Update README.md

2023-10-05 11:17:00 +08:00 · 2023-10-04 19:41:15 +08:00 · 2023-10-04 19:32:58 +08:00 · 2023-10-04 19:15:06 +08:00 · 2023-10-04 19:03:13 +08:00 · 2023-10-04 19:02:03 +08:00
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 # 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
@@ -2,168 +2,211 @@

 ![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) |
+| <img src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/12e2bfc6-438e-4d16-bead-9957ced3bae1" width="190"/><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="190"/><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="190"/><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="190"/><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="190"/><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="190"/><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="190"/><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="190"/><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)
+# Contents
+- [Workflow Template](workflow-template)
+    - [Template01 - Simple Shape Flow](#template01---simple-shape-flow)
+- [Image Preprocess Guide](#image-preprocess-guide)
+    - [Character Gen Prompting Guide](#character-gen-prompting-guide)
+    - [Mouth Open Guide (Inpaint)](#mouth-open-guide-inpaint)
+- [Custom Nodes](custom-nodes)
+    - [Image Segmentation Nodes](#image-segmentation-nodes)
+    - [Mesh Edit Nodes](#mesh-edit-nodes)
+    - [Shape Keys Nodes](#shape-keys-nodes)
+    - [Avatar Output Nodes](#avatar-output-nodes)
 - [Shape Flow](#shape-flow)
 - [Installation](#installation)
 - [Development](#development)
- [Join Discord 💬](https://discord.gg/WNtBYksDwF)

-# Basic Rigging Workflow Template
+# Workflow Template

-### 1. Creating an eye blink and lipsync avatar
+## Template01 - Simple Shape Flow
+To enable the character to blink eyes and talking.

-![ComfyUI_00668_](https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/72a0abe8-2482-4a0d-8436-8eb231fd2f6d)
+> **🎯Notice**
+> 
+> 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.

-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.
+[💡Generate new image Guide](#character-gen-prompting-guide)

-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)
+[💡Make your character mouth open Guide](#mouth-open-guide-inpaint)

-### 2. Creating an eye blink and lipsync emoji avatar
+![eye+mouth movement](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270634138-8a237b9d-05fc-4e4a-b802-6465911f0d77.png)

-| ![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: 📂[Template01 - Simple Shape Flow](https://github.com/avatechai/avatar-graph-comfyui/blob/main/workflow_templates/SimpleEye+MouthMovement.json)
+### Download: 📂[Template01 - ControlNet Gen](https://github.com/avatechai/avatar-graph-comfyui/tree/main/workflow_templates/TemplateGen01)
+_If you don't want to modify any values in the custom nodes, you can download the ControlNet Gen Template to generate your own image._

-Download: Save the image, and drag into Comfyui
+<details> 
+<summary> Template01 - Nodes Value Setting Guide </summary>

-| ![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) | 
-| :--: | :--: |
+## Template01 - Nodes Value Setting Guide

-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"/>
+> ### Basic Eyeblink & Talking
+> 1. Click **[Segmentation (SAM)]/ Edit prompt** button
+>    
+> 2. Add new layer and rename
+>
+> 3. Drag layer to **[Create Mesh Layer]/image**
+> 
+> 4. **[Create Mesh Layer]/ face_threshold, shape_threshold**, To control mesh threshold, recommend value: 0.6~0.7
+>    
+> 5. **[Create Mesh Layer]/ scale_x, scale_y, extrude_x, extrude_y**, To control mesh threshold, recommend value: 1.2~1.4
+>
+> 6. **[Modify Shape Key]/ rotate** Setting Reference, If Head tilted to the left, set a positive number angle 
+> 
+> |  <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/271264869-abf2a843-8ca5-44a6-9611-c334d55928d1.png" width="300"> |  <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/271264902-37658a8e-6f46-4c5b-bfd6-adec270df60b.png" width="300"> |  <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/271264910-0fae0c27-428d-4a5d-8296-6634c9717b95.png" width="300"> |  <img src="https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/271264920-4fea7882-cc51-4a5a-af9a-e66589810f92.png" width="300"> |
+> | --- | --- | --- | --- |
+> | 0 | 5 | -5 | -15 |

 </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)
+<details> 
+    
+<summary> Template01 - ControlNet Gen Guide </summary>

 Place normal and openpose image with reference to images.

-Download: [ControlNet Gen](https://github.com/avatechai/avatar-graph-comfyui/tree/main/workflow_templates/TemplateGen01)
+![image](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270943267-c3cae113-2df4-45f2-a19c-885cbee75450.png)
+    
+</details>


-# Recommend Checkpoint Model List
+    
+# Image Preprocess Guide

-##### Anime Style SD1.5
+### 💡If you want to generate a new character image
+> you can download this Template and refer to the Guide!
+> <details> 
+> <summary> Character Gen Prompting Guide </summary>
+>
+> # Character Gen Prompting Guide
+>> **🎯Notice**
+>> 
+>> 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```
+>  
+>![image](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270609114-acea9933-359b-4398-8d2a-582bf02bef99.png)
+> 
+> ### Download: 📂[Character Gen Template](https://github.com/avatechai/avatar-graph-comfyui/blob/main/workflow_templates/SimpleCharacterGen.json)
+> Feel free to change any checkpoint model that suits your needs.
+>  
+> </details>

- 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
+### 💡If you have a character image but it's not mouth open
+> you can download this Template and refer to the Guide!
+> <details> 
+> <summary> Mouth Open Guide (Inpaint) </summary>
+>
+> # Mouth Open Guide (Inpaint)
+> To maintain consistency with the base image, it is recommended to utilize a checkpoint model that aligns with its style.
+>
+> ![inpaint_workflow](https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/public-download/github-readme/270589181-d11d840b-7ea6-4b47-bc26-a2af7c8c27a5.png)
+>
+> ### Download: 📂[MouthOpen Template](https://github.com/avatechai/avatar-graph-comfyui/blob/main/workflow_templates/MouthOpen_(inpaint).json)
+>
+> ### Inpaint Demonstration 
+>
+> <video src="https://github.com/avatechai/avatar-graph-comfyui/assets/73209427/e3b77295-a1bf-4d96-9551-7cc423a4af73"/>
+> 
+> ### Recommend Checkpoint Model List 
+> 
+> ##### Anime Style SD1.5
+>- https://civitai.com/models/35960/flat-2d-animerge
+>- https://civitai.com/models/24149/mistoonanime
+>- https://civitai.com/models/22364/kizuki-anime-hentai-checkpoint
+>##### 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
+> 
+> </details>

 # Custom Nodes
-
-Expand to see all the available nodes description.
+Expand to see all the available nodes description
+Mesh Edit Nodes
+Shape Keys Nodes
+Avatar Output Nodes

 <details>
-<summary> All Custom Nodes </summary>
+<summary> Image Segmentation 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"> |
+## Image Segmentation Nodes
+| 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"> |

 </details>

-# Shape Flow
+<details>
+<summary> Mesh Edit Nodes </summary>

+## Mesh Edit Nodes
+
+| 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"> |
+
+
+</details>
+
+<details>
+<summary> Shape Keys Nodes </summary>
+
+## Shape Keys Nodes
+| 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"> |
+
+</details>
+
+<details>
+<summary> Avatar Output Nodes </summary>
+
+## Avatar Output Nodes
+| Name                         | Description                                                                                           | Preview                                                                                                                        |
+| ---------------------------- | ----------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
+| `Avatar Main Output`         | The primary output of the .ava file. The embeded 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`
+Clone the repository to custom_nodes in your [ComfyUI](https://github.com/comfyanonymous/ComfyUI) directory:

 1. `cd custom_nodes`

 2. `git clone https://github.com/avatechgg/avatar-graph-comfyui.git`

-3. `cd avatar-graph-comfyui && python -m pip install -r requirements.txt`
+3. Install deps `cd avatar-graph-comfyui && python -m pip install -r requirements.txt`

