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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* 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
+text_encoder/tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,77 @@
+TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
+Tencent HunyuanVideo Release Date: December 3, 2024
+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 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 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, text/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, including, without limitation to, Tencent HunyuanVideo released at [https://github.com/Tencent/HunyuanVideo].
+k.	“Tencent Hunyuan 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 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 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 Works; and (ii) mark the products or services developed by using the Tencent Hunyuan 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 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 (including as regards the Territory). 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.
+a.	Your use of the Tencent Hunyuan 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 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 Works and You must provide notice to subsequent users to whom You distribute that Tencent Hunyuan Works are subject to the use restrictions in these Sections 5(a) and 5(b).
+b.	You must not use the Tencent Hunyuan Works or any Output or results of the Tencent Hunyuan Works to improve any other AI model (other than Tencent Hunyuan or Model Derivatives thereof).
+c.	You must not use, reproduce, modify, distribute, or display the Tencent Hunyuan Works, Output or results of the Tencent Hunyuan Works outside the Territory. Any such use outside the Territory is unlicensed and unauthorized under this Agreement.
+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”) 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 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 Works or to grant any license thereto.
+b.	UNLESS AND ONLY TO THE EXTENT REQUIRED BY APPLICABLE LAW, THE TENCENT HUNYUAN 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 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 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 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 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. You agree not to use Tencent Hunyuan 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 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.

Notice

@@ -0,0 +1,233 @@
+Usage and Legal Notices:
+
+Tencent is pleased to support the open source community by making Tencent HunyuanVideo available.
+
+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.
+
+Tencent HunyuanVideo is licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT except for the third-party components listed below. Tencent HunyuanVideo 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, Tencent HunyuanVideo 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 may be made publicly available by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
+
+
+Other dependencies and licenses:
+
+
+Open Source Model Licensed under the Apache License Version 2.0:
+The below software in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
+--------------------------------------------------------------------
+1. diffusers
+Copyright (c) diffusers original author and authors
+Please note this software has been modified by Tencent in this distribution.
+
+2. transformers
+Copyright (c) transformers original author and authors
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+3. safetensors
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+4. flux
+Copyright (c) flux original author and authors
+
+
+Terms of the Apache License Version 2.0:
+--------------------------------------------------------------------
+Apache License 
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+Version 2.0, January 2004
+
+http://www.apache.org/licenses/ 
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+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
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+
+"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
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+"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
+
+"Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
+
+"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work 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 Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
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+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 Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
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+
+6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
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+7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (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 Work and assume any risks associated with Your exercise of permissions under this License.
+
+8. 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 Work (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.
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+9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works 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.
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+END OF TERMS AND CONDITIONS
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+Open Source Software Licensed under the BSD 2-Clause License:
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+Open Source Software Licensed under the BSD 3-Clause License:
+--------------------------------------------------------------------
+1. torchvision
+Copyright (c) Soumith Chintala 2016, 
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+2. flash-attn
+Copyright (c) 2022, the respective contributors, as shown by the AUTHORS file.
+All rights reserved.
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+--------------------------------------------------------------------
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+Open Source Software Licensed under the BSD 3-Clause License and Other Licenses of the Third-Party Components therein:
+--------------------------------------------------------------------
+1. torch
+Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
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+--------------------------------------------------------------------
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+Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
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+For the license of other third party components, please refer to the following URL:
+https://github.com/pandas-dev/pandas/tree/v2.0.3/LICENSES
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+--------------------------------------------------------------------
+1. numpy
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+Copyright (c) 2018 Alex Rogozhnikov
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+Copyright (c) 2017
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+Open Source Software Licensed under the MIT License and Other Licenses of the Third-Party Components therein:
+--------------------------------------------------------------------
+1. tqdm
+Copyright (c) 2013 noamraph
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+https://github.com/tqdm/tqdm/blob/v4.66.2/LICENCE
+
+
+
+Open Source Model Licensed under the MIT License:
+--------------------------------------------------------------------
+1. clip-large
+Copyright (c) 2021 OpenAI
+
+
+A copy of the MIT is included in this file.
+
+
+--------------------------------------------------------------------
+We may also use other third-party components:
+
+1. llava-llama3
+
+Copyright (c) llava-llama3 original author and authors
+
+URL: https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers#model

