Merge pull request #440 from MrForExample/dev

Small Update for Hunyuan3D-2, Merge 2 pull requests, and add Decimate Mesh node
Merge branch 'main' into dev
2025-01-24 12:39:40 -06:00 · 2025-01-24 12:32:22 -06:00 · 2025-01-24 12:31:59 -06:00 · 2025-01-24 12:16:53 -06:00 · 2025-01-24 12:15:54 -06:00 · 2025-01-23 16:41:59 -08:00
71 changed files with 204658 additions and 18 deletions
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+TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT
+Tencent Hunyuan 3D 2.0 Release Date: January 21, 2025
+THIS LICENSE AGREEMENT DOES NOT APPLY IN THE EUROPEAN UNION, UNITED KINGDOM AND SOUTH KOREA AND IS EXPRESSLY LIMITED TO THE TERRITORY, AS DEFINED BELOW.
+By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan 3D 2.0 Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+1.	DEFINITIONS.
+a.	“Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A.
+b.	“Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of Tencent Hunyuan 3D 2.0 Works or any portion or element thereof set forth herein.
+c.	“Documentation” shall mean the specifications, manuals and documentation for Tencent Hunyuan 3D 2.0 made publicly available by Tencent.
+d.	“Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
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+f.	“Materials” shall mean, collectively, Tencent’s proprietary Tencent Hunyuan 3D 2.0 and Documentation (and any portion thereof) as made available by Tencent under this Agreement.
+g.	“Model Derivatives” shall mean all: (i) modifications to Tencent Hunyuan 3D 2.0 or any Model Derivative of Tencent Hunyuan 3D 2.0; (ii) works based on Tencent Hunyuan 3D 2.0 or any Model Derivative of Tencent Hunyuan 3D 2.0; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent Hunyuan 3D 2.0 or any Model Derivative of Tencent Hunyuan 3D 2.0, to that model in order to cause that model to perform similarly to Tencent Hunyuan 3D 2.0 or a Model Derivative of Tencent Hunyuan 3D 2.0, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent Hunyuan 3D 2.0 or a Model Derivative of Tencent Hunyuan 3D 2.0 for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives.
+h.	“Output” shall mean the information and/or content output of Tencent Hunyuan 3D 2.0 or a Model Derivative that results from operating or otherwise using Tencent Hunyuan 3D 2.0 or a Model Derivative, including via a Hosted Service.
+i.	“Tencent,” “We” or “Us” shall mean THL A29 Limited.
+j.	“Tencent Hunyuan 3D 2.0” shall mean the 3D generation models and their software and algorithms, including trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made publicly available by Us at https://github.com/Tencent/Hunyuan3D-2.
+k.	“Tencent Hunyuan 3D 2.0 Works” shall mean: (i) the Materials; (ii) Model Derivatives; and (iii) all derivative works thereof.
+l.	“Territory” shall mean the worldwide territory, excluding the territory of the European Union, United Kingdom and South Korea. 
+m.	“Third Party” or “Third Parties” shall mean individuals or legal entities that are not under common control with Us or You.
+n.	“including” shall mean including but not limited to.
+2.	GRANT OF RIGHTS.
+We grant You, for the Territory only, a non-exclusive, non-transferable and royalty-free limited license under Tencent’s intellectual property or other rights owned by Us embodied in or utilized by the Materials to use, reproduce, distribute, create derivative works of (including Model Derivatives), and make modifications to the Materials, only in accordance with the terms of this Agreement and the Acceptable Use Policy, and You must not violate (or encourage or permit anyone else to violate) any term of this Agreement or the Acceptable Use Policy.
+3.	DISTRIBUTION.
+You may, subject to Your compliance with this Agreement, distribute or make available to Third Parties the Tencent Hunyuan 3D 2.0 Works, exclusively in the Territory, provided that You meet all of the following conditions:
+a.	You must provide all such Third Party recipients of the Tencent Hunyuan 3D 2.0 Works or products or services using them a copy of this Agreement;
+b.	You must cause any modified files to carry prominent notices stating that You changed the files;
+c.	You are encouraged to: (i) publish at least one technology introduction blogpost or one public statement expressing Your experience of using the Tencent Hunyuan 3D 2.0 Works; and (ii) mark the products or services developed by using the Tencent Hunyuan 3D 2.0 Works to indicate that the product/service is “Powered by Tencent Hunyuan”; and
+d.	All distributions to Third Parties (other than through a Hosted Service) must be accompanied by a “Notice” text file that contains the following notice: “Tencent Hunyuan 3D 2.0 is licensed under the Tencent Hunyuan 3D 2.0 Community License Agreement, Copyright © 2025 Tencent. All Rights Reserved. The trademark rights of “Tencent Hunyuan” are owned by Tencent or its affiliate.”
+You may add Your own copyright statement to Your modifications and, except as set forth in this Section and in Section 5, may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Model Derivatives as a whole, provided Your use, reproduction, modification, distribution, performance and display of the work otherwise complies with the terms and conditions of this Agreement (including as regards the Territory). If You receive Tencent Hunyuan 3D 2.0 Works from a Licensee as part of an integrated end user product, then this Section 3 of this Agreement will not apply to You.
+4.	ADDITIONAL COMMERCIAL TERMS.
+If, on the Tencent Hunyuan 3D 2.0 version release date, the monthly active users of all products or services made available by or for Licensee is greater than 1 million monthly active users in the preceding calendar month, You must request a license from Tencent, which Tencent may grant to You in its sole discretion, and You are not authorized to exercise any of the rights under this Agreement unless or until Tencent otherwise expressly grants You such rights. 
+Subject to Tencent's written approval, you may request a license for the use of Tencent Hunyuan 3D 2.0 by submitting the following information to hunyuan3d@tencent.com:
+a.	Your company’s name and associated business sector that plans to use Tencent Hunyuan 3D 2.0.
+b.	Your intended use case and the purpose of using Tencent Hunyuan 3D 2.0.
+5.	RULES OF USE.
+a.	Your use of the Tencent Hunyuan 3D 2.0 Works must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Tencent Hunyuan 3D 2.0 Works, which is hereby incorporated by reference into this Agreement. You must include the use restrictions referenced in these Sections 5(a) and 5(b) as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Tencent Hunyuan 3D 2.0 Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan 3D 2.0 Works are subject to the use restrictions in these Sections 5(a) and 5(b).
+b.	You must not use the Tencent Hunyuan 3D 2.0 Works or any Output or results of the Tencent Hunyuan 3D 2.0 Works to improve any other AI model (other than Tencent Hunyuan 3D 2.0 or Model Derivatives thereof).
+c.	You must not use, reproduce, modify, distribute, or display the Tencent Hunyuan 3D 2.0 Works, Output or results of the Tencent Hunyuan 3D 2.0 Works outside the Territory. Any such use outside the Territory is unlicensed and unauthorized under this Agreement.
+6.	INTELLECTUAL PROPERTY.
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+b.	No trademark licenses are granted under this Agreement, and in connection with the Tencent Hunyuan 3D 2.0 Works, Licensee may not use any name or mark owned by or associated with Tencent or any of its affiliates, except as required for reasonable and customary use in describing and distributing the Tencent Hunyuan 3D 2.0 Works. Tencent hereby grants You a license to use “Tencent Hunyuan” (the “Mark”) in the Territory solely as required to comply with the provisions of Section 3(c), provided that You comply with any applicable laws related to trademark protection. All goodwill arising out of Your use of the Mark will inure to the benefit of Tencent.
+c.	If You commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any person or entity alleging that the Materials or any Output, or any portion of any of the foregoing, infringe any intellectual property or other right owned or licensable by You, then all licenses granted to You under this Agreement shall terminate as of the date such lawsuit or other proceeding is filed. You will defend, indemnify and hold harmless Us from and against any claim by any Third Party arising out of or related to Your or the Third Party’s use or distribution of the Tencent Hunyuan 3D 2.0 Works.
+d.	Tencent claims no rights in Outputs You generate. You and Your users are solely responsible for Outputs and their subsequent uses.
+7.	DISCLAIMERS OF WARRANTY AND LIMITATIONS OF LIABILITY.
+a.	We are not obligated to support, update, provide training for, or develop any further version of the Tencent Hunyuan 3D 2.0 Works or to grant any license thereto.
+b.	UNLESS AND ONLY TO THE EXTENT REQUIRED BY APPLICABLE LAW, THE TENCENT HUNYUAN 3D 2.0 WORKS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED “AS IS” WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES OF ANY KIND INCLUDING ANY WARRANTIES OF TITLE, MERCHANTABILITY, NONINFRINGEMENT, COURSE OF DEALING, USAGE OF TRADE, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING, REPRODUCING, MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR OR A THIRD PARTY’S USE OR DISTRIBUTION OF ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND PERMISSIONS UNDER THIS AGREEMENT.
+c.	TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT SHALL TENCENT OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, FOR ANY DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL OR PUNITIVE DAMAGES, OR LOST PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO ANY OF THE TENCENT HUNYUAN 3D 2.0 WORKS OR OUTPUTS, EVEN IF TENCENT OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
+8.	SURVIVAL AND TERMINATION.
+a.	The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
+b.	We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan 3D 2.0 Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement.
+9.	GOVERNING LAW AND JURISDICTION.
+a.	This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
+b.	Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
+ 
+EXHIBIT A
+ACCEPTABLE USE POLICY
+
+Tencent reserves the right to update this Acceptable Use Policy from time to time.
+Last modified: November 5, 2024
+
+Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan 3D 2.0. You agree not to use Tencent Hunyuan 3D 2.0 or Model Derivatives:
+1.	Outside the Territory;
+2.	In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
+3.	To harm Yourself or others;
+4.	To repurpose or distribute output from Tencent Hunyuan 3D 2.0 or any Model Derivatives to harm Yourself or others; 
+5.	To override or circumvent the safety guardrails and safeguards We have put in place;
+6.	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+7.	To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
+8.	To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
+9.	To intentionally defame, disparage or otherwise harass others;
+10.	To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
+11.	To generate or disseminate personal identifiable information with the purpose of harming others;
+12.	To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
+13.	To impersonate another individual without consent, authorization, or legal right;
+14.	To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
+15.	In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
+16.	To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
+17.	For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
+18.	To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+19.	For military purposes;
+20.	To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
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+Usage and Legal Notices:
+
+Tencent is pleased to support the open source community by making Hunyuan 3D 2.0 available.
+
+Copyright (C) 2025 THL A29 Limited, a Tencent company.  All rights reserved. The below software and/or models in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+Hunyuan 3D 2.0 is licensed under the TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT except for the third-party components listed below, which is licensed under different terms. Hunyuan 3D 2.0 does not impose any additional limitations beyond what is outlined in the respective licenses of these third-party components. Users must comply with all terms and conditions of original licenses of these third-party components and must ensure that the usage of the third party components adheres to all relevant laws and regulations. 
+
+For avoidance of doubts, Hunyuan 3D 2.0 means inference-enabling code, parameters, and weights of this Model only, which are made publicly available by Tencent in accordance with TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT.
+
+
+Other dependencies and licenses:
+
+
+Open Source Model Licensed under the MIT and CreativeML Open RAIL++-M License:
+--------------------------------------------------------------------
+1. Stable Diffusion
+Copyright (c) 2022 Stability AI
+
+
+Terms of the MIT and CreativeML Open RAIL++-M License:
+--------------------------------------------------------------------
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
+CreativeML Open RAIL++-M License
+dated November 24, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
+
+This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+1. Definitions
+
+- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
+- "Data" means a collection of information and/or content extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License.
+- "Output" means the results of operating a Model as embodied in informational content resulting therefrom.
+- "Model" means any accompanying machine-learning based assemblies (including checkpoints), consisting of learnt weights, parameters (including optimizer states), corresponding to the model architecture as embodied in the Complementary Material, that have been trained or tuned, in whole or in part on the Data, using the Complementary Material.
+- "Derivatives of the Model" means all modifications to the Model, works based on the Model, or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or output of the Model, to the other model, in order to cause the other model to perform similarly to the Model, including - but not limited to - distillation methods entailing the use of intermediate data representations or methods based on the generation of synthetic data by the Model for training the other model.
+- "Complementary Material" means the accompanying source code and scripts used to define, run, load, benchmark or evaluate the Model, and used to prepare data for training or evaluation, if any. This includes any accompanying documentation, tutorials, examples, etc, if any.
+- "Distribution" means any transmission, reproduction, publication or other sharing of the Model or Derivatives of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means - e.g. API-based or web access.
+- "Licensor" means the copyright owner or entity authorized by the copyright owner that is granting the License, including the persons or entities that may have rights in the Model and/or distributing the Model.
+- "You" (or "Your") means an individual or Legal Entity exercising permissions granted by this License and/or making use of the Model for whichever purpose and in any field of use, including usage of the Model in an end-use application - e.g. chatbot, translator, image generator.
+- "Third Parties" means individuals or legal entities that are not under common control with Licensor or You.
+- "Contribution" means any work of authorship, including the original version of the Model and any modifications or additions to that Model or Derivatives of the Model thereof, that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+- "Contributor" means Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
+
+Section II: INTELLECTUAL PROPERTY RIGHTS
+
+Both copyright and patent grants apply to the Model, Derivatives of the Model and Complementary Material. The Model and Derivatives of the Model are subject to additional terms as described in Section III.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare, publicly display, publicly perform, sublicense, and distribute the Complementary Material, the Model, and Derivatives of the Model.
+3. Grant of Patent License. Subject to the terms and conditions of this License and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this paragraph) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Model and the Complementary Material, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model and/or Complementary Material or a Contribution incorporated within the Model and/or Complementary Material constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for the Model and/or Work shall terminate as of the date such litigation is asserted or filed.
+
+Section III: CONDITIONS OF USAGE, DISTRIBUTION AND REDISTRIBUTION
+
+4. Distribution and Redistribution. You may host for Third Party remote access purposes (e.g. software-as-a-service), reproduce and distribute copies of the Model or Derivatives of the Model thereof in any medium, with or without modifications, provided that You meet the following conditions:
+Use-based restrictions as referenced in paragraph 5 MUST be included as an enforceable provision by You in any type of legal agreement (e.g. a license) governing the use and/or distribution of the Model or Derivatives of the Model, and You shall give notice to subsequent users You Distribute to, that the Model or Derivatives of the Model are subject to paragraph 5. This provision does not apply to the use of Complementary Material.
+You must give any Third Party recipients of the Model or Derivatives of the Model a copy of this License;
+You must cause any modified files to carry prominent notices stating that You changed the files;
+You must retain all copyright, patent, trademark, and attribution notices excluding those notices that do not pertain to any part of the Model, Derivatives of the Model.
+You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions - respecting paragraph 4.a. - for use, reproduction, or Distribution of Your modifications, or for any such Derivatives of the Model as a whole, provided Your use, reproduction, and Distribution of the Model otherwise complies with the conditions stated in this License.
+5. Use-based restrictions. The restrictions set forth in Attachment A are considered Use-based restrictions. Therefore You cannot use the Model and the Derivatives of the Model for the specified restricted uses. You may use the Model subject to this License, including only for lawful purposes and in accordance with the License. Use may include creating any content with, finetuning, updating, running, training, evaluating and/or reparametrizing the Model. You shall require all of Your users who use the Model or a Derivative of the Model to comply with the terms of this paragraph (paragraph 5).
+6. The Output You Generate. Except as set forth herein, Licensor claims no rights in the Output You generate using the Model. You are accountable for the Output you generate and its subsequent uses. No use of the output can contravene any provision as stated in the License.
+
+Section IV: OTHER PROVISIONS
+
+7. Updates and Runtime Restrictions. To the maximum extent permitted by law, Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License.
+8. Trademarks and related. Nothing in this License permits You to make use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
+9. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model and the Complementary Material (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivatives of the Model, and the Complementary Material and assume any risks associated with Your exercise of permissions under this License.
+10. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Model and the Complementary Material (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+11. Accepting Warranty or Additional Liability. While redistributing the Model, Derivatives of the Model and the Complementary Material thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+12. If any provision of this License is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
+
+END OF TERMS AND CONDITIONS
+
+
+
+
+Attachment A
+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+
+- In any way that violates any applicable national, federal, state, local or international law or regulation;
+- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
+- To generate or disseminate personal identifiable information that can be used to harm an individual;
+- To defame, disparage or otherwise harass others;
+- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
+- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
+- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories;
+- To provide medical advice and medical results interpretation;
+- To generate or disseminate information for the purpose to be used for administration of justice, law enforcement, immigration or asylum processes, such as predicting an individual will commit fraud/crime commitment (e.g. by text profiling, drawing causal relationships between assertions made in documents, indiscriminate and arbitrarily-targeted use).
+
+
+
+Open Source Model Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT and Other Licenses of the Third-Party Components therein:
+--------------------------------------------------------------------
+1. HunyuanDiT
+Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved. 
+
+
+Terms of the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT:
+--------------------------------------------------------------------
+TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
+Tencent Hunyuan Release Date: 2024/5/14
+By clicking to agree or by using, reproducing, modifying, distributing, performing or displaying any portion or element of the Tencent Hunyuan Works, including via any Hosted Service, You will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+1.	DEFINITIONS.
+a.	“Acceptable Use Policy” shall mean the policy made available by Tencent as set forth in the Exhibit A.
+b.	“Agreement” shall mean the terms and conditions for use, reproduction, distribution, modification, performance and displaying of the Hunyuan Works or any portion or element thereof set forth herein.
+c.	“Documentation” shall mean the specifications, manuals and documentation for Tencent Hunyuan made publicly available by Tencent.
+d.	“Hosted Service” shall mean a hosted service offered via an application programming interface (API), web access, or any other electronic or remote means.
+e.	“Licensee,” “You” or “Your” shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Tencent Hunyuan Works for any purpose and in any field of use.
+f.	“Materials” shall mean, collectively, Tencent’s proprietary Tencent Hunyuan and Documentation (and any portion thereof) as made available by Tencent under this Agreement.
+g.	“Model Derivatives” shall mean all: (i) modifications to Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; (ii) works based on Tencent Hunyuan or any Model Derivative of Tencent Hunyuan; or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Tencent Hunyuan or any Model Derivative of Tencent Hunyuan, to that model in order to cause that model to perform similarly to Tencent Hunyuan or a Model Derivative of Tencent Hunyuan, including distillation methods, methods that use intermediate data representations, or methods based on the generation of synthetic data Outputs by Tencent Hunyuan or a Model Derivative of Tencent Hunyuan for training that model. For clarity, Outputs by themselves are not deemed Model Derivatives.
+h.	“Output” shall mean the information and/or content output of Tencent Hunyuan or a Model Derivative that results from operating or otherwise using Tencent Hunyuan or a Model Derivative, including via a Hosted Service.
+i.	“Tencent,” “We” or “Us” shall mean THL A29 Limited.
+j.	“Tencent Hunyuan” shall mean the large language models, image/video/audio/3D generation models, and multimodal large language models and their software and algorithms, including trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made publicly available by Us at  https://huggingface.co/Tencent-Hunyuan/HunyuanDiT and https://github.com/Tencent/HunyuanDiT .
+k.	“Tencent Hunyuan Works” shall mean: (i) the Materials; (ii) Model Derivatives; and (iii) all derivative works thereof.
+l.	“Third Party” or “Third Parties” shall mean individuals or legal entities that are not under common control with Us or You.
+m.	“including” shall mean including but not limited to.
+2.	GRANT OF RIGHTS.
+We grant You a non-exclusive, worldwide, non-transferable and royalty-free limited license under Tencent’s intellectual property or other rights owned by Us embodied in or utilized by the Materials to use, reproduce, distribute, create derivative works of (including Model Derivatives), and make modifications to the Materials, only in accordance with the terms of this Agreement and the Acceptable Use Policy, and You must not violate (or encourage or permit anyone else to violate) any term of this Agreement or the Acceptable Use Policy.
+3.	DISTRIBUTION.
+You may, subject to Your compliance with this Agreement, distribute or make available to Third Parties the Tencent Hunyuan Works, provided that You meet all of the following conditions:
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+d.	All distributions to Third Parties (other than through a Hosted Service) must be accompanied by a “Notice” text file that contains the following notice: “Tencent Hunyuan is licensed under the Tencent Hunyuan Community License Agreement, Copyright © 2024 Tencent. All Rights Reserved. The trademark rights of “Tencent Hunyuan” are owned by Tencent or its affiliate.”
+You may add Your own copyright statement to Your modifications and, except as set forth in this Section and in Section 5, may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Model Derivatives as a whole, provided Your use, reproduction, modification, distribution, performance and display of the work otherwise complies with the terms and conditions of this Agreement. If You receive Tencent Hunyuan Works from a Licensee as part of an integrated end user product, then this Section 3 of this Agreement will not apply to You.
+4.	ADDITIONAL COMMERCIAL TERMS.
+If, on the Tencent Hunyuan version release date, the monthly active users of all products or services made available by or for Licensee is greater than 100 million monthly active users in the preceding calendar month, You must request a license from Tencent, which Tencent may grant to You in its sole discretion, and You are not authorized to exercise any of the rights under this Agreement unless or until Tencent otherwise expressly grants You such rights.
+5.	RULES OF USE.
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+b.	You must not use the Tencent Hunyuan Works or any Output or results of the Tencent Hunyuan Works to improve any other large language model (other than Tencent Hunyuan or Model Derivatives thereof).
+6.	INTELLECTUAL PROPERTY.
+a.	Subject to Tencent’s ownership of Tencent Hunyuan Works made by or for Tencent and intellectual property rights therein, conditioned upon Your compliance with the terms and conditions of this Agreement, as between You and Tencent, You will be the owner of any derivative works and modifications of the Materials and any Model Derivatives that are made by or for You.
+b.	No trademark licenses are granted under this Agreement, and in connection with the Tencent Hunyuan Works, Licensee may not use any name or mark owned by or associated with Tencent or any of its affiliates, except as required for reasonable and customary use in describing and distributing the Tencent Hunyuan Works. Tencent hereby grants You a license to use “Tencent Hunyuan” (the “Mark”) solely as required to comply with the provisions of Section 3(c), provided that You comply with any applicable laws related to trademark protection. All goodwill arising out of Your use of the Mark will inure to the benefit of Tencent.
+c.	If You commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against Us or any person or entity alleging that the Materials or any Output, or any portion of any of the foregoing, infringe any intellectual property or other right owned or licensable by You, then all licenses granted to You under this Agreement shall terminate as of the date such lawsuit or other proceeding is filed. You will defend, indemnify and hold harmless Us from and against any claim by any Third Party arising out of or related to Your or the Third Party’s use or distribution of the Tencent Hunyuan Works.
+d.	Tencent claims no rights in Outputs You generate. You and Your users are solely responsible for Outputs and their subsequent uses.
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+a.	The term of this Agreement shall commence upon Your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
+b.	We may terminate this Agreement if You breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, You must promptly delete and cease use of the Tencent Hunyuan Works. Sections 6(a), 6(c), 7 and 9 shall survive the termination of this Agreement.
+9.	GOVERNING LAW AND JURISDICTION.
+a.	This Agreement and any dispute arising out of or relating to it will be governed by the laws of the Hong Kong Special Administrative Region of the People’s Republic of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
+b.	Exclusive jurisdiction and venue for any dispute arising out of or relating to this Agreement will be a court of competent jurisdiction in the Hong Kong Special Administrative Region of the People’s Republic of China, and Tencent and Licensee consent to the exclusive jurisdiction of such court with respect to any such dispute.
+ 
+
+EXHIBIT A
+ACCEPTABLE USE POLICY
+
+Tencent reserves the right to update this Acceptable Use Policy from time to time.
+Last modified: 2024/5/14
+
+Tencent endeavors to promote safe and fair use of its tools and features, including Tencent Hunyuan. You agree not to use Tencent Hunyuan or Model Derivatives:
+1.	In any way that violates any applicable national, federal, state, local, international or any other law or regulation;
+2.	To harm Yourself or others;
+3.	To repurpose or distribute output from Tencent Hunyuan or any Model Derivatives to harm Yourself or others; 
+4.	To override or circumvent the safety guardrails and safeguards We have put in place;
+5.	For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
+6.	To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections;
+7.	To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement;
+8.	To intentionally defame, disparage or otherwise harass others;
+9.	To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems;
+10.	To generate or disseminate personal identifiable information with the purpose of harming others;
+11.	To generate or disseminate information (including images, code, posts, articles), and place the information in any public context (including –through the use of bot generated tweets), without expressly and conspicuously identifying that the information and/or content is machine generated;
+12.	To impersonate another individual without consent, authorization, or legal right;
+13.	To make high-stakes automated decisions in domains that affect an individual’s safety, rights or wellbeing (e.g., law enforcement, migration, medicine/health, management of critical infrastructure, safety components of products, essential services, credit, employment, housing, education, social scoring, or insurance);
+14.	In a manner that violates or disrespects the social ethics and moral standards of other countries or regions;
+15.	To perform, facilitate, threaten, incite, plan, promote or encourage violent extremism or terrorism;