-4. Restart ComfyUI with enable-cors-header `python main.py --enable-cors-header` or (for mac) `python main.py --force-fp16 --enable-cors-header`
+4. Restart comfyui
+
+5. 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
+<details>
+<summary> If you are interested in contributing expand to see development details </summary>
+

 For comfyui frontend extension, frontend js located at `avatar-graph-comfyui/js`

@@ -193,21 +236,9 @@ For each changes, simply refresh the comfyui page to see the changes.
    ]
 }
 ```
+
 </details>

-## Update blender node types
+</details>

-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.
@@ -43,29 +43,7 @@ def append_to_sys_path(path):
    if path not in sys.path:
        sys.path.append(path)

-
-folder_paths.folder_names_and_paths["sams"] = (
-    [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)
-
+folder_paths.folder_names_and_paths["sams"] = ([os.path.join(folder_paths.models_dir, "sams")], folder_paths.supported_pt_extensions)

 def download_sam_model():
    model_dir = get_folder_paths("sams")[0]
@@ -75,35 +53,27 @@ def download_sam_model():
    add_model_folder_path("sams", model_dir)

    files = get_filename_list("sams")
-    if "sam_vit_h_4b8939.pth" not in files:
+    if len(files) == 0:
        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")
+        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(f"{model_dir}/sam_vit_h_4b8939.pth", "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)


 download_sam_model()

-
-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 = []

@@ -1,6 +1,6 @@
 import platform
 import blender_node
-from mesh_utils import upload_avatar_file, open_in_blender as open_blender, export_gltf
+from mesh_utils import assign_texture, open_in_blender as open_blender, export_gltf
 import folder_paths

 global_blender_path = ''
@@ -47,16 +47,14 @@ class AvatarMainOutput(blender_node.ObjectOps):
        }),
        "model_type": (["AVA","GLB", "GLTF_EMBEDDED"],),
        "write_mode": (["Overwrite", "Increment"],),
-        "upload_to_cloud": ("BOOLEAN", {
-            "default": False
-        }),
+
        "SHAPE_FLOW": ("SHAPE_FLOW",),
    }

    OUTPUT_NODE = True
    RETURN_TYPES = ()

-    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=''):
+    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='', 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'
@@ -74,24 +72,4 @@ class AvatarMainOutput(blender_node.ObjectOps):
        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
-        }
+        return {"ui": {"gltfFilename": {filepath.replace(f"{self.output_dir}/", "")}, "SHAPE_FLOW": {SHAPE_FLOW}, "auto_save": {'true' if auto_save else 'false'},}}
@@ -5,17 +5,17 @@ class VECTOR3D:
            "required": {
                "x": ("FLOAT", {
                    "default": 0,
-                    "step": 0.01,
+                    "step": 0.1,
                    "display": "number"
                }),
                "y": ("FLOAT", {
                    "default": 0,
-                    "step": 0.01,
+                    "step": 0.1,
                    "display": "number"
                }),
                "z": ("FLOAT", {
                    "default": 0,
-                    "step": 0.01,
+                    "step": 0.1,
                    "display": "number"
                }),
            },
@@ -5,17 +5,17 @@ class VECTOR4D:
            "required": {
                "x": ("FLOAT", {
                    "default": 0,
-                    "step":  0.01,
+                    "step": 0.1,
                    "display": "number"
                }),
                "y": ("FLOAT", {
                    "default": 0,
-                    "step":  0.01,
+                    "step": 0.1,
                    "display": "number"
                }),
                "z": ("FLOAT", {
                    "default": 0,
-                    "step":  0.01,
+                    "step": 0.1,
                    "display": "number"
                }),
                "u": ("FLOAT", {
@@ -1,7 +1,6 @@
 import inspect
 import re
 import json
-import os

 BPY_OBJS = "BPY_OBJS"
 BPY_OBJ = "BPY_OBJ"
@@ -11,13 +10,12 @@ BPY_OBJS_TYPE = {
 }

 node_input_types = {}
-with open(f"{os.path.dirname(__file__)}/input_types.txt") as f:
+with open("custom_nodes/avatar-graph-comfyui/blender/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
@@ -49,29 +47,22 @@ class ObjectOps:

    @classmethod
    def INPUT_TYPES(cls):
-        if type_generation:
-            import global_bpy
+        return node_input_types[cls.__name__]

-            bpy = global_bpy.get_bpy()
-            result = {
-                "required": {},
-                "optional": {
-                    **cls.get_base_input_types(bpy),
-                    **cls.get_extra_input_types(bpy),
-                },
-            }
+        # import global_bpy
+        # bpy = global_bpy.get_bpy()
+        # result = {
+        #     "required": {},
+        #     "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),
-                },
-            }
+        # with open("input_types.txt", "a") as f:
+        #     f.write(cls.__name__ + "|" + json.dumps(result) + "\n")
+
+        # return result

    @classmethod
    def NODE_CLASS_MAPPINGS(cls):
@@ -100,14 +91,14 @@ class ObjectOps:
        import global_bpy
        bpy = global_bpy.get_bpy()

-        if props.get("BPY_OBJ") is not None:
+        if props.get("BPY_OBJ") != None:
            bpy.context.view_layer.objects.active = props["BPY_OBJ"]

        results = self.blender_process(bpy, **props)

        if results is None:
            # print(results)
-            if props.get("BPY_OBJ") is not None:
+            if props.get("BPY_OBJ") != None:
                return (props["BPY_OBJ"], )
            else:
                return (bpy.context.view_layer.objects.active, )
@@ -260,8 +251,7 @@ def create_primitive_shape_class(cls, path, name=None, name_prefix=''):


 def assign_and_return(BPY_OBJ, name, value):
-    setattr(BPY_OBJ, name, value)
-    # BPY_OBJ[name] = value
+    BPY_OBJ[name] = value
    # print(BPY_OBJ,name, BPY_OBJ[name])
    return None

@@ -1,32 +0,0 @@
-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"
-}
@@ -1,3 +1,17 @@
+Object_VertexGroupNewWithName|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "name": ["STRING", {"multiline": false, "default": "Group"}], "assign_selected": ["BOOLEAN", {"default": true}]}}
+EditOps|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+ContextSet_TransformPivotPoint|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "pivot": [["BOUNDING_BOX_CENTER", "CURSOR", "INDIVIDUAL_ORIGINS", "MEDIAN_POINT", "ACTIVE_ELEMENT"]]}}
+Object_AddShapeKeys|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "shape_keys": ["STRING", {"default": "key1,key2", "multiline": true}], "from_mix": ["BOOLEAN", {"default": false}]}}
+Object_MeshFromTexture|{"required": {}, "optional": {"image": ["IMAGE"], "seed": ["INT", {"default": 0, "min": 0, "max": 18446744073709551615}]}}
+Object_MatchTextureAspectRatio|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "image": ["IMAGE"], "scale": ["FLOAT", {"default": 0.001, "display": "number", "step": 0.001}]}}
+AssignVertexGroupOps|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "name": ["STRING", {"multiline": false, "default": "Group"}]}}
+Object_VertexGroupNewWithName|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "name": ["STRING", {"multiline": false, "default": "Group"}], "assign_selected": ["BOOLEAN", {"default": true}]}}
+Object_CreateMeshLayer|{"required": {}, "optional": {"image": ["IMAGE"], "convex_hull": ["BOOLEAN", {"default": true}], "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": 18446744073709551615}]}}
+GetImageWidthHeight|{"required": {}, "optional": {"image": ["IMAGE"], "scale": ["FLOAT", {"default": 1.0}]}}
+GetFirstObjOps|{"required": {}, "optional": {"BPY_OBJS": ["BPY_OBJS"]}}