README.md

@@ -0,0 +1,268 @@
+---
+pipeline_tag: text-to-video
+license: other
+license_name: tencent-hunyuan-community
+license_link: LICENSE
+---
+
+<!-- ## **HunyuanVideo** -->
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/refs/heads/main/assets/logo.png"  height=100>
+</p>
+
+# HunyuanVideo: A Systematic Framework For Large Video Generation Model Training
+
+-----
+
+This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring HunyuanVideo. You can find more visualizations on our [project page](https://aivideo.hunyuan.tencent.com).
+
+> [**HunyuanVideo: A Systematic Framework For Large Video Generation Model Training**](https://arxiv.org/abs/2412.03603) <br>
+
+## 🔥🔥🔥 News!!
+* Dec 3, 2024: 🤗 We release the inference code and model weights of HunyuanVideo.
+
+## 📑 Open-source Plan
+
+- HunyuanVideo (Text-to-Video Model)
+  - [x] Inference 
+  - [x] Checkpoints 
+  - [ ] Penguin Video Benchmark
+  - [ ] Web Demo (Gradio) 
+  - [ ] ComfyUI
+  - [ ] Diffusers 
+- HunyuanVideo (Image-to-Video Model)
+  - [ ] Inference 
+  - [ ] Checkpoints 
+
+## Contents
+- [HunyuanVideo: A Systematic Framework For Large Video Generation Model Training](#hunyuanvideo--a-systematic-framework-for-large-video-generation-model-training)
+  - [🔥🔥🔥 News!!](#-news!!)
+  - [📑 Open-source Plan](#-open-source-plan)
+  - [Contents](#contents)
+  - [**Abstract**](#abstract)
+  - [**HunyuanVideo Overall Architechture**](#-hunyuanvideo-overall-architechture)
+  - [🎉 **HunyuanVideo Key Features**](#-hunyuanvideo-key-features)
+    - [**Unified Image and Video Generative Architecture**](#unified-image-and-video-generative-architecture)
+    - [**MLLM Text Encoder**](#mllm-text-encoder)
+    - [**3D VAE**](#3d-vae)
+    - [**Prompt Rewrite**](#prompt-rewrite)
+  - [📈 Comparisons](#-comparisons)
+  - [📜 Requirements](#-requirements)
+  - [🛠️ Dependencies and Installation](#-dependencies-and-installation)
+    - [Installation Guide for Linux](#installation-guide-for-linux)
+  - [🧱 Download Pretrained Models](#-download-pretrained-models)
+  - [🔑 Inference](#-inference)
+    - [Using Command Line](#using-command-line)
+    - [More Configurations](#more-configurations)
+  - [🔗 BibTeX](#-bibtex)
+  - [Acknowledgements](#acknowledgements)
+---
+
+## **Abstract**
+We present HunyuanVideo, a novel open-source video foundation model that exhibits performance in video generation that is comparable to, if not superior to, leading closed-source models. HunyuanVideo features a comprehensive framework that integrates several key contributions, including data curation, image-video joint model training, and an efficient infrastructure designed to facilitate large-scale model training and inference. Additionally, through an effective strategy for scaling model architecture and dataset, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models. 
+
+We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion diversity, text-video alignment, and generation stability. According to professional human evaluation results, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and 3 top performing Chinese video generative models. By releasing the code and weights of the foundation model and its applications, we aim to bridge the gap between closed-source and open-source video foundation models. This initiative will empower everyone in the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem. 
+
+## **HunyuanVideo Overall Architechture**
+
+HunyuanVideo is trained on a spatial-temporally
+compressed latent space, which is compressed through Causal 3D VAE. Text prompts are encoded
+using a large language model, and used as the condition. Gaussian noise and condition are taken as
+input, our generate model generates an output latent, which is decoded to images or videos through
+the 3D VAE decoder.
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/refs/heads/main/assets/overall.png"  height=300>
+</p>
+
+## 🎉 **HunyuanVideo Key Features**
+### **Unified Image and Video Generative Architecture**
+HunyuanVideo introduces the Transformer design and employs a Full Attention mechanism for unified image and video generation. 
+Specifically, we use a "Dual-stream to Single-stream" hybrid model design for video generation. In the dual-stream phase, video and text
+tokens are processed independently through multiple Transformer blocks, enabling each modality to learn its own appropriate modulation mechanisms without interference. In the single-stream phase, we concatenate the video and text
+tokens and feed them into subsequent Transformer blocks for effective multimodal information fusion.
+This design captures complex interactions between visual and semantic information, enhancing
+overall model performance.
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/refs/heads/main/assets/backbone.png"  height=350>
+</p>
+
+### **MLLM Text Encoder**
+Some previous text-to-video model typically use pretrainednCLIP and T5-XXL as text encoders where CLIP uses Transformer Encoder and T5 uses a Encoder-Decoder structure. In constrast, we utilize a pretrained Multimodal Large Language Model (MLLM) with a Decoder-Only structure as our text encoder, which has following advantages: (i) Compared with T5, MLLM after visual instruction finetuning has better image-text alignment in the feature space, which alleviates the difficulty of instruction following in diffusion models; (ii)