+16.	For any use intended to discriminate against or harm individuals or groups based on protected characteristics or categories, online or offline social behavior or known or predicted personal or personality characteristics;
+17.	To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
+18.	For military purposes;
+19.	To engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or other professional practices.
+
+For the license of other third party components, please refer to the following URL:
+https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/Notice
+
+--------------------------------------------------------------------
+
+This Model also incorporates insights from Flux's neural network architechtures (https://github.com/black-forest-labs/flux?tab=readme-ov-file). Credits are given to the orginal authors.
@@ -0,0 +1,224 @@
+---
+library_name: hunyuan3d-2.0
+license: other
+license_name: tencent-hunyuan-community
+license_link: https://huggingface.co/tencent/Hunyuan3D-2/blob/main/LICENSE.txt
+language:
+  - en
+  - zh
+tags:
+  - image-to-3d
+  - text-to-3d
+pipeline_tag: image-to-3d
+---
+
+<p align="center">
+  <img src="./assets/images/teaser.jpg">
+</p>
+
+<div align="center">
+  <a href=https://3d.hunyuan.tencent.com target="_blank"><img src=https://img.shields.io/badge/Hunyuan3D-black.svg?logo=homepage height=22px></a>
+  <a href=https://huggingface.co/spaces/tencent/Hunyuan3D-2  target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Demo-276cb4.svg height=22px></a>
+  <a href=https://huggingface.co/tencent/Hunyuan3D-2 target="_blank"><img src=https://img.shields.io/badge/%F0%9F%A4%97%20Models-d96902.svg height=22px></a>
+  <a href=https://3d-models.hunyuan.tencent.com/ target="_blank"><img src= https://img.shields.io/badge/Page-bb8a2e.svg?logo=github height=22px></a>
+<a href=https://discord.gg/GuaWYwzKbX target="_blank"><img src= https://img.shields.io/badge/Discord-white.svg?logo=discord height=22px></a>
+    <a href=https://github.com/Tencent/Hunyuan3D-2/blob/main/assets/report/Tencent_Hunyuan3D_2_0.pdf target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>
+</div>
+
+
+[//]: # (  <a href=# target="_blank"><img src=https://img.shields.io/badge/Report-b5212f.svg?logo=arxiv height=22px></a>)
+
+[//]: # (  <a href=# target="_blank"><img src= https://img.shields.io/badge/Colab-8f2628.svg?logo=googlecolab height=22px></a>)
+
+[//]: # (  <a href="#"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/v/mulankit?logo=pypi"  height=22px></a>)
+
+<br>
+<p align="center">
+“ Living out everyone’s imagination on creating and manipulating 3D assets.”
+</p>
+
+This repository contains the models of the paper [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://huggingface.co/papers/2501.12202).
+For code and more details on how to use it, refer to the [Github repository](https://github.com/Tencent/Hunyuan3D-2).
+
+## 🔥 News
+
+- Jan 21, 2025: 💬 Release [Hunyuan3D 2.0](https://huggingface.co/spaces/tencent/Hunyuan3D-2). Please give it a try!
+
+## **Abstract**
+
+We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets.
+This system includes two foundation components: a large-scale shape generation model - Hunyuan3D-DiT, and a large-scale
+texture synthesis model - Hunyuan3D-Paint.
+The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly
+aligns with a given condition image, laying a solid foundation for downstream applications.
+The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant
+texture maps for either generated or hand-crafted meshes.
+Furthermore, we build Hunyuan3D-Studio - a versatile, user-friendly production platform that simplifies the re-creation
+process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes
+efficiently.
+We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models,
+including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and
+e.t.c.
+
+<p align="center">
+  <img src="assets/images/system.jpg">
+</p>
+
+## ☯️ **Hunyuan3D 2.0**
+
+### Architecture
+
+Hunyuan3D 2.0 features a two-stage generation pipeline, starting with the creation of a bare mesh, followed by the
+synthesis of a texture map for that mesh. This strategy is effective for decoupling the difficulties of shape and
+texture generation and also provides flexibility for texturing either generated or handcrafted meshes.
+
+<p align="left">
+  <img src="assets/images/arch.jpg">
+</p>
+
+### Performance
+
+We have evaluated Hunyuan3D 2.0 with other open-source as well as close-source 3d-generation methods.
+The numerical results indicate that Hunyuan3D 2.0 surpasses all baselines in the quality of generated textured 3D assets
+and the condition following ability.
+
+| Model                   | CMMD(⬇)   | FID_CLIP(⬇) | FID(⬇)      | CLIP-score(⬆) |
+|-------------------------|-----------|-------------|-------------|---------------|
+| Top Open-source Model1  | 3.591     | 54.639      | 289.287     | 0.787         |
+| Top Close-source Model1 | 3.600     | 55.866      | 305.922     | 0.779         |
+| Top Close-source Model2 | 3.368     | 49.744      | 294.628     | 0.806         |
+| Top Close-source Model3 | 3.218     | 51.574      | 295.691     | 0.799         |
+| Hunyuan3D 2.0           | **3.193** | **49.165**  | **282.429** | **0.809**     |
+
+Generation results of Hunyuan3D 2.0:
+<p align="left">
+  <img src="assets/images/e2e-1.gif"  height=300>
+  <img src="assets/images/e2e-2.gif"  height=300>
+</p>
+
+### Pretrained Models
+
+| Model                | Date       | Huggingface                                            |
+|----------------------|------------|--------------------------------------------------------| 
+| Hunyuan3D-DiT-v2-0   | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) |
+| Hunyuan3D-Paint-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2) |
+| Hunyuan3D-Delight-v2-0 | 2025-01-21 | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-delight-v2-0) |
+
+## 🤗 Get Started with Hunyuan3D 2.0
+
+You may follow the next steps to use Hunyuan3D 2.0 via code or the Gradio App.
+
+### Install Requirements
+
+Please install Pytorch via the [official](https://pytorch.org/) site. Then install the other requirements via
+
+```bash
+pip install -r requirements.txt
+# for texture
+cd hy3dgen/texgen/custom_rasterizer
+python3 setup.py install
+cd ../../..
+cd hy3dgen/texgen/differentiable_renderer
+bash compile_mesh_painter.sh OR python3 setup.py install (on Windows)
+```
+
+### API Usage
+
+We designed a diffusers-like API to use our shape generation model - Hunyuan3D-DiT and texture synthesis model -
+Hunyuan3D-Paint.
+
+You could assess **Hunyuan3D-DiT** via:
+
+```python
+from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
+
+pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
+mesh = pipeline(image='assets/demo.png')[0]
+```
+
+The output mesh is a [trimesh object](https://trimesh.org/trimesh.html), which you could save to glb/obj (or other
+format) file.
+
+For **Hunyuan3D-Paint**, do the following:
+
+```python
+from hy3dgen.texgen import Hunyuan3DPaintPipeline
+from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
+
+# let's generate a mesh first
+pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2')
+mesh = pipeline(image='assets/demo.png')[0]
+
+pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2')
+mesh = pipeline(mesh, image='assets/demo.png')
+```
+
+Please visit [minimal_demo.py](minimal_demo.py) for more advanced usage, such as **text to 3D** and **texture generation
+for handcrafted mesh**.
+
+### Gradio App
+
+You could also host a [Gradio](https://www.gradio.app/) App in your own computer via:
+
+```bash
+pip3 install gradio==3.39.0
+python3 gradio_app.py
+```
+
+Don't forget to visit [Hunyuan3D](https://3d.hunyuan.tencent.com) for quick use, if you don't want to host yourself.
+
+## 📑 Open-Source Plan
+
+- [x] Inference Code
+- [x] Model Checkpoints
+- [x] Technical Report
+- [ ] ComfyUI
+- [ ] TensorRT Version
+
+## 🔗 BibTeX
+
+If you found this repository helpful, please cite our report:
+
+```bibtex
+@misc{hunyuan3d22025tencent,
+    title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation},
+    author={Tencent Hunyuan3D Team},
+    year={2025},
+    eprint={2501.12202},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+
+@misc{yang2024tencent,
+    title={Tencent Hunyuan3D-1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation},
+    author={Tencent Hunyuan3D Team},
+    year={2024},
+    eprint={2411.02293},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+## Community Resources
+
+Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0:
+
+- [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper)
+- [Hunyuan3D-2-for-windows](https://github.com/sdbds/Hunyuan3D-2-for-windows)
+- [📦 A bundle for running on Windows | 整合包](https://github.com/YanWenKun/Comfy3D-WinPortable/releases/tag/r8-hunyuan3d2)
+
+## Acknowledgements
+
+We would like to thank the contributors to
+the [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers)
+and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
+
+## Star History
+
+<a href="https://star-history.com/#Tencent/Hunyuan3D-2&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Tencent/Hunyuan3D-2&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=Tencent/Hunyuan3D-2&type=Date" />
+   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=Tencent/Hunyuan3D-2&type=Date" />
+ </picture>
+</a>
@@ -0,0 +1,5 @@
+{
+  "Name": [
+    "Hunyuan3D-2"
+  ],
+}
@@ -0,0 +1,27 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "shortest_edge": 224
+  }
+}
@@ -0,0 +1,38 @@
+{
+  "_class_name": "StableDiffusionInstructPix2PixPipeline",
+  "_diffusers_version": "0.30.1",
+  "_name_or_path": "",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "image_encoder": [
+    null,
+    null
+  ],
+  "requires_safety_checker": false,
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
@@ -0,0 +1,20 @@
+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.30.1",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "skip_prk_steps": true,
+  "steps_offset": 1,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null
+}
@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float16",
+  "transformers_version": "4.45.0.dev0",
+  "vocab_size": 49408
+}
@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "!",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
@@ -0,0 +1,38 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "!",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49406": {
+      "content": "<|startoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "49407": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 77,
+  "pad_token": "!",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": "<|endoftext|>"
+}
@@ -0,0 +1,73 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.30.1",
+  "_name_or_path": "",
+  "act_fn": "silu",
+  "addition_embed_type": null,
+  "addition_embed_type_num_heads": 64,
+  "addition_time_embed_dim": null,
+  "attention_head_dim": [
+    5,
+    10,
+    20,
+    20
+  ],
+  "attention_type": "default",
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "class_embeddings_concat": false,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": 1024,
+  "cross_attention_norm": null,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dropout": 0.0,
+  "dual_cross_attention": false,
+  "encoder_hid_dim": null,
+  "encoder_hid_dim_type": null,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 8,
+  "layers_per_block": 2,
+  "mid_block_only_cross_attention": null,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_attention_heads": null,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "projection_class_embeddings_input_dim": null,
+  "resnet_out_scale_factor": 1.0,
+  "resnet_skip_time_act": false,
+  "resnet_time_scale_shift": "default",
+  "reverse_transformer_layers_per_block": null,
+  "sample_size": 96,
+  "time_cond_proj_dim": null,
+  "time_embedding_act_fn": null,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "transformer_layers_per_block": 1,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "upcast_attention": true,
+  "use_linear_projection": true
+}
@@ -0,0 +1,38 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.30.1",
+  "_name_or_path": "",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 4,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 768,
+  "scaling_factor": 0.18215,
+  "shift_factor": null,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": true,
+  "use_quant_conv": true
+}
@@ -0,0 +1,68 @@
+model:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.Hunyuan3DDiT
+  params:
+    in_channels: 64
+    context_in_dim: 1536
+    hidden_size: 1024
+    mlp_ratio: 4.0
+    num_heads: 16
+    depth: 16
+    depth_single_blocks: 32
+    axes_dim: [ 64 ]
+    theta: 10000
+    qkv_bias: True
+
+vae:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.ShapeVAE
+  params:
+    num_latents: 3072
+    embed_dim: 64
+    num_freqs: 8
+    include_pi: false
+    heads: 16
+    width: 1024
+    num_decoder_layers: 16
+    qkv_bias: false
+    qk_norm: true
+    scale_factor: 0.9990943042622529
+
+conditioner:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.models.SingleImageEncoder
+  params:
+    main_image_encoder:
+      type: DinoImageEncoder # dino giant
+      kwargs:
+        config:
+          attention_probs_dropout_prob: 0.0
+          drop_path_rate: 0.0
+          hidden_act: gelu
+          hidden_dropout_prob: 0.0
+          hidden_size: 1536
+          image_size: 518
+          initializer_range: 0.02
+          layer_norm_eps: 1.e-6
+          layerscale_value: 1.0
+          mlp_ratio: 4
+          model_type: dinov2
+          num_attention_heads: 24
+          num_channels: 3
+          num_hidden_layers: 40
+          patch_size: 14
+          qkv_bias: true
+          torch_dtype: float32
+          use_swiglu_ffn: true
+        image_size: 518
+
+scheduler:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.schedulers.FlowMatchEulerDiscreteScheduler
+  params:
+    num_train_timesteps: 1000
+
+image_processor:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.preprocessors.ImageProcessorV2
+  params:
+    size: 512
+    border_ratio: 0.15
+
+pipeline:
+  target: Hunyuan3D_V2.hy3dgen.shapegen.pipelines.Hunyuan3DDiTFlowMatchingPipeline
@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
@@ -0,0 +1,20 @@
+{
+  "crop_size": 224,
+  "do_center_crop": true,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": 224
+}
@@ -0,0 +1,33 @@
+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.23.1",
+  "feature_extractor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "requires_safety_checker": false,
+  "safety_checker": [
+    null,
+    null
+  ],
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "unet": [
+    "modules",
+    "UNet2p5DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}
@@ -0,0 +1,15 @@
+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.23.1",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "set_alpha_to_one": true,
+  "steps_offset": 1,
+  "trained_betas": null,
+  "timestep_spacing": "trailing",
+  "rescale_betas_zero_snr": true
+}
@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "stabilityai/stable-diffusion-2",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_size": 1024,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 23,
+  "pad_token_id": 1,
+  "projection_dim": 512,
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.0.dev0",
+  "vocab_size": 49408
+}
@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "!",
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
@@ -0,0 +1,34 @@
+{
+  "add_prefix_space": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "do_lower_case": true,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "errors": "replace",
+  "model_max_length": 77,
+  "name_or_path": "stabilityai/stable-diffusion-2",
+  "pad_token": "<|endoftext|>",
+  "special_tokens_map_file": "./special_tokens_map.json",
+  "tokenizer_class": "CLIPTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}
@@ -0,0 +1,45 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.10.0.dev0",
+  "act_fn": "silu",
+  "attention_head_dim": [
+    5,
+    10,
+    20,
+    20
+  ],
+  "block_out_channels": [
+    320,
+    640,
+    1280,
+    1280
+  ],
+  "center_input_sample": false,
+  "cross_attention_dim": 1024,
+  "down_block_types": [
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dual_cross_attention": false,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 4,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 4,
+  "sample_size": 64,
+  "up_block_types": [
+    "UpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D"
+  ],
+  "use_linear_projection": true
+}
@@ -0,0 +1,437 @@
+import os
+import json
+from typing import Any, Dict, Optional
+from diffusers.models import UNet2DConditionModel
+
+import numpy
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+import torch.distributed
+from PIL import Image
+from einops import rearrange
+from typing import Any, Callable, Dict, List, Optional, Union, Tuple
+
+import diffusers
+from diffusers import (
+    AutoencoderKL,
+    DDPMScheduler,
+    DiffusionPipeline,
+    EulerAncestralDiscreteScheduler,
+    UNet2DConditionModel,
+    ImagePipelineOutput
+)
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models.attention_processor import Attention, AttnProcessor, XFormersAttnProcessor, AttnProcessor2_0
+from diffusers.utils.import_utils import is_xformers_available
+
+
+from diffusers.utils import deprecate
+
+from diffusers.models.transformers.transformer_2d import BasicTransformerBlock
+
+
+
+def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
+    # "feed_forward_chunk_size" can be used to save memory
+    if hidden_states.shape[chunk_dim] % chunk_size != 0:
+        raise ValueError(
+            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+        )
+
+    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
+    ff_output = torch.cat(
+        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+        dim=chunk_dim,
+    )
+    return ff_output
+
+
+class Basic2p5DTransformerBlock(torch.nn.Module):
+    def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True) -> None:
+        super().__init__()
+        self.transformer = transformer
+        self.layer_name = layer_name
+        self.use_ma = use_ma
+        self.use_ra = use_ra
+
+        # multiview attn
+        if self.use_ma:
+            self.attn_multiview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+
+        # ref attn
+        if self.use_ra:
+            self.attn_refview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.transformer, name)
+        
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1)
+        mode = cross_attention_kwargs.pop('mode', None)
+        mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
+        ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
+        condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None)
+
+
+        if self.norm_type == "ada_norm":
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.norm_type == "ada_norm_zero":
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif self.norm_type == "ada_norm_single":
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+        else:
+            raise ValueError("Incorrect norm used")
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        # 1. Prepare GLIGEN inputs
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+        if self.norm_type == "ada_norm_zero":
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.norm_type == "ada_norm_single":
+            attn_output = gate_msa * attn_output
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+        
+        # 1.2 Reference Attention
+        if 'w' in mode:
+            condition_embed_dict[self.layer_name] = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch) # B, (N L), C
+
+        if 'r' in mode and self.use_ra:
+            condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1,num_in_batch,1,1) # B N L C
+            condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c')
+
+            attn_output = self.attn_refview(
+                norm_hidden_states,
+                encoder_hidden_states=condition_embed,
+                attention_mask=None,
+                **cross_attention_kwargs
+            )
+            ref_scale_timing = ref_scale
+            if isinstance(ref_scale, torch.Tensor):
+                ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
+                for _ in range(attn_output.ndim - 1):
+                    ref_scale_timing = ref_scale_timing.unsqueeze(-1)
+            hidden_states = ref_scale_timing * attn_output + hidden_states
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+            
+
+        # 1.3 Multiview Attention
+        if num_in_batch > 1 and self.use_ma:
+            multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch)
+
+            attn_output = self.attn_multiview(
+                multivew_hidden_states,
+                encoder_hidden_states=multivew_hidden_states,
+                **cross_attention_kwargs
+            )
+
+            attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch)
+
+            hidden_states = mva_scale * attn_output + hidden_states
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+
+        # 1.2 GLIGEN Control
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            if self.norm_type == "ada_norm":
+                norm_hidden_states = self.norm2(hidden_states, timestep)
+            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
+                norm_hidden_states = self.norm2(hidden_states)
+            elif self.norm_type == "ada_norm_single":
+                # For PixArt norm2 isn't applied here:
+                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
+                norm_hidden_states = hidden_states
+            elif self.norm_type == "ada_norm_continuous":
+                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
+            else:
+                raise ValueError("Incorrect norm")
+
+            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
+                norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+            
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        # i2vgen doesn't have this norm 🤷‍♂️
+        if self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif not self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm3(hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.norm_type == "ada_norm_single":
+            ff_output = gate_mlp * ff_output
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+import copy
+class UNet2p5DConditionModel(torch.nn.Module):
+    def __init__(self, unet: UNet2DConditionModel) -> None:
+        super().__init__()
+        self.unet = unet
+
+        self.use_ma  = True
+        self.use_ra  = True
+        self.use_camera_embedding = True
+        self.use_dual_stream = True
+
+        if self.use_dual_stream:
+            self.unet_dual = copy.deepcopy(unet)
+            self.init_attention(self.unet_dual)
+        self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra)
+        self.init_condition()
+        self.init_camera_embedding()
+
+    
+    @staticmethod
+    def from_pretrained(pretrained_model_name_or_path, **kwargs):
+        torch_dtype = kwargs.pop('torch_dtype', torch.float32)
+        config_path = os.path.join(pretrained_model_name_or_path, 'config.json')
+        unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin')
+        with open(config_path, 'r', encoding='utf-8') as file:
+            config = json.load(file)
+        unet = UNet2DConditionModel(**config)
+        unet = UNet2p5DConditionModel(unet)
+        unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True)
+        unet.load_state_dict(unet_ckpt, strict=True)
+        unet = unet.to(torch_dtype)
+        return unet
+
+    def init_condition(self):
+        self.unet.conv_in = torch.nn.Conv2d(
+            12, 
+            self.unet.conv_in.out_channels, 
+            kernel_size=self.unet.conv_in.kernel_size, 
+            stride=self.unet.conv_in.stride, 
+            padding=self.unet.conv_in.padding, 
+            dilation=self.unet.conv_in.dilation, 
+            groups=self.unet.conv_in.groups, 
+            bias=self.unet.conv_in.bias is not None)
+        self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1,77,1024))
+        self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1,77,1024))
+
+    def init_camera_embedding(self):
+
+        self.max_num_ref_image = 5
+        self.max_num_gen_image = 12*3+4*2
+
+        if self.use_camera_embedding:
+            time_embed_dim = 1280
+            self.unet.class_embedding = nn.Embedding(self.max_num_ref_image+self.max_num_gen_image, time_embed_dim)
+
+
+    def init_attention(self, unet, use_ma=False, use_ra=False):
+
+        for down_block_i, down_block in enumerate(unet.down_blocks):
+            if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention:
+                for attn_i, attn in enumerate(down_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'down_{down_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra)
+                            
+
+        if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention:
+            for attn_i, attn in enumerate(unet.mid_block.attentions):
+                for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                    if isinstance(transformer, BasicTransformerBlock):
+                        attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'mid_{attn_i}_{transformer_i}', use_ma, use_ra)
+
+        for up_block_i, up_block in enumerate(unet.up_blocks):
+            if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention:
+                for attn_i, attn in enumerate(up_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer, f'up_{up_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra)
+
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.unet, name)
+        
+    def forward(
+        self, sample, timestep, encoder_hidden_states,
+        *args, down_intrablock_additional_residuals=None,
+        down_block_res_samples=None, mid_block_res_sample=None,
+        **cached_condition,
+    ):
+        B, N_gen, _, H, W = sample.shape
+        assert H == W
+
+        if self.use_camera_embedding:
+            camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image
+            camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)')
+        else:
+            camera_info_gen = None
+
+        sample = [sample]
+        if 'normal_imgs' in cached_condition:
+            sample.append(cached_condition["normal_imgs"])
+        if 'position_imgs' in cached_condition:
+            sample.append(cached_condition["position_imgs"])
+        sample = torch.cat(sample, dim=2)
+
+        sample = rearrange(sample, 'b n c h w -> (b n) c h w')
+
+        encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1)
+        encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c')
+
+        if self.use_ra:
+            if 'condition_embed_dict' in cached_condition:
+                condition_embed_dict = cached_condition['condition_embed_dict']
+            else:
+                condition_embed_dict = {}
+                ref_latents = cached_condition['ref_latents']
+                N_ref = ref_latents.shape[1]
+                if self.use_camera_embedding:
+                    camera_info_ref = cached_condition['camera_info_ref']
+                    camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)')
+                else:
+                    camera_info_ref = None
+                
+                ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w')
+
+                encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1)
+                encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c')
+
+                noisy_ref_latents = ref_latents
+                timestep_ref = 0
+
+                if self.use_dual_stream:
+                    unet_ref = self.unet_dual
+                else:
+                    unet_ref = self.unet
+                unet_ref(
+                    noisy_ref_latents, timestep_ref,
+                    encoder_hidden_states=encoder_hidden_states_ref,
+                    class_labels=camera_info_ref,
+                    # **kwargs
+                    return_dict=False,
+                    cross_attention_kwargs={
+                        'mode':'w', 'num_in_batch':N_ref, 
+                        'condition_embed_dict':condition_embed_dict},
+                )
+                cached_condition['condition_embed_dict'] = condition_embed_dict
+        else:
+            condition_embed_dict = None
+
+
+        mva_scale = cached_condition.get('mva_scale', 1.0)
+        ref_scale = cached_condition.get('ref_scale', 1.0)
+
+        return self.unet(
+            sample, timestep,
+            encoder_hidden_states_gen, *args,
+            class_labels=camera_info_gen,
+            down_intrablock_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals
+            ] if down_intrablock_additional_residuals is not None else None,
+            down_block_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples
+            ] if down_block_res_samples is not None else None,
+            mid_block_additional_residual=(
+                mid_block_res_sample.to(dtype=self.unet.dtype)
+                if mid_block_res_sample is not None else None
+            ),
+            return_dict=False,
+            cross_attention_kwargs={
+                'mode':'r', 'num_in_batch':N_gen, 
+                'condition_embed_dict':condition_embed_dict, 
+                'mva_scale': mva_scale,
+                'ref_scale': ref_scale,
+            },
+        ) 
@@ -0,0 +1,29 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.10.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 3,
+  "latent_channels": 4,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 768,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}
@@ -0,0 +1,23 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
@@ -0,0 +1,36 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+from PIL import Image
+from rembg import remove, new_session
+
+
+class BackgroundRemover():
+    def __init__(self):
+        self.session = new_session()
+
+    def __call__(self, image: Image.Image):
+        output = remove(image, session=self.session, bgcolor=[255, 255, 255, 0])
+        return output
@@ -0,0 +1,27 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+from .pipelines import Hunyuan3DDiTPipeline, Hunyuan3DDiTFlowMatchingPipeline
+from .postprocessors import FaceReducer, FloaterRemover, DegenerateFaceRemover
+from .preprocessors import ImageProcessorV2, IMAGE_PROCESSORS, DEFAULT_IMAGEPROCESSOR
@@ -0,0 +1,28 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+from .conditioner import DualImageEncoder, SingleImageEncoder, DinoImageEncoder, CLIPImageEncoder
+from .hunyuan3ddit import Hunyuan3DDiT
+from .vae import ShapeVAE
@@ -0,0 +1,165 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from transformers import (
+    CLIPVisionModelWithProjection,
+    CLIPVisionConfig,
+    Dinov2Model,
+    Dinov2Config,
+)
+
+
+class ImageEncoder(nn.Module):
+    def __init__(
+        self,
+        version=None,
+        config=None,
+        use_cls_token=True,
+        image_size=224,
+        **kwargs,
+    ):
+        super().__init__()
+
+        if config is None:
+            self.model = self.MODEL_CLASS.from_pretrained(version)
+        else:
+            self.model = self.MODEL_CLASS(self.MODEL_CONFIG_CLASS.from_dict(config))
+        self.model.eval()
+        self.model.requires_grad_(False)
+        self.use_cls_token = use_cls_token
+        self.size = image_size // 14
+        self.num_patches = (image_size // 14) ** 2
+        if self.use_cls_token:
+            self.num_patches += 1
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(image_size, transforms.InterpolationMode.BILINEAR, antialias=True),
+                transforms.CenterCrop(image_size),
+                transforms.Normalize(
