
-                if (len(item) == 0):
-                    h, w, c = image[0].shape
-                    result.append(torch.zeros(1, h, w, c))
-                    continue
-                point_coords = np.array([[p['x'], p['y']] for p in item])
-                point_labels = np.array([p['label'] for p in item])
-
-                masks, _, _ = predictor.predict(
-                    point_coords=point_coords,
-                    point_labels=point_labels,
-                )
-                masks = torch.from_numpy(masks)
-                masks = rearrange(masks[0], 'h w -> 1 h w')
-                out_image = repeat(masks, '1 h w -> 1 h w c', c=3) * image
-                result.append(out_image)
-        return result
-
-
-NODE_CLASS_MAPPINGS = {"SAM": SAM}
-
-NODE_DISPLAY_NAME_MAPPINGS = {"SAM": "Segmentation (SAM)"}

sam/sam_load_emb.py

@@ -1,52 +0,0 @@
-import folder_paths
-import torch
-from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
-from einops import rearrange, repeat
-
-
-class SAM_Load_Embedding:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "filename": ("STRING", {
-                    "multiline": False,
-                    "default": "embeddings"
-                }),
-                "embedding_id": ("STRING", {
-                    "multiline": False,
-                    "default": "embedding"
-                }),
-            }
-        }
-
-    RETURN_TYPES = ("EMBEDDINGS",)
-    RETURN_NAMES = ("EMBEDDINGS",)
-
-    OUTPUT_NODE = True
-
-    FUNCTION = "process"
-
-    CATEGORY = "image"
-
-    def process(self, filename, embedding_id):
-        import json
-        import numpy as np
-
-        data = {}
-        with open(filename, 'r') as f:
-            data = json.load(f)
-
-        # Convert list to numpy ndarray
-        data['image_embedding'] = np.array(data['image_embedding'])
-
-        return (data, )
-
-
-NODE_CLASS_MAPPINGS = {
-    "SAM_Load_Embedding": SAM_Load_Embedding
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "SAM_Load_Embedding": "SAM_Load_Embedding "
-}

sam/sam_loader.py

@@ -1,42 +0,0 @@
-import folder_paths
-import torch
-from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
-from einops import rearrange, repeat
-import requests
-from PIL import Image
-import io
-import numpy as np
-from segment_anything import SamAutomaticMaskGenerator, sam_model_registry, SamPredictor
-from einops import rearrange, repeat
-
-class SAM_Remote_Emb:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model_type": (["vit_h", "vit_l", "vit_b"],),
-                "ckpt": (folder_paths.get_filename_list("sam"),),
-            },
-        }
-
-    RETURN_TYPES = ("SAM", "SAMPREDICTOR")
-    RETURN_NAMES = ("sam", "predictor")
-
-    FUNCTION = "segment"
-
-    CATEGORY = "image"
-
-    def segment(self, model_type, ckpt):
-        ckpt = folder_paths.get_full_path("sam", ckpt)
-        sam = sam_model_registry[model_type](checkpoint=ckpt)
-        predictor = SamPredictor(sam)
-
-        return (sam, predictor)
-
-NODE_CLASS_MAPPINGS = {
-    "SAM_Loader": SAM_Remote_Emb
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "SAM_Loader": "SAM Loader"
-}