+Object_AssignTexture|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "texture": ["IMAGE"], "texture_name": ["STRING", {"multiline": false, "default": "my_image"}]}}
+Mesh_JoinMesh|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "BPY_OBJ2": ["BPY_OBJ"]}}
+Mesh_ModifyShapeKey|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "shape_key_name": ["STRING", {"multiline": false, "default": "EyeBlinkLeft"}], "target_vertex_group": ["STRING", {"multiline": false, "default": ""}], "scale_x": ["FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}], "scale_y": ["FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}], "offset_x": ["FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}], "offset_y": ["FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}], "rotate": ["FLOAT", {"default": 0, "min": -360, "max": 360.0, "step": 0.01, "display": "number"}], "origin_offset_x": ["FLOAT", {"default": 0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}], "origin_offset_y": ["FLOAT", {"default": 0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}], "transform_radius": ["FLOAT", {"default": 1.0, "min": 0, "max": 1, "step": 0.01, "display": "number"}], "falloff": ["FLOAT", {"default": 0, "min": -10.0, "max": 10.0, "step": 0.01, "display": "number"}]}}
 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}]}}
@@ -593,5 +607,8 @@ UV_SnapCursor|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "target": [[
 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"]}}
+ToGroupOps|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"]}}
+AvatarMainOutput|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "BPY_OBJS": ["BPY_OBJS"], "open_in_blender": ["BOOLEAN", {"default": false}], "auto_save": ["BOOLEAN", {"default": false}], "blender_path_override": ["STRING", {"multiline": false, "default": ""}], "filename": ["STRING", {"multiline": false, "default": "out"}], "model_type": [["AVA", "GLB", "GLTF_EMBEDDED"]], "write_mode": [["Overwrite", "Increment"]], "SHAPE_FLOW": ["SHAPE_FLOW"]}}
+GroupOps|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "BPY_OBJ2": ["BPY_OBJ"]}}
+Object_PlaneTextureUnwrap|{"required": {}, "optional": {"BPY_OBJ": ["BPY_OBJ"], "image": ["IMAGE"], "scale": ["FLOAT", {"default": 1, "display": "number", "step": 0.01}], "texture_name": ["STRING", {"default": "Texture"}]}}
@@ -1,55 +0,0 @@
-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"
-}
@@ -1,9 +1,6 @@
 import atexit
 import subprocess
 import os
-import folder_paths
-import requests
-import json

 def genreate_mesh_from_texture(bpy, image):
    import torch
@@ -17,9 +14,6 @@ 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]

@@ -92,7 +86,7 @@ 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], alpha = True)
+        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:
@@ -124,7 +118,6 @@ 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)
@@ -145,8 +138,6 @@ 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
@@ -245,42 +236,7 @@ def export_gltf(output_dir, bpy_objects, filename, model_type, write_mode, metad
        # print(filepath)
        if filepath.endswith('.ava.glb'):
            new_filepath = filepath.replace('.ava.glb', '.ava')
-            os.replace(filepath, new_filepath)
+            os.rename(filepath, new_filepath)
            filepath = new_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
+    return filepath
@@ -41,7 +41,7 @@ class Object_CreateMeshLayer(blender_node.ObjectOps):
        bpy.ops.mesh.select_all(action='SELECT')
        bpy.ops.mesh.edge_face_add()

-        bpy.ops.transform.resize(value=(float(scale_x), float(scale_y), 1))
+        bpy.ops.transform.resize(value=(scale_x, scale_y, 1))

        bpy.context.object.vertex_groups.new(name=mesh_layer_name)
        bpy.ops.object.vertex_group_assign()
@@ -1,62 +0,0 @@
-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)
@@ -8,6 +8,5 @@ class GroupOps(blender_node.ObjectOps):
    RETURN_TYPES = (blender_node.BPY_OBJS,)

    def blender_process(self, bpy, BPY_OBJ, BPY_OBJ2, **props):
-        prop_values = props.values()
-        return ([BPY_OBJ, BPY_OBJ2, *prop_values],)
+        return ([BPY_OBJ, BPY_OBJ2],)

@@ -11,10 +11,8 @@ class Mesh_JoinMesh(blender_node.ObjectOps):
    def blender_process(self, bpy, BPY_OBJ, **props):
        prop_values = props.values()        
        for obj in list(prop_values) + [BPY_OBJ]:
-            if obj is not None:
-                obj.select_set(True)
-        if bpy.context.view_layer.objects is not None:
-            bpy.context.view_layer.objects.active = BPY_OBJ
+            obj.select_set(True)
+        bpy.context.view_layer.objects.active = BPY_OBJ
        bpy.ops.object.join()

        return (BPY_OBJ,)
@@ -1,24 +0,0 @@
-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)
@@ -1,26 +0,0 @@
-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,)
@@ -1,132 +0,0 @@
-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)
@@ -1,82 +0,0 @@
-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"
-}
@@ -0,0 +1,50 @@
+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"
+}
@@ -1,17 +0,0 @@
-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")
@@ -3,25 +3,8 @@ 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);
@@ -31,12 +14,13 @@ export function Alert() {
  return div(
    {
      class: () =>
-        "absolute z-[100] bottom-8 flex justify-center w-full " +
+        "absolute bottom-8 flex justify-center w-full " +
        (alertDialog.val.text ? "" : "hidden"),
    },
    div(
      {
-        class: () => `${color.val} border-t-4 rounded-sm p-2`,
+        class:
+          "bg-orange-100 border-t-4 border-orange-500 rounded-sm text-orange-700 p-2",
      },
      () => span(alertDialog.val.text)
    )
@@ -1,28 +0,0 @@
-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")
-  );
-}
@@ -1,774 +1,25 @@
 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);
-    }
-  });
-}
+const { button, iframe, div, img } = van.tags;
+import { showEditor, previewUrl } from "./state.js";

 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({
+  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 min-w-[350px] min-h-[350px] z-[100] pointer-events-auto flex border-none overflow-hidden bg-transparent" +
-        (showPreview.val ? "" : "hidden"),
+        "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",
      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);
-  }
-}
@@ -1,146 +0,0 @@
-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"
-            )
-          )
-        )
-      )
-  );
-}
@@ -1,24 +1,21 @@
-import { LayerEditor } from "./LayerEditor.js";
-import { ShapeFlowEditor } from "./ShapeFlowEditor.js";
-import { van } from "./van.js";
-import { AvatarPreview } from "./AvatarPreview.js";
-import { Loading } from "./Loading.js";
-import { Alert } from "./Alert.js";
-import { AppHeader } from "./AppHeader.js";
-import { CombinePointsDialog } from "./CombinePointsDialog.js";
+import { LayerEditor } from './LayerEditor.js';
+import { ShapeFlowEditor } from './ShapeFlowEditor.js';
+import { van } from './van.js';
+import { AvatarPreview } from './AvatarPreview.js';
+import { Loading } from './Loading.js';
+import { Alert } from './Alert.js';
 const { button, iframe, div, img } = van.tags;

 export function Container() {
  return div(
    {
-      class: "fixed left-0 top-0 w-full h-full z-[1000] pointer-events-none",
-      id: "avatech-editor",
+      class: 'fixed left-0 top-0 w-full h-full z-[1000] pointer-events-none',
+      id: 'avatech-editor',
    },
-    CombinePointsDialog(),
    ShapeFlowEditor(),
    LayerEditor(),
    AvatarPreview(),
    Loading(),
-    Alert()
+    Alert(),
  );
 }
@@ -1,29 +0,0 @@
-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"
-      )
-    )
-  );
-}
@@ -1,7 +1,5 @@
 import { SideBar } from "./SideBar.js";
-import { api } from "./api.js";
-import { app } from "./app.js";
-import { runONNX } from "./onnx.js";
+import { initModel, runONNX } from "./onnx.js";
 import {
  showImageEditor,
  point_label,
@@ -13,295 +11,11 @@ import {
  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) {