+                    mean=self.mean,
+                    std=self.std,
+                ),
+            ]
+        )
+
+    def forward(self, image, mask=None, value_range=(-1, 1)):
+        if value_range is not None:
+            low, high = value_range
+            image = (image - low) / (high - low)
+
+        image = image.to(self.model.device, dtype=self.model.dtype)
+        inputs = self.transform(image)
+        outputs = self.model(inputs)
+
+        last_hidden_state = outputs.last_hidden_state
+        if not self.use_cls_token:
+            last_hidden_state = last_hidden_state[:, 1:, :]
+
+        return last_hidden_state
+
+    def unconditional_embedding(self, batch_size):
+        device = next(self.model.parameters()).device
+        dtype = next(self.model.parameters()).dtype
+        zero = torch.zeros(
+            batch_size,
+            self.num_patches,
+            self.model.config.hidden_size,
+            device=device,
+            dtype=dtype,
+        )
+
+        return zero
+
+
+class CLIPImageEncoder(ImageEncoder):
+    MODEL_CLASS = CLIPVisionModelWithProjection
+    MODEL_CONFIG_CLASS = CLIPVisionConfig
+    mean = [0.48145466, 0.4578275, 0.40821073]
+    std = [0.26862954, 0.26130258, 0.27577711]
+
+
+class DinoImageEncoder(ImageEncoder):
+    MODEL_CLASS = Dinov2Model
+    MODEL_CONFIG_CLASS = Dinov2Config
+    mean = [0.485, 0.456, 0.406]
+    std = [0.229, 0.224, 0.225]
+
+
+def build_image_encoder(config):
+    if config['type'] == 'CLIPImageEncoder':
+        return CLIPImageEncoder(**config['kwargs'])
+    elif config['type'] == 'DinoImageEncoder':
+        return DinoImageEncoder(**config['kwargs'])
+    else:
+        raise ValueError(f'Unknown image encoder type: {config["type"]}')
+
+
+class DualImageEncoder(nn.Module):
+    def __init__(
+        self,
+        main_image_encoder,
+        additional_image_encoder,
+    ):
+        super().__init__()
+        self.main_image_encoder = build_image_encoder(main_image_encoder)
+        self.additional_image_encoder = build_image_encoder(additional_image_encoder)
+
+    def forward(self, image, mask=None):
+        outputs = {
+            'main': self.main_image_encoder(image, mask=mask),
+            'additional': self.additional_image_encoder(image, mask=mask),
+        }
+        return outputs
+
+    def unconditional_embedding(self, batch_size):
+        outputs = {
+            'main': self.main_image_encoder.unconditional_embedding(batch_size),
+            'additional': self.additional_image_encoder.unconditional_embedding(batch_size),
+        }
+        return outputs
+
+
+class SingleImageEncoder(nn.Module):
+    def __init__(
+        self,
+        main_image_encoder,
+    ):
+        super().__init__()
+        self.main_image_encoder = build_image_encoder(main_image_encoder)
+
+    def forward(self, image, mask=None):
+        outputs = {
+            'main': self.main_image_encoder(image, mask=mask),
+        }
+        return outputs
+
+    def unconditional_embedding(self, batch_size):
+        outputs = {
+            'main': self.main_image_encoder.unconditional_embedding(batch_size),
+        }
+        return outputs
@@ -0,0 +1,390 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import math
+from dataclasses import dataclass
+from typing import List, Tuple, Optional
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+
+def attention(q: Tensor, k: Tensor, v: Tensor, **kwargs) -> Tensor:
+    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+    x = rearrange(x, "B H L D -> B L (H D)")
+    return x
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
+        t.device
+    )
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int):
+        super().__init__()
+        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+        self.silu = nn.SiLU()
+        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.scale = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x: Tensor):
+        x_dtype = x.dtype
+        x = x.float()
+        rrms = torch.rsqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + 1e-6)
+        return (x * rrms).to(dtype=x_dtype) * self.scale
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int):
+        super().__init__()
+        self.query_norm = RMSNorm(dim)
+        self.key_norm = RMSNorm(dim)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tuple[Tensor, Tensor]:
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.norm = QKNorm(head_dim)
+        self.proj = nn.Linear(dim, dim)
+
+    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+        qkv = self.qkv(x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+        x = attention(q, k, v, pe=pe)
+        x = self.proj(x)
+        return x
+
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate: Tensor
+
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+
+    def forward(self, vec: Tensor) -> Tuple[ModulationOut, Optional[ModulationOut]]:
+        out = self.lin(nn.functional.silu(vec))[:, None, :]
+        out = out.chunk(self.multiplier, dim=-1)
+
+        return (
+            ModulationOut(*out[:3]),
+            ModulationOut(*out[3:]) if self.is_double else None,
+        )
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float,
+        qkv_bias: bool = False,
+    ):
+        super().__init__()
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_mod = Modulation(hidden_size, double=True)
+        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.img_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+        self.txt_mod = Modulation(hidden_size, double=True)
+        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)
+
+        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.txt_mlp = nn.Sequential(
+            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            nn.GELU(approximate="tanh"),
+            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+        )
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> Tuple[Tensor, Tensor]:
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+        img_modulated = self.img_norm1(img)
+        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        q = torch.cat((txt_q, img_q), dim=2)
+        k = torch.cat((txt_k, img_k), dim=2)
+        v = torch.cat((txt_v, img_v), dim=2)
+
+        attn = attention(q, k, v, pe=pe)
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
+
+        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+
+        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
+        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: Optional[float] = None,
+    ):
+        super().__init__()
+
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        # proj and mlp_out
+        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+        self.norm = QKNorm(head_dim)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+        self.modulation = Modulation(hidden_size, double=False)
+
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+        mod, _ = self.modulation(vec)
+
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        return x + mod.gate * output
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x
+
+
+class Hunyuan3DDiT(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 64,
+        context_in_dim: int = 1536,
+        hidden_size: int = 1024,
+        mlp_ratio: float = 4.0,
+        num_heads: int = 16,
+        depth: int = 16,
+        depth_single_blocks: int = 32,
+        axes_dim: List[int] = [64],
+        theta: int = 10_000,
+        qkv_bias: bool = True,
+        time_factor: float = 1000,
+        ckpt_path: Optional[str] = None,
+        **kwargs,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.context_in_dim = context_in_dim
+        self.hidden_size = hidden_size
+        self.mlp_ratio = mlp_ratio
+        self.num_heads = num_heads
+        self.depth = depth
+        self.depth_single_blocks = depth_single_blocks
+        self.axes_dim = axes_dim
+        self.theta = theta
+        self.qkv_bias = qkv_bias
+        self.time_factor = time_factor
+        self.out_channels = self.in_channels
+
+        if hidden_size % num_heads != 0:
+            raise ValueError(
+                f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}"
+            )
+        pe_dim = hidden_size // num_heads
+        if sum(axes_dim) != pe_dim:
+            raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.latent_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
+        self.cond_in = nn.Linear(context_in_dim, self.hidden_size)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                )
+                for _ in range(depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=mlp_ratio,
+                )
+                for _ in range(depth_single_blocks)
+            ]
+        )
+
+        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
+
+        if ckpt_path is not None:
+            print('restored denoiser ckpt', ckpt_path)
+
+            ckpt = torch.load(ckpt_path, map_location="cpu")
+            if 'state_dict' not in ckpt:
+                # deepspeed ckpt
+                state_dict = {}
+                for k in ckpt.keys():
+                    new_k = k.replace('_forward_module.', '')
+                    state_dict[new_k] = ckpt[k]
+            else:
+                state_dict = ckpt["state_dict"]
+
+            final_state_dict = {}
+            for k, v in state_dict.items():
+                if k.startswith('model.'):
+                    final_state_dict[k.replace('model.', '')] = v
+                else:
+                    final_state_dict[k] = v
+            missing, unexpected = self.load_state_dict(final_state_dict, strict=False)
+            print('unexpected keys:', unexpected)
+            print('missing keys:', missing)
+
+    def forward(
+        self,
+        x,
+        t,
+        contexts,
+        **kwargs,
+    ) -> Tensor:
+        cond = contexts['main']
+        latent = self.latent_in(x)
+        vec = self.time_in(timestep_embedding(t, 256, self.time_factor).to(dtype=latent.dtype))
+        cond = self.cond_in(cond)
+        pe = None
+
+        for block in self.double_blocks:
+            latent, cond = block(img=latent, txt=cond, vec=vec, pe=pe)
+
+        latent = torch.cat((cond, latent), 1)
+        for block in self.single_blocks:
+            latent = block(latent, vec=vec, pe=pe)
+
+        latent = latent[:, cond.shape[1]:, ...]
+        latent = self.final_layer(latent, vec)
+        return latent
@@ -0,0 +1,636 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+from typing import Tuple, List, Union, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from skimage import measure
+from tqdm import tqdm
+
+
+class FourierEmbedder(nn.Module):
+    """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts
+    each feature dimension of `x[..., i]` into:
+        [
+            sin(x[..., i]),
+            sin(f_1*x[..., i]),
+            sin(f_2*x[..., i]),
+            ...
+            sin(f_N * x[..., i]),
+            cos(x[..., i]),
+            cos(f_1*x[..., i]),
+            cos(f_2*x[..., i]),
+            ...
+            cos(f_N * x[..., i]),
+            x[..., i]     # only present if include_input is True.
+        ], here f_i is the frequency.
+
+    Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs].
+    If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...];
+    Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)].
+
+    Args:
+        num_freqs (int): the number of frequencies, default is 6;
+        logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
+            otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)];
+        input_dim (int): the input dimension, default is 3;
+        include_input (bool): include the input tensor or not, default is True.
+
+    Attributes:
+        frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
+                otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1);
+
+        out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1),
+            otherwise, it is input_dim * num_freqs * 2.
+
+    """
+
+    def __init__(self,
+                 num_freqs: int = 6,
+                 logspace: bool = True,
+                 input_dim: int = 3,
+                 include_input: bool = True,
+                 include_pi: bool = True) -> None:
+
+        """The initialization"""
+
+        super().__init__()
+
+        if logspace:
+            frequencies = 2.0 ** torch.arange(
+                num_freqs,
+                dtype=torch.float32
+            )
+        else:
+            frequencies = torch.linspace(
+                1.0,
+                2.0 ** (num_freqs - 1),
+                num_freqs,
+                dtype=torch.float32
+            )
+
+        if include_pi:
+            frequencies *= torch.pi
+
+        self.register_buffer("frequencies", frequencies, persistent=False)
+        self.include_input = include_input
+        self.num_freqs = num_freqs
+
+        self.out_dim = self.get_dims(input_dim)
+
+    def get_dims(self, input_dim):
+        temp = 1 if self.include_input or self.num_freqs == 0 else 0
+        out_dim = input_dim * (self.num_freqs * 2 + temp)
+
+        return out_dim
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """ Forward process.
+
+        Args:
+            x: tensor of shape [..., dim]
+
+        Returns:
+            embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)]
+                where temp is 1 if include_input is True and 0 otherwise.
+        """
+
+        if self.num_freqs > 0:
+            embed = (x[..., None].contiguous() * self.frequencies).view(*x.shape[:-1], -1)
+            if self.include_input:
+                return torch.cat((x, embed.sin(), embed.cos()), dim=-1)
+            else:
+                return torch.cat((embed.sin(), embed.cos()), dim=-1)
+        else:
+            return x
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+        This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+        the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+        See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+        changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+        'survival rate' as the argument.
+
+        """
+        if self.drop_prob == 0. or not self.training:
+            return x
+        keep_prob = 1 - self.drop_prob
+        shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+        random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+        if keep_prob > 0.0 and self.scale_by_keep:
+            random_tensor.div_(keep_prob)
+        return x * random_tensor
+
+    def extra_repr(self):
+        return f'drop_prob={round(self.drop_prob, 3):0.3f}'
+
+
+class MLP(nn.Module):
+    def __init__(
+        self, *,
+        width: int,
+        output_width: int = None,
+        drop_path_rate: float = 0.0
+    ):
+        super().__init__()
+        self.width = width
+        self.c_fc = nn.Linear(width, width * 4)
+        self.c_proj = nn.Linear(width * 4, output_width if output_width is not None else width)
+        self.gelu = nn.GELU()
+        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(self, x):
+        return self.drop_path(self.c_proj(self.gelu(self.c_fc(x))))
+
+
+class QKVMultiheadCrossAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        heads: int,
+        n_data: Optional[int] = None,
+        width=None,
+        qk_norm=False,
+        norm_layer=nn.LayerNorm
+    ):
+        super().__init__()
+        self.heads = heads
+        self.n_data = n_data
+        self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+
+    def forward(self, q, kv):
+        _, n_ctx, _ = q.shape
+        bs, n_data, width = kv.shape
+        attn_ch = width // self.heads // 2
+        q = q.view(bs, n_ctx, self.heads, -1)
+        kv = kv.view(bs, n_data, self.heads, -1)
+        k, v = torch.split(kv, attn_ch, dim=-1)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
+        out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
+
+        return out
+
+
+class MultiheadCrossAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        width: int,
+        heads: int,
+        qkv_bias: bool = True,
+        n_data: Optional[int] = None,
+        data_width: Optional[int] = None,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False
+    ):
+        super().__init__()
+        self.n_data = n_data
+        self.width = width
+        self.heads = heads
+        self.data_width = width if data_width is None else data_width
+        self.c_q = nn.Linear(width, width, bias=qkv_bias)
+        self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias)
+        self.c_proj = nn.Linear(width, width)
+        self.attention = QKVMultiheadCrossAttention(
+            heads=heads,
+            n_data=n_data,
+            width=width,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm
+        )
+
+    def forward(self, x, data):
+        x = self.c_q(x)
+        data = self.c_kv(data)
+        x = self.attention(x, data)
+        x = self.c_proj(x)
+        return x
+
+
+class ResidualCrossAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_data: Optional[int] = None,
+        width: int,
+        heads: int,
+        data_width: Optional[int] = None,
+        qkv_bias: bool = True,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False
+    ):
+        super().__init__()
+
+        if data_width is None:
+            data_width = width
+
+        self.attn = MultiheadCrossAttention(
+            n_data=n_data,
+            width=width,
+            heads=heads,
+            data_width=data_width,
+            qkv_bias=qkv_bias,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm
+        )
+        self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+        self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6)
+        self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+        self.mlp = MLP(width=width)
+
+    def forward(self, x: torch.Tensor, data: torch.Tensor):
+        x = x + self.attn(self.ln_1(x), self.ln_2(data))
+        x = x + self.mlp(self.ln_3(x))
+        return x
+
+
+class QKVMultiheadAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        heads: int,
+        n_ctx: int,
+        width=None,
+        qk_norm=False,
+        norm_layer=nn.LayerNorm
+    ):
+        super().__init__()
+        self.heads = heads
+        self.n_ctx = n_ctx
+        self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+
+    def forward(self, qkv):
+        bs, n_ctx, width = qkv.shape
+        attn_ch = width // self.heads // 3
+        qkv = qkv.view(bs, n_ctx, self.heads, -1)
+        q, k, v = torch.split(qkv, attn_ch, dim=-1)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
+        out = F.scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
+        return out
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        qkv_bias: bool,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False,
+        drop_path_rate: float = 0.0
+    ):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.heads = heads
+        self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias)
+        self.c_proj = nn.Linear(width, width)
+        self.attention = QKVMultiheadAttention(
+            heads=heads,
+            n_ctx=n_ctx,
+            width=width,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm
+        )
+        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(self, x):
+        x = self.c_qkv(x)
+        x = self.attention(x)
+        x = self.drop_path(self.c_proj(x))
+        return x
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        qkv_bias: bool = True,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False,
+        drop_path_rate: float = 0.0,
+    ):
+        super().__init__()
+        self.attn = MultiheadAttention(
+            n_ctx=n_ctx,
+            width=width,
+            heads=heads,
+            qkv_bias=qkv_bias,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm,
+            drop_path_rate=drop_path_rate
+        )
+        self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+        self.mlp = MLP(width=width, drop_path_rate=drop_path_rate)
+        self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_ctx: int,
+        width: int,
+        layers: int,
+        heads: int,
+        qkv_bias: bool = True,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False,
+        drop_path_rate: float = 0.0
+    ):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.ModuleList(
+            [
+                ResidualAttentionBlock(
+                    n_ctx=n_ctx,
+                    width=width,
+                    heads=heads,
+                    qkv_bias=qkv_bias,
+                    norm_layer=norm_layer,
+                    qk_norm=qk_norm,
+                    drop_path_rate=drop_path_rate
+                )
+                for _ in range(layers)
+            ]
+        )
+
+    def forward(self, x: torch.Tensor):
+        for block in self.resblocks:
+            x = block(x)
+        return x
+
+
+class CrossAttentionDecoder(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        num_latents: int,
+        out_channels: int,
+        fourier_embedder: FourierEmbedder,
+        width: int,
+        heads: int,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        label_type: str = "binary"
+    ):
+        super().__init__()
+
+        self.fourier_embedder = fourier_embedder
+
+        self.query_proj = nn.Linear(self.fourier_embedder.out_dim, width)
+
+        self.cross_attn_decoder = ResidualCrossAttentionBlock(
+            n_data=num_latents,
+            width=width,
+            heads=heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm
+        )
+
+        self.ln_post = nn.LayerNorm(width)
+        self.output_proj = nn.Linear(width, out_channels)
+        self.label_type = label_type
+
+    def forward(self, queries: torch.FloatTensor, latents: torch.FloatTensor):
+        queries = self.query_proj(self.fourier_embedder(queries).to(latents.dtype))
+        x = self.cross_attn_decoder(queries, latents)
+        x = self.ln_post(x)
+        occ = self.output_proj(x)
+        return occ
+
+
+def generate_dense_grid_points(bbox_min: np.ndarray,
+                               bbox_max: np.ndarray,
+                               octree_depth: int,
+                               indexing: str = "ij",
+                               octree_resolution: int = None,
+                               ):
+    length = bbox_max - bbox_min
+    num_cells = np.exp2(octree_depth)
+    if octree_resolution is not None:
+        num_cells = octree_resolution
+
+    x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
+    y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
+    z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
+    [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
+    xyz = np.stack((xs, ys, zs), axis=-1)
+    xyz = xyz.reshape(-1, 3)
+    grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
+
+    return xyz, grid_size, length
+
+
+def center_vertices(vertices):
+    """Translate the vertices so that bounding box is centered at zero."""
+    vert_min = vertices.min(dim=0)[0]
+    vert_max = vertices.max(dim=0)[0]
+    vert_center = 0.5 * (vert_min + vert_max)
+    return vertices - vert_center
+
+
+class Latent2MeshOutput:
+
+    def __init__(self, mesh_v=None, mesh_f=None):
+        self.mesh_v = mesh_v
+        self.mesh_f = mesh_f
+
+
+class ShapeVAE(nn.Module):
+    def __init__(
+        self,
+        *,
+        num_latents: int,
+        embed_dim: int,
+        width: int,
+        heads: int,
+        num_decoder_layers: int,
+        num_freqs: int = 8,
+        include_pi: bool = True,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        label_type: str = "binary",
+        drop_path_rate: float = 0.0,
+        scale_factor: float = 1.0,
+    ):
+        super().__init__()
+        self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
+
+        self.post_kl = nn.Linear(embed_dim, width)
+
+        self.transformer = Transformer(
+            n_ctx=num_latents,
+            width=width,
+            layers=num_decoder_layers,
+            heads=heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            drop_path_rate=drop_path_rate
+        )
+
+        self.geo_decoder = CrossAttentionDecoder(
+            fourier_embedder=self.fourier_embedder,
+            out_channels=1,
+            num_latents=num_latents,
+            width=width,
+            heads=heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm,
+            label_type=label_type,
+        )
+
+        self.scale_factor = scale_factor
+        self.latent_shape = (num_latents, embed_dim)
+
+    def forward(self, latents):
+        latents = self.post_kl(latents)
+        latents = self.transformer(latents)
+        return latents
+
+    @torch.no_grad()
+    def latents2mesh(
+        self,
+        latents: torch.FloatTensor,
+        bounds: Union[Tuple[float], List[float], float] = 1.1,
+        octree_depth: int = 7,
+        num_chunks: int = 10000,
+        mc_level: float = -1 / 512,
+        octree_resolution: int = None,
+        mc_algo: str = 'dmc',
+    ):
+        device = latents.device
+
+        # 1. generate query points
+        if isinstance(bounds, float):
+            bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+        bbox_min = np.array(bounds[0:3])
+        bbox_max = np.array(bounds[3:6])
+        bbox_size = bbox_max - bbox_min
+        xyz_samples, grid_size, length = generate_dense_grid_points(
+            bbox_min=bbox_min,
+            bbox_max=bbox_max,
+            octree_depth=octree_depth,
+            octree_resolution=octree_resolution,
+            indexing="ij"
+        )
+        xyz_samples = torch.FloatTensor(xyz_samples)
+
+        # 2. latents to 3d volume
+        batch_logits = []
+        batch_size = latents.shape[0]
+        for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
+                          desc=f"MC Level {mc_level} Implicit Function:"):
+            queries = xyz_samples[start: start + num_chunks, :].to(device)
+            queries = queries.half()
+            batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
+
+            logits = self.geo_decoder(batch_queries.to(latents.dtype), latents)
+            if mc_level == -1:
+                mc_level = 0
+                logits = torch.sigmoid(logits) * 2 - 1
+                print(f'Training with soft labels, inference with sigmoid and marching cubes level 0.')
+            batch_logits.append(logits)
+        grid_logits = torch.cat(batch_logits, dim=1)
+        grid_logits = grid_logits.view((batch_size, grid_size[0], grid_size[1], grid_size[2])).float()
+
+        # 3. extract surface
+        outputs = []
+        for i in range(batch_size):
+            try:
+                if mc_algo == 'mc':
+                    vertices, faces, normals, _ = measure.marching_cubes(
+                        grid_logits[i].cpu().numpy(),
+                        mc_level,
+                        method="lewiner"
+                    )
+                    vertices = vertices / grid_size * bbox_size + bbox_min
+                elif mc_algo == 'dmc':
+                    if not hasattr(self, 'dmc'):
+                        try:
+                            from diso import DiffDMC
+                        except:
+                            raise ImportError("Please install diso via `pip install diso`, or set mc_algo to 'mc'")
+                        self.dmc = DiffDMC(dtype=torch.float32).to(device)
+                    octree_resolution = 2 ** octree_depth if octree_resolution is None else octree_resolution
+                    sdf = -grid_logits[i] / octree_resolution
+                    verts, faces = self.dmc(sdf, deform=None, return_quads=False, normalize=True)
+                    verts = center_vertices(verts)
+                    vertices = verts.detach().cpu().numpy()
+                    faces = faces.detach().cpu().numpy()[:, ::-1]
+                else:
+                    raise ValueError(f"mc_algo {mc_algo} not supported.")
+
+                outputs.append(
+                    Latent2MeshOutput(
+                        mesh_v=vertices.astype(np.float32),
+                        mesh_f=np.ascontiguousarray(faces)
+                    )
+                )
+
+            except ValueError:
+                outputs.append(None)
+            except RuntimeError:
+                outputs.append(None)
+
+        return outputs
@@ -0,0 +1,578 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import copy
+import importlib
+import inspect
+import logging
+import os
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+import trimesh
+import yaml
+from PIL import Image
+from diffusers.utils.torch_utils import randn_tensor
+from tqdm import tqdm
+
+import comfy.utils
+
+logger = logging.getLogger(__name__)
+
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+def export_to_trimesh(mesh_output):
+    if isinstance(mesh_output, list):
+        outputs = []
+        for mesh in mesh_output:
+            if mesh is None:
+                outputs.append(None)
+            else:
+                mesh.mesh_f = mesh.mesh_f[:, ::-1]
+                mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f)
+                outputs.append(mesh_output)
+        return outputs
+    else:
+        mesh_output.mesh_f = mesh_output.mesh_f[:, ::-1]
+        mesh_output = trimesh.Trimesh(mesh_output.mesh_v, mesh_output.mesh_f)
+        return mesh_output
+
+
+def get_obj_from_str(string, reload=False):
+    module, cls = string.rsplit(".", 1)
+    if reload:
+        module_imp = importlib.import_module(module)
+        importlib.reload(module_imp)
+    return getattr(importlib.import_module(module, package=None), cls)
+
+
+def instantiate_from_config(config, **kwargs):
+    if "target" not in config:
+        raise KeyError("Expected key `target` to instantiate.")
+    cls = get_obj_from_str(config["target"])
+    params = config.get("params", dict())
+    kwargs.update(params)
+    instance = cls(**kwargs)
+    return instance
+
+
+class Hunyuan3DDiTPipeline:
+    @classmethod
+    def from_single_file(
+        cls,
+        ckpt_path,
+        config_path,
+        device='cuda',
+        dtype=torch.float16,
+        use_safetensors=None,
+        **kwargs,
+    ):
+        # load config
+        with open(config_path, 'r') as f:
+            config = yaml.safe_load(f)
+
+        # load ckpt
+        if use_safetensors:
+            ckpt_path = ckpt_path.replace('.ckpt', '.safetensors')
+        if not os.path.exists(ckpt_path):
+            raise FileNotFoundError(f"Model file {ckpt_path} not found")
+        logger.info(f"Loading model from {ckpt_path}")
+
+        if use_safetensors:
+            # parse safetensors
+            import safetensors.torch
+            safetensors_ckpt = safetensors.torch.load_file(ckpt_path, device='cpu')
+            ckpt = {}
+            for key, value in safetensors_ckpt.items():
+                model_name = key.split('.')[0]
+                new_key = key[len(model_name) + 1:]
+                if model_name not in ckpt:
+                    ckpt[model_name] = {}
+                ckpt[model_name][new_key] = value
+        else:
+            ckpt = torch.load(ckpt_path, map_location='cpu')
+        # load model
+        model = instantiate_from_config(config['model'])
+        model.load_state_dict(ckpt['model'])
+        vae = instantiate_from_config(config['vae'])
+        vae.load_state_dict(ckpt['vae'])
+        conditioner = instantiate_from_config(config['conditioner'])
+        if 'conditioner' in ckpt:
+            conditioner.load_state_dict(ckpt['conditioner'])
+        image_processor = instantiate_from_config(config['image_processor'])
+        scheduler = instantiate_from_config(config['scheduler'])
+
+        model_kwargs = dict(
+            vae=vae,
+            model=model,
+            scheduler=scheduler,
+            conditioner=conditioner,