sam/sam_multilayer.py

@@ -0,0 +1,385 @@
+import folder_paths
+import os
+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
+import mediapipe as mp
+from math import sqrt
+
+BaseOptions = mp.tasks.BaseOptions
+FaceLandmarker = mp.tasks.vision.FaceLandmarker
+FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions
+PoseLandmarker = mp.tasks.vision.PoseLandmarker
+PoseLandmarkerOptions = mp.tasks.vision.PoseLandmarkerOptions
+VisionRunningMode = mp.tasks.vision.RunningMode
+
+global_predictor = None
+face_landmarker = None
+pose_landmarker = None
+
+# For auto-segmentation
+layerMapping = {
+    "L_eye": {
+        "useMiddle": False,
+        "positiveOffsetX": 0,
+        "positiveOffsetY": 0,
+        "negativeOffsetX": 0,
+        "negativeOffsetY": 0,
+        "positiveScale": 0,
+        "negativeScale": 0.5,
+        "indices": mp.solutions.face_mesh.FACEMESH_LEFT_EYE,
+    },
+    "R_eye": {
+        "useMiddle": False,
+        "positiveOffsetX": 0,
+        "positiveOffsetY": 0,
+        "negativeOffsetX": 0,
+        "negativeOffsetY": 0,
+        "positiveScale": 0,
+        "negativeScale": 0.5,
+        "indices": mp.solutions.face_mesh.FACEMESH_RIGHT_EYE,
+    },
+    "L_iris": {
+        "useMiddle": False,
+        "positiveOffsetX": 0,
+        "positiveOffsetY": 0,
+        "negativeOffsetX": 0,
+        "negativeOffsetY": 0,
+        "positiveScale": -0.2,
+        "negativeScale": 0.5,
+        "indices": mp.solutions.face_mesh.FACEMESH_LEFT_IRIS,
+    },
+    "R_iris": {
+        "useMiddle": False,
+        "positiveOffsetX": 0,
+        "positiveOffsetY": 0,
+        "negativeOffsetX": 0,
+        "negativeOffsetY": 0,
+        "positiveScale": -0.2,
+        "negativeScale": 0.5,
+        "indices": mp.solutions.face_mesh.FACEMESH_RIGHT_IRIS,
+    },
+    "face": {
+        "useMiddle": False,
+        "positiveOffsetX": 0,
+        "positiveOffsetY": 40,
+        "negativeOffsetX": 0,
+        "negativeOffsetY": 60,
+        "positiveScale": 0.2,
+        "negativeScale": 0.6,
+        "indices": mp.solutions.face_mesh.FACEMESH_FACE_OVAL,
+    },
+    "mouth": {
+        "useMiddle": False,
+        "positiveOffsetX": 0,
+        "positiveOffsetY": 0,
+        "negativeOffsetX": 0,
+        "negativeOffsetY": 0,
+        "positiveScale": -0.3,
+        "negativeScale": 0.3,
+        # https://stackoverflow.com/questions/66649492/how-to-get-specific-landmark-of-face-like-lips-or-eyes-using-tensorflow-js-face
+        "indices": [[x, x] for x in [61, 37, 270, 91, 314]],
+    },
+    "mouth_in": {
+        "useMiddle": False,
+        "positiveOffsetX": 0,
+        "positiveOffsetY": 0,
+        "negativeOffsetX": 0,
+        "negativeOffsetY": 0,
+        "positiveScale": -0.5,
+        "negativeScale": 0.5,
+        # https://stackoverflow.com/questions/66649492/how-to-get-specific-landmark-of-face-like-lips-or-eyes-using-tensorflow-js-face
+        "indices": [[x, x] for x in [310, 88]],
+    },
+}
+
+
+class SAMMultiLayer:
+    def __init__(self):
+        self.predictor = None
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "ckpt": (folder_paths.get_filename_list("sams"),),
+                "embedding_id": (
+                    "STRING",
+                    {"multiline": False, "default": "embedding"},
+                ),
+                "image_prompts_json": ("STRING", {"multiline": False, "default": "[]"}),
+            },
+        }
+
+    CATEGORY = "image"
+
+    RETURN_TYPES = ("SAM_PROMPT",)
+    FUNCTION = "load_image"
+
+    def load_models(self, ckpt, model_type):
+        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")
+        global_predictor = SamPredictor(sam)
+
+        face_landmarker_model_path = os.path.join(
+            os.path.dirname(__file__), "../mediapipe_models/face_landmarker.task"
+        )
+        face_landmarker_options = FaceLandmarkerOptions(
+            base_options=BaseOptions(model_asset_path=face_landmarker_model_path),
+            running_mode=VisionRunningMode.IMAGE,
+        )
+        face_landmarker = FaceLandmarker.create_from_options(face_landmarker_options)
+
+        pose_landmarker_model_path = os.path.join(
+            os.path.dirname(__file__), "../mediapipe_models/pose_landmarker_full.task"
+        )
+        pose_landmarker_options = PoseLandmarkerOptions(
+            base_options=BaseOptions(model_asset_path=pose_landmarker_model_path),
+            running_mode=VisionRunningMode.IMAGE,
+        )
+        pose_landmarker = PoseLandmarker.create_from_options(pose_landmarker_options)
+        return global_predictor, face_landmarker, pose_landmarker