@@ -312,18 +26,6 @@ export function updateImagePrompts() {

    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);
@@ -331,44 +33,7 @@ export function updateImagePrompts() {
  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) {
+function handleClick(e) {
  const rect = e.target.getBoundingClientRect();
  const x = e.clientX - rect.left;
  const y = e.clientY - rect.top;
@@ -380,28 +45,22 @@ async function handleClick(e) {
      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 },
+    { x: relativeX, y: relativeY, label: e.isRight ? 0 : 1 },
  ];
-  await drawSegment(getClicks());
+
  updateImagePrompts();
+  drawSegment(getClicks());
 }

-async function handlePointClick(e, point) {
+function handlePointClick(e, point) {
  e.preventDefault();
  imagePrompts.val = imagePrompts.val.filter(
    (x) => !(x.x === point.x && x.y === point.y)
  );
-  await drawSegment(getClicks());
  updateImagePrompts();
+  drawSegment(getClicks());
 }

 function handleImageSize(image) {
@@ -415,16 +74,15 @@ function handleImageSize(image) {
  return { height: h, width: w, samScale, imgScale };
 }

-export function getClicks(prompts) {
-  return (prompts || imagePrompts.val).map((point) => ({
+export function getClicks() {
+  return imagePrompts.val.map((point) => ({
    x: point.x,
    y: point.y,
    clickType: point.label,
-    isAuto: point.isAuto,
  }));
 }

-export async function drawSegment(clicks, layer, drawBox = true) {
+export function drawSegment(clicks) {
  const canvas = document.getElementById("mask-canvas");
  const ctx = canvas.getContext("2d");
  if (clicks.length === 0) {
@@ -432,34 +90,19 @@ export async function drawSegment(clicks, layer, drawBox = true) {
    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);
+    runONNX(clicks, embeddings.val).then((mask) => {
+      if (mask) {
+        ctx.clearRect(0, 0, canvas.width, canvas.height);
+        ctx.drawImage(mask, 0, 0);
      }
-    }
+    });
  }
 }

+initModel();
+
 export function LayerEditor() {
  let realTimeSegment = true;
-
-  const showSidebar = van.state(true);
-
  document.addEventListener("keydown", (e) => {
    if (showImageEditor.val && e.code === "Tab") {
      e.preventDefault();
@@ -473,97 +116,29 @@ export function LayerEditor() {
  return div(
    {
      class: () =>
-        "absolute flex bg-gray-900 bg-opacity-50 top-0 w-full h-full pointer-events-auto z-[1000] " +
+        "absolute flex bg-gray-900 bg-opacity-50 top-0 w-full h-full pointer-events-auto " +
        (showImageEditor.val ? "" : "hidden"),
    },
-    div(
+    button(
      {
-        class:
-          "absolute top-4 left-4 right-0 flex w-full gap-2 justify-start z-[200]",
+        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;
+        },
      },
-      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"))
-      )
+      span({
+        class: "iconify text-lg",
+        "data-icon": "ic:baseline-arrow-back",
+        "data-inline": "false",
+      }),
+      div("Back")
    ),
    div(
      {
        class:
-          "hidden w-full justify-center absolute top-0 left-0 right-0 items-center",
+          "hidden w-full flex justify-center absolute top-0 left-0 right-0 items-center",
      },
      button(
        {
@@ -590,11 +165,10 @@ export function LayerEditor() {
        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) => {
+        onload: (e) => {
          imageSize.val = handleImageSize(e.target);

          document.getElementById("image-container").style.scale =
@@ -609,13 +183,13 @@ export function LayerEditor() {
          canvas.width = e.target.naturalWidth;
          canvas.height = e.target.naturalHeight;
        },
-        oncontextmenu: async (e) => {
+        oncontextmenu: (e) => {
          e.preventDefault();
          e.isRight = true;
-          await handleClick(e);
+          handleClick(e);
        },
-        onclick: async (e) => {
-          await handleClick(e);
+        onclick: (e) => {
+          handleClick(e);
        },
        onmouseleave: (e) => {
          drawSegment(getClicks());
@@ -649,25 +223,19 @@ export function LayerEditor() {
          }
        },
      }),
-      () =>
-        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(
+      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;`,
          },
-          ...(enableAutoSegment.val
-            ? imagePrompts.val
-            : imagePrompts.val?.filter((click) => !click.isAuto)
-          ).map((point) => {
+          ...imagePrompts.val?.map((point) => {
            return button({
              style: () =>
                `left: ${
@@ -681,16 +249,17 @@ export function LayerEditor() {
                  point.label === 1 ? "bg-green-500" : "bg-red-500"
                }`,

-              oncontextmenu: async (e) => {
-                await handlePointClick(e, point);
+              oncontextmenu: (e) => {
+                handlePointClick(e, point);
              },
-              onclick: async (e) => {
-                await handlePointClick(e, point);
+              onclick: (e) => {
+                handlePointClick(e, point);
              },
            });
          })
-        )
+        );
+      }
    ),
-    () => (showSidebar.val ? SideBar() : div())
+    SideBar()
  );
 }
@@ -6,7 +6,7 @@ export function Loading() {
  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 z-[1001] " +
+        "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 " +
        (showLoading.val ? "" : "hidden"),
    },
    span({
@@ -42,7 +42,7 @@ export function ShapeFlowEditor() {
        {
          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: "text-black" }, "This is a dialog"),
        div(
          { class: "modal-action" },
          form(
@@ -5,8 +5,6 @@ import {
  imagePromptsMulti,
  targetNode,
  showImageEditor,
-  allImagePrompts,
-  samPrompts,
 } from "./state.js";
 import { van } from "./van.js";
 const {
@@ -25,41 +23,36 @@ 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"
+    targetNode.val.outputs && targetNode.val.type === 'SAM'
  ) {
-    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" ? "IMAGE" : "SAM_PROMPT"
+        );
+      });
+      targetNode.val.graph.change();
+    }
  }
 });

@@ -67,232 +60,185 @@ 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",
-      },
-      () => {
-        const layers = Object.entries(imagePromptsMulti.val);
-        return ul(
-          {
-            class: "menu bg-base-200 w-56 rounded-box text-base-content ",
-          },
-          button(
-            {
-              onclick: () => {
-                layers.map(([key, value]) => {
-                  imagePrompts.val = [];
-                  imagePromptsMulti.val[key] = [];
-                });
-                drawSegment([]);
-                updateImagePrompts();
-              },
-              class: "btn btn-ghost normal-case flex",
-            },
-            "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(
+    {
+      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);
+      return ul(
+        {
+          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: "flex items-center justify-between",
+                class: () =>
+                  `normal-case text-start items-start flex items-center justify-between  ${
+                    selectedLayer.val === key ? "active" : ""
+                  }`,
                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 = [];
-                }
-              }
-              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(
-              {
-                class: "gap-2 flex flex-col",
-                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];
+                  selectedLayer.val = key;
+                  imagePrompts.val = imagePromptsMulti.val[key];
                  drawSegment(getClicks());
-                  updateImagePrompts();
                },
              },
-              button(
-                {