+            image_processor=image_processor,
+            device=device,
+            dtype=dtype,
+        )
+        model_kwargs.update(kwargs)
+
+        return cls(
+            **model_kwargs
+        )
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        model_path,
+        ckpt_name='model.ckpt',
+        config_name='config.yaml',
+        device='cuda',
+        dtype=torch.float16,
+        use_safetensors=None,
+        **kwargs,
+    ):
+        model_path = os.path.join(model_path, 'hunyuan3d-dit-v2-0')
+
+        config_path = os.path.join(model_path, config_name)
+        ckpt_path = os.path.join(model_path, ckpt_name)
+
+        if not os.path.exists(model_path):
+            raise FileNotFoundError(f"Model path {model_path} not found")
+
+        return cls.from_single_file(
+            ckpt_path,
+            config_path,
+            device=device,
+            dtype=dtype,
+            use_safetensors=use_safetensors,
+            **kwargs
+        )
+
+    def __init__(
+        self,
+        vae,
+        model,
+        scheduler,
+        conditioner,
+        image_processor,
+        device='cuda',
+        dtype=torch.float16,
+        **kwargs
+    ):
+        self.vae = vae
+        self.model = model
+        self.scheduler = scheduler
+        self.conditioner = conditioner
+        self.image_processor = image_processor
+
+        self.to(device, dtype)
+
+    def to(self, device=None, dtype=None):
+        if device is not None:
+            self.device = torch.device(device)
+            self.vae.to(device)
+            self.model.to(device)
+            self.conditioner.to(device)
+        if dtype is not None:
+            self.dtype = dtype
+            self.vae.to(dtype=dtype)
+            self.model.to(dtype=dtype)
+            self.conditioner.to(dtype=dtype)
+            
+        return self
+
+    def encode_cond(self, image, mask, do_classifier_free_guidance, dual_guidance):
+        bsz = image.shape[0]
+        cond = self.conditioner(image=image, mask=mask)
+
+        if do_classifier_free_guidance:
+            un_cond = self.conditioner.unconditional_embedding(bsz)
+
+            if dual_guidance:
+                un_cond_drop_main = copy.deepcopy(un_cond)
+                un_cond_drop_main['additional'] = cond['additional']
+
+                def cat_recursive(a, b, c):
+                    if isinstance(a, torch.Tensor):
+                        return torch.cat([a, b, c], dim=0).to(self.dtype)
+                    out = {}
+                    for k in a.keys():
+                        out[k] = cat_recursive(a[k], b[k], c[k])
+                    return out
+
+                cond = cat_recursive(cond, un_cond_drop_main, un_cond)
+            else:
+                un_cond = self.conditioner.unconditional_embedding(bsz)
+
+                def cat_recursive(a, b):
+                    if isinstance(a, torch.Tensor):
+                        return torch.cat([a, b], dim=0).to(self.dtype)
+                    out = {}
+                    for k in a.keys():
+                        out[k] = cat_recursive(a[k], b[k])
+                    return out
+
+                cond = cat_recursive(cond, un_cond)
+        return cond
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def prepare_latents(self, batch_size, dtype, device, generator, latents=None):
+        shape = (batch_size, *self.vae.latent_shape)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * getattr(self.scheduler, 'init_noise_sigma', 1.0)
+        return latents
+
+    def prepare_image(self, image):
+        if isinstance(image, str) and not os.path.exists(image):
+            raise FileNotFoundError(f"Couldn't find image at path {image}")
+
+        if not isinstance(image, list):
+            image = [image]
+        image_pts = []
+        mask_pts = []
+        for img in image:
+            image_pt, mask_pt = self.image_processor(img, return_mask=True)
+            image_pts.append(image_pt)
+            mask_pts.append(mask_pt)
+
+        image_pts = torch.cat(image_pts, dim=0).to(self.device, dtype=self.dtype)
+        if mask_pts[0] is not None:
+            mask_pts = torch.cat(mask_pts, dim=0).to(self.device, dtype=self.dtype)
+        else:
+            mask_pts = None
+        return image_pts, mask_pts
+
+    def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+        """
+        See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+
+        Args:
+            timesteps (`torch.Tensor`):
+                generate embedding vectors at these timesteps
+            embedding_dim (`int`, *optional*, defaults to 512):
+                dimension of the embeddings to generate
+            dtype:
+                data type of the generated embeddings
+
+        Returns:
+            `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)`
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Union[str, List[str], Image.Image] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        eta: float = 0.0,
+        guidance_scale: float = 7.5,
+        dual_guidance_scale: float = 10.5,
+        dual_guidance: bool = True,
+        generator=None,
+        box_v=1.01,
+        octree_resolution=384,
+        mc_level=-1 / 512,
+        num_chunks=8000,
+        mc_algo='mc',
+        output_type: Optional[str] = "trimesh",
+        enable_pbar=True,
+        **kwargs,
+    ) -> List[List[trimesh.Trimesh]]:
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        device = self.device
+        dtype = self.dtype
+        do_classifier_free_guidance = guidance_scale >= 0 and \
+                                      getattr(self.model, 'guidance_cond_proj_dim', None) is None
+        dual_guidance = dual_guidance_scale >= 0 and dual_guidance
+
+        image, mask = self.prepare_image(image)
+        cond = self.encode_cond(image=image,
+                                mask=mask,
+                                do_classifier_free_guidance=do_classifier_free_guidance,
+                                dual_guidance=dual_guidance)
+        batch_size = image.shape[0]
+
+        t_dtype = torch.long
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas)
+
+        latents = self.prepare_latents(batch_size, dtype, device, generator)
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        guidance_cond = None
+        if getattr(self.model, 'guidance_cond_proj_dim', None) is not None:
+            print('Using lcm guidance scale')
+            guidance_scale_tensor = torch.tensor(guidance_scale - 1).repeat(batch_size)
+            guidance_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.model.guidance_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+            
+        
+        for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:", leave=False)):
+            # expand the latents if we are doing classifier free guidance
+            if do_classifier_free_guidance:
+                latent_model_input = torch.cat([latents] * (3 if dual_guidance else 2))
+            else:
+                latent_model_input = latents
+            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+            # predict the noise residual
+            timestep_tensor = torch.tensor([t], dtype=t_dtype, device=device)
+            timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0])
+            noise_pred = self.model(latent_model_input, timestep_tensor, cond, guidance_cond=guidance_cond)
+
+            # no drop, drop clip, all drop
+            if do_classifier_free_guidance:
+                if dual_guidance:
+                    noise_pred_clip, noise_pred_dino, noise_pred_uncond = noise_pred.chunk(3)
+                    noise_pred = (
+                        noise_pred_uncond
+                        + guidance_scale * (noise_pred_clip - noise_pred_dino)
+                        + dual_guidance_scale * (noise_pred_dino - noise_pred_uncond)
+                    )
+                else:
+                    noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
+            latents = outputs.prev_sample
+
+            if callback is not None and i % callback_steps == 0:
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, outputs)
+
+        return self._export(
+            latents,
+            output_type,
+            box_v, mc_level, num_chunks, octree_resolution, mc_algo,
+        )
+
+    def _export(self, latents, output_type, box_v, mc_level, num_chunks, octree_resolution, mc_algo):
+        if not output_type == "latent":
+            latents = 1. / self.vae.scale_factor * latents
+            latents = self.vae(latents)
+            outputs = self.vae.latents2mesh(
+                latents,
+                bounds=box_v,
+                mc_level=mc_level,
+                num_chunks=num_chunks,
+                octree_resolution=octree_resolution,
+                mc_algo=mc_algo,
+            )
+        else:
+            outputs = latents
+
+        if output_type == 'trimesh':
+            outputs = export_to_trimesh(outputs)
+
+        return outputs
+
+
+class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline):
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Union[str, List[str], Image.Image] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        eta: float = 0.0,
+        guidance_scale: float = 7.5,
+        generator=None,
+        box_v=1.01,
+        octree_resolution=384,
+        mc_level=0.0,
+        mc_algo='mc',
+        num_chunks=8000,
+        output_type: Optional[str] = "trimesh",
+        enable_pbar=True,
+        **kwargs,
+    ) -> List[List[trimesh.Trimesh]]:
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        device = self.device
+        dtype = self.dtype
+        do_classifier_free_guidance = guidance_scale >= 0 and not (
+            hasattr(self.model, 'guidance_embed') and
+            self.model.guidance_embed is True
+        )
+
+        image, mask = self.prepare_image(image)
+        cond = self.encode_cond(
+            image=image,
+            mask=mask,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            dual_guidance=False,
+        )
+        batch_size = image.shape[0]
+
+        # 5. Prepare timesteps
+        # NOTE: this is slightly different from common usage, we start from 0.
+        sigmas = np.linspace(0, 1, num_inference_steps) if sigmas is None else sigmas
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+        )
+        latents = self.prepare_latents(batch_size, dtype, device, generator)
+
+        guidance = None
+        if hasattr(self.model, 'guidance_embed') and \
+            self.model.guidance_embed is True:
+            guidance = torch.tensor([guidance_scale] * batch_size, device=device, dtype=dtype)
+
+        comfy_pbar = comfy.utils.ProgressBar(num_inference_steps)
+        for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:")):
+            # expand the latents if we are doing classifier free guidance
+            if do_classifier_free_guidance:
+                latent_model_input = torch.cat([latents] * 2)
+            else:
+                latent_model_input = latents
+
+            # NOTE: we assume model get timesteps ranged from 0 to 1
+            timestep = t.expand(latent_model_input.shape[0]).to(
+                latents.dtype) / self.scheduler.config.num_train_timesteps
+            noise_pred = self.model(latent_model_input, timestep, cond, guidance=guidance)
+
+            if do_classifier_free_guidance:
+                noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            outputs = self.scheduler.step(noise_pred, t, latents)
+            latents = outputs.prev_sample
+
+            if callback is not None and i % callback_steps == 0:
+                step_idx = i // getattr(self.scheduler, "order", 1)
+                callback(step_idx, t, outputs)
+
+            comfy_pbar.update_absolute(i + 1)
+
+        return self._export(
+            latents,
+            output_type,
+            box_v, mc_level, num_chunks, octree_resolution, mc_algo,
+        )
@@ -0,0 +1,175 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import os
+import tempfile
+from typing import Union
+
+import pymeshlab
+import trimesh
+
+from .models.vae import Latent2MeshOutput
+
+
+def load_mesh(path):
+    if path.endswith(".glb"):
+        mesh = trimesh.load(path)
+    else:
+        mesh = pymeshlab.MeshSet()
+        mesh.load_new_mesh(path)
+    return mesh
+
+
+def reduce_face(mesh: pymeshlab.MeshSet, max_facenum: int = 200000):
+    mesh.apply_filter(
+        "meshing_decimation_quadric_edge_collapse",
+        targetfacenum=max_facenum,
+        qualitythr=1.0,
+        preserveboundary=True,
+        boundaryweight=3,
+        preservenormal=True,
+        preservetopology=True,
+        autoclean=True
+    )
+    return mesh
+
+
+def remove_floater(mesh: pymeshlab.MeshSet):
+    mesh.apply_filter("compute_selection_by_small_disconnected_components_per_face",
+                      nbfaceratio=0.005)
+    mesh.apply_filter("compute_selection_transfer_face_to_vertex", inclusive=False)
+    mesh.apply_filter("meshing_remove_selected_vertices_and_faces")
+    return mesh
+
+
+def pymeshlab2trimesh(mesh: pymeshlab.MeshSet):
+    temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True)
+    temp_file.close()
+    temp_file_name = temp_file.name
+    
+    mesh.save_current_mesh(temp_file_name)
+    mesh = trimesh.load(temp_file_name)
+    if os.path.exists(temp_file_name):
+        os.remove(temp_file_name)
+          
+    # 检查加载的对象类型
+    if isinstance(mesh, trimesh.Scene):
+        combined_mesh = trimesh.Trimesh()
+        # 如果是Scene，遍历所有的geometry并合并
+        for geom in mesh.geometry.values():
+            combined_mesh = trimesh.util.concatenate([combined_mesh, geom])
+        mesh = combined_mesh
+    return mesh
+
+
+def trimesh2pymeshlab(mesh: trimesh.Trimesh):
+    temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True)
+    temp_file.close()
+    temp_file_name = temp_file.name
+    
+    if isinstance(mesh, trimesh.scene.Scene):
+        for idx, obj in enumerate(mesh.geometry.values()):
+            if idx == 0:
+                temp_mesh = obj
+            else:
+                temp_mesh = temp_mesh + obj
+        mesh = temp_mesh
+    mesh.export(temp_file_name)
+    mesh = pymeshlab.MeshSet()
+    mesh.load_new_mesh(temp_file_name)
+    if os.path.exists(temp_file_name):
+        os.remove(temp_file_name)
+          
+    return mesh
+
+
+def export_mesh(input, output):
+    if isinstance(input, pymeshlab.MeshSet):
+        mesh = output
+    elif isinstance(input, Latent2MeshOutput):
+        output = Latent2MeshOutput()
+        output.mesh_v = output.current_mesh().vertex_matrix()
+        output.mesh_f = output.current_mesh().face_matrix()
+        mesh = output
+    else:
+        mesh = pymeshlab2trimesh(output)
+    return mesh
+
+
+def import_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str]) -> pymeshlab.MeshSet:
+    if isinstance(mesh, str):
+        mesh = load_mesh(mesh)
+    elif isinstance(mesh, Latent2MeshOutput):
+        mesh = pymeshlab.MeshSet()
+        mesh_pymeshlab = pymeshlab.Mesh(vertex_matrix=mesh.mesh_v, face_matrix=mesh.mesh_f)
+        mesh.add_mesh(mesh_pymeshlab, "converted_mesh")
+
+    if isinstance(mesh, (trimesh.Trimesh, trimesh.scene.Scene)):
+        mesh = trimesh2pymeshlab(mesh)
+
+    return mesh
+
+
+class FaceReducer:
+    def __call__(
+        self,
+        mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str],
+        max_facenum: int = 40000
+    ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh]:
+        ms = import_mesh(mesh)
+        ms = reduce_face(ms, max_facenum=max_facenum)
+        mesh = export_mesh(mesh, ms)
+        return mesh
+
+
+class FloaterRemover:
+    def __call__(
+        self,
+        mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str],
+    ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]:
+        ms = import_mesh(mesh)
+        ms = remove_floater(ms)
+        mesh = export_mesh(mesh, ms)
+        return mesh
+
+
+class DegenerateFaceRemover:
+    def __call__(
+        self,
+        mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str],
+    ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]:
+        ms = import_mesh(mesh)
+
+        temp_file = tempfile.NamedTemporaryFile(suffix='.ply', delete=True)
+        temp_file.close()
+        temp_file_name = temp_file.name
+
+        ms.save_current_mesh(temp_file_name)
+        ms = pymeshlab.MeshSet()
+        ms.load_new_mesh(temp_file_name)
+        if os.path.exists(temp_file_name):
+            os.remove(temp_file_name)
+               
+        mesh = export_mesh(mesh, ms)
+        return mesh
@@ -0,0 +1,127 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from einops import repeat, rearrange
+
+
+def array_to_tensor(np_array):
+    image_pt = torch.tensor(np_array).float()
+    image_pt = image_pt / 255 * 2 - 1
+    image_pt = rearrange(image_pt, "h w c -> c h w")
+    image_pts = repeat(image_pt, "c h w -> b c h w", b=1)
+    return image_pts
+
+
+class ImageProcessorV2:
+    def __init__(self, size=512, border_ratio=None):
+        self.size = size
+        self.border_ratio = border_ratio
+
+    @staticmethod
+    def recenter(image, border_ratio: float = 0.2):
+        """ recenter an image to leave some empty space at the image border.
+
+        Args:
+            image (ndarray): input image, float/uint8 [H, W, 3/4]
+            mask (ndarray): alpha mask, bool [H, W]
+            border_ratio (float, optional): border ratio, image will be resized to (1 - border_ratio). Defaults to 0.2.
+
+        Returns:
+            ndarray: output image, float/uint8 [H, W, 3/4]
+        """
+
+        if image.shape[-1] == 4:
+            mask = image[..., 3]
+        else:
+            mask = np.ones_like(image[..., 0:1]) * 255
+            image = np.concatenate([image, mask], axis=-1)
+            mask = mask[..., 0]
+
+        H, W, C = image.shape
+
+        size = max(H, W)
+        result = np.zeros((size, size, C), dtype=np.uint8)
+
+        coords = np.nonzero(mask)
+        x_min, x_max = coords[0].min(), coords[0].max()
+        y_min, y_max = coords[1].min(), coords[1].max()
+        h = x_max - x_min
+        w = y_max - y_min
+        if h == 0 or w == 0:
+            raise ValueError('input image is empty')
+        desired_size = int(size * (1 - border_ratio))
+        scale = desired_size / max(h, w)
+        h2 = int(h * scale)
+        w2 = int(w * scale)
+        x2_min = (size - h2) // 2
+        x2_max = x2_min + h2
+
+        y2_min = (size - w2) // 2
+        y2_max = y2_min + w2
+
+        result[x2_min:x2_max, y2_min:y2_max] = cv2.resize(image[x_min:x_max, y_min:y_max], (w2, h2),
+                                                          interpolation=cv2.INTER_AREA)
+
+        bg = np.ones((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255
+        # bg = np.zeros((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255
+        mask = result[..., 3:].astype(np.float32) / 255
+        result = result[..., :3] * mask + bg * (1 - mask)
+
+        mask = mask * 255
+        result = result.clip(0, 255).astype(np.uint8)
+        mask = mask.clip(0, 255).astype(np.uint8)
+        return result, mask
+
+    def __call__(self, image, border_ratio=0.15, to_tensor=True, return_mask=False, **kwargs):
+        if self.border_ratio is not None:
+            border_ratio = self.border_ratio
+            print(f"Using border_ratio from init: {border_ratio}")
+        if isinstance(image, str):
+            image = cv2.imread(image, cv2.IMREAD_UNCHANGED)
+            image, mask = self.recenter(image, border_ratio=border_ratio)
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        elif isinstance(image, Image.Image):
+            image = np.asarray(image)
+            image, mask = self.recenter(image, border_ratio=border_ratio)
+
+        image = cv2.resize(image, (self.size, self.size), interpolation=cv2.INTER_CUBIC)
+        mask = cv2.resize(mask, (self.size, self.size), interpolation=cv2.INTER_NEAREST)
+        mask = mask[..., np.newaxis]
+
+        if to_tensor:
+            image = array_to_tensor(image)
+            mask = array_to_tensor(mask)
+        if return_mask:
+            return image, mask
+        return image
+
+
+IMAGE_PROCESSORS = {
+    "v2": ImageProcessorV2,
+}
+
+DEFAULT_IMAGEPROCESSOR = 'v2'
@@ -0,0 +1,307 @@
+# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.utils import BaseOutput, logging
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+
+    Args:
+        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+            denoising loop.
+    """
+
+    prev_sample: torch.FloatTensor
+
+
+class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
+    """
+    NOTE: this is very similar to diffusers.FlowMatchEulerDiscreteScheduler. Except our timesteps are reversed
+
+    Euler scheduler.
+
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
+    methods the library implements for all schedulers such as loading and saving.
+
+    Args:
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        timestep_spacing (`str`, defaults to `"linspace"`):
+            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
+            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
+        shift (`float`, defaults to 1.0):
+            The shift value for the timestep schedule.
+    """
+
+    _compatibles = []
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        shift: float = 1.0,
+        use_dynamic_shifting=False,
+    ):
+        timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32).copy()
+        timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
+
+        sigmas = timesteps / num_train_timesteps
+        if not use_dynamic_shifting:
+            # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
+            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+
+        self.timesteps = sigmas * num_train_timesteps
+
+        self._step_index = None
+        self._begin_index = None
+
+        self.sigmas = sigmas.to("cpu")  # to avoid too much CPU/GPU communication
+        self.sigma_min = self.sigmas[-1].item()
+        self.sigma_max = self.sigmas[0].item()
+
+    @property
+    def step_index(self):
+        """
+        The index counter for current timestep. It will increase 1 after each scheduler step.
+        """
+        return self._step_index
+
+    @property
+    def begin_index(self):
+        """
+        The index for the first timestep. It should be set from pipeline with `set_begin_index` method.
+        """
+        return self._begin_index
+
+    # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index
+    def set_begin_index(self, begin_index: int = 0):
+        """
+        Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
+
+        Args:
+            begin_index (`int`):
+                The begin index for the scheduler.
+        """
+        self._begin_index = begin_index
+
+    def scale_noise(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        noise: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        """
+        Forward process in flow-matching
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The input sample.
+            timestep (`int`, *optional*):
+                The current timestep in the diffusion chain.
+
+        Returns:
+            `torch.FloatTensor`:
+                A scaled input sample.
+        """
+        # Make sure sigmas and timesteps have the same device and dtype as original_samples
+        sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype)
+
+        if sample.device.type == "mps" and torch.is_floating_point(timestep):
+            # mps does not support float64
+            schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32)
+            timestep = timestep.to(sample.device, dtype=torch.float32)
+        else:
+            schedule_timesteps = self.timesteps.to(sample.device)
+            timestep = timestep.to(sample.device)
+
+        # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index
+        if self.begin_index is None:
+            step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep]
+        elif self.step_index is not None:
+            # add_noise is called after first denoising step (for inpainting)
+            step_indices = [self.step_index] * timestep.shape[0]
+        else:
+            # add noise is called before first denoising step to create initial latent(img2img)
+            step_indices = [self.begin_index] * timestep.shape[0]
+
+        sigma = sigmas[step_indices].flatten()
+        while len(sigma.shape) < len(sample.shape):
+            sigma = sigma.unsqueeze(-1)
+
+        sample = sigma * noise + (1.0 - sigma) * sample
+
+        return sample
+
+    def _sigma_to_t(self, sigma):
+        return sigma * self.config.num_train_timesteps
+
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int = None,
+        device: Union[str, torch.device] = None,
+        sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
+    ):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+            device (`str` or `torch.device`, *optional*):
+                The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        """
+
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError(" you have a pass a value for `mu` when `use_dynamic_shifting` is set to be `True`")
+
+        if sigmas is None:
+            self.num_inference_steps = num_inference_steps
+            timesteps = np.linspace(
+                self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps
+            )
+
+            sigmas = timesteps / self.config.num_train_timesteps
+
+        if self.config.use_dynamic_shifting:
+            sigmas = self.time_shift(mu, 1.0, sigmas)
+        else:
+            sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
+
+        sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
+        timesteps = sigmas * self.config.num_train_timesteps
+
+        self.timesteps = timesteps.to(device=device)
+        self.sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)])
+
+        self._step_index = None
+        self._begin_index = None
+
+    def index_for_timestep(self, timestep, schedule_timesteps=None):
+        if schedule_timesteps is None:
+            schedule_timesteps = self.timesteps
+
+        indices = (schedule_timesteps == timestep).nonzero()
+
+        # The sigma index that is taken for the **very** first `step`
+        # is always the second index (or the last index if there is only 1)
+        # This way we can ensure we don't accidentally skip a sigma in
+        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
+        pos = 1 if len(indices) > 1 else 0
+
+        return indices[pos].item()
+
+    def _init_step_index(self, timestep):
+        if self.begin_index is None:
+            if isinstance(timestep, torch.Tensor):
+                timestep = timestep.to(self.timesteps.device)
+            self._step_index = self.index_for_timestep(timestep)
+        else:
+            self._step_index = self._begin_index
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: Union[float, torch.FloatTensor],
+        sample: torch.FloatTensor,
+        s_churn: float = 0.0,
+        s_tmin: float = 0.0,
+        s_tmax: float = float("inf"),
+        s_noise: float = 1.0,
+        generator: Optional[torch.Generator] = None,
+        return_dict: bool = True,
+    ) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
+        process from the learned model outputs (most often the predicted noise).
+
+        Args:
+            model_output (`torch.FloatTensor`):
+                The direct output from learned diffusion model.
+            timestep (`float`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                A current instance of a sample created by the diffusion process.
+            s_churn (`float`):
+            s_tmin  (`float`):
+            s_tmax  (`float`):
+            s_noise (`float`, defaults to 1.0):
+                Scaling factor for noise added to the sample.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            return_dict (`bool`):
+                Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or
+                tuple.
+
+        Returns:
+            [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is
+                returned, otherwise a tuple is returned where the first element is the sample tensor.
+        """
+
+        if (
+            isinstance(timestep, int)
+            or isinstance(timestep, torch.IntTensor)
+            or isinstance(timestep, torch.LongTensor)
+        ):
+            raise ValueError(
+                (
+                    "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
+                    " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
+                    " one of the `scheduler.timesteps` as a timestep."
+                ),
+            )
+
+        if self.step_index is None:
+            self._init_step_index(timestep)
+
+        # Upcast to avoid precision issues when computing prev_sample
+        sample = sample.to(torch.float32)
+
+        sigma = self.sigmas[self.step_index]
+        sigma_next = self.sigmas[self.step_index + 1]
+
+        prev_sample = sample + (sigma_next - sigma) * model_output
+
+        # Cast sample back to model compatible dtype
+        prev_sample = prev_sample.to(model_output.dtype)
+
+        # upon completion increase step index by one
+        self._step_index += 1
+
+        if not return_dict:
+            return (prev_sample,)
+
+        return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample)
+
+    def __len__(self):
+        return self.config.num_train_timesteps
@@ -0,0 +1,26 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+from .pipelines import Hunyuan3DPaintPipeline, Hunyuan3DTexGenConfig
@@ -0,0 +1,23 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
@@ -0,0 +1,116 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import math
+
+import numpy as np
+import torch
+
+
+def transform_pos(mtx, pos, keepdim=False):
+    t_mtx = torch.from_numpy(mtx).to(
+        pos.device) if isinstance(
+        mtx, np.ndarray) else mtx
+    if pos.shape[-1] == 3:
+        posw = torch.cat(
+            [pos, torch.ones([pos.shape[0], 1]).to(pos.device)], axis=1)
+    else:
+        posw = pos
+
+    if keepdim:
+        return torch.matmul(posw, t_mtx.t())[...]
+    else:
+        return torch.matmul(posw, t_mtx.t())[None, ...]
+
+
+def get_mv_matrix(elev, azim, camera_distance, center=None):
+    elev = -elev
+    azim += 90
+
+    elev_rad = math.radians(elev)
+    azim_rad = math.radians(azim)
+
+    camera_position = np.array([camera_distance * math.cos(elev_rad) * math.cos(azim_rad),
+                                camera_distance *
+                                math.cos(elev_rad) * math.sin(azim_rad),
+                                camera_distance * math.sin(elev_rad)])
+
+    if center is None:
+        center = np.array([0, 0, 0])
+    else:
+        center = np.array(center)
+
+    lookat = center - camera_position
+    lookat = lookat / np.linalg.norm(lookat)
+
+    up = np.array([0, 0, 1.0])
+    right = np.cross(lookat, up)
+    right = right / np.linalg.norm(right)
+    up = np.cross(right, lookat)
+    up = up / np.linalg.norm(up)
+
+    c2w = np.concatenate(
+        [np.stack([right, up, -lookat], axis=-1), camera_position[:, None]], axis=-1)
+
+    w2c = np.zeros((4, 4))
+    w2c[:3, :3] = np.transpose(c2w[:3, :3], (1, 0))
+    w2c[:3, 3:] = -np.matmul(np.transpose(c2w[:3, :3], (1, 0)), c2w[:3, 3:])
+    w2c[3, 3] = 1.0
+
+    return w2c.astype(np.float32)
+
+
+def get_orthographic_projection_matrix(
+    left=-1, right=1, bottom=-1, top=1, near=0, far=2):
+    """
+    计算正交投影矩阵。
+
+    参数:
+        left (float): 投影区域左侧边界。
+        right (float): 投影区域右侧边界。
+        bottom (float): 投影区域底部边界。
+        top (float): 投影区域顶部边界。
+        near (float): 投影区域近裁剪面距离。
+        far (float): 投影区域远裁剪面距离。
+
+    返回:
+        numpy.ndarray: 正交投影矩阵。
+    """
+    ortho_matrix = np.eye(4, dtype=np.float32)
