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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# ==========================================
# Custom settings
# ==========================================
# For MacOS
.DS_Store
# For IDEs
.idea/
.vscode/
# For global settings
__*/
tmp*.*
# Model checkpoints
*.pt
*.ckpt
*.pth
*.safetensors
log_EXP
results/
trt/activate.sh
trt/deactivate.sh
*.onnx
ckpts/text_encoder
ckpts/text_encoder_2
ckpts/hunyuan-video-t2v-720p
ckpts/hunyuan-video-t2v-720p/vae
ckpts/hunyuan-video-t2v-720p/transformers
models/*
\ No newline at end of file
LICENSE.txt 0 → 100644
View file @ e2ec88b4
TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT
Tencent HunyuanVideo-I2V Release Date: March 5, 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 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-I2V released at [ https://github.com/Tencent/HunyuanVideo-I2V ].
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 © 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 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 → 100644
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Usage and Legal Notices:
Tencent is pleased to support the open source community by making HunyuanVideo-I2V 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.
HunyuanVideo-I2V is licensed under the Tencent Hunyuan Community License Agreement, which can be found in this repository called "LICENSE", except for the third-party components listed below. HunyuanVideo-I2V 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, HunyuanVideo-I2V 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.
Other dependencies and licenses:
Open Source Software Licensed under the MIT License:
The below software in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2025 THL A29 Limited.
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1. Chinese-CLIP
Copyright (c) 2012-2022 OFA-Sys Team
Copyright (c) 2012-2022 Gabriel Ilharco, Mitchell Wortsman, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, Ludwig Schmidt
2. vae
Copyright (c) sd-vae-ft-ema original author and authors
3. clip
Copyright (c) 2012-2022 OFA-Sys Team
Copyright (c) 2012-2022 Gabriel Ilharco, Mitchell Wortsman, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, Ludwig Schmidt
4. improved-diffusion
Copyright (c) 2021 OpenAI
5. ComfyUI-Diffusers
Copyright (c) 2023 Limitex
Terms of the MIT License:
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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.
Open Source Software Licensed under the Apache License Version 2.0:
The below software in this distribution may have been modified by Tencent.
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1. text-to-text-transfer-transformer
Copyright 2024 The T5 Authors.
Please note this software has been modified by Tencent in this distribution.
2. pytorch-fid
Copyright (c) pytorch-fid orignal author and authors
Please note this software has been modified by Tencent in this distribution.
3. IP-Adapter
Copyright (c) IP-Adapter original author and authors
Please note this software has been modified by Tencent in this distribution.
4. mT5
Copyright (c) mT5 original author and authors
Please note this software has been modified by Tencent in this distribution.
5. LLaVA
Copyright (c) 2024 Mistral AI, All rights reserved
Please note this software has been modified by Tencent in this distribution.
6. peft
Copyright 2023 The HuggingFace Team. All rights reserved.
Please note this software has been modified by Tencent in this distribution.
7. glide-text2im
Copyright 2023 The HuggingFace Team. All rights reserved.
Please note this software has been modified by Tencent in this distribution.
8. dwpose
Copyright 2018-2020 Open-MMLab.
Please note this software has been modified by Tencent in this distribution.
Terms of the Apache License Version 2.0:
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Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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.
"Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
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"Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
"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."
"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
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.
3. Grant of Patent 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 (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, 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 Work 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 Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:
You must give any other recipients of the Work or Derivative Works a copy of this License; and
You must cause any modified files to carry prominent notices stating that You changed the files; and
You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and
If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.
You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
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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.
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.
END OF TERMS AND CONDITIONS
README.md 0 → 100644
View file @ e2ec88b4
<!-- ## **HunyuanVideo** -->
[中文阅读](./README_zh.md)
<p align="center">
<img src="./assets/logo.png" height=100>
</p>
# **HunyuanVideo-I2V** 🌅
<div align="center">
<a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-I2V Code&message=Github&color=blue"></a> &ensp;
<a href="https://aivideo.hunyuan.tencent.com"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Web&color=green"></a> &ensp;
<a href="https://video.hunyuan.tencent.com"><img src="https://img.shields.io/static/v1?label=Playground&message=Web&color=green"></a>
</div>
<div align="center">
<a href="https://arxiv.org/abs/2412.03603"><img src="https://img.shields.io/static/v1?label=Tech Report&message=Arxiv&color=red"></a> &ensp;
<a href="https://aivideo.hunyuan.tencent.com/hunyuanvideo.pdf"><img src="https://img.shields.io/static/v1?label=Tech Report&message=High-Quality Version (~350M)&color=red"></a>
</div>
<div align="center">
<a href="https://huggingface.co/tencent/HunyuanVideo-I2V"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-I2V&message=HuggingFace&color=yellow"></a> &ensp;
<!-- <a href="https://huggingface.co/docs/diffusers/main/api/pipelines/hunyuan_video"><img src="https://img.shields.io/static/v1?label=HunyuanVideo&message=Diffusers&color=yellow"></a> &ensp; -->
<!-- <a href="https://huggingface.co/tencent/HunyuanVideo-PromptRewrite"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-PromptRewrite&message=HuggingFace&color=yellow"></a> -->
<!--
[![Replicate](https://replicate.com/zsxkib/hunyuan-video/badge)](https://replicate.com/zsxkib/hunyuan-video) -->
</div>
<p align="center">
👋 Join our <a href="assets/WECHAT.md" target="_blank">WeChat</a> and <a href="https://discord.gg/tv7FkG4Nwf" target="_blank">Discord</a>
</p>
<p align="center">
-----
Following the great successful open-sourcing of our [HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo), we proudly present the [HunyuanVideo-I2V](https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V), a new image-to-video generation framework to accelerate open-source community exploration!