-                  type: "button",
-                  class:
-                    "btn btn-sm btn-circle btn-ghost absolute right-2 top-2",
-                  onclick: (e) => {
-                    e.stopPropagation();
-                    my_modal_3.close();
+              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();
+                    },
                  },
-                },
-                "✕"
-              ),
-              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"
+                  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 = [];
+              }
+            }
+            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(
+            {
+              class: "gap-2 flex flex-col",
+              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();
+              },
+            },
+            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
-              )
-            )
-          )
-        );
-      }
-    )
+      )
  );
 }

@@ -1,33 +0,0 @@
-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()
@@ -5,7 +5,6 @@ import {
  imageUrl,
  imagePrompts,
  targetNode,
-  combinePointsNode,
  fileName,
  embeddings,
  imagePromptsMulti,
@@ -13,37 +12,20 @@ import {
  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 { initModel, loadNpyTensor } from "./onnx.js";
+import { loadNpyTensor } from "./onnx.js";
 import "https://code.iconify.design/3/3.1.0/iconify.min.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";
+import { drawSegment, getClicks } from "./LayerEditor.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);
+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;
@@ -249,35 +231,15 @@ 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} */
-  const isGeneratedImage = true;
-  return [isGeneratedImage, generatedImages[saveImageNode.id]];
+  return nodea.widgets.find((x) => x.name === widgetName).value;
 }

 /**
@@ -285,87 +247,45 @@ function getInputWidgetValue(node, inputIndex, widgetName) {
 * @param {LGraphNode} node
 */
 function showMyImageEditor(node) {
-  let [isGeneratedImage, connectedImageFileName] = getInputWidgetValue(
-    node,
-    0,
-    "image"
+  let connectedImageFileName = getInputWidgetValue(node, 0, "image");
+  const split = connectedImageFileName.split("/");
+  if (split.length > 1) connectedImageFileName = split[1];
+
+  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) {
-    alertDialog.val = {
-      text: "Please connect or generate an image first",
-      time: 3000,
-    };
-    return;
+
+  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;
  }
-
-  loadingCaption.val = "Loading SAM model...";
-  showLoading.val = true;
-
-  const ckpt = node.widgets.find((x) => x.name === "ckpt").value;
-  const modelType = ckpt.match(/vit_[lbh]/)?.[0];
-  initModel(modelType).then((res) => {
-    loadingCaption.val = "Computing image embedding...";
-
-    const split = connectedImageFileName.split("/");
-    let id = connectedImageFileName;
-    if (split.length > 1) id = split[1];
-
-    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;
-      });
+  showImageEditor.val = true;
+  imageUrl.val = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      connectedImageFileName
+    )}&type=input&subfolder=${split.length > 1 ? split[0] : ""}`
+  );
+  const embeedingUrl = api.apiURL(
+    `/view?filename=${encodeURIComponent(
+      `${embeddingFilename}.npy`
+    )}&type=output&subfolder=`
+  );
+  loadNpyTensor(embeedingUrl).then((tensor) => {
+    embeddings.val = tensor;
+    drawSegment(getClicks());
  });
+  targetNode.val = node;
 }

 /** @typedef {import('../../../web/types/comfy.js').ComfyExtension} ComfyExtension*/
@@ -374,38 +294,47 @@ const ext = {
  getCustomWidgets(app) {
    return {
      SAM_PROMPTS(node, inputName, inputData, app) {
-        const asd = document.createElement("div");
-        Object.assign(asd, {
-          id: "sam",
-          onclick: () => {
-            showMyImageEditor(node);
-          },
-        });
-        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();
+          let connectedImageFileName = getInputWidgetValue(node, 0, "image");
+          if (!connectedImageFileName) {
+            alertDialog.val = {
+              text: "Please connect an image first",
+              time: 3000,
+            };
+            return;
          }
-        };

-        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;
+          loadingCaption.val = "Computing image embedding...";
+          showLoading.val = true;
+
+          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;
+
+          api
+            .fetchApi("/sam_model", {
+              method: "POST",
+              body: JSON.stringify({
+                image: connectedImageFileName,
+                embedding_id: id,
+                ckpt,
+              }),
+            })
+            .then(() => {
+              showLoading.val = false;
+              showMyImageEditor(node);
+            })
+            .catch((err) => {
+              console.log(err);
+              showLoading.val = false;
+            });
        });
+        btn.serialize = false;
+
        return {
          widget: btn,
        };
@@ -448,24 +377,6 @@ 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,
-        };
-      },
    };
  },

@@ -482,7 +393,6 @@ const ext = {

      node.computeParentGroupResize();
    };
-    injectUIComponentToComfyuimenu();
  },

  async setup() {
@@ -495,10 +405,6 @@ 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"
@@ -536,7 +442,7 @@ const ext = {

    window.addEventListener(
      "keydown",
-      async (event) => {
+      (event) => {
        if (event.key === "Escape") {
          event.preventDefault();
          if (my_modal_3.open) {
@@ -544,14 +450,6 @@ const ext = {
          } 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),
-            //   }),
-            // });
          }
        }
      },
@@ -692,7 +590,7 @@ const ext = {
          });
        });
        break;
-      case "SAM MultiLayer":
+      case "SAM_Prompt_Image":
        nodeData.input.required.sam = ["SAM_PROMPTS"];
        // nodeData.input.required.upload = ['IMAGEUPLOAD'];
        // nodeData.input.required.prompts_points = ["IMAGEUPLOAD"];
@@ -705,9 +603,10 @@ const ext = {
          });
        });
        break;
-      case "Combine Points":
-        nodeData.input.required.sam = ["COMBINE_POINTS"];
-
+      case "SAM":
+        nodeData.input.required.sam = ["SAM_PROMPTS"];
+        // nodeData.input.required.upload = ['IMAGEUPLOAD'];
+        // nodeData.input.required.prompts_points = ["IMAGEUPLOAD"];
        addMenuHandler(nodeType, function (_, options) {
          options.unshift({
            content: "Open In Points Editor (Local)",
@@ -724,197 +623,10 @@ const ext = {
        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);
@@ -1,22 +1,23 @@
-import "https://cdn.jsdelivr.net/npm/onnxruntime-web@1.16.3/dist/ort.min.js";
+import "https://cdn.jsdelivr.net/npm/onnxruntime-web/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@1.16.3/dist/";
+ort.env.wasm.wasmPaths = "https://cdn.jsdelivr.net/npm/onnxruntime-web/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 (type) => {
+export const initModel = async () => {
  try {
-    if (!model || modelType !== type) {
-      modelType = type;
-      model = await ort.InferenceSession.create(
-        `${location.protocol}//${location.host}/sam_model?type=${modelType}`
-      );
-    }
+    if (MODEL_DIR === undefined) return;
+    const URL = MODEL_DIR;
+    model = await ort.InferenceSession.create(URL);
  } catch (e) {
    console.log(e);
  }
@@ -24,12 +25,14 @@ export const initModel = async (type) => {

 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, box) => {
+export const runONNX = async (clicks, tensor) => {
  // console.log('tensor', tensor);
  try {
    if (
@@ -46,7 +49,6 @@ export const runONNX = async (clicks, tensor, box) => {
        clicks,
        tensor,