+
+    def auto_segment(self, image, face_landmarks, pose_landmarks):
+        H, W, C = image.shape
+        imagePromptsMulti = {}
+        boxesMulti = {}
+
+        for key, value in layerMapping.items():
+            positivePoints = []
+            middlePoints = []
+            negativePoints = []
+
+            for index in value["indices"]:
+                start, end = index
+                startPoint = face_landmarks[start]
+
+                startX = startPoint.x * W
+                startY = startPoint.y * H
+
+                if len(middlePoints) == 0:
+                    middlePoints.append({"x": startX, "y": startY, "label": 1})
+                else:
+                    middlePoints[0]["x"] += startX
+                    middlePoints[0]["y"] += startY
+
+                positivePoints.append({"x": startX, "y": startY, "label": 1})
+
+            len_indices = len(value["indices"])
+            middlePoints[0]["x"] /= len_indices
+            middlePoints[0]["y"] /= len_indices
+
+            if value["useMiddle"]:
+                imagePromptsMulti[key] = middlePoints
+            else:
+                for i, index in enumerate(value["indices"]):
+                    start, end = index
+                    startPoint = face_landmarks[start]
+
+                    startX = startPoint.x * W
+                    startY = startPoint.y * H
+
+                    middlePoint = middlePoints[0]
+                    directionVector = {
+                        "x": middlePoint["x"] - startX,
+                        "y": middlePoint["y"] - startY,
+                    }
+                    directionVectorLength = sqrt(
+                        directionVector["x"] * directionVector["x"]
+                        + directionVector["y"] * directionVector["y"]
+                    )
+
+                    if value["negativeScale"] != 0:
+                        negativePointDistance = (
+                            value["negativeScale"] * directionVectorLength
+                        )
+                        negativePoint = {
+                            "x": startX
+                            - (negativePointDistance * directionVector["x"])
+                            / directionVectorLength
+                            - value["negativeOffsetX"],
+                            "y": startY
+                            - (negativePointDistance * directionVector["y"])
+                            / directionVectorLength
+                            - value["negativeOffsetY"],
+                            "label": 0,
+                        }
+                        negativePoints.append(negativePoint)
+
+                    positivePointDistance = (
+                        value["positiveScale"] * directionVectorLength
+                    )
+                    positivePoints[i] = {
+                        "x": positivePoints[i]["x"]
+                        - (positivePointDistance * directionVector["x"])
+                        / directionVectorLength
+                        - value["positiveOffsetX"],
+                        "y": positivePoints[i]["y"]
+                        - (positivePointDistance * directionVector["y"])
+                        / directionVectorLength
+                        - value["positiveOffsetY"],
+                        "label": 1,
+                    }
+
+                imagePromptsMulti[key] = positivePoints + negativePoints
+
+            points = negativePoints if len(negativePoints) > 0 else positivePoints
+            box = np.array(
+                [
+                    min(x["x"] for x in points),
+                    min(x["y"] for x in points),
+                    max(x["x"] for x in points),
+                    max(x["y"] for x in points),
+                ]
+            )
+            boxesMulti[key] = box
+
+            if pose_landmarks is not None:
+                positiveBreathX = (
+                    (pose_landmarks[11].x + pose_landmarks[12].x) / 2
+                ) * W
+                positiveBreathY = (
+                    (pose_landmarks[11].y + pose_landmarks[12].y) / 2
+                ) * H
+                negativeBreathX1 = pose_landmarks[0].x * W
+                negativeBreathY1 = pose_landmarks[0].y * H
+                negativeBreathX2 = pose_landmarks[9].x * W
+                negativeBreathY2 = pose_landmarks[9].y * H
+                negativeBreathX3 = pose_landmarks[10].x * W
+                negativeBreathY3 = pose_landmarks[10].y * H
+                imagePromptsMulti["breath"] = [
+                    {"x": positiveBreathX, "y": positiveBreathY, "label": 1},