+    ortho_matrix[0, 0] = 2 / (right - left)
+    ortho_matrix[1, 1] = 2 / (top - bottom)
+    ortho_matrix[2, 2] = -2 / (far - near)
+    ortho_matrix[0, 3] = -(right + left) / (right - left)
+    ortho_matrix[1, 3] = -(top + bottom) / (top - bottom)
+    ortho_matrix[2, 3] = -(far + near) / (far - near)
+    return ortho_matrix
+
+
+def get_perspective_projection_matrix(fovy, aspect_wh, near, far):
+    fovy_rad = math.radians(fovy)
+    return np.array([[1.0 / (math.tan(fovy_rad / 2.0) * aspect_wh), 0, 0, 0],
+                     [0, 1.0 / math.tan(fovy_rad / 2.0), 0, 0],
+                     [0, 0, -(far + near) / (far - near), -
+                     2.0 * far * near / (far - near)],
+                     [0, 0, -1, 0]]).astype(np.float32)
@@ -0,0 +1,3 @@
+FOR /F "tokens=*" %%i IN ('python -m pybind11 --includes') DO SET PYINCLUDES=%%i
+echo %PYINCLUDES%
+g++ -O3 -Wall -shared -std=c++11 -fPIC %PYINCLUDES% mesh_processor.cpp -o mesh_processor.pyd -lpython3.12
@@ -0,0 +1 @@
+c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` mesh_processor.cpp -o mesh_processor`python3-config --extension-suffix`
@@ -0,0 +1,161 @@
+#include <vector>
+#include <queue>
+#include <cmath>
+#include <algorithm>
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+#include <pybind11/stl.h>
+
+namespace py = pybind11;
+using namespace std;
+
+std::pair<py::array_t<float>,
+  py::array_t<uint8_t>>  meshVerticeInpaint_smooth(py::array_t<float> texture,
+py::array_t<uint8_t> mask,
+                 py::array_t<float> vtx_pos, py::array_t<float> vtx_uv, 
+                 py::array_t<int> pos_idx, py::array_t<int> uv_idx) {
+    auto texture_buf = texture.request();
+    auto mask_buf = mask.request();
+    auto vtx_pos_buf = vtx_pos.request();
+    auto vtx_uv_buf = vtx_uv.request();
+    auto pos_idx_buf = pos_idx.request();
+    auto uv_idx_buf = uv_idx.request();
+
+    int texture_height = texture_buf.shape[0];
+    int texture_width = texture_buf.shape[1];
+    int texture_channel = texture_buf.shape[2];
+    float* texture_ptr = static_cast<float*>(texture_buf.ptr);
+    uint8_t* mask_ptr = static_cast<uint8_t*>(mask_buf.ptr);
+
+    int vtx_num = vtx_pos_buf.shape[0];
+    float* vtx_pos_ptr = static_cast<float*>(vtx_pos_buf.ptr);
+    float* vtx_uv_ptr = static_cast<float*>(vtx_uv_buf.ptr);
+    int* pos_idx_ptr = static_cast<int*>(pos_idx_buf.ptr);
+    int* uv_idx_ptr = static_cast<int*>(uv_idx_buf.ptr);
+
+    vector<float> vtx_mask(vtx_num, 0.0f);
+    vector<vector<float>> vtx_color(vtx_num, vector<float>(texture_channel, 0.0f));
+    vector<int> uncolored_vtxs;
+
+    vector<vector<int>> G(vtx_num);
+
+    for (int i = 0; i < uv_idx_buf.shape[0]; ++i) {
+        for (int k = 0; k < 3; ++k) {
+            int vtx_uv_idx = uv_idx_ptr[i * 3 + k];
+            int vtx_idx = pos_idx_ptr[i * 3 + k];
+            int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1));
+            int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1));
+
+            if (mask_ptr[uv_u * texture_width + uv_v] > 0) {
+                vtx_mask[vtx_idx] = 1.0f;
+                for (int c = 0; c < texture_channel; ++c) {
+                    vtx_color[vtx_idx][c] = texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c];
+                }
+            }else{
+                uncolored_vtxs.push_back(vtx_idx);
+            }
+
+            G[pos_idx_ptr[i * 3 + k]].push_back(pos_idx_ptr[i * 3 + (k + 1) % 3]);
+        }
+    }
+
+    int smooth_count = 2;
+    int last_uncolored_vtx_count = 0;
+    while (smooth_count>0) {
+        int uncolored_vtx_count = 0;
+
+        for (int vtx_idx : uncolored_vtxs) {
+
+            vector<float> sum_color(texture_channel, 0.0f);
+            float total_weight = 0.0f;
+
+            array<float, 3> vtx_0 = {vtx_pos_ptr[vtx_idx * 3],
+vtx_pos_ptr[vtx_idx * 3 + 1], vtx_pos_ptr[vtx_idx * 3 + 2]};
+            for (int connected_idx : G[vtx_idx]) {
+                if (vtx_mask[connected_idx] > 0) {
+                    array<float, 3> vtx1 = {vtx_pos_ptr[connected_idx * 3],
+                    vtx_pos_ptr[connected_idx * 3 + 1], vtx_pos_ptr[connected_idx * 3 + 2]};
+                    float dist_weight = 1.0f / max(sqrt(pow(vtx_0[0] - vtx1[0], 2) + pow(vtx_0[1] - vtx1[1], 2) + \
+                     pow(vtx_0[2] - vtx1[2], 2)), 1E-4);
+                    dist_weight = dist_weight * dist_weight;
+                    for (int c = 0; c < texture_channel; ++c) {
+                        sum_color[c] += vtx_color[connected_idx][c] * dist_weight;
+                    }
+                    total_weight += dist_weight;
+                }
+            }
+
+            if (total_weight > 0.0f) {
+                for (int c = 0; c < texture_channel; ++c) {
+                    vtx_color[vtx_idx][c] = sum_color[c] / total_weight;
+                }
+                vtx_mask[vtx_idx] = 1.0f;
+            } else {
+                uncolored_vtx_count++;
+            }
+            
+        }
+
+        if(last_uncolored_vtx_count==uncolored_vtx_count){
+            smooth_count--;
+        }else{
+            smooth_count++;
+        }
+        last_uncolored_vtx_count = uncolored_vtx_count;
+    }
+
+    // Create new arrays for the output
+    py::array_t<float> new_texture(texture_buf.size);
+    py::array_t<uint8_t> new_mask(mask_buf.size);
+
+    auto new_texture_buf = new_texture.request();
+    auto new_mask_buf = new_mask.request();
+
+    float* new_texture_ptr = static_cast<float*>(new_texture_buf.ptr);
+    uint8_t* new_mask_ptr = static_cast<uint8_t*>(new_mask_buf.ptr);
+    // Copy original texture and mask to new arrays
+    std::copy(texture_ptr, texture_ptr + texture_buf.size, new_texture_ptr);
+    std::copy(mask_ptr, mask_ptr + mask_buf.size, new_mask_ptr);
+
+    for (int face_idx = 0; face_idx < uv_idx_buf.shape[0]; ++face_idx) {
+        for (int k = 0; k < 3; ++k) {
+            int vtx_uv_idx = uv_idx_ptr[face_idx * 3 + k];
+            int vtx_idx = pos_idx_ptr[face_idx * 3 + k];
+
+            if (vtx_mask[vtx_idx] == 1.0f) {
+                int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1));
+                int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1));
+
+                for (int c = 0; c < texture_channel; ++c) {
+                    new_texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c] = vtx_color[vtx_idx][c];
+                }
+                new_mask_ptr[uv_u * texture_width + uv_v] = 255;
+            }
+        }
+    }
+
+    // Reshape the new arrays to match the original texture and mask shapes
+    new_texture.resize({texture_height, texture_width, 3});
+    new_mask.resize({texture_height, texture_width});
+  return std::make_pair(new_texture, new_mask);
+}
+
+
+std::pair<py::array_t<float>, py::array_t<uint8_t>> meshVerticeInpaint(py::array_t<float> texture,
+          py::array_t<uint8_t> mask,
+          py::array_t<float> vtx_pos, py::array_t<float> vtx_uv,
+          py::array_t<int> pos_idx, py::array_t<int> uv_idx, const std::string& method = "smooth") {
+    if (method == "smooth") {
+        return meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx);
+    } else {
+        throw std::invalid_argument("Invalid method. Use 'smooth' or 'forward'.");
+    }
+}
+
+PYBIND11_MODULE(mesh_processor, m) {
+    m.def("meshVerticeInpaint", &meshVerticeInpaint, "A function to process mesh",
+          py::arg("texture"), py::arg("mask"),
+          py::arg("vtx_pos"), py::arg("vtx_uv"),
+          py::arg("pos_idx"), py::arg("uv_idx"),
+          py::arg("method") = "smooth");
+}
@@ -0,0 +1,70 @@
+import numpy as np
+
+def meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx):
+    texture_height, texture_width, texture_channel = texture.shape
+    vtx_num = vtx_pos.shape[0]
+
+    vtx_mask = np.zeros(vtx_num, dtype=np.float32)
+    vtx_color = [np.zeros(texture_channel, dtype=np.float32) for _ in range(vtx_num)]
+    uncolored_vtxs = []
+    G = [[] for _ in range(vtx_num)]
+
+    for i in range(uv_idx.shape[0]):
+        for k in range(3):
+            vtx_uv_idx = uv_idx[i, k]
+            vtx_idx = pos_idx[i, k]
+            uv_v = int(round(vtx_uv[vtx_uv_idx, 0] * (texture_width - 1)))
+            uv_u = int(round((1.0 - vtx_uv[vtx_uv_idx, 1]) * (texture_height - 1)))
+            if mask[uv_u, uv_v] > 0:
+                vtx_mask[vtx_idx] = 1.0
+                vtx_color[vtx_idx] = texture[uv_u, uv_v]
+            else:
+                uncolored_vtxs.append(vtx_idx)
+            G[pos_idx[i, k]].append(pos_idx[i, (k + 1) % 3])
+
+    smooth_count = 2
+    last_uncolored_vtx_count = 0
+    while smooth_count > 0:
+        uncolored_vtx_count = 0
+        for vtx_idx in uncolored_vtxs:
+            sum_color = np.zeros(texture_channel, dtype=np.float32)
+            total_weight = 0.0
+            vtx_0 = vtx_pos[vtx_idx]
+            for connected_idx in G[vtx_idx]:
+                if vtx_mask[connected_idx] > 0:
+                    vtx1 = vtx_pos[connected_idx]
+                    dist = np.sqrt(np.sum((vtx_0 - vtx1) ** 2))
+                    dist_weight = 1.0 / max(dist, 1e-4)
+                    dist_weight *= dist_weight
+                    sum_color += vtx_color[connected_idx] * dist_weight
+                    total_weight += dist_weight
+            if total_weight > 0:
+                vtx_color[vtx_idx] = sum_color / total_weight
+                vtx_mask[vtx_idx] = 1.0
+            else:
+                uncolored_vtx_count += 1
+
+        if last_uncolored_vtx_count == uncolored_vtx_count:
+            smooth_count -= 1
+        else:
+            smooth_count += 1
+        last_uncolored_vtx_count = uncolored_vtx_count
+
+    new_texture = texture.copy()
+    new_mask = mask.copy()
+    for face_idx in range(uv_idx.shape[0]):
+        for k in range(3):
+            vtx_uv_idx = uv_idx[face_idx, k]
+            vtx_idx = pos_idx[face_idx, k]
+            if vtx_mask[vtx_idx] == 1.0:
+                uv_v = int(round(vtx_uv[vtx_uv_idx, 0] * (texture_width - 1)))
+                uv_u = int(round((1.0 - vtx_uv[vtx_uv_idx, 1]) * (texture_height - 1)))
+                new_texture[uv_u, uv_v] = vtx_color[vtx_idx]
+                new_mask[uv_u, uv_v] = 255
+    return new_texture, new_mask
+
+def meshVerticeInpaint(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx, method="smooth"):
+    if method == "smooth":
+        return meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx)
+    else:
+        raise ValueError("Invalid method. Use 'smooth' or 'forward'.")
@@ -0,0 +1,833 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+import trimesh
+from PIL import Image
+
+from .camera_utils import (
+    transform_pos,
+    get_mv_matrix,
+    get_orthographic_projection_matrix,
+    get_perspective_projection_matrix,
+)
+from .mesh_processor import meshVerticeInpaint
+from .mesh_utils import load_mesh, save_mesh
+
+
+def stride_from_shape(shape):
+    stride = [1]
+    for x in reversed(shape[1:]):
+        stride.append(stride[-1] * x)
+    return list(reversed(stride))
+
+
+def scatter_add_nd_with_count(input, count, indices, values, weights=None):
+    # input: [..., C], D dimension + C channel
+    # count: [..., 1], D dimension
+    # indices: [N, D], long
+    # values: [N, C]
+
+    D = indices.shape[-1]
+    C = input.shape[-1]
+    size = input.shape[:-1]
+    stride = stride_from_shape(size)
+
+    assert len(size) == D
+
+    input = input.view(-1, C)  # [HW, C]
+    count = count.view(-1, 1)
+
+    flatten_indices = (indices * torch.tensor(stride,
+                                              dtype=torch.long, device=indices.device)).sum(-1)  # [N]
+
+    if weights is None:
+        weights = torch.ones_like(values[..., :1])
+
+    input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values)
+    count.scatter_add_(0, flatten_indices.unsqueeze(1), weights)
+
+    return input.view(*size, C), count.view(*size, 1)
+
+
+def linear_grid_put_2d(H, W, coords, values, return_count=False):
+    # coords: [N, 2], float in [0, 1]
+    # values: [N, C]
+
+    C = values.shape[-1]
+
+    indices = coords * torch.tensor(
+        [H - 1, W - 1], dtype=torch.float32, device=coords.device
+    )
+    indices_00 = indices.floor().long()  # [N, 2]
+    indices_00[:, 0].clamp_(0, H - 2)
+    indices_00[:, 1].clamp_(0, W - 2)
+    indices_01 = indices_00 + torch.tensor(
+        [0, 1], dtype=torch.long, device=indices.device
+    )
+    indices_10 = indices_00 + torch.tensor(
+        [1, 0], dtype=torch.long, device=indices.device
+    )
+    indices_11 = indices_00 + torch.tensor(
+        [1, 1], dtype=torch.long, device=indices.device
+    )
+
+    h = indices[..., 0] - indices_00[..., 0].float()
+    w = indices[..., 1] - indices_00[..., 1].float()
+    w_00 = (1 - h) * (1 - w)
+    w_01 = (1 - h) * w
+    w_10 = h * (1 - w)
+    w_11 = h * w
+
+    result = torch.zeros(H, W, C, device=values.device,
+                         dtype=values.dtype)  # [H, W, C]
+    count = torch.zeros(H, W, 1, device=values.device,
+                        dtype=values.dtype)  # [H, W, 1]
+    weights = torch.ones_like(values[..., :1])  # [N, 1]
+
+    result, count = scatter_add_nd_with_count(
+        result, count, indices_00, values * w_00.unsqueeze(1), weights * w_00.unsqueeze(1))
+    result, count = scatter_add_nd_with_count(
+        result, count, indices_01, values * w_01.unsqueeze(1), weights * w_01.unsqueeze(1))
+    result, count = scatter_add_nd_with_count(
+        result, count, indices_10, values * w_10.unsqueeze(1), weights * w_10.unsqueeze(1))
+    result, count = scatter_add_nd_with_count(
+        result, count, indices_11, values * w_11.unsqueeze(1), weights * w_11.unsqueeze(1))
+
+    if return_count:
+        return result, count
+
+    mask = (count.squeeze(-1) > 0)
+    result[mask] = result[mask] / count[mask].repeat(1, C)
+
+    return result
+
+
+class MeshRender():
+    def __init__(
+        self,
+        camera_distance=1.45, camera_type='orth',
+        default_resolution=1024, texture_size=1024,
+        use_antialias=True, max_mip_level=None, filter_mode='linear',
+        bake_mode='linear', raster_mode='cr', device='cuda'):
+
+        self.device = device
+
+        self.set_default_render_resolution(default_resolution)
+        self.set_default_texture_resolution(texture_size)
+
+        self.camera_distance = camera_distance
+        self.use_antialias = use_antialias
+        self.max_mip_level = max_mip_level
+        self.filter_mode = filter_mode
+
+        self.bake_angle_thres = 75
+        self.bake_unreliable_kernel_size = int(
+            (2 / 512) * max(self.default_resolution[0], self.default_resolution[1]))
+        self.bake_mode = bake_mode
+
+        self.raster_mode = raster_mode
+        if self.raster_mode == 'cr':
+            import custom_rasterizer as cr
+            self.raster = cr
+        else:
+            raise f'No raster named {self.raster_mode}'
+
+        if camera_type == 'orth':
+            self.ortho_scale = 1.2
+            self.camera_proj_mat = get_orthographic_projection_matrix(
+                left=-self.ortho_scale * 0.5, right=self.ortho_scale * 0.5,
+                bottom=-self.ortho_scale * 0.5, top=self.ortho_scale * 0.5,
+                near=0.1, far=100
+            )
+        elif camera_type == 'perspective':
+            self.camera_proj_mat = get_perspective_projection_matrix(
+                49.13, self.default_resolution[1] / self.default_resolution[0],
+                0.01, 100.0
+            )
+        else:
+            raise f'No camera type {camera_type}'
+
+    def raster_rasterize(self, pos, tri, resolution, ranges=None, grad_db=True):
+
+        if self.raster_mode == 'cr':
+            rast_out_db = None
+            if pos.dim() == 2:
+                pos = pos.unsqueeze(0)
+            findices, barycentric = self.raster.rasterize(pos, tri, resolution)
+            rast_out = torch.cat((barycentric, findices.unsqueeze(-1)), dim=-1)
+            rast_out = rast_out.unsqueeze(0)
+        else:
+            raise f'No raster named {self.raster_mode}'
+
+        return rast_out, rast_out_db
+
+    def raster_interpolate(self, uv, rast_out, uv_idx, rast_db=None, diff_attrs=None):
+
+        if self.raster_mode == 'cr':
+            textd = None
+            barycentric = rast_out[0, ..., :-1]
+            findices = rast_out[0, ..., -1]
+            if uv.dim() == 2:
+                uv = uv.unsqueeze(0)
+            textc = self.raster.interpolate(uv, findices, barycentric, uv_idx)
+        else:
+            raise f'No raster named {self.raster_mode}'
+
+        return textc, textd
+
+    def raster_texture(self, tex, uv, uv_da=None, mip_level_bias=None, mip=None, filter_mode='auto',
+                       boundary_mode='wrap', max_mip_level=None):
+
+        if self.raster_mode == 'cr':
+            raise f'Texture is not implemented in cr'
+        else:
+            raise f'No raster named {self.raster_mode}'
+
+        return color
+
+    def raster_antialias(self, color, rast, pos, tri, topology_hash=None, pos_gradient_boost=1.0):
+
+        if self.raster_mode == 'cr':
+            # Antialias has not been supported yet
+            color = color
+        else:
+            raise f'No raster named {self.raster_mode}'
+
+        return color
+
+    def load_mesh(
+        self,
+        mesh,
+        scale_factor=1.15,
+        auto_center=True,
+    ):
+        vtx_pos, pos_idx, vtx_uv, uv_idx, texture_data = load_mesh(mesh)
+        self.mesh_copy = mesh
+        self.set_mesh(vtx_pos, pos_idx,
+                      vtx_uv=vtx_uv, uv_idx=uv_idx,
+                      scale_factor=scale_factor, auto_center=auto_center
+                      )
+        if texture_data is not None:
+            self.set_texture(texture_data)
+
+    def save_mesh(self):
+        texture_data = self.get_texture()
+        texture_data = Image.fromarray((texture_data * 255).astype(np.uint8))
+        return save_mesh(self.mesh_copy, texture_data)
+
+    def set_mesh(
+        self,
+        vtx_pos, pos_idx,
+        vtx_uv=None, uv_idx=None,
+        scale_factor=1.15, auto_center=True
+    ):
+
+        self.vtx_pos = torch.from_numpy(vtx_pos).to(self.device).float()
+        self.pos_idx = torch.from_numpy(pos_idx).to(self.device).to(torch.int)
+        if (vtx_uv is not None) and (uv_idx is not None):
+            self.vtx_uv = torch.from_numpy(vtx_uv).to(self.device).float()
+            self.uv_idx = torch.from_numpy(uv_idx).to(self.device).to(torch.int)
+        else:
+            self.vtx_uv = None
+            self.uv_idx = None
+
+        self.vtx_pos[:, [0, 1]] = -self.vtx_pos[:, [0, 1]]
+        self.vtx_pos[:, [1, 2]] = self.vtx_pos[:, [2, 1]]
+        if (vtx_uv is not None) and (uv_idx is not None):
+            self.vtx_uv[:, 1] = 1.0 - self.vtx_uv[:, 1]
+
+        if auto_center:
+            max_bb = (self.vtx_pos - 0).max(0)[0]
+            min_bb = (self.vtx_pos - 0).min(0)[0]
+            center = (max_bb + min_bb) / 2
+            scale = torch.norm(self.vtx_pos - center, dim=1).max() * 2.0
+            self.vtx_pos = (self.vtx_pos - center) * \
+                           (scale_factor / float(scale))
+            self.scale_factor = scale_factor
+
+    def set_texture(self, tex):
+        if isinstance(tex, np.ndarray):
+            tex = Image.fromarray((tex * 255).astype(np.uint8))
+        elif isinstance(tex, torch.Tensor):
+            tex = tex.cpu().numpy()
+            tex = Image.fromarray((tex * 255).astype(np.uint8))
+
+        tex = tex.resize(self.texture_size).convert('RGB')
+        tex = np.array(tex) / 255.0
+        self.tex = torch.from_numpy(tex).to(self.device)
+        self.tex = self.tex.float()
+
+    def set_default_render_resolution(self, default_resolution):
+        if isinstance(default_resolution, int):
+            default_resolution = (default_resolution, default_resolution)
+        self.default_resolution = default_resolution
+
+    def set_default_texture_resolution(self, texture_size):
+        if isinstance(texture_size, int):
+            texture_size = (texture_size, texture_size)
+        self.texture_size = texture_size
+
+    def get_mesh(self):
+        vtx_pos = self.vtx_pos.cpu().numpy()
+        pos_idx = self.pos_idx.cpu().numpy()
+        vtx_uv = self.vtx_uv.cpu().numpy()
+        uv_idx = self.uv_idx.cpu().numpy()
+
+        # 坐标变换的逆变换
+        vtx_pos[:, [1, 2]] = vtx_pos[:, [2, 1]]
+        vtx_pos[:, [0, 1]] = -vtx_pos[:, [0, 1]]
+
+        vtx_uv[:, 1] = 1.0 - vtx_uv[:, 1]
+        return vtx_pos, pos_idx, vtx_uv, uv_idx
+
+    def get_texture(self):
+        return self.tex.cpu().numpy()
+
+    def to(self, device):
+        self.device = device
+
+        for attr_name in dir(self):
+            attr_value = getattr(self, attr_name)
+            if isinstance(attr_value, torch.Tensor):
+                setattr(self, attr_name, attr_value.to(self.device))
+
+    def color_rgb_to_srgb(self, image):
+        if isinstance(image, Image.Image):
+            image_rgb = torch.tesnor(
+                np.array(image) /
+                255.0).float().to(
+                self.device)
+        elif isinstance(image, np.ndarray):
+            image_rgb = torch.tensor(image).float()
+        else:
+            image_rgb = image.to(self.device)
+
+        image_srgb = torch.where(
+            image_rgb <= 0.0031308,
+            12.92 * image_rgb,
+            1.055 * torch.pow(image_rgb, 1 / 2.4) - 0.055
+        )
+
+        if isinstance(image, Image.Image):
+            image_srgb = Image.fromarray(
+                (image_srgb.cpu().numpy() *
+                 255).astype(
+                    np.uint8))
+        elif isinstance(image, np.ndarray):
+            image_srgb = image_srgb.cpu().numpy()
+        else:
+            image_srgb = image_srgb.to(image.device)
+
+        return image_srgb
+
+    def _render(
+        self,
+        glctx,
+        mvp,
+        pos,
+        pos_idx,
+        uv,
+        uv_idx,
+        tex,
+        resolution,
+        max_mip_level,
+        keep_alpha,
+        filter_mode
+    ):
+        pos_clip = transform_pos(mvp, pos)
+        if isinstance(resolution, (int, float)):
+            resolution = [resolution, resolution]
+        rast_out, rast_out_db = self.raster_rasterize(
+            glctx, pos_clip, pos_idx, resolution=resolution)
+
+        tex = tex.contiguous()
+        if filter_mode == 'linear-mipmap-linear':
+            texc, texd = self.raster_interpolate(
+                uv[None, ...], rast_out, uv_idx, rast_db=rast_out_db, diff_attrs='all')
+            color = self.raster_texture(
+                tex[None, ...], texc, texd, filter_mode='linear-mipmap-linear', max_mip_level=max_mip_level)
+        else:
+            texc, _ = self.raster_interpolate(uv[None, ...], rast_out, uv_idx)
+            color = self.raster_texture(tex[None, ...], texc, filter_mode=filter_mode)
+
+        visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
+        color = color * visible_mask  # Mask out background.
+        if self.use_antialias:
+            color = self.raster_antialias(color, rast_out, pos_clip, pos_idx)
+
+        if keep_alpha:
+            color = torch.cat([color, visible_mask], dim=-1)
+        return color[0, ...]
+
+    def render(
+        self,
+        elev,
+        azim,
+        camera_distance=None,
+        center=None,
+        resolution=None,
+        tex=None,
+        keep_alpha=True,
+        bgcolor=None,
+        filter_mode=None,
+        return_type='th'
+    ):
+
+        proj = self.camera_proj_mat
+        r_mv = get_mv_matrix(
+            elev=elev,
+            azim=azim,
+            camera_distance=self.camera_distance if camera_distance is None else camera_distance,
+            center=center)
+        r_mvp = np.matmul(proj, r_mv).astype(np.float32)
+        if tex is not None:
+            if isinstance(tex, Image.Image):
+                tex = torch.tensor(np.array(tex) / 255.0)
+            elif isinstance(tex, np.ndarray):
+                tex = torch.tensor(tex)
+            if tex.dim() == 2:
+                tex = tex.unsqueeze(-1)
+            tex = tex.float().to(self.device)
+        image = self._render(r_mvp, self.vtx_pos, self.pos_idx, self.vtx_uv, self.uv_idx,
+                             self.tex if tex is None else tex,
+                             self.default_resolution if resolution is None else resolution,
+                             self.max_mip_level, True, filter_mode if filter_mode else self.filter_mode)
+        mask = (image[..., [-1]] == 1).float()
+        if bgcolor is None:
+            bgcolor = [0 for _ in range(image.shape[-1] - 1)]
+        image = image * mask + (1 - mask) * \
+                torch.tensor(bgcolor + [0]).to(self.device)
+        if keep_alpha == False:
+            image = image[..., :-1]
+        if return_type == 'np':
+            image = image.cpu().numpy()
+        elif return_type == 'pl':
+            image = image.squeeze(-1).cpu().numpy() * 255
+            image = Image.fromarray(image.astype(np.uint8))
+        return image
+
+    def render_normal(
+        self,
+        elev,
+        azim,
+        camera_distance=None,
+        center=None,
+        resolution=None,
+        bg_color=[1, 1, 1],
+        use_abs_coor=False,
+        normalize_rgb=True,
+        return_type='th'
+    ):
+
+        pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center)
+        if resolution is None:
+            resolution = self.default_resolution
+        if isinstance(resolution, (int, float)):
+            resolution = [resolution, resolution]
+        rast_out, rast_out_db = self.raster_rasterize(
+            pos_clip, self.pos_idx, resolution=resolution)
+
+        if use_abs_coor:
+            mesh_triangles = self.vtx_pos[self.pos_idx[:, :3], :]
+        else:
+            pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4]
+            mesh_triangles = pos_camera[self.pos_idx[:, :3], :]
+        face_normals = F.normalize(
+            torch.cross(mesh_triangles[:,
+                        1,
+                        :] - mesh_triangles[:,
+                             0,
+                             :],
+                        mesh_triangles[:,
+                        2,
+                        :] - mesh_triangles[:,
+                             0,
+                             :],
+                        dim=-1),
+            dim=-1)
+
+        vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0],
+                                                              faces=self.pos_idx.cpu(),
+                                                              face_normals=face_normals.cpu(), )
+        vertex_normals = torch.from_numpy(
+            vertex_normals).float().to(self.device).contiguous()
+
+        # Interpolate normal values across the rasterized pixels
+        normal, _ = self.raster_interpolate(
+            vertex_normals[None, ...], rast_out, self.pos_idx)
+
+        visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
+        normal = normal * visible_mask + \
+                 torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 -
+                                                                                    visible_mask)  # Mask out background.
+
+        if normalize_rgb:
+            normal = (normal + 1) * 0.5
+        if self.use_antialias:
+            normal = self.raster_antialias(normal, rast_out, pos_clip, self.pos_idx)
+
+        image = normal[0, ...]
+        if return_type == 'np':
+            image = image.cpu().numpy()
+        elif return_type == 'pl':
+            image = image.cpu().numpy() * 255
+            image = Image.fromarray(image.astype(np.uint8))
+
+        return image
+
+    def convert_normal_map(self, image):
+        # blue is front, red is left, green is top
+        if isinstance(image, Image.Image):
+            image = np.array(image)
+        mask = (image == [255, 255, 255]).all(axis=-1)
+
+        image = (image / 255.0) * 2.0 - 1.0
+
+        image[..., [1]] = -image[..., [1]]
+        image[..., [1, 2]] = image[..., [2, 1]]
+        image[..., [0]] = -image[..., [0]]
+
+        image = (image + 1.0) * 0.5
+
+        image = (image * 255).astype(np.uint8)
+        image[mask] = [127, 127, 255]
+
+        return Image.fromarray(image)
+
+    def get_pos_from_mvp(self, elev, azim, camera_distance, center):
+        proj = self.camera_proj_mat
+        r_mv = get_mv_matrix(
+            elev=elev,
+            azim=azim,
+            camera_distance=self.camera_distance if camera_distance is None else camera_distance,
+            center=center)
+
+        pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True)
+        pos_clip = transform_pos(proj, pos_camera)
+
+        return pos_camera, pos_clip
+
+    def render_depth(
+        self,
+        elev,
+        azim,
+        camera_distance=None,
+        center=None,
+        resolution=None,
+        return_type='th'
+    ):
+        pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center)
+
+        if resolution is None:
+            resolution = self.default_resolution
+        if isinstance(resolution, (int, float)):
+            resolution = [resolution, resolution]
+        rast_out, rast_out_db = self.raster_rasterize(
+            pos_clip, self.pos_idx, resolution=resolution)
+
+        pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4]
+        tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous()
+
+        # Interpolate depth values across the rasterized pixels
+        depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx)
+
+        visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
+        depth_max, depth_min = depth[visible_mask >
+                                     0].max(), depth[visible_mask > 0].min()
+        depth = (depth - depth_min) / (depth_max - depth_min)
+
+        depth = depth * visible_mask  # Mask out background.
+        if self.use_antialias:
+            depth = self.raster_antialias(depth, rast_out, pos_clip, self.pos_idx)
+
+        image = depth[0, ...]
+        if return_type == 'np':
+            image = image.cpu().numpy()
+        elif return_type == 'pl':
+            image = image.squeeze(-1).cpu().numpy() * 255
+            image = Image.fromarray(image.astype(np.uint8))
+        return image
+
+    def render_position(self, elev, azim, camera_distance=None, center=None,
+                        resolution=None, bg_color=[1, 1, 1], return_type='th'):
+        pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center)
+        if resolution is None:
+            resolution = self.default_resolution
+        if isinstance(resolution, (int, float)):
+            resolution = [resolution, resolution]
+        rast_out, rast_out_db = self.raster_rasterize(
+            pos_clip, self.pos_idx, resolution=resolution)
+
+        tex_position = 0.5 - self.vtx_pos[:, :3] / self.scale_factor
+        tex_position = tex_position.contiguous()
+
+        # Interpolate depth values across the rasterized pixels
+        position, _ = self.raster_interpolate(
+            tex_position[None, ...], rast_out, self.pos_idx)
+
+        visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)
+
+        position = position * visible_mask + \
+                   torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 -
+                                                                                      visible_mask)  # Mask out background.
+        if self.use_antialias:
+            position = self.raster_antialias(position, rast_out, pos_clip, self.pos_idx)
+
+        image = position[0, ...]
+
+        if return_type == 'np':
+            image = image.cpu().numpy()
+        elif return_type == 'pl':
+            image = image.squeeze(-1).cpu().numpy() * 255
+            image = Image.fromarray(image.astype(np.uint8))
+        return image
+
+    def render_uvpos(self, return_type='th'):
+        image = self.uv_feature_map(self.vtx_pos * 0.5 + 0.5)
+        if return_type == 'np':
+            image = image.cpu().numpy()
+        elif return_type == 'pl':