        modelScale: imageSize.val,
-        box,
      });
      if (feeds === undefined) return;
      // Run the SAM ONNX model with the feeds returned from modelData()
@@ -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, box }) => {
+const modelData = ({ clicks, tensor, modelScale }) => {
  const imageEmbedding = tensor;
  let pointCoords;
  let pointLabels;
@@ -18,9 +18,8 @@ const modelData = ({ clicks, tensor, modelScale, box }) => {
    // 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.
-    const numPoints = box ? n + 3 : n + 1;
-    pointCoords = new Float32Array(2 * numPoints);
-    pointLabels = new Float32Array(numPoints);
+    pointCoords = new Float32Array(2 * (n + 1));
+    pointLabels = new Float32Array(n + 1);

    // Add clicks and scale to what SAM expects
    for (let i = 0; i < n; i++) {
@@ -29,25 +28,15 @@ const modelData = ({ clicks, tensor, modelScale, box }) => {
      pointLabels[i] = clicks[i].clickType;
    }

-    if (box) {
-      pointCoords[2 * n] = box.x1 * modelScale.samScale;
-      pointCoords[2 * n + 1] = box.y1 * modelScale.samScale;
-      pointLabels[n] = 2;
-
-      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;
-    }
+    // 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, numPoints, 2]);
-    pointLabelsTensor = new ort.Tensor("float32", pointLabels, [1, numPoints]);
+    pointCoordsTensor = new ort.Tensor("float32", pointCoords, [1, n + 1, 2]);
+    pointLabelsTensor = new ort.Tensor("float32", pointLabels, [1, n + 1]);
  }
  const imageSizeTensor = new ort.Tensor("float32", [
    modelScale.height,
@@ -9,42 +9,23 @@
 * @typedef {Object} Point
 * @property {number} x - The x coordinate
 * @property {number} y - The y coordinate
- * @property {>} label - The label
- *
- * @typedef {Object} Box
- * @property {number} x1
- * @property {number} y1
- * @property {number} x2
- * @property {number} y2
+ * @property {number} label - The label
 */

 import { van } from "./van.js";
+
 export const iframeSrc = van.state("https://editor.avatech.ai?comfyui=true");
 export const showEditor = van.state(false);
-// localStorage.getItem("showPreview") == 'true'
-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("https://editor.avatech.ai/viewer?avatarId=default&hideUI=true&debug=true&width=300&height=300&showAudioControl=true");
 // 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 fileName = van.state('');
 export const showImageEditor = van.state(false);
 export const showLoading = van.state(false);
 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("");
@@ -54,17 +35,9 @@ export const imageContainerSize = van.state({
  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({});

@@ -76,8 +49,3 @@ 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({});
@@ -1,146 +1,3 @@
-@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;
-}
+@tailwind utilities;
@@ -11,7 +11,7 @@
  "license": "ISC",
  "devDependencies": {
    "chokidar": "^3.5.3",
-    "daisyui": "^4.0.7",
+    "daisyui": "^3.7.5",
    "tailwindcss": "^3.3.3"
  }
 }
@@ -8,9 +8,12 @@ devDependencies:
  chokidar:
    specifier: ^3.5.3
    version: 3.5.3
+  concurrently:
+    specifier: ^8.2.1
+    version: 8.2.1
  daisyui:
-    specifier: ^4.0.7
-    version: 4.0.7(postcss@8.4.29)
+    specifier: ^3.7.5
+    version: 3.7.5
  tailwindcss:
    specifier: ^3.3.3
    version: 3.3.3
@@ -22,6 +25,13 @@ 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'}
@@ -73,6 +83,18 @@ 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
@@ -117,6 +139,14 @@ 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'}
@@ -132,6 +162,30 @@ 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'}
@@ -141,6 +195,22 @@ 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:
@@ -154,21 +224,24 @@ packages:
    hasBin: true
    dev: true

-  /culori@3.2.0:
-    resolution: {integrity: sha512-HIEbTSP7vs1mPq/2P9In6QyFE0Tkpevh0k9a+FkjhD+cwsYm9WRSbn4uMdW9O0yXlNYC3ppxL3gWWPOcvEl57w==}
-    engines: {node: ^12.20.0 || ^14.13.1 || >=16.0.0}
-    dev: true
-
-  /daisyui@4.0.7(postcss@8.4.29):
-    resolution: {integrity: sha512-D84DnNDZKcamwNsxCMrwYaddyz5kC6VO6oe30nM1x67GzCAfarfd3Ar1rpLGXCIqSsEoNZUHO8EcXvX93W2ZkA==}
+  /daisyui@3.7.5:
+    resolution: {integrity: sha512-udhiBJYVvcPGXa+mL5IElke6EdddKecjbbz6m43+9IVqzK8GBwetg092Edclo42TkNboLD9nzodeesJygqZt2A==}
    engines: {node: '>=16.9.0'}
    dependencies:
+      colord: 2.9.3
      css-selector-tokenizer: 0.8.0
-      culori: 3.2.0
-      picocolors: 1.0.0
+      postcss: 8.4.29
      postcss-js: 4.0.1(postcss@8.4.29)
+      tailwindcss: 3.3.3
    transitivePeerDependencies:
-      - postcss
+      - ts-node
+    dev: true
+
+  /date-fns@2.30.0:
+    resolution: {integrity: sha512-fnULvOpxnC5/Vg3NCiWelDsLiUc9bRwAPs/+LfTLNvetFCtCTN+yQz15C/fs4AwX1R9K5GLtLfn8QW+dWisaAw==}
+    engines: {node: '>=0.11'}
+    dependencies:
+      '@babel/runtime': 7.22.11
    dev: true

  /didyoumean@1.2.2:
@@ -179,6 +252,15 @@ 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'}
@@ -223,6 +305,11 @@ 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'}
@@ -248,6 +335,11 @@ 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'}
@@ -284,6 +376,11 @@ 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'}
@@ -310,6 +407,10 @@ 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'}
@@ -479,6 +580,15 @@ 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
@@ -499,11 +609,41 @@ 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'}
@@ -518,6 +658,20 @@ 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'}
@@ -574,19 +728,60 @@ 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
@@ -3,9 +3,7 @@ numpy
 opencv-python
 opencv-contrib-python
 einops
-bpy==3.6.0
+bpy
 segment-anything
 tqdm
-python-dotenv
-mediapipe
 # -e git+https://github.com/facebookresearch/segment-anything.git#egg=segment_anything
@@ -1,8 +1,6 @@
 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
@@ -10,15 +8,6 @@ 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):
@@ -43,35 +32,29 @@ for item in server.PromptServer.instance.routes._items:
    routes.append(item)
 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):
-    model_type = request.rel_url.query.get("type", "vit_h")
-    filename = os.path.join(folder_paths.base_path, f"web/models/sam_{model_type}.onnx")
+    filename = os.path.join(folder_paths.base_path, "web/models/sam.onnx")
+    # print(filename)
    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(
-            f"https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/models/sam_{model_type}.onnx"
+            "https://avatech-avatar-dev1.nyc3.cdn.digitaloceanspaces.com/models/sam.onnx"
        )
        response.raise_for_status()
        with open(filename, "wb") as f:
            f.write(response.content)
-        print(f"ONNX model downloaded")
+        print(f"ONNX model downloaded: {filename}")
    return web.FileResponse(filename)


-def load_image(image, is_generated_image):
-    if is_generated_image:
-        image_path = f"{folder_paths.get_output_directory()}/{image}"
-    else:
-        image_path = folder_paths.get_annotated_filepath(image)
+def load_image(image):
+    image_path = folder_paths.get_annotated_filepath(image)
    i = Image.open(image_path)
    i = ImageOps.exif_transpose(i)
    image = i.convert("RGB")
@@ -82,349 +65,26 @@ def load_image(image, is_generated_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")
-    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"
-    )
+    emb_filename = f"{folder_paths.get_output_directory()}/{emb_id}.npy"
    if not os.path.exists(emb_filename):
-        image = load_image(post.get("image"), is_generated_image)
-        if remote:
-            # Run embed in remote server
-            image = Image.fromarray((image * 255).astype(np.uint8))
-            buffered = io.BytesIO()
-            image.save(buffered, format="PNG")
-            image = base64.b64encode(buffered.getvalue()).decode()
-            res = requests.post(
-                "https://avatechgg--sam-embed.modal.run",
-                headers={