+                    {"x": negativeBreathX1, "y": negativeBreathY1, "label": 0},
+                    {"x": negativeBreathX2, "y": negativeBreathY2, "label": 0},
+                    {"x": negativeBreathX3, "y": negativeBreathY3, "label": 0},
+                ]
+
+        return imagePromptsMulti, boxesMulti
+
+    def detect_face(self, np_image):
+        global face_landmarker, pose_landmarker
+        mp_image = mp.Image(
+            image_format=mp.ImageFormat.SRGB, data=(np_image * 255).astype(np.uint8)
+        )
+        face_landmarks = face_landmarker.detect(mp_image).face_landmarks
+        if len(face_landmarks) == 0:
+            print("Warning: no face detected")
+            return None, None
+
+        pose_landmarks = pose_landmarker.detect(mp_image).pose_landmarks
+        if len(pose_landmarks) == 0:
+            print("Warning: no pose detected")
+            return None, None
+
+        imagePromptsMulti, boxesMulti = self.auto_segment(
+            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"
+        if os.path.exists(order_file):
+            # Frontend uploads segments images to backend => backend reads all segments images and passes them to next nodes
+            with open(order_file) as f:
+                order = json.load(f)
+
+            result = [image_prompts]
+
+            for segment in order:
+                image = Image.open(
+                    f"{self.output_dir}/segments_{embedding_id}/{segment}.png"
+                )
+                image = np.array(image).astype(np.float32) / 255.0
+                image = torch.from_numpy(image)[None,]
+                result.append(image)
+
+            return result
+        else:
+            # Frontend uploads clicks coordinates to backend => backend runs SAM and passes the segments to next nodes
+            model_type = re.findall(r"vit_[lbh]", ckpt)[0]
+
+            global global_predictor
+            if global_predictor is None:
+                global_predictor, _, _ = self.load_models(ckpt, model_type)
+
+            if image.shape[3] == 4:
+                image = image[:, :, :, :3]
+
+            emb_filename = f"{self.output_dir}/{embedding_id}_{model_type}.npy"
+            if not os.path.exists(emb_filename):
+                image_np = (image[0].numpy() * 255).astype(np.uint8)
+                global_predictor.set_image(image_np)
+                emb = global_predictor.get_image_embedding().cpu().numpy()
+                np.save(emb_filename, emb)
+
+                with open(
+                    f"{self.output_dir}/{embedding_id}_{model_type}.json", "w"
+                ) as f:
+                    data = {
+                        "input_size": global_predictor.input_size,
+                        "original_size": global_predictor.original_size,
+                    }
+                    json.dump(data, f)
+            else:
+                emb = np.load(emb_filename)
+
+                with open(f"{self.output_dir}/{embedding_id}_{model_type}.json") as f:
+                    data = json.load(f)
+                    global_predictor.input_size = data["input_size"]
+                    global_predictor.features = torch.from_numpy(emb)
+                    global_predictor.is_image_set = True
+                    global_predictor.original_size = data["original_size"]
+
+            imagePromptsMulti, boxesMulti = self.detect_face(image[0].numpy())
+
+            image_prompts = json.loads(image_prompts_json.replace("'", '"'))
+            result = [image_prompts]  # use imagePromptsMulti
+
+            if isinstance(image_prompts, list):
+                pass
+            elif all(isinstance(item, list) for item in image_prompts.values()):
+                for key, item in image_prompts.items():
+                    if len(item) == 0:
+                        h, w, c = image[0].shape
+                        result.append(torch.zeros(1, h, w, c))
+                        continue
+
+                    points = (
+                        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])
+
+                    masks, _, _ = global_predictor.predict(
+                        point_coords=point_coords,
+                        point_labels=point_labels,
+                        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")
+                    out_image = repeat(masks, "1 h w -> 1 h w c", c=3) * image
+                    result.append(out_image)
+            return result
+
+
+NODE_CLASS_MAPPINGS = {"SAM MultiLayer": SAMMultiLayer}
+
+NODE_DISPLAY_NAME_MAPPINGS = {"SAM MultiLayer": "SAM MultiLayer"}