+            image = image.cpu().numpy() * 255
+            image = Image.fromarray(image.astype(np.uint8))
+        return image
+
+    def uv_feature_map(self, vert_feat, bg=None):
+        vtx_uv = self.vtx_uv * 2 - 1.0
+        vtx_uv = torch.cat(
+            [vtx_uv, torch.zeros_like(self.vtx_uv)], dim=1).unsqueeze(0)
+        vtx_uv[..., -1] = 1
+        uv_idx = self.uv_idx
+        rast_out, rast_out_db = self.raster_rasterize(
+            vtx_uv, uv_idx, resolution=self.texture_size)
+        feat_map, _ = self.raster_interpolate(vert_feat[None, ...], rast_out, uv_idx)
+        feat_map = feat_map[0, ...]
+        if bg is not None:
+            visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...]
+            feat_map[visible_mask == 0] = bg
+        return feat_map
+
+    def render_sketch_from_geometry(self, normal_image, depth_image):
+        normal_image_np = normal_image.cpu().numpy()
+        depth_image_np = depth_image.cpu().numpy()
+
+        normal_image_np = (normal_image_np * 255).astype(np.uint8)
+        depth_image_np = (depth_image_np * 255).astype(np.uint8)
+        normal_image_np = cv2.cvtColor(normal_image_np, cv2.COLOR_RGB2GRAY)
+
+        normal_edges = cv2.Canny(normal_image_np, 80, 150)
+        depth_edges = cv2.Canny(depth_image_np, 30, 80)
+
+        combined_edges = np.maximum(normal_edges, depth_edges)
+
+        sketch_image = torch.from_numpy(combined_edges).to(
+            normal_image.device).float() / 255.0
+        sketch_image = sketch_image.unsqueeze(-1)
+
+        return sketch_image
+
+    def render_sketch_from_depth(self, depth_image):
+        depth_image_np = depth_image.cpu().numpy()
+        depth_image_np = (depth_image_np * 255).astype(np.uint8)
+        depth_edges = cv2.Canny(depth_image_np, 30, 80)
+        combined_edges = depth_edges
+        sketch_image = torch.from_numpy(combined_edges).to(
+            depth_image.device).float() / 255.0
+        sketch_image = sketch_image.unsqueeze(-1)
+        return sketch_image
+
+    def back_project(self, image, elev, azim,
+                     camera_distance=None, center=None, method=None):
+        if isinstance(image, Image.Image):
+            image = torch.tensor(np.array(image) / 255.0)
+        elif isinstance(image, np.ndarray):
+            image = torch.tensor(image)
+        if image.dim() == 2:
+            image = image.unsqueeze(-1)
+        image = image.float().to(self.device)
+        resolution = image.shape[:2]
+        channel = image.shape[-1]
+        texture = torch.zeros(self.texture_size + (channel,)).to(self.device)
+        cos_map = torch.zeros(self.texture_size + (1,)).to(self.device)
+
+        proj = self.camera_proj_mat
+        r_mv = get_mv_matrix(
+            elev=elev,
+            azim=azim,
+            camera_distance=self.camera_distance if camera_distance is None else camera_distance,
+            center=center)
+        pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True)
+        pos_clip = transform_pos(proj, pos_camera)
+        pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4]
+        v0 = pos_camera[self.pos_idx[:, 0], :]
+        v1 = pos_camera[self.pos_idx[:, 1], :]
+        v2 = pos_camera[self.pos_idx[:, 2], :]
+        face_normals = F.normalize(
+            torch.cross(
+                v1 - v0,
+                v2 - v0,
+                dim=-1),
+            dim=-1)
+        vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0],
+                                                              faces=self.pos_idx.cpu(),
+                                                              face_normals=face_normals.cpu(), )
+        vertex_normals = torch.from_numpy(
+            vertex_normals).float().to(self.device).contiguous()
+        tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous()
+        rast_out, rast_out_db = self.raster_rasterize(
+            pos_clip, self.pos_idx, resolution=resolution)
+        visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...]
+
+        normal, _ = self.raster_interpolate(
+            vertex_normals[None, ...], rast_out, self.pos_idx)
+        normal = normal[0, ...]
+        uv, _ = self.raster_interpolate(self.vtx_uv[None, ...], rast_out, self.uv_idx)
+        depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx)
+        depth = depth[0, ...]
+
+        depth_max, depth_min = depth[visible_mask >
+                                     0].max(), depth[visible_mask > 0].min()
+        depth_normalized = (depth - depth_min) / (depth_max - depth_min)
+        depth_image = depth_normalized * visible_mask  # Mask out background.
+
+        sketch_image = self.render_sketch_from_depth(depth_image)
+
+        lookat = torch.tensor([[0, 0, -1]], device=self.device)
+        cos_image = torch.nn.functional.cosine_similarity(
+            lookat, normal.view(-1, 3))
+        cos_image = cos_image.view(normal.shape[0], normal.shape[1], 1)
+
+        cos_thres = np.cos(self.bake_angle_thres / 180 * np.pi)
+        cos_image[cos_image < cos_thres] = 0
+
+        # shrink
+        kernel_size = self.bake_unreliable_kernel_size * 2 + 1
+        kernel = torch.ones(
+            (1, 1, kernel_size, kernel_size), dtype=torch.float32).to(
+            sketch_image.device)
+
+        visible_mask = visible_mask.permute(2, 0, 1).unsqueeze(0).float()
+        visible_mask = F.conv2d(
+            1.0 - visible_mask,
+            kernel,
+            padding=kernel_size // 2)
+        visible_mask = 1.0 - (visible_mask > 0).float()  # 二值化
+        visible_mask = visible_mask.squeeze(0).permute(1, 2, 0)
+
+        sketch_image = sketch_image.permute(2, 0, 1).unsqueeze(0)
+        sketch_image = F.conv2d(sketch_image, kernel, padding=kernel_size // 2)
+        sketch_image = (sketch_image > 0).float()  # 二值化
+        sketch_image = sketch_image.squeeze(0).permute(1, 2, 0)
+        visible_mask = visible_mask * (sketch_image < 0.5)
+
+        cos_image[visible_mask == 0] = 0
+
+        method = self.bake_mode if method is None else method
+
+        if method == 'linear':
+            proj_mask = (visible_mask != 0).view(-1)
+            uv = uv.squeeze(0).contiguous().view(-1, 2)[proj_mask]
+            image = image.squeeze(0).contiguous().view(-1, channel)[proj_mask]
+            cos_image = cos_image.contiguous().view(-1, 1)[proj_mask]
+            sketch_image = sketch_image.contiguous().view(-1, 1)[proj_mask]
+
+            texture = linear_grid_put_2d(
+                self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], image)
+            cos_map = linear_grid_put_2d(
+                self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], cos_image)
+            boundary_map = linear_grid_put_2d(
+                self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], sketch_image)
+        else:
+            raise f'No bake mode {method}'
+
+        return texture, cos_map, boundary_map
+
+    def bake_texture(self, colors, elevs, azims,
+                     camera_distance=None, center=None, exp=6, weights=None):
+        for i in range(len(colors)):
+            if isinstance(colors[i], Image.Image):
+                colors[i] = torch.tensor(
+                    np.array(
+                        colors[i]) / 255.0,
+                    device=self.device).float()
+        if weights is None:
+            weights = [1.0 for _ in range(colors)]
+        textures = []
+        cos_maps = []
+        for color, elev, azim, weight in zip(colors, elevs, azims, weights):
+            texture, cos_map, _ = self.back_project(
+                color, elev, azim, camera_distance, center)
+            cos_map = weight * (cos_map ** exp)
+            textures.append(texture)
+            cos_maps.append(cos_map)
+
+        texture_merge, trust_map_merge = self.fast_bake_texture(
+            textures, cos_maps)
+        return texture_merge, trust_map_merge
+
+    @torch.no_grad()
+    def fast_bake_texture(self, textures, cos_maps):
+
+        channel = textures[0].shape[-1]
+        texture_merge = torch.zeros(
+            self.texture_size + (channel,)).to(self.device)
+        trust_map_merge = torch.zeros(self.texture_size + (1,)).to(self.device)
+        for texture, cos_map in zip(textures, cos_maps):
+            view_sum = (cos_map > 0).sum()
+            painted_sum = ((cos_map > 0) * (trust_map_merge > 0)).sum()
+            if painted_sum / view_sum > 0.99:
+                continue
+            texture_merge += texture * cos_map
+            trust_map_merge += cos_map
+        texture_merge = texture_merge / torch.clamp(trust_map_merge, min=1E-8)
+
+        return texture_merge, trust_map_merge > 1E-8
+
+    def uv_inpaint(self, texture, mask):
+
+        if isinstance(texture, torch.Tensor):
+            texture_np = texture.cpu().numpy()
+        elif isinstance(texture, np.ndarray):
+            texture_np = texture
+        elif isinstance(texture, Image.Image):
+            texture_np = np.array(texture) / 255.0
+
+        vtx_pos, pos_idx, vtx_uv, uv_idx = self.get_mesh()
+
+        texture_np, mask = meshVerticeInpaint(
+            texture_np, mask, vtx_pos, vtx_uv, pos_idx, uv_idx)
+
+        texture_np = cv2.inpaint(
+            (texture_np *
+             255).astype(
+                np.uint8),
+            255 -
+            mask,
+            3,
+            cv2.INPAINT_NS)
+
+        return texture_np
@@ -0,0 +1,44 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import trimesh
+
+
+def load_mesh(mesh):
+    vtx_pos = mesh.vertices if hasattr(mesh, 'vertices') else None
+    pos_idx = mesh.faces if hasattr(mesh, 'faces') else None
+
+    vtx_uv = mesh.visual.uv if hasattr(mesh.visual, 'uv') else None
+    uv_idx = mesh.faces if hasattr(mesh, 'faces') else None
+
+    texture_data = None
+
+    return vtx_pos, pos_idx, vtx_uv, uv_idx, texture_data
+
+
+def save_mesh(mesh, texture_data):
+    material = trimesh.visual.texture.SimpleMaterial(image=texture_data, diffuse=(255, 255, 255))
+    texture_visuals = trimesh.visual.TextureVisuals(uv=mesh.visual.uv, image=texture_data, material=material)
+    mesh.visual = texture_visuals
+    return mesh
@@ -0,0 +1,47 @@
+from setuptools import setup, Extension
+import pybind11
+import sys
+import platform
+
+# Common compile arguments
+common_compile_args = []
+
+if sys.platform == 'win32':
+    # Windows-specific compile arguments
+    extra_compile_args = [
+        '/O2',  # Optimization level
+        '/std:c++14',  # C++ standard (using C++14 for better compatibility)
+        '/EHsc',  # Exception handling model
+        '/MP',  # Multi-process compilation
+        '/DWIN32_LEAN_AND_MEAN',  # Exclude rarely-used Windows headers
+    ]
+    extra_link_args = []
+else:
+    # Linux/Unix compile arguments
+    extra_compile_args = [
+        '-O3',  # Optimization level
+        '-std=c++14',  # C++ standard
+        '-fPIC',  # Position independent code
+    ]
+    extra_link_args = ['-fPIC']
+
+ext_modules = [
+    Extension(
+        "mesh_processor",
+        ["mesh_processor.cpp"],
+        include_dirs=[
+            pybind11.get_include(),
+            pybind11.get_include(user=True)
+        ],
+        language='c++',
+        extra_compile_args=common_compile_args + extra_compile_args,
+        extra_link_args=extra_link_args,
+    ),
+]
+
+setup(
+    name="mesh_processor",
+    ext_modules=ext_modules,
+    install_requires=['pybind11>=2.6.0'],
+    python_requires='>=3.6',
+)
@@ -0,0 +1,23 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
@@ -0,0 +1,554 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy
+import numpy as np
+import torch
+import torch.distributed
+import torch.utils.checkpoint
+from PIL import Image
+from diffusers import (
+    AutoencoderKL,
+    DiffusionPipeline,
+    ImagePipelineOutput
+)
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.image_processor import PipelineImageInput
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline, retrieve_timesteps, \
+    rescale_noise_cfg
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils import deprecate
+from einops import rearrange
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from .unet.modules import UNet2p5DConditionModel
+
+
+def to_rgb_image(maybe_rgba: Image.Image):
+    if maybe_rgba.mode == 'RGB':
+        return maybe_rgba
+    elif maybe_rgba.mode == 'RGBA':
+        rgba = maybe_rgba
+        img = numpy.random.randint(127, 128, size=[rgba.size[1], rgba.size[0], 3], dtype=numpy.uint8)
+        img = Image.fromarray(img, 'RGB')
+        img.paste(rgba, mask=rgba.getchannel('A'))
+        return img
+    else:
+        raise ValueError("Unsupported image type.", maybe_rgba.mode)
+
+
+class HunyuanPaintPipeline(StableDiffusionPipeline):
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2p5DConditionModel,
+        scheduler: KarrasDiffusionSchedulers,
+        feature_extractor: CLIPImageProcessor,
+        safety_checker=None,
+        use_torch_compile=False,
+    ):
+        DiffusionPipeline.__init__(self)
+
+        safety_checker = None
+        self.register_modules(
+            vae=torch.compile(vae) if use_torch_compile else vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=torch.compile(feature_extractor) if use_torch_compile else feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    @torch.no_grad()
+    def encode_images(self, images):
+        B = images.shape[0]
+        images = rearrange(images, 'b n c h w -> (b n) c h w')
+
+        dtype = next(self.vae.parameters()).dtype
+        images = (images - 0.5) * 2.0
+        posterior = self.vae.encode(images.to(dtype)).latent_dist
+        latents = posterior.sample() * self.vae.config.scaling_factor
+
+        latents = rearrange(latents, '(b n) c h w -> b n c h w', b=B)
+        return latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        image: Image.Image = None,
+        prompt=None,
+        negative_prompt='watermark, ugly, deformed, noisy, blurry, low contrast',
+        *args,
+        num_images_per_prompt: Optional[int] = 1,
+        guidance_scale=2.0,
+        output_type: Optional[str] = "pil",
+        width=512,
+        height=512,
+        num_inference_steps=28,
+        return_dict=True,
+        **cached_condition,
+    ):
+        if image is None:
+            raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.")
+        assert not isinstance(image, torch.Tensor)
+
+        image = to_rgb_image(image)
+
+        image_vae = torch.tensor(np.array(image) / 255.0)
+        image_vae = image_vae.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(0)
+        image_vae = image_vae.to(device=self.vae.device, dtype=self.vae.dtype)
+
+        batch_size = image_vae.shape[0]
+        assert batch_size == 1
+        assert num_images_per_prompt == 1
+
+        ref_latents = self.encode_images(image_vae)
+
+        def convert_pil_list_to_tensor(images):
+            bg_c = [1., 1., 1.]
+            images_tensor = []
+            for batch_imgs in images:
+                view_imgs = []
+                for pil_img in batch_imgs:
+                    img = numpy.asarray(pil_img, dtype=numpy.float32) / 255.
+                    if img.shape[2] > 3:
+                        alpha = img[:, :, 3:]
+                        img = img[:, :, :3] * alpha + bg_c * (1 - alpha)
+                    img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).contiguous().half().to("cuda")
+                    view_imgs.append(img)
+                view_imgs = torch.cat(view_imgs, dim=0)
+                images_tensor.append(view_imgs.unsqueeze(0))
+
+            images_tensor = torch.cat(images_tensor, dim=0)
+            return images_tensor
+
+        if "normal_imgs" in cached_condition:
+
+            if isinstance(cached_condition["normal_imgs"], List):
+                cached_condition["normal_imgs"] = convert_pil_list_to_tensor(cached_condition["normal_imgs"])
+
+            cached_condition['normal_imgs'] = self.encode_images(cached_condition["normal_imgs"])
+
+        if "position_imgs" in cached_condition:
+
+            if isinstance(cached_condition["position_imgs"], List):
+                cached_condition["position_imgs"] = convert_pil_list_to_tensor(cached_condition["position_imgs"])
+
+            cached_condition["position_imgs"] = self.encode_images(cached_condition["position_imgs"])
+
+        if 'camera_info_gen' in cached_condition:
+            camera_info = cached_condition['camera_info_gen']  # B,N
+            if isinstance(camera_info, List):
+                camera_info = torch.tensor(camera_info)
+            camera_info = camera_info.to(image_vae.device).to(torch.int64)
+            cached_condition['camera_info_gen'] = camera_info
+        if 'camera_info_ref' in cached_condition:
+            camera_info = cached_condition['camera_info_ref']  # B,N
+            if isinstance(camera_info, List):
+                camera_info = torch.tensor(camera_info)
+            camera_info = camera_info.to(image_vae.device).to(torch.int64)
+            cached_condition['camera_info_ref'] = camera_info
+
+        cached_condition['ref_latents'] = ref_latents
+
+        if guidance_scale > 1:
+            negative_ref_latents = torch.zeros_like(cached_condition['ref_latents'])
+            cached_condition['ref_latents'] = torch.cat([negative_ref_latents, cached_condition['ref_latents']])
+            cached_condition['ref_scale'] = torch.as_tensor([0.0, 1.0]).to(cached_condition['ref_latents'])
+            if "normal_imgs" in cached_condition:
+                cached_condition['normal_imgs'] = torch.cat(
+                    (cached_condition['normal_imgs'], cached_condition['normal_imgs']))
+
+            if "position_imgs" in cached_condition:
+                cached_condition['position_imgs'] = torch.cat(
+                    (cached_condition['position_imgs'], cached_condition['position_imgs']))
+
+            if 'position_maps' in cached_condition:
+                cached_condition['position_maps'] = torch.cat(
+                    (cached_condition['position_maps'], cached_condition['position_maps']))
+
+            if 'camera_info_gen' in cached_condition:
+                cached_condition['camera_info_gen'] = torch.cat(
+                    (cached_condition['camera_info_gen'], cached_condition['camera_info_gen']))
+            if 'camera_info_ref' in cached_condition:
+                cached_condition['camera_info_ref'] = torch.cat(
+                    (cached_condition['camera_info_ref'], cached_condition['camera_info_ref']))
+
+        prompt_embeds = self.unet.learned_text_clip_gen.repeat(num_images_per_prompt, 1, 1)
+        negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+
+        latents: torch.Tensor = self.denoise(
+            None,
+            *args,
+            cross_attention_kwargs=None,
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=num_images_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            num_inference_steps=num_inference_steps,
+            output_type='latent',
+            width=width,
+            height=height,
+            **cached_condition
+        ).images
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+        else:
+            image = latents
+
+        image = self.image_processor.postprocess(image, output_type=output_type)
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)
+
+    def denoise(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        timesteps: List[int] = None,
+        sigmas: List[float] = None,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        ip_adapter_image: Optional[PipelineImageInput] = None,
+        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        clip_skip: Optional[int] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        **kwargs,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            sigmas (`List[float]`, *optional*):
+                Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
+                their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
+                will be used.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                A higher guidance scale value encourages the model to generate images closely linked to the text
+                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
+                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+            ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
+            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
+                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
+                IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
+                contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
+                provided, embeddings are computed from the `ip_adapter_image` input argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            guidance_rescale (`float`, *optional*, defaults to 0.0):
+                Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are
+                Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when
+                using zero terminal SNR.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
+                otherwise a `tuple` is returned where the first element is a list with the generated images and the
+                second element is a list of `bool`s indicating whether the corresponding generated image contains
+                "not-safe-for-work" (nsfw) content.
+        """
+
+        callback = kwargs.pop("callback", None)
+        callback_steps = kwargs.pop("callback_steps", None)
+
+        if callback is not None:
+            deprecate(
+                "callback",
+                "1.0.0",
+                "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+        if callback_steps is not None:
+            deprecate(
+                "callback_steps",
+                "1.0.0",
+                "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
+            )
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+        # to deal with lora scaling and other possible forward hooks
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            height,
+            width,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            ip_adapter_image,
+            ip_adapter_image_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._cross_attention_kwargs = cross_attention_kwargs
+        self._interrupt = False
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+
+        # 3. Encode input prompt
+        lora_scale = (
+            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
+        )
+
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            self.do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=lora_scale,
+            clip_skip=self.clip_skip,
+        )
+
+        # For classifier free guidance, we need to do two forward passes.
+        # Here we concatenate the unconditional and text embeddings into a single batch
+        # to avoid doing two forward passes
+        if self.do_classifier_free_guidance:
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+
+        if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
+            image_embeds = self.prepare_ip_adapter_image_embeds(
+                ip_adapter_image,
+                ip_adapter_image_embeds,
+                device,
+                batch_size * num_images_per_prompt,
+                self.do_classifier_free_guidance,
+            )
+
+        # 4. Prepare timesteps
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler, num_inference_steps, device, timesteps, sigmas
+        )
+        assert num_images_per_prompt == 1
+        # 5. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * kwargs['num_in_batch'],  # num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 6.1 Add image embeds for IP-Adapter
+        added_cond_kwargs = (
+            {"image_embeds": image_embeds}
+            if (ip_adapter_image is not None or ip_adapter_image_embeds is not None)
+            else None
+        )
+
+        # 6.2 Optionally get Guidance Scale Embedding
+        timestep_cond = None
+        if self.unet.config.time_cond_proj_dim is not None:
+            guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
+            timestep_cond = self.get_guidance_scale_embedding(
+                guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim
+            ).to(device=device, dtype=latents.dtype)
+
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # expand the latents if we are doing classifier free guidance
+                latents = rearrange(latents, '(b n) c h w -> b n c h w', n=kwargs['num_in_batch'])
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                latent_model_input = rearrange(latent_model_input, 'b n c h w -> (b n) c h w')
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+                latent_model_input = rearrange(latent_model_input, '(b n) c h w ->b n c h w', n=kwargs['num_in_batch'])
+
+                # predict the noise residual
+
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    timestep_cond=timestep_cond,
+                    cross_attention_kwargs=self.cross_attention_kwargs,
+                    added_cond_kwargs=added_cond_kwargs,
+                    return_dict=False, **kwargs
+                )[0]
+                latents = rearrange(latents, 'b n c h w -> (b n) c h w')
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = \
+                    self.scheduler.step(noise_pred, t, latents[:, :num_channels_latents, :, :], **extra_step_kwargs,
+                                        return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        step_idx = i // getattr(self.scheduler, "order", 1)
+                        callback(step_idx, t, latents)
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
+                0
+            ]
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
@@ -0,0 +1,23 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
@@ -0,0 +1,440 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+import copy
+import json
+import os
+from typing import Any, Dict, Optional
+
+import torch
+import torch.nn as nn
+from diffusers.models import UNet2DConditionModel
+from diffusers.models.attention_processor import Attention
+from diffusers.models.transformers.transformer_2d import BasicTransformerBlock
+from einops import rearrange
+
+
+def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
+    # "feed_forward_chunk_size" can be used to save memory
+    if hidden_states.shape[chunk_dim] % chunk_size != 0:
+        raise ValueError(
+            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+        )
+
+    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
+    ff_output = torch.cat(
+        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+        dim=chunk_dim,
+    )
+    return ff_output
+
+
+class Basic2p5DTransformerBlock(torch.nn.Module):
+    def __init__(self, transformer: BasicTransformerBlock, layer_name, use_ma=True, use_ra=True) -> None:
+        super().__init__()
+        self.transformer = transformer
+        self.layer_name = layer_name
+        self.use_ma = use_ma
+        self.use_ra = use_ra
+
+        # multiview attn
+        if self.use_ma:
+            self.attn_multiview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+
+        # ref attn
+        if self.use_ra:
+            self.attn_refview = Attention(
+                query_dim=self.dim,
+                heads=self.num_attention_heads,
+                dim_head=self.attention_head_dim,
+                dropout=self.dropout,
+                bias=self.attention_bias,
+                cross_attention_dim=None,
+                upcast_attention=self.attn1.upcast_attention,
+                out_bias=True,
+            )
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.transformer, name)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        timestep: Optional[torch.LongTensor] = None,
+        cross_attention_kwargs: Dict[str, Any] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+    ) -> torch.Tensor:
+
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1)
+        mode = cross_attention_kwargs.pop('mode', None)
+        mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0)
+        ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0)
+        condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None)
+
+        if self.norm_type == "ada_norm":
+            norm_hidden_states = self.norm1(hidden_states, timestep)
+        elif self.norm_type == "ada_norm_zero":
+            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+            )
+        elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]:
+            norm_hidden_states = self.norm1(hidden_states)
+        elif self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif self.norm_type == "ada_norm_single":
+            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
+                self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
+            ).chunk(6, dim=1)
+            norm_hidden_states = self.norm1(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa
+        else:
+            raise ValueError("Incorrect norm used")
+
+        if self.pos_embed is not None:
+            norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+        # 1. Prepare GLIGEN inputs
+        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+        gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+        attn_output = self.attn1(
+            norm_hidden_states,
+            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+            attention_mask=attention_mask,
+            **cross_attention_kwargs,
+        )
+
+        if self.norm_type == "ada_norm_zero":
+            attn_output = gate_msa.unsqueeze(1) * attn_output
+        elif self.norm_type == "ada_norm_single":
+            attn_output = gate_msa * attn_output
+
+        hidden_states = attn_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        # 1.2 Reference Attention
+        if 'w' in mode:
+            condition_embed_dict[self.layer_name] = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c',
+                                                              n=num_in_batch)  # B, (N L), C
+
+        if 'r' in mode and self.use_ra:
+            condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1, num_in_batch, 1,
+                                                                                        1)  # B N L C
+            condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c')
+
+            attn_output = self.attn_refview(
+                norm_hidden_states,
+                encoder_hidden_states=condition_embed,
+                attention_mask=None,
+                **cross_attention_kwargs
+            )
+            ref_scale_timing = ref_scale
+            if isinstance(ref_scale, torch.Tensor):
+                ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1)
+                for _ in range(attn_output.ndim - 1):
+                    ref_scale_timing = ref_scale_timing.unsqueeze(-1)
+            hidden_states = ref_scale_timing * attn_output + hidden_states
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+
+        # 1.3 Multiview Attention
+        if num_in_batch > 1 and self.use_ma:
+            multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch)
+
+            attn_output = self.attn_multiview(
+                multivew_hidden_states,
+                encoder_hidden_states=multivew_hidden_states,
+                **cross_attention_kwargs
+            )
+
+            attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch)
+
+            hidden_states = mva_scale * attn_output + hidden_states
+            if hidden_states.ndim == 4:
+                hidden_states = hidden_states.squeeze(1)
+
+        # 1.2 GLIGEN Control
+        if gligen_kwargs is not None:
+            hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            if self.norm_type == "ada_norm":
+                norm_hidden_states = self.norm2(hidden_states, timestep)