-                    "Content-type": "application/json",
-                    "Accept": "application/json",
+        image = load_image(post.get("image"))
+        ckpt = post.get("ckpt")
+        model_type = re.findall(r'vit_[lbh]', ckpt)[0]
+        ckpt = folder_paths.get_full_path("sams", ckpt)
+        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(f"{folder_paths.get_output_directory()}/{emb_id}.json", "w") as f:
+            json.dump(
+                {
+                    "input_size": predictor.input_size,
+                    "original_size": predictor.original_size,
                },
-                data=json.dumps(
-                    {
-                        "image": image,
-                    }
-                ),
-            ).json()
-            emb, input_size, original_size = (
-                res["emb"],
-                res["input_size"],
-                res["original_size"],
+                f,
            )
-            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})
@@ -1,27 +0,0 @@
-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"}
@@ -1,66 +0,0 @@
-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"}
@@ -1,43 +0,0 @@
-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"}
@@ -1,31 +0,0 @@
-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"}
@@ -0,0 +1,108 @@
+import folder_paths
+import os
+import numpy as np
+import torch
+import re
+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",),
+                "ckpt": (folder_paths.get_filename_list("sams"),),
+                "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, ckpt, embedding_id, image_prompts_json):
+        import json
+
+        global global_predictor
+
+        if global_predictor is None:
+            ckpt = folder_paths.get_full_path("sams", ckpt)
+            model_type = re.findall(r'vit_[lbh]', ckpt)[0]
+            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)
+
+            with open(f"{self.output_dir}/{embedding_id}.json", "w") as f:
+                data = {
+                    "input_size": predictor.input_size,
+                    "original_size": predictor.original_size,
+                }
+                json.dump(data, f)
+        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)"}
@@ -1,385 +0,0 @@
-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"}
@@ -3,9 +3,6 @@ module.exports = {
  // content: ['./js/**/*.{html,js}'],
  content: ['./js/**/*.{html,js}'],
  theme: {
-    fontFamily: {
-      'gabarito': ['Gabarito'],
-    },
    extend: {},
  },
  daisyui: {
@@ -0,0 +1,467 @@
+{
+  "last_node_id": 316,
+  "last_link_id": 561,
+  "nodes": [
+    {
+      "id": 181,
+      "type": "CheckpointLoaderSimple",
+      "pos": [
+        40,
+        200
+      ],
+      "size": {
+        "0": 340.6639709472656,
+        "1": 98
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "MODEL",
+          "type": "MODEL",
+          "links": [
+            552
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "CLIP",
+          "type": "CLIP",
+          "links": [
+            527,
+            558
+          ],
+          "shape": 3,
+          "slot_index": 1
+        },
+        {
+          "name": "VAE",
+          "type": "VAE",
+          "links": [
+            249,
+            547
+          ],
+          "shape": 3,
+          "slot_index": 2
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "CheckpointLoaderSimple"
+      },
+      "widgets_values": [
+        "meinamix_meinaV10.safetensors"
+      ]
+    },
+    {
+      "id": 176,
+      "type": "KSampler",
+      "pos": [
+        900,
+        230
+      ],
+      "size": {
+        "0": 326.9862060546875,
+        "1": 262.3746643066406
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 552
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 557,
+          "slot_index": 1
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 554
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 540,
+          "slot_index": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            248
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        509878269555324,
+        "randomize",
+        25,
+        7,
+        "dpmpp_2m",
+        "karras",
+        1
+      ]
+    },
+    {
+      "id": 175,
+      "type": "CLIPTextEncode",
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+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            26
+          ],
+          "shape": 3
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "ComfyUI_temp_nycbp_00001_.png",
+        "image"
+      ]
+    },
+    {
+      "id": 1,
+      "type": "LoadImage",
+      "pos": [
+        1020,
+        468
+      ],
+      "size": [
+        315,
+        314.00001525878906
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            7
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "ComfyUI_temp_rlaph_00001_s.png",
+        "image"
+      ]
+    },
+    {
+      "id": 9,
+      "type": "KSampler",
+      "pos": [
+        1379,
+        305
+      ],
+      "size": {
+        "0": 315,
+        "1": 262
+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "model",
+          "type": "MODEL",
+          "link": 12
+        },
+        {
+          "name": "positive",
+          "type": "CONDITIONING",
+          "link": 9
+        },
+        {
+          "name": "negative",
+          "type": "CONDITIONING",
+          "link": 13,
+          "slot_index": 2
+        },
+        {
+          "name": "latent_image",
+          "type": "LATENT",
+          "link": 18,
+          "slot_index": 3
+        }
+      ],
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            15
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KSampler"
+      },
+      "widgets_values": [
+        291874885197794,
+        "randomize",
+        25,
+        7,
+        "dpmpp_2m",
+        "karras",
+        1
+      ]
+    },
+    {
+      "id": 8,
+      "type": "ControlNetLoader",
+      "pos": [
+        1020,
+        190
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "CONTROL_NET",
+          "type": "CONTROL_NET",
+          "links": [
+            8
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ControlNetLoader"
+      },
+      "widgets_values": [
+        "control_v11p_sd15_normalbae_fp16.safetensors"
+      ]
+    },
+    {
+      "id": 20,
+      "type": "ControlNetLoader",
+      "pos": [
+        665,
+        190
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "CONTROL_NET",
+          "type": "CONTROL_NET",
+          "links": [
+            22
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ControlNetLoader"
+      },
+      "widgets_values": [
+        "control_v11p_sd15_openpose_fp16.safetensors"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "EmptyLatentImage",
+      "pos": [
+        1377,
+        150
+      ],
+      "size": {
+        "0": 315,
+        "1": 106
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "LATENT",
+          "type": "LATENT",
+          "links": [
+            18
+          ],
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "EmptyLatentImage"
+      },
+      "widgets_values": [
+        512,
+        768,
+        1
+      ]
+    }
+  ],
+  "links": [
+    [
+      7,
+      1,
+      0,
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+      2,
+      "IMAGE"
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+      1,
+      "CONTROL_NET"
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+      "CONDITIONING"
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+    [
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+    [
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+    [
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+      0,
+      "IMAGE"
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+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}
Author	SHA1	Message	Date
AvatechGGG	e8179d72d9	Update README.md	2023-10-05 11:17:00 +08:00