sam/sam_node_from_emb.py

@@ -1,110 +0,0 @@
-import torch
-from einops import rearrange, repeat
-import numpy as np
-import cv2
-
-class SAM_Predict:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "embeddings": ("EMBEDDINGS",),
-                "predictor": ("SAMPREDICTOR",),
-            },
-            "optional": {
-                "prompt": ("SAM_PROMPT", ),
-                "mask": ("IMAGE",),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE","IMAGE", "IMAGE")
-    RETURN_NAMES = ("image","out_image", "mask")
-
-    FUNCTION = "segment"
-
-    CATEGORY = "image"
-
-    def segment(self, image, embeddings, predictor, prompt, mask=None):
-        image_embedding_list = embeddings['image_embedding']
-        shape = tuple(embeddings['shape'])
-        input_size = tuple(embeddings['input_size'])
-
-        # Convert the list back to a numpy array with the original shape
-        image_embedding_np = np.array(image_embedding_list, dtype=np.single).reshape(shape)
-
-        # Convert the numpy array to a PyTorch tensor
-        image_embedding_tensor = torch.from_numpy(image_embedding_np)
-
-        # Set the image embeddings for the model
-        # predictor.set_torch_image(image_embedding_tensor, image.shape[:2])
-        # print(image[0].shape[:2])
-        predictor.input_size = input_size
-        predictor.features = image_embedding_tensor
-        predictor.is_image_set = True
-        predictor.original_size = image[0].shape[:2]
-
-        # prompt = [{"x":364,"y":153,"label":1},{"x":296,"y":189,"label":1},{"x":277,"y":246,"label":1}]
-
-        # if point_1 != None:
-            # x, y, z = point_1
-
-        # point_coords = np.array([[x, y]])
-        # point_labels = np.array([1])
-
-        if prompt == None or len(prompt) == 0:
-            return (image, image, None)
-
-        point_coords = np.array([[p['x'], p['y']] for p in prompt])
-        point_labels = np.array([p['label'] for p in prompt])
-
-        masks, iou_predictions, low_res_masks = predictor.predict(
-            point_coords=point_coords,
-            point_labels=point_labels,
-        )
-
-        if mask != None:
-            # scale the mask to 256x256
-            cv2_mask = cv2.resize(np.array(mask[0]), (256, 256))
-            cv2_mask = cv2_mask[np.newaxis, :, :]
-            cv2_mask = (cv2_mask * 255).astype(int)
-
-            true_locations = np.array(np.where(cv2_mask[0] == 255))
-            if true_locations.shape[1] > 0:
-                # Randomly select a point in the mask
-                rand_index = np.random.randint(true_locations.shape[1])
-                y, x = true_locations[:, rand_index]
-                point_coords = np.array([[x, y]])
-                point_labels = np.array([1])
-
-                masks, iou_predictions, low_res_masks = predictor.predict(
-                    point_coords=point_coords,
-                    point_labels=point_labels,
-                    mask_input=cv2_mask
-                )
-            else:
-                # No detected mask
-                h, w, c = image[0].shape
-                masks = np.zeros((1, h, w))
-
-        masks = torch.from_numpy(masks)
-
-        masks = rearrange(masks[0], 'h w -> 1 h w')
-        # masks = rearrange(masks, 'c h w -> 1 c h w')
-        out_image = repeat(masks, '1 h w -> 1 h w c', c=3) * image
-
-        print(masks.shape, torch.max(masks), torch.min(masks))
-        print(image.shape, torch.max(image), torch.min(image))
-
-        # print(emb)
-
-        # print(masks, out_image)
-        return (image, out_image, masks)
-
-NODE_CLASS_MAPPINGS = {
-    "SAM_Predict": SAM_Predict
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "SAM_Predict": "SAM Predict"
-}