+Compared with CLIP, MLLM has been demonstrated superior ability in image detail description
+and complex reasoning; (iii) MLLM can play as a zero-shot learner by following system instructions prepended to user prompts, helping text features pay more attention to key information. In addition, MLLM is based on causal attention while T5-XXL utilizes bidirectional attention that produces better text guidance for diffusion models. Therefore, we introduce an extra bidirectional token refiner for enhacing text features.
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/refs/heads/main/assets/text_encoder.png"  height=275>
+</p>
+
+### **3D VAE**
+HunyuanVideo trains a 3D VAE with CausalConv3D to compress pixel-space videos and images into a compact latent space. We set the compression ratios of video length, space and channel to 4, 8 and 16 respectively. This can significantly reduce the number of tokens for the subsequent diffusion transformer model, allowing us to train videos at the original resolution and frame rate.
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Tencent/HunyuanVideo/refs/heads/main/assets/3dvae.png"  height=150>
+</p>
+
+### **Prompt Rewrite**
+To address the variability in linguistic style and length of user-provided prompts, we fine-tune the [Hunyuan-Large model](https://github.com/Tencent/Tencent-Hunyuan-Large) as our prompt rewrite model to adapt the original user prompt to model-preferred prompt.
+
+We provide two rewrite modes: Normal mode and Master mode, which can be called using different prompts. The Normal mode is designed to enhance the video generation model's comprehension of user intent, facilitating a more accurate interpretation of the instructions provided. The Master mode enhances the description of aspects such as composition, lighting, and camera movement, which leans towards generating videos with a higher visual quality. However, this emphasis may occasionally result in the loss of some semantic details. 
+
+The Prompt Rewrite Model can be directly deployed and inferred using the [Hunyuan-Large original code](https://github.com/Tencent/Tencent-Hunyuan-Large). We release the weights of the Prompt Rewrite Model [here](https://huggingface.co/Tencent/HunyuanVideo-PromptRewrite).
+
+## 📈 Comparisons
+
+To evaluate the performance of HunyuanVideo, we selected five strong baselines from closed-source video generation models. In total, we utilized 1,533 text prompts, generating an equal number of video samples with HunyuanVideo in a single run. For a fair comparison, we conducted inference only once, avoiding any cherry-picking of results. When comparing with the baseline methods, we maintained the default settings for all selected models, ensuring consistent video resolution. Videos were assessed based on three criteria: Text Alignment, Motion Quality and Visual Quality. More than 60 professional evaluators performed the evaluation. Notably, HunyuanVideo demonstrated the best overall performance, particularly excelling in motion quality.
+
+<p align="center">
+<table> 
+<thead> 
+<tr> 
+    <th rowspan="2">Model</th> <th rowspan="2">Open Source</th> <th>Duration</th> <th>Text Alignment</th> <th>Motion Quality</th> <th rowspan="2">Visual Quality</th> <th rowspan="2">Overall</th>  <th rowspan="2">Ranking</th>
+</tr> 
+</thead> 
+<tbody> 
+<tr> 
+    <td>HunyuanVideo (Ours)</td> <td> ✔ </td> <td>5s</td> <td>61.8%</td> <td>66.5%</td> <td>95.7%</td> <td>41.3%</td> <td>1</td>
+</tr> 
+<tr> 
+    <td>CNTopA (API)</td> <td> &#10008 </td> <td>5s</td> <td>62.6%</td> <td>61.7%</td> <td>95.6%</td> <td>37.7%</td> <td>2</td>
+</tr> 
+<tr> 
+    <td>CNTopB (Web)</td> <td> &#10008</td> <td>5s</td> <td>60.1%</td> <td>62.9%</td> <td>97.7%</td> <td>37.5%</td> <td>3</td>
+</tr> 
+<tr> 
+    <td>GEN-3 alpha (Web)</td> <td>&#10008</td> <td>6s</td> <td>47.7%</td> <td>54.7%</td> <td>97.5%</td> <td>27.4%</td> <td>4</td> 
+</tr> 
+<tr> 
+    <td>Luma1.6 (API)</td><td>&#10008</td> <td>5s</td> <td>57.6%</td> <td>44.2%</td> <td>94.1%</td> <td>24.8%</td> <td>6</td>
+</tr>
+<tr> 
+    <td>CNTopC (Web)</td> <td>&#10008</td> <td>5s</td> <td>48.4%</td> <td>47.2%</td> <td>96.3%</td> <td>24.6%</td> <td>5</td>
+</tr> 
+</tbody>
+</table>
+</p>
+
+## 📜 Requirements
+
+The following table shows the requirements for running HunyuanVideo model (batch size = 1) to generate videos:
+
+|     Model    |  Setting<br/>(height/width/frame) | GPU Peak Memory  |
+|:------------:|:--------------------------------:|:----------------:|
+| HunyuanVideo   |        720px1280px129f          |       60GB        |
+| HunyuanVideo   |        544px960px129f           |       45GB        |
+
+* An NVIDIA GPU with CUDA support is required. 
+  * The model is tested on a single 80G GPU.
+  * **Minimum**: The minimum GPU memory required is 60GB for 720px1280px129f and 45G for 544px960px129f.
+  * **Recommended**: We recommend using a GPU with 80GB of memory for better generation quality.