+            elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]:
+                norm_hidden_states = self.norm2(hidden_states)
+            elif self.norm_type == "ada_norm_single":
+                # For PixArt norm2 isn't applied here:
+                # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103
+                norm_hidden_states = hidden_states
+            elif self.norm_type == "ada_norm_continuous":
+                norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"])
+            else:
+                raise ValueError("Incorrect norm")
+
+            if self.pos_embed is not None and self.norm_type != "ada_norm_single":
+                norm_hidden_states = self.pos_embed(norm_hidden_states)
+
+            attn_output = self.attn2(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                **cross_attention_kwargs,
+            )
+
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        # i2vgen doesn't have this norm 🤷‍♂️
+        if self.norm_type == "ada_norm_continuous":
+            norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"])
+        elif not self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm3(hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        if self.norm_type == "ada_norm_single":
+            norm_hidden_states = self.norm2(hidden_states)
+            norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
+
+        if self._chunk_size is not None:
+            # "feed_forward_chunk_size" can be used to save memory
+            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.norm_type == "ada_norm_zero":
+            ff_output = gate_mlp.unsqueeze(1) * ff_output
+        elif self.norm_type == "ada_norm_single":
+            ff_output = gate_mlp * ff_output
+
+        hidden_states = ff_output + hidden_states
+        if hidden_states.ndim == 4:
+            hidden_states = hidden_states.squeeze(1)
+
+        return hidden_states
+
+
+class UNet2p5DConditionModel(torch.nn.Module):
+    def __init__(self, unet: UNet2DConditionModel) -> None:
+        super().__init__()
+        self.unet = unet
+
+        self.use_ma = True
+        self.use_ra = True
+        self.use_camera_embedding = True
+        self.use_dual_stream = True
+
+        if self.use_dual_stream:
+            self.unet_dual = copy.deepcopy(unet)
+            self.init_attention(self.unet_dual)
+        self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra)
+        self.init_condition()
+        self.init_camera_embedding()
+
+    @staticmethod
+    def from_pretrained(pretrained_model_name_or_path, **kwargs):
+        torch_dtype = kwargs.pop('torch_dtype', torch.float32)
+        config_path = os.path.join(pretrained_model_name_or_path, 'config.json')
+        unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin')
+        with open(config_path, 'r', encoding='utf-8') as file:
+            config = json.load(file)
+        unet = UNet2DConditionModel(**config)
+        unet = UNet2p5DConditionModel(unet)
+        unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True)
+        unet.load_state_dict(unet_ckpt, strict=True)
+        unet = unet.to(torch_dtype)
+        return unet
+
+    def init_condition(self):
+        self.unet.conv_in = torch.nn.Conv2d(
+            12,
+            self.unet.conv_in.out_channels,
+            kernel_size=self.unet.conv_in.kernel_size,
+            stride=self.unet.conv_in.stride,
+            padding=self.unet.conv_in.padding,
+            dilation=self.unet.conv_in.dilation,
+            groups=self.unet.conv_in.groups,
+            bias=self.unet.conv_in.bias is not None)
+
+        self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1, 77, 1024))
+        self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1, 77, 1024))
+
+    def init_camera_embedding(self):
+
+        if self.use_camera_embedding:
+            time_embed_dim = 1280
+            self.max_num_ref_image = 5
+            self.max_num_gen_image = 12 * 3 + 4 * 2
+            self.unet.class_embedding = nn.Embedding(self.max_num_ref_image + self.max_num_gen_image, time_embed_dim)
+
+    def init_attention(self, unet, use_ma=False, use_ra=False):
+
+        for down_block_i, down_block in enumerate(unet.down_blocks):
+            if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention:
+                for attn_i, attn in enumerate(down_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer,
+                                                                                               f'down_{down_block_i}_{attn_i}_{transformer_i}',
+                                                                                               use_ma, use_ra)
+
+        if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention:
+            for attn_i, attn in enumerate(unet.mid_block.attentions):
+                for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                    if isinstance(transformer, BasicTransformerBlock):
+                        attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer,
+                                                                                           f'mid_{attn_i}_{transformer_i}',
+                                                                                           use_ma, use_ra)
+
+        for up_block_i, up_block in enumerate(unet.up_blocks):
+            if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention:
+                for attn_i, attn in enumerate(up_block.attentions):
+                    for transformer_i, transformer in enumerate(attn.transformer_blocks):
+                        if isinstance(transformer, BasicTransformerBlock):
+                            attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock(transformer,
+                                                                                               f'up_{up_block_i}_{attn_i}_{transformer_i}',
+                                                                                               use_ma, use_ra)
+
+    def __getattr__(self, name: str):
+        try:
+            return super().__getattr__(name)
+        except AttributeError:
+            return getattr(self.unet, name)
+
+    def forward(
+        self, sample, timestep, encoder_hidden_states,
+        *args, down_intrablock_additional_residuals=None,
+        down_block_res_samples=None, mid_block_res_sample=None,
+        **cached_condition,
+    ):
+        B, N_gen, _, H, W = sample.shape
+        assert H == W
+
+        if self.use_camera_embedding:
+            camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image
+            camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)')
+        else:
+            camera_info_gen = None
+
+        sample = [sample]
+        if 'normal_imgs' in cached_condition:
+            sample.append(cached_condition["normal_imgs"])
+        if 'position_imgs' in cached_condition:
+            sample.append(cached_condition["position_imgs"])
+        sample = torch.cat(sample, dim=2)
+
+        sample = rearrange(sample, 'b n c h w -> (b n) c h w')
+
+        encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1)
+        encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c')
+
+        if self.use_ra:
+            if 'condition_embed_dict' in cached_condition:
+                condition_embed_dict = cached_condition['condition_embed_dict']
+            else:
+                condition_embed_dict = {}
+                ref_latents = cached_condition['ref_latents']
+                N_ref = ref_latents.shape[1]
+                if self.use_camera_embedding:
+                    camera_info_ref = cached_condition['camera_info_ref']
+                    camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)')
+                else:
+                    camera_info_ref = None
+
+                ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w')
+
+                encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1)
+                encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c')
+
+                noisy_ref_latents = ref_latents
+                timestep_ref = 0
+
+                if self.use_dual_stream:
+                    unet_ref = self.unet_dual
+                else:
+                    unet_ref = self.unet
+                unet_ref(
+                    noisy_ref_latents, timestep_ref,
+                    encoder_hidden_states=encoder_hidden_states_ref,
+                    class_labels=camera_info_ref,
+                    # **kwargs
+                    return_dict=False,
+                    cross_attention_kwargs={
+                        'mode': 'w', 'num_in_batch': N_ref,
+                        'condition_embed_dict': condition_embed_dict},
+                )
+                cached_condition['condition_embed_dict'] = condition_embed_dict
+        else:
+            condition_embed_dict = None
+
+        mva_scale = cached_condition.get('mva_scale', 1.0)
+        ref_scale = cached_condition.get('ref_scale', 1.0)
+
+        return self.unet(
+            sample, timestep,
+            encoder_hidden_states_gen, *args,
+            class_labels=camera_info_gen,
+            down_intrablock_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals
+            ] if down_intrablock_additional_residuals is not None else None,
+            down_block_additional_residuals=[
+                sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples
+            ] if down_block_res_samples is not None else None,
+            mid_block_additional_residual=(
+                mid_block_res_sample.to(dtype=self.unet.dtype)
+                if mid_block_res_sample is not None else None
+            ),
+            return_dict=False,
+            cross_attention_kwargs={
+                'mode': 'r', 'num_in_batch': N_gen,
+                'condition_embed_dict': condition_embed_dict,
+                'mva_scale': mva_scale,
+                'ref_scale': ref_scale,
+            },
+        )
@@ -0,0 +1,244 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+import logging
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+
+import comfy.utils
+
+from .differentiable_renderer.mesh_render import MeshRender
+from .utils.dehighlight_utils import Light_Shadow_Remover
+from .utils.multiview_utils import Multiview_Diffusion_Net
+from .utils.uv_warp_utils import mesh_uv_wrap
+
+logger = logging.getLogger(__name__)
+
+
+class Hunyuan3DTexGenConfig:
+
+    def __init__(self, light_remover_ckpt_path, multiview_ckpt_path):
+        self.device = 'cuda'
+        self.light_remover_ckpt_path = light_remover_ckpt_path
+        self.multiview_ckpt_path = multiview_ckpt_path
+
+        self.candidate_camera_azims = [0, 90, 180, 270, 0, 180]
+        self.candidate_camera_elevs = [0, 0, 0, 0, 90, -90]
+        self.candidate_view_weights = [1, 0.1, 0.5, 0.1, 0.05, 0.05]
+
+        self.render_size = 2048
+        self.texture_size = 1024
+        self.bake_exp = 4
+        self.merge_method = 'fast'
+
+
+class Hunyuan3DPaintPipeline:
+    @classmethod
+    def from_pretrained(cls, model_path, device='cuda', dtype=torch.float16, **kwargs):
+        # try local path	
+        delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0')
+        multiview_model_path = os.path.join(model_path, 'hunyuan3d-paint-v2-0')
+        
+        if not (os.path.exists(delight_model_path) and os.path.exists(multiview_model_path)):
+            raise FileNotFoundError(f"Model path {delight_model_path} or {multiview_model_path} not found")
+
+        return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path))
+
+
+    def __init__(self, config):
+        self.config = config
+        self.models = {}
+        self.render = MeshRender(
+            default_resolution=self.config.render_size,
+            texture_size=self.config.texture_size)
+
+        self.load_models()
+
+    def load_models(self):
+        # empty cude cache
+        torch.cuda.empty_cache()
+        # Load model
+        self.models['delight_model'] = Light_Shadow_Remover(self.config)
+        self.models['multiview_model'] = Multiview_Diffusion_Net(self.config)
+        
+    def to(self, device=None, dtype=None):
+        if device is not None:
+            for model in self.models.values():
+                model.pipeline.to(device)
+        if dtype is not None:
+            for model in self.models.values():
+                model.pipeline.to(dtype=dtype)
+
+        return self
+
+    def render_normal_multiview(self, camera_elevs, camera_azims, use_abs_coor=True):
+        normal_maps = []
+        for elev, azim in zip(camera_elevs, camera_azims):
+            normal_map = self.render.render_normal(
+                elev, azim, use_abs_coor=use_abs_coor, return_type='pl')
+            normal_maps.append(normal_map)
+
+        return normal_maps
+
+    def render_position_multiview(self, camera_elevs, camera_azims):
+        position_maps = []
+        for elev, azim in zip(camera_elevs, camera_azims):
+            position_map = self.render.render_position(
+                elev, azim, return_type='pl')
+            position_maps.append(position_map)
+
+        return position_maps
+
+    def bake_from_multiview(self, views, camera_elevs,
+                            camera_azims, view_weights, method='graphcut'):
+        project_textures, project_weighted_cos_maps = [], []
+        project_boundary_maps = []
+        for view, camera_elev, camera_azim, weight in zip(
+            views, camera_elevs, camera_azims, view_weights):
+            project_texture, project_cos_map, project_boundary_map = self.render.back_project(
+                view, camera_elev, camera_azim)
+            project_cos_map = weight * (project_cos_map ** self.config.bake_exp)
+            project_textures.append(project_texture)
+            project_weighted_cos_maps.append(project_cos_map)
+            project_boundary_maps.append(project_boundary_map)
+
+        if method == 'fast':
+            texture, ori_trust_map = self.render.fast_bake_texture(
+                project_textures, project_weighted_cos_maps)
+        else:
+            raise f'no method {method}'
+        return texture, ori_trust_map > 1E-8
+
+    def texture_inpaint(self, texture, mask):
+
+        texture_np = self.render.uv_inpaint(texture, mask)
+        texture = torch.tensor(texture_np / 255).float().to(texture.device)
+
+        return texture
+    
+    def recenter_image(self, image, border_ratio=0.2):
+        if image.mode == 'RGB':
+            return image
+        elif image.mode == 'L':
+            image = image.convert('RGB')
+            return image
+
+        alpha_channel = np.array(image)[:, :, 3]
+        non_zero_indices = np.argwhere(alpha_channel > 0)
+        if non_zero_indices.size == 0:
+            raise ValueError("Image is fully transparent")
+
+        min_row, min_col = non_zero_indices.min(axis=0)
+        max_row, max_col = non_zero_indices.max(axis=0)
+
+        cropped_image = image.crop((min_col, min_row, max_col + 1, max_row + 1))
+
+        width, height = cropped_image.size
+        border_width = int(width * border_ratio)
+        border_height = int(height * border_ratio)
+
+        new_width = width + 2 * border_width
+        new_height = height + 2 * border_height
+
+        square_size = max(new_width, new_height)
+
+        new_image = Image.new('RGBA', (square_size, square_size), (255, 255, 255, 0))
+
+        paste_x = (square_size - new_width) // 2 + border_width
+        paste_y = (square_size - new_height) // 2 + border_height
+
+        new_image.paste(cropped_image, (paste_x, paste_y))
+        return new_image
+
+    @torch.no_grad()
+    def __call__(self, mesh, image):
+
+        comfy_pbar = comfy.utils.ProgressBar(4)
+        step_num_i = 0
+
+        print("Load image and predict delight")
+        if isinstance(image, str):
+            image_prompt = Image.open(image)
+        else:
+            image_prompt = image
+
+        image_prompt = self.recenter_image(image_prompt)
+        image_prompt = self.models['delight_model'](image_prompt)
+
+        step_num_i += 1
+        comfy_pbar.update_absolute(step_num_i)
+
+        mesh = mesh_uv_wrap(mesh)
+
+        print("Rendering normal and position maps of the mesh")
+        self.render.load_mesh(mesh)
+        
+        selected_camera_elevs, selected_camera_azims, selected_view_weights = \
+            self.config.candidate_camera_elevs, self.config.candidate_camera_azims, self.config.candidate_view_weights
+
+        normal_maps = self.render_normal_multiview(
+            selected_camera_elevs, selected_camera_azims, use_abs_coor=True)
+        position_maps = self.render_position_multiview(
+            selected_camera_elevs, selected_camera_azims)
+        
+        step_num_i += 1
+        comfy_pbar.update_absolute(step_num_i)
+
+        print("Predicting multiview texture")
+        camera_info = [(((azim // 30) + 9) % 12) // {-20: 1, 0: 1, 20: 1, -90: 3, 90: 3}[
+            elev] + {-20: 0, 0: 12, 20: 24, -90: 36, 90: 40}[elev] for azim, elev in
+                       zip(selected_camera_azims, selected_camera_elevs)]
+        multiviews = self.models['multiview_model'](image_prompt, normal_maps + position_maps, camera_info)
+
+        for i in range(len(multiviews)):
+            multiviews[i] = multiviews[i].resize(
+                (self.config.render_size, self.config.render_size))
+        
+        step_num_i += 1
+        comfy_pbar.update_absolute(step_num_i)
+
+        print("Baking texture on to the mesh")
+        texture, mask = self.bake_from_multiview(multiviews,
+                                                 selected_camera_elevs, selected_camera_azims, selected_view_weights,
+                                                 method=self.config.merge_method)
+
+        step_num_i += 1
+        comfy_pbar.update_absolute(step_num_i)
+
+        print("Inpainting texture")        
+        mask_np = (mask.squeeze(-1).cpu().numpy() * 255).astype(np.uint8)
+
+        texture = self.texture_inpaint(texture, mask_np)
+
+        self.render.set_texture(texture)
+        textured_mesh = self.render.save_mesh()
+
+        step_num_i += 1
+        comfy_pbar.update_absolute(step_num_i)
+
+        return textured_mesh
@@ -0,0 +1,23 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
@@ -0,0 +1,132 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+import torch
+from diffusers import EulerAncestralDiscreteScheduler
+from diffusers import StableDiffusionControlNetPipeline, StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, \
+    AutoencoderKL
+
+
+class Img2img_Control_Ip_adapter:
+    def __init__(self, device):
+        controlnet = ControlNetModel.from_pretrained('lllyasviel/control_v11f1p_sd15_depth', torch_dtype=torch.float16,
+                                                     variant="fp16", use_safetensors=True)
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            'runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
+        )
+        pipe.load_ip_adapter('h94/IP-Adapter', subfolder="models", weight_name="ip-adapter-plus_sd15.safetensors")
+        pipe.set_ip_adapter_scale(0.7)
+
+        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+        # pipe.enable_model_cpu_offload()
+        self.pipe = pipe.to(device)
+
+    def __call__(
+        self,
+        prompt,
+        control_image,
+        ip_adapter_image,
+        negative_prompt,
+        height=512,
+        width=512,
+        num_inference_steps=20,
+        guidance_scale=8.0,
+        controlnet_conditioning_scale=1.0,
+        output_type="pil",
+        **kwargs,
+    ):
+        results = self.pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=control_image,
+            ip_adapter_image=ip_adapter_image,
+            generator=torch.manual_seed(42),
+            seed=42,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            controlnet_conditioning_scale=controlnet_conditioning_scale,
+            strength=1,
+            # clip_skip=2,
+            height=height,
+            width=width,
+            output_type=output_type,
+            **kwargs,
+        ).images[0]
+        return results
+
+
+################################################################
+
+class HesModel:
+    def __init__(self, ):
+        controlnet_depth = ControlNetModel.from_pretrained(
+            'diffusers/controlnet-depth-sdxl-1.0',
+            torch_dtype=torch.float16,
+            variant="fp16",
+            use_safetensors=True
+        )
+        self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
+            'stabilityai/stable-diffusion-xl-base-1.0',
+            torch_dtype=torch.float16,
+            variant="fp16",
+            controlnet=controlnet_depth,
+            use_safetensors=True,
+        )
+        self.pipe.vae = AutoencoderKL.from_pretrained(
+            'madebyollin/sdxl-vae-fp16-fix',
+            torch_dtype=torch.float16
+        )
+
+        self.pipe.load_ip_adapter('h94/IP-Adapter', subfolder="sdxl_models", weight_name="ip-adapter_sdxl.safetensors")
+        self.pipe.set_ip_adapter_scale(0.7)
+        self.pipe.to("cuda")
+
+    def __call__(self,
+                 init_image,
+                 control_image,
+                 ip_adapter_image=None,
+                 prompt='3D image',
+                 negative_prompt='2D image',
+                 seed=42,
+                 strength=0.8,
+                 num_inference_steps=40,
+                 guidance_scale=7.5,
+                 controlnet_conditioning_scale=0.5,
+                 **kwargs
+                 ):
+        image = self.pipe(
+            prompt=prompt,
+            image=init_image,
+            control_image=control_image,
+            ip_adapter_image=ip_adapter_image,
+            negative_prompt=negative_prompt,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            strength=strength,
+            controlnet_conditioning_scale=controlnet_conditioning_scale,
+            seed=seed,
+            **kwargs
+        ).images[0]
+        return image
@@ -0,0 +1,58 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+class RunningStats():
+    def __init__(self) -> None:
+        self.count = 0
+        self.sum = 0
+        self.mean = 0
+        self.min = None
+        self.max = None
+
+    def add_value(self, value):
+        self.count += 1
+        self.sum += value
+        self.mean = self.sum / self.count
+
+        if self.min is None or value < self.min:
+            self.min = value
+
+        if self.max is None or value > self.max:
+            self.max = value
+
+    def get_count(self):
+        return self.count
+
+    def get_sum(self):
+        return self.sum
+
+    def get_mean(self):
+        return self.mean
+
+    def get_min(self):
+        return self.min
+
+    def get_max(self):
+        return self.max
@@ -0,0 +1,84 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+
+
+class Light_Shadow_Remover():
+    def __init__(self, config):
+        self.device = config.device
+        self.cfg_image = 1.5
+        self.cfg_text = 1.0
+
+        pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained(
+            config.light_remover_ckpt_path,
+            torch_dtype=torch.float16,
+            safety_checker=None,
+        )
+        pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config)
+        pipeline.set_progress_bar_config(disable=True)
+
+        self.pipeline = pipeline.to(self.device, torch.float16)
+
+    @torch.no_grad()
+    def __call__(self, image):
+
+        image = image.resize((512, 512))
+
+        if image.mode == 'RGBA':
+            image_array = np.array(image)
+            alpha_channel = image_array[:, :, 3]
+            erosion_size = 3
+            kernel = np.ones((erosion_size, erosion_size), np.uint8)
+            alpha_channel = cv2.erode(alpha_channel, kernel, iterations=1)
+            image_array[alpha_channel == 0, :3] = 255
+            image_array[:, :, 3] = alpha_channel
+            image = Image.fromarray(image_array)
+
+            image_tensor = torch.tensor(np.array(image) / 255.0).to(self.device)
+            alpha = image_tensor[:, :, 3:]
+            rgb_target = image_tensor[:, :, :3]
+        else:
+            image_tensor = torch.tensor(np.array(image) / 255.0).to(self.device)
+            alpha = torch.ones_like(image_tensor)[:, :, :1]
+            rgb_target = image_tensor[:, :, :3]
+
+        image = image.convert('RGB')
+
+        image = self.pipeline(
+            prompt="",
+            image=image,
+            generator=torch.manual_seed(42),
+            height=512,
+            width=512,
+            num_inference_steps=50,
+            image_guidance_scale=self.cfg_image,
+            guidance_scale=self.cfg_text,
+        ).images[0]
+
+        return image
@@ -0,0 +1,86 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import os
+import random
+
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline
+from diffusers import EulerAncestralDiscreteScheduler
+
+
+class Multiview_Diffusion_Net():
+    def __init__(self, config) -> None:
+        self.device = config.device
+        self.view_size = 512
+        multiview_ckpt_path = config.multiview_ckpt_path
+
+        current_file_path = os.path.abspath(__file__)
+        custom_pipeline_path = os.path.join(os.path.dirname(current_file_path), '..', 'hunyuanpaint')
+
+        pipeline = DiffusionPipeline.from_pretrained(
+            multiview_ckpt_path,
+            custom_pipeline=custom_pipeline_path, torch_dtype=torch.float16)
+
+        pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config,
+                                                                         timestep_spacing='trailing')
+
+        pipeline.set_progress_bar_config(disable=True)
+        self.pipeline = pipeline.to(self.device)
+
+    def seed_everything(self, seed):
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        os.environ["PL_GLOBAL_SEED"] = str(seed)
+
+    def __call__(self, input_image, control_images, camera_info):
+
+        self.seed_everything(0)
+
+        input_image = input_image.resize((self.view_size, self.view_size))
+        for i in range(len(control_images)):
+            control_images[i] = control_images[i].resize((self.view_size, self.view_size))
+            if control_images[i].mode == 'L':
+                control_images[i] = control_images[i].point(lambda x: 255 if x > 1 else 0, mode='1')
+
+        kwargs = dict(generator=torch.Generator(device=self.pipeline.device).manual_seed(0))
+
+        num_view = len(control_images) // 2
+        normal_image = [[control_images[i] for i in range(num_view)]]
+        position_image = [[control_images[i + num_view] for i in range(num_view)]]
+
+        camera_info_gen = [camera_info]
+        camera_info_ref = [[0]]
+        kwargs['width'] = self.view_size
+        kwargs['height'] = self.view_size
+        kwargs['num_in_batch'] = num_view
+        kwargs['camera_info_gen'] = camera_info_gen
+        kwargs['camera_info_ref'] = camera_info_ref
+        kwargs["normal_imgs"] = normal_image
+        kwargs["position_imgs"] = position_image
+
+        mvd_image = self.pipeline(input_image, num_inference_steps=30, **kwargs).images
+        return mvd_image
@@ -0,0 +1,46 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import trimesh
+
+
+def remesh_mesh(mesh_path, remesh_path, method='trimesh'):
+    if method == 'trimesh':
+        mesh_simplify_trimesh(mesh_path, remesh_path)
+    else:
+        raise f'Method {method} has not been implemented.'
+
+
+def mesh_simplify_trimesh(inputpath, outputpath):
+    import pymeshlab
+    ms = pymeshlab.MeshSet()
+    ms.load_new_mesh(inputpath, load_in_a_single_layer=True)
+    ms.save_current_mesh(outputpath.replace('.glb', '.obj'), save_textures=False)
+
+    courent = trimesh.load(outputpath.replace('.glb', '.obj'), force='mesh')
+    face_num = courent.faces.shape[0]
+
+    if face_num > 100000:
+        courent = courent.simplify_quadric_decimation(40000)
+    courent.export(outputpath)
@@ -0,0 +1,39 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+import trimesh
+import xatlas
+
+
+def mesh_uv_wrap(mesh):
+    if isinstance(mesh, trimesh.Scene):
+        mesh = mesh.dump(concatenate=True)
+
+    vmapping, indices, uvs = xatlas.parametrize(mesh.vertices, mesh.faces)
+
+    mesh.vertices = mesh.vertices[vmapping]
+    mesh.faces = indices
+    mesh.visual.uv = uvs
+
+    return mesh
@@ -0,0 +1,92 @@
+# Open Source Model Licensed under the Apache License Version 2.0
+# and Other Licenses of the Third-Party Components therein:
+# The below Model in this distribution may have been modified by THL A29 Limited
+# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+
+# Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved.
+# The below software and/or models in this distribution may have been
+# modified by THL A29 Limited ("Tencent Modifications").
+# All Tencent Modifications are Copyright (C) THL A29 Limited.
+
+# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
+# except for the third-party components listed below.
+# Hunyuan 3D does not impose any additional limitations beyond what is outlined
+# in the repsective licenses of these third-party components.
+# Users must comply with all terms and conditions of original licenses of these third-party
+# components and must ensure that the usage of the third party components adheres to
+# all relevant laws and regulations.
+
+# For avoidance of doubts, Hunyuan 3D means the large language models and
+# their software and algorithms, including trained model weights, parameters (including
+# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
+# fine-tuning enabling code and other elements of the foregoing made publicly available
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+import os
+import random
+
+import numpy as np
+import torch
+from diffusers import AutoPipelineForText2Image
+
+
+def seed_everything(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    os.environ["PL_GLOBAL_SEED"] = str(seed)
+
+
+class HunyuanDiTPipeline:
+    def __init__(
+        self,
+        model_path="Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled",
+        device='cuda'
+    ):
+        self.device = device
+        self.pipe = AutoPipelineForText2Image.from_pretrained(
+            model_path,
+            torch_dtype=torch.float16,
+            enable_pag=True,
+            pag_applied_layers=["blocks.(16|17|18|19)"]
+        ).to(device)
+        self.pos_txt = ",白色背景,3D风格,最佳质量"
+        self.neg_txt = "文本,特写,裁剪,出框,最差质量,低质量,JPEG伪影,PGLY,重复,病态," \
+                       "残缺,多余的手指,变异的手,画得不好的手,画得不好的脸,变异,畸形,模糊,脱水,糟糕的解剖学," \
+                       "糟糕的比例,多余的肢体,克隆的脸,毁容,恶心的比例,畸形的肢体,缺失的手臂,缺失的腿," \
+                       "额外的手臂,额外的腿,融合的手指,手指太多,长脖子"
+
+    def compile(self):
+        # accelarate hunyuan-dit transformer,first inference will cost long time
+        torch.set_float32_matmul_precision('high')
+        self.pipe.transformer = torch.compile(self.pipe.transformer, fullgraph=True)
+        # self.pipe.vae.decode = torch.compile(self.pipe.vae.decode, fullgraph=True)
+        generator = torch.Generator(device=self.pipe.device)  # infer once for hot-start
+        out_img = self.pipe(
+            prompt='美少女战士',
+            negative_prompt='模糊',
+            num_inference_steps=25,
+            pag_scale=1.3,
+            width=1024,
+            height=1024,
+            generator=generator,
+            return_dict=False
+        )[0][0]
+
+    @torch.no_grad()
+    def __call__(self, prompt, seed=0):
+        seed_everything(seed)
+        generator = torch.Generator(device=self.pipe.device)
+        generator = generator.manual_seed(int(seed))
+        out_img = self.pipe(
+            prompt=prompt[:60] + self.pos_txt,
+            negative_prompt=self.neg_txt,
+            num_inference_steps=25,
+            pag_scale=1.3,
+            width=1024,
+            height=1024,
+            generator=generator,
+            return_dict=False
+        )[0][0]
+        return out_img
@@ -43,9 +43,8 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py