sam/sam_node_remote.py

@@ -1,88 +0,0 @@
-import requests
-from PIL import Image
-import io
-import numpy as np
-import folder_paths
-
-class SAM_Embedding:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "embedding_id": ("STRING", {
-                    "multiline": False,
-                    "default": "embedding"
-                }),
-            },
-            "optional": {
-                "predictor": ("SAMPREDICTOR",)
-            }
-        }
-
-    RETURN_TYPES = ("EMBEDDINGS", )
-    RETURN_NAMES = ("embeddings", )
-
-    FUNCTION = "segment"
-
-    CATEGORY = "image"
-
-    def segment(self, image, embedding_id, predictor=None):
-        # Convert PyTorch tensor to numpy array
-        image_np = (image[0].numpy() * 255).astype(np.uint8)
-        
-        if predictor != None:
-            predictor.set_image(image_np)
-            emb = predictor.get_image_embedding().cpu().numpy()
-            output = {
-                "image_embedding": emb,
-                "shape": emb.shape,
-                "input_size": predictor.input_size
-            }
-        else:
-            # Convert numpy array to PIL Image
-            img = Image.fromarray(image_np)
-
-            # Create an in-memory bytes buffer
-            img_byte_arr = io.BytesIO()
-
-            # Save the PIL Image to the bytes buffer in PNG format
-            img.save(img_byte_arr, format='PNG')
-
-            # Get the bytes value of the buffer
-            img_byte_arr = img_byte_arr.getvalue()
-
-            # Create a dictionary with the image bytes
-            files = {'image': ('image.png', img_byte_arr)}
-
-            # Send the POST request
-            response = requests.post('https://avatechgg--segment-anything-entrypoint.modal.run', files=files)
-
-            # Check if the request was successful
-            if response.status_code == 200:
-                # Parse the JSON response
-                output = response.json()
-            else:
-                print(f"Request failed with status code {response.status_code}")
-                output = None
-
-            # sam = sam_model_registry[model_type](checkpoint=ckpt)
-            # predictor = SamPredictor(sam)
-            # masks = predictor.set_torch_image
-            # masks = predictor.predict
-
-            # print(output)
-
-        np.save(f"{self.output_dir}/{embedding_id}.npy", output["image_embedding"])
-        return (output, )
-
-NODE_CLASS_MAPPINGS = {
-    "SAM_Embedding": SAM_Embedding
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "SAM_Embedding": "SAM Embedding"
-}

sam/sam_prompt.py

@@ -1,67 +0,0 @@
-import folder_paths
-import os
-import numpy as np
-from segment_anything import sam_model_registry, SamPredictor
-
-
-class SAM_Prompt_Image:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-
-    @classmethod
-    def INPUT_TYPES(s):
-        input_dir = folder_paths.get_input_directory()
-        files = [
-            f
-            for f in os.listdir(input_dir)
-            if os.path.isfile(os.path.join(input_dir, f))
-        ]
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "model_type": (["vit_h", "vit_l", "vit_b"],),
-                "ckpt": (folder_paths.get_filename_list("sam"),),
-                "embedding_id": (
-                    "STRING",
-                    {"multiline": False, "default": "embedding"},
-                ),
-                # "image": (sorted(files), ),
-                "image_prompts_json": ("STRING", {"multiline": False, "default": "[]"}),
-            },
-        }
-
-    CATEGORY = "image"
-
-    RETURN_TYPES = ("SAM_PROMPT",)
-    FUNCTION = "load_image"
-
-    def load_image(self, image, model_type, ckpt, embedding_id, image_prompts_json):
-        import json
-
-        emb_filename = f"{self.output_dir}/{embedding_id}.npy"
-        if not os.path.exists(emb_filename):
-            ckpt = folder_paths.get_full_path("sam", ckpt)
-            sam = sam_model_registry[model_type](checkpoint=ckpt)
-            predictor = SamPredictor(sam)
-
-            image_np = (image[0].numpy() * 255).astype(np.uint8)
-            predictor.set_image(image_np)
-            emb = predictor.get_image_embedding().cpu().numpy()
-            np.save(emb_filename, emb)
-
-        image_prompts = json.loads(image_prompts_json)
-
-        result = (image_prompts,)
-
-        if isinstance(image_prompts, list):
-            pass
-        elif all(isinstance(item, list) for item in image_prompts.values()):
-            for item in image_prompts.values():
-                result += (item,)
-
-        return result
-
-
-NODE_CLASS_MAPPINGS = {"SAM_Prompt_Image": SAM_Prompt_Image}
-
-NODE_DISPLAY_NAME_MAPPINGS = {"SAM_Prompt_Image": "SAM_Prompt_Image "}