+* Tested operating system: Linux
+
+## 🛠️ Dependencies and Installation
+
+Begin by cloning the repository:
+```shell
+git clone https://github.com/tencent/HunyuanVideo
+cd HunyuanVideo
+```
+
+### Installation Guide for Linux
+
+We provide an `environment.yml` file for setting up a Conda environment.
+Conda's installation instructions are available [here](https://docs.anaconda.com/free/miniconda/index.html).
+
+We recommend CUDA versions 11.8 and 12.0+.
+
+```shell
+# 1. Prepare conda environment
+conda env create -f environment.yml
+
+# 2. Activate the environment
+conda activate HunyuanVideo
+
+# 3. Install pip dependencies
+python -m pip install -r requirements.txt
+
+# 4. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
+python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.5.9.post1
+```
+
+Additionally, HunyuanVideo also provides a pre-built Docker image:
+[docker_hunyuanvideo](https://hub.docker.com/repository/docker/hunyuanvideo/hunyuanvideo/general). 
+
+```shell
+# 1. Use the following link to download the docker image tar file (For CUDA 12).
+wget https://aivideo.hunyuan.tencent.com/download/HunyuanVideo/hunyuan_video_cu12.tar
+
+# 2. Import the docker tar file and show the image meta information (For CUDA 12).
+docker load -i hunyuan_video.tar
+
+docker image ls
+
+# 3. Run the container based on the image
+docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged  docker_image_tag
+```
+
+
+## 🧱 Download Pretrained Models
+
+The details of download pretrained models are shown [here](https://github.com/Tencent/HunyuanVideo/blob/main/ckpts/README.md).
+
+## 🔑 Inference
+We list the height/width/frame settings we support in the following table.
+
+|      Resolution       |           h/w=9:16           |    h/w=16:9     |     h/w=4:3     |     h/w=3:4     |     h/w=1:1     |
+|:---------------------:|:----------------------------:|:---------------:|:---------------:|:---------------:|:---------------:|
+|         540p          |        544px960px129f        |  960px544px129f | 624px832px129f  |  832px624px129f |  720px720px129f |
+| 720p (recommended)    |       720px1280px129f        | 1280px720px129f | 1104px832px129f | 832px1104px129f | 960px960px129f  |
+
+### Using Command Line
+
+```bash
+cd HunyuanVideo
+
+python3 sample_video.py \
+    --video-size 720 1280 \
+    --video-length 129 \
+    --infer-steps 30 \
+    --prompt "a cat is running, realistic." \
+    --flow-reverse \
+    --seed 0 \
+    --use-cpu-offload \
+    --save-path ./results
+```
+
+### More Configurations
+
+We list some more useful configurations for easy usage:
+
+|        Argument        |  Default  |                Description                |
+|:----------------------:|:---------:|:-----------------------------------------:|
+|       `--prompt`       |   None    |   The text prompt for video generation    |
+|     `--video-size`     | 720 1280  |      The size of the generated video      |
+|    `--video-length`    |    129    |     The length of the generated video     |
+|    `--infer-steps`     |    30     |     The number of steps for sampling      |
+| `--embedded-cfg-scale` |    6.0    |    Embeded  Classifier free guidance scale       |
+|     `--flow-shift`     |    9.0    | Shift factor for flow matching schedulers |
+|     `--flow-reverse`   |    False  | If reverse, learning/sampling from t=1 -> t=0 |
+|     `--neg-prompt`     |   None    | The negative prompt for video generation  |
+|        `--seed`        |     0     |   The random seed for generating video    |
+|  `--use-cpu-offload`   |   False   |    Use CPU offload for the model load to save more memory, necessary for high-res video generation    |
+|     `--save-path`      | ./results |     Path to save the generated video      |
+
+
+## 🔗 BibTeX
+If you find [HunyuanVideo](https://arxiv.org/abs/2412.03603) useful for your research and applications, please cite using this BibTeX:
+
+```BibTeX
+@misc{kong2024hunyuanvideo,
+      title={HunyuanVideo: A Systematic Framework For Large Video Generative Models}, 
+      author={Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, Kathrina Wu, Qin Lin, Aladdin Wang, Andong Wang, Changlin Li, Duojun Huang, Fang Yang, Hao Tan, Hongmei Wang, Jacob Song, Jiawang Bai, Jianbing Wu, Jinbao Xue, Joey Wang, Junkun Yuan, Kai Wang, Mengyang Liu, Pengyu Li, Shuai Li, Weiyan Wang, Wenqing Yu, Xinchi Deng, Yang Li, Yanxin Long, Yi Chen, Yutao Cui, Yuanbo Peng, Zhentao Yu, Zhiyu He, Zhiyong Xu, Zixiang Zhou, Yangyu Tao, Qinglin Lu, Songtao Liu, Dax Zhou, Hongfa Wang, Yong Yang, Di Wang, Yuhong Liu, and Jie Jiang, along with Caesar Zhong},
+      year={2024},
+      archivePrefix={arXiv preprint arXiv:2412.03603},
+      primaryClass={cs.CV}
+}
+```
+
+## Acknowledgements
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [FLUX](https://github.com/black-forest-labs/flux), [Llama](https://github.com/meta-llama/llama), [LLaVA](https://github.com/haotian-liu/LLaVA), [Xtuner](https://github.com/InternLM/xtuner), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research and exploration.
+Additionally, we also thank the Tencent Hunyuan Multimodal team for their help with the text encoder. 