Edwin Wong	2f38c8467c	Update README.md	2023-10-04 19:41:15 +08:00
Edwin Wong	3039963272	Update README.md	2023-10-04 19:32:58 +08:00
Edwin Wong	34fb26d6fb	Merge branch 'readme'	2023-10-04 19:15:06 +08:00
Edwin Wong	cb8b6679da	Merge branch 'readme' # Conflicts: # README.md	2023-10-04 19:03:13 +08:00
Edwin Wong	8422acfc89	Update README.md	2023-10-04 19:02:03 +08:00
Edwin Wong	c066eea984	fix: readme	2023-10-04 18:59:42 +08:00
Edwin Wong	ce1b4702e0	fix: image in cdn	2023-10-04 18:53:42 +08:00
Edwin Wong	0818a6f1c7	fix: readme	2023-10-04 18:53:30 +08:00
Edwin Wong	80493fa7bc	fix: readme	2023-10-04 18:53:30 +08:00
Edwin Wong	4dd87437a4	replace all image to digital ocean	2023-10-04 18:53:22 +08:00
Radionic	b1f562c8e8	update preview url	2023-10-04 18:46:41 +08:00
Edwin Wong	c693166d98	Update README.md	2023-10-04 18:41:01 +08:00
AvatechGGG	976ddc8348	Update README.md	2023-10-04 18:30:44 +08:00
Edwin Wong	6cc075422b	fix: image in cdn	2023-10-04 18:27:02 +08:00
Edwin Wong	d2c2984555	fix: readme	2023-10-04 18:25:42 +08:00
Edwin Wong	e9ff1e8b64	fix: readme	2023-10-04 18:21:23 +08:00
Edwin Wong	b39bfc0693	replace all image to digital ocean	2023-10-04 17:57:34 +08:00
AvatechGGG	486000d2b5	Update README.md	2023-10-04 13:33:57 +08:00
jonaaathan	2907800373	update 2 videos	2023-10-03 17:43:20 +08:00
Radionic	e928630a7a	fix: sams folder path	2023-10-03 14:08:03 +08:00
San45600	71d066a8d6	Update README.md	2023-10-03 14:03:28 +08:00
Edwin Wong	3077a2dbae	fix: viewer error	2023-10-03 13:43:25 +08:00
Edwin Wong	323967dc08	fix: viewer	2023-10-03 13:35:21 +08:00
Radionic	e4cfb86114	feat: reset scene	2023-10-03 12:36:50 +08:00
Edwin Wong	788596525e	Update README.md update gif in readme	2023-10-02 22:30:15 +08:00
Edwin Wong	3ee3714664	feat: back and save	2023-09-30 01:43:19 +08:00
jonaaathan	cada51a729	swap 2 videos	2023-09-29 18:08:33 +08:00
San45600	b7961e025b	Update README.md	2023-09-29 17:36:11 +08:00
Edwin Wong	abac0ed6aa	fix: copy and paste	2023-09-29 17:05:07 +08:00
Radionic	ff1ed96a74	feat: update template	2023-09-29 16:42:03 +08:00
Radionic	fb81ea509f	fix: image metadata not saved	2023-09-29 16:14:41 +08:00
Radionic	9c7721bb76	fix: dialog close	2023-09-29 15:28:58 +08:00
Radionic	dfdc97b27b	chore: remove unused nodes	2023-09-29 13:14:12 +08:00
BennyKok	07904311ff	fix	2023-09-29 00:11:08 +08:00
heiume	71f6c679b9	Update README.md	2023-09-28 19:23:15 +08:00
Radionic	c121ff37cf	fix: occasional segmentation fault	2023-09-28 18:49:30 +08:00
BennyKok	3931d6c7ed	feat: options to disable convex_hull	2023-09-28 16:44:24 +08:00
BennyKok	71211782aa	fix	2023-09-28 12:59:27 +08:00
BennyKok	3979064eee	add styles	2023-09-28 12:59:27 +08:00
heiume	6f973ecd93	Update README.md	2023-09-28 12:41:26 +08:00
Radionic	acc52d905a	feat: import bpy inside custom node	2023-09-27 18:36:59 +08:00
heiume	7803ac467b	Update README.md	2023-09-27 18:16:13 +08:00
heiume	0c20b73d11	Update README.md	2023-09-27 18:12:58 +08:00
BennyKok	b2eb735bf4	clean up	2023-09-27 18:10:52 +08:00
heiume	50929a29b8	Update README.md	2023-09-27 18:10:24 +08:00
heiume	5cb899d9e1	Merge branch 'main' of https://github.com/avatechgg/avatar-graph-comfyui	2023-09-27 18:09:58 +08:00
heiume	ab2b239690	move folder	2023-09-27 18:09:49 +08:00
heiume	7bf1de543f	Update README.md	2023-09-27 18:03:21 +08:00
heiume	850598abe8	TemplateGen01	2023-09-27 18:02:35 +08:00
BennyKok	85b2de6303	fix styling	2023-09-27 17:45:32 +08:00
BennyKok	6854f9b59b	fix	2023-09-27 17:30:16 +08:00
heiume	bb50059376	Update README.md	2023-09-27 17:02:09 +08:00
heiume	a0f055f0cd	Update README.md	2023-09-27 17:01:14 +08:00
heiume	c5560a8084	Update README.md	2023-09-27 16:56:55 +08:00
Edwin Wong	ab7f660f95	feat: auto save ava format	2023-09-27 16:56:17 +08:00
heiume	6af9e0c62f	Update README.md	2023-09-27 16:50:56 +08:00
BennyKok	545ec8a3be	refactor! renam ui components	2023-09-27 16:41:47 +08:00
heiume	dc97bbc540	Update README.md	2023-09-27 16:31:35 +08:00
heiume	4432c680cc	Update README.md	2023-09-27 16:02:28 +08:00
heiume	aef869e6d1	Update README.md	2023-09-27 15:53:16 +08:00
heiume	6810145f0b	Update README.md	2023-09-27 15:51:19 +08:00
heiume	1a39c236ff	Update README.md	2023-09-27 15:47:32 +08:00
Radionic	b843eb7c10	fix: switch to layer when created	2023-09-27 15:44:30 +08:00
heiume	a48648025d	Update README.md	2023-09-27 15:40:13 +08:00
heiume	8897d5c430	Update README.md	2023-09-27 13:48:19 +08:00
BennyKok	9f96f7bff1	fix	2023-09-27 13:45:55 +08:00
heiume	3510381894	Update README.md	2023-09-27 13:43:47 +08:00
BennyKok	f21426106b	fix: duplicated layer name	2023-09-27 13:42:00 +08:00
heiume	acbb05572c	Update README.md	2023-09-27 13:41:58 +08:00
Radionic	56000fbe45	feat: show alert when image not connected	2023-09-27 13:40:46 +08:00
heiume	4c3864ec4f	Update README.md	2023-09-27 13:23:02 +08:00
heiume	99481e00c0	Update README.md	2023-09-27 13:22:22 +08:00
BennyKok	2f0a6f688f	will auto check if there are any missing output slots, will auto create	2023-09-27 13:19:47 +08:00
BennyKok	6133c6c9fb	auto clean up output slot for sam node	2023-09-27 13:19:47 +08:00
heiume	c5c080d896	Update README.md	2023-09-27 12:56:49 +08:00
Radionic	f13829a975	feat: hide loading when caught error	2023-09-27 12:37:55 +08:00
Radionic	535e77a0d9	feat: loading caption	2023-09-27 12:37:55 +08:00
BennyKok	035439e1fe	Update README.md	2023-09-27 12:07:05 +08:00
BennyKok	83a7ce7899	Update README.md	2023-09-26 19:22:38 +08:00
BennyKok	0effef3fe2	Update README.md	2023-09-26 19:09:06 +08:00
BennyKok	a436e3846d	Update README.md	2023-09-26 19:07:52 +08:00
BennyKok	83d6a73cfd	feat!: add scale x and y to create mesh layer	2023-09-26 19:05:32 +08:00
BennyKok	5516baa5c2	update workflow	2023-09-26 19:05:12 +08:00
BennyKok	744cb75552	fix default value	2023-09-26 19:05:04 +08:00
Radionic	722542e92a	feat: infer model type from checkpoint name	2023-09-26 18:34:46 +08:00
Radionic	6e3820b502	chore: hide logging	2023-09-26 18:21:52 +08:00
Edwin Wong	6ce009d984	fix: filename	2023-09-26 18:11:19 +08:00
BennyKok	94874357cd	Update README.md	2023-09-26 18:05:56 +08:00
Radionic	d37f9a87f3	feat: auto download sam model	2023-09-26 17:45:32 +08:00
heiume	0729bf982e	Merge pull request #2 from avatechai/readme-patch-1 Update README.md	2023-09-26 17:36:23 +08:00
heiume	9f1bd85c6b	Update README.md	2023-09-26 17:36:14 +08:00
heiume	082f96262b	Workflow json	2023-09-26 17:35:02 +08:00
BennyKok	b3340d6085	docs: update readme	2023-09-26 17:30:32 +08:00
BennyKok	c269a1e340	fix number display	2023-09-26 17:17:56 +08:00
heiume	02a6b37971	Merge pull request #1 from avatechai/readme-patch-1 Update README.md	2023-09-26 17:13:15 +08:00
heiume	18958a22b0	Update README.md	2023-09-26 17:13:03 +08:00
Radionic	df96815448	feat: move import to __init__.py	2023-09-26 17:12:43 +08:00
BennyKok	de211a79e4	Update README.md	2023-09-26 17:12:28 +08:00
BennyKok	ae22626e6d	Update README.md	2023-09-26 17:11:36 +08:00
heiume	5313370518	Update README.md	2023-09-26 17:11:04 +08:00
heiume	b9fe7cce14	Update README.md	2023-09-26 17:07:30 +08:00
BennyKok	ad6a2aea56	feat: add ava display	2023-09-26 16:49:12 +08:00
heiume	fdc1268ea3	Update README.md	2023-09-26 16:45:59 +08:00
Radionic	b27b6094e2	fix: variable name	2023-09-26 16:44:50 +08:00
BennyKok	3820d98942	Update README.md	2023-09-26 16:39:35 +08:00
Radionic	723dbe4a71	fix: output image type	2023-09-26 16:37:48 +08:00
heiume	d20d6c1b10	Update README.md	2023-09-26 16:37:27 +08:00
heiume	2cbf3da2d0	Update README.md	2023-09-26 16:33:08 +08:00
heiume	3e49f992aa	Merge branch 'main' of https://github.com/avatechgg/avatar-graph-comfyui	2023-09-26 16:31:35 +08:00
heiume	f438146927	add inpaint mouth open template file and workflow json	2023-09-26 16:31:22 +08:00
BennyKok	3391bae9ff	Update README.md	2023-09-26 16:24:22 +08:00
Edwin Wong	c5b903b9d1	fix	2023-09-26 15:54:14 +08:00
BennyKok	834b7abfdf	Update README.md	2023-09-26 15:51:17 +08:00
BennyKok	a20b23488a	Update README.md	2023-09-26 15:47:10 +08:00
Radionic	be5f2c8d1b	feat: save metadata in glb/gltf	2023-09-26 15:40:27 +08:00
BennyKok	eb2fb63d24	Update README.md	2023-09-26 15:39:47 +08:00