sam/sam_save_emb.py

@@ -1,64 +0,0 @@
-import folder_paths
-import torch
-import os
-from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
-from einops import rearrange, repeat
-
-class SAM_Save_Embedding:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
-    
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "embeddings": ("EMBEDDINGS",),
-                "filename": ("STRING", {
-                    "multiline": False,
-                    "default": "embeddings"
-                }),
-                "write_mode": (["Overwrite", "Increment"],),
-            }
-        }
-
-    RETURN_TYPES = ()
-    RETURN_NAMES = ()
-
-    OUTPUT_NODE = True
-
-    FUNCTION = "process"
-
-    CATEGORY = "image"
-
-    def process(self, embeddings, filename, write_mode):
-        import json
-        import numpy as np
-
-        filepath = self.output_dir + "/" + filename + '.json'
-
-        if write_mode == "Increment":
-            count = 0
-            # while file exists, increment count
-            while os.path.exists(self.output_dir + "/" + filename + '_' + str(count) + '.json'):
-                count += 1
-
-            filepath = self.output_dir + "/" + filename + '_' + str(count) + '.json'
-
-        # print(embeddings)
-        if isinstance(embeddings['image_embedding'], np.ndarray):
-            embeddings['image_embedding'] = embeddings['image_embedding'].tolist()
-        with open(filepath, 'w') as f:
-            json.dump(embeddings, f)
-        
-        return { "ui" : { "file": { filepath.replace(f"{self.output_dir}/", "") } } }
-
-
-NODE_CLASS_MAPPINGS = {
-    "SAM_Save_Embedding": SAM_Save_Embedding
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "SAM_Save_Embedding": "SAM_Save_Embedding "
-}

style-watcher.js

@@ -1,24 +0,0 @@
-const fs = require('fs');
-const chokidar = require('chokidar');
-const path = require('path');
-
-// Initialize watcher.
-const watcher = chokidar.watch(path.resolve(__dirname, './js/output.css'), {
-  persistent: true
-});
-
-// Add event listeners.
-watcher
-  .on('change', filePath => {
-    console.log(`File ${filePath} has been changed`);
-    let cssContent = fs.readFileSync(filePath, 'utf8');
-    let jsContent = `let style = document.createElement('style');
-style.innerHTML = \`${cssContent.replace(/\\/g, '\\\\')}\`;
-document.head.appendChild(style);`;
-    fs.writeFileSync(path.resolve(__dirname, './js/style-inject.js'), jsContent);
-  });
-
-// On script exit, close the watcher
-process.on('exit', (code) => {
-  watcher.close();
-});

tailwind.config.js

@@ -3,6 +3,9 @@ module.exports = {
   // content: ['./js/**/*.{html,js}'],
   content: ['./js/**/*.{html,js}'],
   theme: {
+    fontFamily: {
+      'gabarito': ['Gabarito'],
+    },
     extend: {},
   },
   daisyui: {