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+}

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text_encoder_2/README.md

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+---
+tags:
+- vision
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: playing music, playing sports
+  example_title: Cat & Dog
+---
+
+# Model Card: CLIP
+
+Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
+
+## Model Details
+
+The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
+
+### Model Date
+
+January 2021
+
+### Model Type
+
+The base model uses a ViT-L/14 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
+
+The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
+
+
+### Documents
+
+- [Blog Post](https://openai.com/blog/clip/)
+- [CLIP Paper](https://arxiv.org/abs/2103.00020)
+
+
+### Use with Transformers
+
+```python
+from PIL import Image
+import requests
+
+from transformers import CLIPProcessor, CLIPModel
+
+model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
+
+outputs = model(**inputs)
+logits_per_image = outputs.logits_per_image # this is the image-text similarity score
+probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
+```
+
+
+## Model Use
+
+### Intended Use
+
+The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
+
+#### Primary intended uses
+
+The primary intended users of these models are AI researchers.
+
+We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
+
+### Out-of-Scope Use Cases
+
+**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 
+
+Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
+
+Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
+
+
+
+## Data
+
+The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
+
+### Data Mission Statement
+
+Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
+
+
+
+## Performance and Limitations
+
+### Performance
+
+We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
+
+- Food101
+- CIFAR10   
+- CIFAR100   
+- Birdsnap
+- SUN397
+- Stanford Cars
+- FGVC Aircraft
+- VOC2007
+- DTD
+- Oxford-IIIT Pet dataset
+- Caltech101
+- Flowers102
+- MNIST   
+- SVHN 
+- IIIT5K   
+- Hateful Memes   
+- SST-2
+- UCF101
+- Kinetics700
+- Country211
+- CLEVR Counting
+- KITTI Distance
+- STL-10
+- RareAct
+- Flickr30
+- MSCOCO
+- ImageNet
+- ImageNet-A
+- ImageNet-R
+- ImageNet Sketch
+- ObjectNet (ImageNet Overlap)
+- Youtube-BB
+- ImageNet-Vid
+
+## Limitations
+
+CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
+
+### Bias and Fairness
+
+We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
+
+We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
+
+
+
+## Feedback
+
+### Where to send questions or comments about the model
+
+Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)

text_encoder_2/config.json

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+{
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+  "architectures": [
+    "CLIPModel"
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+      "1": "LABEL_1"
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+    "hidden_act": "quick_gelu",
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+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
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+    "is_decoder": false,
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