 ## Features:
- For use cases please check out [Example Workflows](./_Example_Workflows/). [**Last update: 16/December/2024**]
+- For use cases please check out [Example Workflows](./_Example_Workflows/). [**Last update: 22/January/2025**]
  - **Note:** you need to put [Example Inputs Files & Folders](_Example_Workflows/_Example_Inputs_Files/) under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow
-  - [tripoSR-layered-diffusion workflow](https://github.com/C0nsumption/Consume-ComfyUI-Workflows/tree/main/assets/tripo_sr/00) by [@Consumption](https://twitter.com/c0nsumption_)

 - **TRELLIS**: [microsoft/TRELLIS](https://github.com/microsoft/TRELLIS)
  - Single image to 3D Mesh with RGB texture
@@ -53,6 +52,14 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py

  <video controls autoplay loop src="https://github.com/user-attachments/assets/f569c561-23ea-471f-a9d3-f2e6d1803e00"></video>

+- **Hunyuan3D_V2** [tencent/Hunyuan3D-2](https://github.com/Tencent/Hunyuan3D-2/)
+  - Two stages pipeline:
+    1. Single image to 3D Mesh shape only
+    2. 3D Mesh shape + Single reference image to 3D Mesh with RGB texture
+  - Model weights: https://huggingface.co/tencent/Hunyuan3D-2/tree/main
+
+  <video controls autoplay loop src="https://github.com/user-attachments/assets/da6a8f8b-8dab-4460-90a6-44769ec9bb09"></video>
+
 - **Hunyuan3D_V1** [tencent/Hunyuan3D-1](https://github.com/Tencent/Hunyuan3D-1/)
  - Two stages pipeline:
    1. Single image to multi-views
@@ -123,6 +130,7 @@ Your ComfyUI Root Directory\python_embeded\python.exe install.py
 - **TripoSR**: [VAST-AI-Research/TripoSR](https://github.com/VAST-AI-Research/TripoSR) | [ComfyUI-Flowty-TripoSR](https://github.com/flowtyone/ComfyUI-Flowty-TripoSR)
  - Generate NeRF representation and using marching cube to turn it into 3D mesh
  - Model weights: https://huggingface.co/stabilityai/TripoSR/tree/main
+  - [tripoSR-layered-diffusion workflow](https://github.com/C0nsumption/Consume-ComfyUI-Workflows/tree/main/assets/tripo_sr/00) by [@Consumption](https://twitter.com/c0nsumption_)
 
  <video controls autoplay loop src="https://github.com/MrForExample/ComfyUI-3D-Pack/assets/62230687/ec4f8df5-5907-4bbf-ba19-c0565fe95a97"></video>

@@ -0,0 +1,601 @@
+{
+  "last_node_id": 22,
+  "last_link_id": 27,
+  "nodes": [
+    {
+      "id": 10,
+      "type": "[Comfy3D] Load Diffusers Pipeline",
+      "pos": [
+        312.5229187011719,
+        153.14285278320312
+      ],
+      "size": [
+        411.5325622558594,
+        185.8739776611328
+      ],
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "pipe",
+          "type": "DIFFUSERS_PIPE",
+          "links": [
+            10
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Load Diffusers Pipeline"
+      },
+      "widgets_values": [
+        "Hunyuan3DDiTFlowMatchingPipeline",
+        "tencent/Hunyuan3D-2",
+        "",
+        false,
+        ""
+      ]
+    },
+    {
+      "id": 14,
+      "type": "InvertMask",
+      "pos": [
+        754.2721557617188,
+        503.93536376953125
+      ],
+      "size": [
+        140,
+        26
+      ],
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 12
+        }
+      ],
+      "outputs": [
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            13,
+            23
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "InvertMask"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 19,
+      "type": "Reroute",
+      "pos": [
+        885.0671997070312,
+        112.03165435791016
+      ],
+      "size": [
+        75,
+        26
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "",
+          "type": "*",
+          "link": 22
+        }
+      ],
+      "outputs": [
+        {
+          "name": "",
+          "type": "IMAGE",
+          "links": [
+            24
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "showOutputText": false,
+        "horizontal": false
+      }
+    },
+    {
+      "id": 20,
+      "type": "Reroute",
+      "pos": [
+        935.6452026367188,
+        170.5956573486328
+      ],
+      "size": [
+        75,
+        26
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "",
+          "type": "*",
+          "link": 23
+        }
+      ],
+      "outputs": [
+        {
+          "name": "",
+          "type": "MASK",
+          "links": [
+            25
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "showOutputText": false,
+        "horizontal": false
+      }
+    },
+    {
+      "id": 11,
+      "type": "[Comfy3D] Load Diffusers Pipeline",
+      "pos": [
+        1452.5572509765625,
+        -17.293842315673828
+      ],
+      "size": [
+        362.7291564941406,
+        184.89788818359375
+      ],
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "pipe",
+          "type": "DIFFUSERS_PIPE",
+          "links": [
+            16
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Load Diffusers Pipeline"
+      },
+      "widgets_values": [
+        "Hunyuan3DPaintPipeline",
+        "tencent/Hunyuan3D-2",
+        "",
+        false,
+        ""
+      ]
+    },
+    {
+      "id": 12,
+      "type": "[Comfy3D] Hunyuan3D V2 DiT Flow Matching Model",
+      "pos": [
+        966.2610473632812,
+        287.9259948730469
+      ],
+      "size": [
+        453.5999755859375,
+        194
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "hunyuan3d_v2_i23d_pipe",
+          "type": "DIFFUSERS_PIPE",
+          "link": 10
+        },
+        {
+          "name": "reference_image",
+          "type": "IMAGE",
+          "link": 11
+        },
+        {
+          "name": "reference_mask",
+          "type": "MASK",
+          "link": 13
+        }
+      ],
+      "outputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "links": [
+            14,
+            17
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Hunyuan3D V2 DiT Flow Matching Model"
+      },
+      "widgets_values": [
+        1234,
+        "fixed",
+        5.5,
+        30,
+        256
+      ]
+    },
+    {
+      "id": 15,
+      "type": "[Comfy3D] Preview 3DMesh",
+      "pos": [
+        1445.0145263671875,
+        491.7875061035156
+      ],
+      "size": [
+        600,
+        500
+      ],
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mesh_file_path",
+          "type": "STRING",
+          "link": 15,
+          "widget": {
+            "name": "mesh_file_path"
+          }
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Preview 3DMesh"
+      },
+      "widgets_values": [
+        "",
+        null
+      ]
+    },
+    {
+      "id": 16,
+      "type": "[Comfy3D] Save 3D Mesh",
+      "pos": [
+        1444.8043212890625,
+        369.44805908203125
+      ],
+      "size": [
+        348.07330322265625,
+        58
+      ],
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "link": 14
+        }
+      ],
+      "outputs": [
+        {
+          "name": "save_path",
+          "type": "STRING",
+          "links": [
+            15
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Save 3D Mesh"
+      },
+      "widgets_values": [
+        "Hunyuan3D_V2_Test/mesh_shape.glb"
+      ]
+    },
+    {
+      "id": 17,
+      "type": "[Comfy3D] Hunyuan3D V2 Paint Model",
+      "pos": [
+        1870.954345703125,
+        226.06764221191406
+      ],
+      "size": [
+        245.1999969482422,
+        94.80000305175781
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "hunyuan3d_v2_texgen_pipe",
+          "type": "DIFFUSERS_PIPE",
+          "link": 16
+        },
+        {
+          "name": "reference_image",
+          "type": "IMAGE",
+          "link": 24
+        },
+        {
+          "name": "reference_mask",
+          "type": "MASK",
+          "link": 25
+        },
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "link": 17
+        }
+      ],
+      "outputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "links": [
+            27
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Hunyuan3D V2 Paint Model"
+      },
+      "widgets_values": []
+    },
+    {
+      "id": 22,
+      "type": "[Comfy3D] Preview 3DMesh",
+      "pos": [
+        2143.500732421875,
+        350.717529296875
+      ],
+      "size": [
+        600,
+        500
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mesh_file_path",
+          "type": "STRING",
+          "link": 26,
+          "widget": {
+            "name": "mesh_file_path"
+          }
+        }
+      ],
+      "outputs": [],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Preview 3DMesh"
+      },
+      "widgets_values": [
+        "",
+        null
+      ]
+    },
+    {
+      "id": 21,
+      "type": "[Comfy3D] Save 3D Mesh",
+      "pos": [
+        2138.554443359375,
+        227.3116455078125
+      ],
+      "size": [
+        348.07330322265625,
+        58
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "mesh",
+          "type": "MESH",
+          "link": 27
+        }
+      ],
+      "outputs": [
+        {
+          "name": "save_path",
+          "type": "STRING",
+          "links": [
+            26
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "[Comfy3D] Save 3D Mesh"
+      },
+      "widgets_values": [
+        "Hunyuan3D_V2_Test/mesh_tex.obj"
+      ]
+    },
+    {
+      "id": 13,
+      "type": "LoadImage",
+      "pos": [
+        358.67254638671875,
+        401.5780029296875
+      ],
+      "size": [
+        362.0579528808594,
+        476.63336181640625
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [],
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            11,
+            22
+          ],
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [
+            12
+          ],
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "White Tiger Girl_1.png",
+        "image"
+      ]
+    }
+  ],
+  "links": [
+    [
+      10,
+      10,
+      0,
+      12,
+      0,
+      "DIFFUSERS_PIPE"
+    ],
+    [
+      11,
+      13,
+      0,
+      12,
+      1,
+      "IMAGE"
+    ],
+    [
+      12,
+      13,
+      1,
+      14,
+      0,
+      "MASK"
+    ],
+    [
+      13,
+      14,
+      0,
+      12,
+      2,
+      "MASK"
+    ],
+    [
+      14,
+      12,
+      0,
+      16,
+      0,
+      "MESH"
+    ],
+    [
+      15,
+      16,
+      0,
+      15,
+      0,
+      "STRING"
+    ],
+    [
+      16,
+      11,
+      0,
+      17,
+      0,
+      "DIFFUSERS_PIPE"
+    ],
+    [
+      17,
+      12,
+      0,
+      17,
+      3,
+      "MESH"
+    ],
+    [
+      22,
+      13,
+      0,
+      19,
+      0,
+      "*"
+    ],
+    [
+      23,
+      14,
+      0,
+      20,
+      0,
+      "*"
+    ],
+    [
+      24,
+      19,
+      0,
+      17,
+      1,
+      "IMAGE"
+    ],
+    [
+      25,
+      20,
+      0,
+      17,
+      2,
+      "MASK"
+    ],
+    [
+      26,
+      21,
+      0,
+      22,
+      0,
+      "STRING"
+    ],
+    [
+      27,
+      17,
+      0,
+      21,
+      0,
+      "MESH"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.7513148009015777,
+      "offset": [
+        -284.1754354938319,
+        66.2572568880548
+      ]
+    }
+  },
+  "version": 0.4
+}
@@ -24,7 +24,7 @@ from build_utils import (

 def read_dependencies(file_path):
    # Download required source libraries from remote
-    if not os.path.exists(LIBS_ROOT_ABS_PATH):
+    if not os.path.exists(LIBS_ROOT_ABS_PATH) or not os.listdir(LIBS_ROOT_ABS_PATH):
        if not git_folder_parallel(build_config.repo_id, build_config.libs_dir_name, recursive=True, root_outdir=LIBS_ROOT_ABS_PATH):
            raise RuntimeError(f"Comfy3D install failed, couldn't download directory {build_config.libs_dir_name} in remote repository {build_config.repo_id}")
    dependencies = [ f.path for f in os.scandir(LIBS_ROOT_ABS_PATH) if f.is_dir() ]
@@ -211,9 +211,22 @@ def poisson_mesh_reconstruction(points, normals=None):


 def decimate_mesh(
-    verts, faces, target, backend="pymeshlab", remesh=False, optimalplacement=True
+    verts, faces, target=5e4, backend="pymeshlab", remesh=False, optimalplacement=True, verbose=True
 ):
-    # optimalplacement: default is True, but for flat mesh must turn False to prevent spike artifect.
+    """ perform mesh decimation.
+
+    Args:
+        verts (np.ndarray): mesh vertices, float [N, 3]
+        faces (np.ndarray): mesh faces, int [M, 3]
+        target (int): targeted number of faces
+        backend (str, optional): algorithm backend, can be "pymeshlab" or "pyfqmr". Defaults to "pymeshlab".
+        remesh (bool, optional): whether to remesh after decimation. Defaults to False.
+        optimalplacement (bool, optional): For flat mesh, use False to prevent spikes. Defaults to True.
+        verbose (bool, optional): whether to print the decimation process. Defaults to True.
+
+    Returns:
+        Tuple[np.ndarray]: vertices and faces after decimation.
+    """

    _ori_vert_shape = verts.shape
    _ori_face_shape = faces.shape
@@ -231,7 +244,7 @@ def decimate_mesh(
        ms.add_mesh(m, "mesh")  # will copy!

        # filters
-        # ms.meshing_decimation_clustering(threshold=pml.Percentage(1))
+        # ms.meshing_decimation_clustering(threshold=pml.PercentageValue(1))
        ms.meshing_decimation_quadric_edge_collapse(
            targetfacenum=int(target), optimalplacement=optimalplacement
        )
@@ -239,17 +252,17 @@ def decimate_mesh(
        if remesh:
            # ms.apply_coord_taubin_smoothing()
            ms.meshing_isotropic_explicit_remeshing(
-                iterations=3, targetlen=pml.Percentage(1)
+                iterations=3, targetlen=pml.PercentageValue(1)
            )

        # extract mesh
        m = ms.current_mesh()
+        m.compact()
        verts = m.vertex_matrix()
        faces = m.face_matrix()

-    print(
-        f"[INFO] mesh decimation: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}"
-    )
+    if verbose:
+        print(f"[INFO] mesh decimation: {_ori_vert_shape} --> {verts.shape}, {_ori_face_shape} --> {faces.shape}")

    return verts, faces

@@ -33,6 +33,7 @@ from diffusers import (
 from huggingface_hub import snapshot_download

 from plyfile import PlyData
+import trimesh
 from PIL import Image

 from .mesh_processer.mesh import Mesh
@@ -45,6 +46,7 @@ from .mesh_processer.mesh_utils import (
    marching_cubes_density_to_mesh,
    color_func_to_albedo,
    interpolate_texture_map_attr,
+    decimate_mesh,
 )

 from FlexiCubes.flexicubes_trainer import FlexiCubesTrainer
@@ -93,8 +95,10 @@ from CRM_T2I_V3.model.crm.sampler import CRMSamplerV3
 from Hunyuan3D_V1.mvd.hunyuan3d_mvd_std_pipeline import HunYuan3D_MVD_Std_Pipeline
 from Hunyuan3D_V1.mvd.hunyuan3d_mvd_lite_pipeline import Hunyuan3D_MVD_Lite_Pipeline
 from Hunyuan3D_V1.infer import Views2Mesh
+from Hunyuan3D_V2.hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, Hunyuan3DDiTFlowMatchingPipeline
+from Hunyuan3D_V2.hy3dgen.texgen import Hunyuan3DPaintPipeline
 from TRELLIS.trellis.pipelines import TrellisImageTo3DPipeline
-from TRELLIS.trellis.utils import render_utils, postprocessing_utils
+from TRELLIS.trellis.utils import postprocessing_utils

 os.environ['SPCONV_ALGO'] = 'native'

@@ -120,6 +124,8 @@ DIFFUSERS_PIPE_DICT = OrderedDict([
    ("Unique3DImageCustomPipeline", StableDiffusionImageCustomPipeline),
    ("HunYuan3DMVDStdPipeline", HunYuan3D_MVD_Std_Pipeline),
    ("Hunyuan3DMVDLitePipeline", Hunyuan3D_MVD_Lite_Pipeline),
+    ("Hunyuan3DDiTFlowMatchingPipeline", Hunyuan3DDiTFlowMatchingPipeline),
+    ("Hunyuan3DPaintPipeline", Hunyuan3DPaintPipeline),
 ])

 DIFFUSERS_SCHEDULER_DICT = OrderedDict([
@@ -168,7 +174,7 @@ WEIGHT_DTYPE = torch.float16
 DEVICE_STR = "cuda" if torch.cuda.is_available() else "cpu"
 DEVICE = torch.device(DEVICE_STR)

-HF_DOWNLOAD_IGNORE = ["*.json", "*.py", ".png", ".jpg"]
+HF_DOWNLOAD_IGNORE = ["*.yaml", "*.json", "*.py", ".png", ".jpg", ".gif"]

 class Preview_3DGS:

@@ -603,6 +609,35 @@ class Fast_Clean_Mesh:
        mesh = Mesh(v=vertices, f=faces, device=DEVICE)

        return (mesh,)
+    
+class Decimate_Mesh:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mesh": ("MESH",),
+                "target": ("INT", {"default": 5e4, "min": 0, "max": 0xffffffffffffffff}),
+                "remesh": ("BOOLEAN", {"default": True},),
+                "optimalplacement": ("BOOLEAN", {"default": True},),
+            },
+        }
+
+    RETURN_TYPES = (
+        "MESH",
+    )
+    RETURN_NAMES = (
+        "mesh",
+    )
+    FUNCTION = "process_mesh"
+    CATEGORY = "Comfy3D/Preprocessor"
+
+    def process_mesh(self, mesh, target, remesh, optimalplacement):
+        
+        vertices, faces = decimate_mesh(mesh.v, mesh.f, target, remesh, optimalplacement)
+        mesh.v = vertices
+        mesh.f = faces
+
+        return (mesh,)

 class Switch_3DGS_Axis:
    @classmethod
@@ -1482,7 +1517,8 @@ class Load_Diffusers_Pipeline:
            custom_pipeline=custom_pipeline,
        ).to(DEVICE)
        
-        pipe.enable_xformers_memory_efficient_attention()
+        if hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
+            pipe.enable_xformers_memory_efficient_attention()
        
        return (pipe, )
    
@@ -3884,6 +3920,104 @@ class Hunyuan3D_V1_Reconstruction_Model:
        mesh.auto_normal()
        
        return (mesh,)
+
+class Hunyuan3D_V2_DiT_Flow_Matching_Model:
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "hunyuan3d_v2_i23d_pipe": ("DIFFUSERS_PIPE",),
+                "reference_image": ("IMAGE",),
+                "reference_mask": ("MASK",),
+                "seed": ("INT", {"default": 1234, "min": 0, "max": 0xffffffffffffffff}),
+                "guidance_scale": ("FLOAT", {"default": 5.5, "min": 0.0, "step": 0.01}),
+                "num_inference_steps": ("INT", {"default": 30, "min": 1}),
+                "octree_resolution": ("INT", {"default": 256, "min": 1}),
+            }
+        }
+    
+    RETURN_TYPES = (
+        "MESH",
+    )
+    RETURN_NAMES = (
+        "mesh",
+    )
+    FUNCTION = "run_model"
+    CATEGORY = "Comfy3D/Algorithm"
+    
+    @torch.no_grad()
+    def run_model(
+        self, 
+        hunyuan3d_v2_i23d_pipe, 
+        reference_image, # [1, H, W, 3]
+        reference_mask,  # [1, H, W]
+        seed,
+        guidance_scale, 
+        num_inference_steps,
+        octree_resolution,
+    ):
+        single_image = torch_imgs_to_pils(reference_image, reference_mask)[0]
+
+        generator = torch.Generator(device=hunyuan3d_v2_i23d_pipe.device).manual_seed(seed)
+        mesh = hunyuan3d_v2_i23d_pipe(
+            image=single_image,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            generator=generator,
+            octree_resolution=octree_resolution
+        )[0]
+
+        mesh = FloaterRemover()(mesh)
+        mesh = DegenerateFaceRemover()(mesh)
+        mesh = FaceReducer()(mesh)
+
+        mesh = Mesh.load_trimesh(given_mesh=mesh)
+
+        return (mesh,)
+
+class Hunyuan3D_V2_Paint_Model:
+    
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "hunyuan3d_v2_texgen_pipe": ("DIFFUSERS_PIPE",),
+                "reference_image": ("IMAGE",),
+                "reference_mask": ("MASK",),
+                "mesh": ("MESH",),
+            }
+        }
+    
+    RETURN_TYPES = (
+        "MESH",
+    )
+    RETURN_NAMES = (
+        "mesh",
+    )
+    FUNCTION = "run_model"
+    CATEGORY = "Comfy3D/Algorithm"
+    
+    @torch.no_grad()
+    def run_model(
+        self, 
+        hunyuan3d_v2_texgen_pipe, 
+        reference_image, # [1, H, W, 3]
+        reference_mask,  # [1, H, W]
+        mesh,
+    ):
+        single_image = torch_imgs_to_pils(reference_image, reference_mask)[0]
+
+        v_np = mesh.v.detach().cpu().numpy()
+        f_np = mesh.f.detach().cpu().numpy()
+
+        mesh = trimesh.Trimesh(vertices=v_np, faces=f_np)
+        mesh = hunyuan3d_v2_texgen_pipe(mesh, single_image)
+
+        mesh = Mesh.load_trimesh(given_mesh=mesh)
+        mesh.auto_normal()
+
+        return (mesh,)
    
 class Load_Trellis_Structured_3D_Latents_Models:
    default_repo_id = "JeffreyXiang/TRELLIS-image-large"
@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-3d-pack"
 description = "Make ComfyUI generates 3D assets as good & convenient as it generates image/video!"
-version = "0.1.3"
+version = "0.1.4"
 license = { file = "LICENSE" }
 dynamic = ["dependencies"]
 [tool.setuptools.dynamic]
@@ -1,11 +1,8 @@
 import server
-import folder_paths as comfy_paths
 import os

 from ..shared_utils.log_utils import cstr

-ROOT_PATH = os.path.join(comfy_paths.get_folder_paths("custom_nodes")[0], "ComfyUI-3D-Pack")
-
 web = server.web

 SUPPORTED_VIEW_EXTENSIONS = (
@@ -38,4 +35,4 @@ async def view_file(request):
        if filepath.lower().endswith(SUPPORTED_VIEW_EXTENSIONS) and os.path.exists(filepath):
            return web.FileResponse(filepath)
    
-    return web.Response(status=404)
+    return web.Response(status=404)
Author	SHA1	Message	Date
Mr. For Example	bdf8a0261d	Merge pull request #440 from MrForExample/dev Small Update for Hunyuan3D-2, Merge 2 pull requests, and add Decimate Mesh node	2025-01-24 12:39:40 -06:00
Mr. For Example	979d770c56	Merge branch 'main' into dev	2025-01-24 12:32:22 -06:00
Mr. For Example	3c12ee16b9	Small update for Hunyuan3D-2	2025-01-24 12:31:59 -06:00
Mr. For Example	a3c515f666	Merge pull request #439 from robinjhuang/main Remove unused variable.	2025-01-24 12:16:53 -06:00
Mr. For Example	50e1f59997	Merge pull request #434 from YanWenKun/patch-lfs Remove images to avoid git lfs "This repository is over its data quota"	2025-01-24 12:15:54 -06:00
Robin Huang	a5f00f20ba	Remove unused variable.	2025-01-23 16:41:59 -08:00
YAN Wenkun	5b5bd64990	Remove images to avoid git lfs "This repository is over its data quota"	2025-01-23 17:54:20 +08:00
Mr. For Example	62a3e7c247	Merge pull request #432 from MrForExample/dev Integrate Hunyuan3D-2	2025-01-22 16:57:50 -06:00
Mr. For Example	0611e0d230	Update README.md	2025-01-22 16:47:35 -06:00
Mr. For Example	dbfa5e1e41	Integrate Hunyuan3D-2	2025-01-22 16:39:14 -06:00
				`@@ -0,0 +1 @@`
				c++ -O3 -Wall -shared -std=c++11 -fPIC `python3 -m pybind11 --includes` mesh_processor.cpp -o mesh_processor`python3-config --extension-suffix`