This repo contains official PyTorch model definitions, pre-trained weights and inference/sampling code. You can find more visualizations on our [project page](https://aivideo.hunyuan.tencent.com). Meanwhile, we have released the LoRA training code for customizable special effects, which can be used to create more interesting video effects.
> [**HunyuanVideo: A Systematic Framework For Large Video Generation Model**](https://arxiv.org/abs/2412.03603) <be>
## 🔥🔥🔥 News!!
* Mar 13, 2025: 🚀 We release the parallel inference code for HunyuanVideo-I2V powered by [xDiT](https://github.com/xdit-project/xDiT).
* Mar 11, 2025: 🎉 We have updated the lora training and inference code after fixing the bug.
* Mar 07, 2025: 🔥 We have fixed the bug in our open-source version that caused ID changes. Please try the new model weights of [HunyuanVideo-I2V](https://huggingface.co/tencent/HunyuanVideo-I2V) to ensure full visual consistency in the first frame and produce higher quality videos.
* Mar 06, 2025: 👋 We release the inference code and model weights of HunyuanVideo-I2V. [Download](https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V/blob/main/ckpts/README.md).
## 🎥 Demo
### I2V Demo
<div align="center">
<video src="https://github.com/user-attachments/assets/442afb73-3092-454f-bc46-02361c285930" width="80%" poster="./assets/video_poster.jpg"> </video>
<p>Co-creator @D-aiY Director Ding Yi</p>
</div>
### First Frame Consistency Demo
| Reference Image | Generated Video |
|:----------------:|:----------------:|
| <img src="https://github.com/user-attachments/assets/83e7a097-ffca-40db-9c72-be01d866aa7d" width="80%"> | <video src="https://github.com/user-attachments/assets/f81d2c88-bb1a-43f8-b40f-1ccc20774563" width="100%"> </video> |
｜ <img src="https://github.com/user-attachments/assets/c385a11f-60c7-4919-b0f1-bc5e715f673c" width="80%"> | <video src="https://github.com/user-attachments/assets/0c29ede9-0481-4d40-9c67-a4b6267fdc2d" width="100%"> </video> |
｜ <img src="https://github.com/user-attachments/assets/5763f5eb-0be5-4b36-866a-5199e31c5802" width="95%"> | <video src="https://github.com/user-attachments/assets/a8da0a1b-ba7d-45a4-a901-5d213ceaf50e" width="100%"> </video> |
### Customizable I2V LoRA Demo
| I2V Lora Effect | Reference Image | Generated Video |
|:---------------:|:--------------------------------:|:----------------:|
| Hair growth | <img src="./assets/demo/i2v_lora/imgs/hair_growth.png" width="40%"> | <video src="https://github.com/user-attachments/assets/06b998ae-bbde-4c1f-96cb-a25a9197d5cb" width="100%"> </video> |
| Embrace | <img src="./assets/demo/i2v_lora/imgs/embrace.png" width="40%"> | <video src="https://github.com/user-attachments/assets/f8c99eb1-2a43-489a-ba02-6bd50a6dd260" width="100%" > </video> |
## 🧩 Community Contributions
If you develop/use HunyuanVideo-I2V in your projects, welcome to let us know.
- ComfyUI-Kijai (FP8 Inference, V2V and IP2V Generation): [ComfyUI-HunyuanVideoWrapper](https://github.com/kijai/ComfyUI-HunyuanVideoWrapper) by [Kijai](https://github.com/kijai)
- HunyuanVideoGP (GPU Poor version): [HunyuanVideoGP](https://github.com/deepbeepmeep/HunyuanVideoGP) by [DeepBeepMeep](https://github.com/deepbeepmeep)
- xDiT compatibility improvement: [xDiT compatibility improvement](https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V/issues/36#issuecomment-2728068507) by [pftq](https://github.com/pftq) and [xibosun](https://github.com/xibosun)
## 📑 Open-source Plan
- HunyuanVideo-I2V (Image-to-Video Model)
- [x] Inference
- [x] Checkpoints
- [x] ComfyUI
- [x] Lora training scripts
- [x] Multi-gpus Sequence Parallel inference (Faster inference speed on more gpus)
## Contents
- [**HunyuanVideo-I2V** 🌅](#hunyuanvideo-i2v-)
- [🔥🔥🔥 News!!](#-news)
- [🎥 Demo](#-demo)
- [I2V Demo](#i2v-demo)
- [Frist Frame Consistency Demo](#frist-frame-consistency-demo)
- [Customizable I2V LoRA Demo](#customizable-i2v-lora-demo)
- [🧩 Community Contributions](#-community-contributions)
- [📑 Open-source Plan](#-open-source-plan)
- [Contents](#contents)
- [**HunyuanVideo-I2V Overall Architecture**](#hunyuanvideo-i2v-overall-architecture)
- [📜 Requirements](#-requirements)
- [🛠️ Dependencies and Installation](#️-dependencies-and-installation)
- [Installation Guide for Linux](#installation-guide-for-linux)
- [🧱 Download Pretrained Models](#-download-pretrained-models)
- [🔑 Single-gpu Inference](#-single-gpu-inference)
- [Tips for Using Image-to-Video Models](#tips-for-using-image-to-video-models)
- [Using Command Line](#using-command-line)
- [More Configurations](#more-configurations)
- [🎉 Customizable I2V LoRA effects training](#-customizable-i2v-lora-effects-training)
- [Requirements](#requirements)
- [Environment](#environment)
- [Training data construction](#training-data-construction)
- [Training](#training)
- [Inference](#inference)
- [🚀 Parallel Inference on Multiple GPUs by xDiT](#-parallel-inference-on-multiple-gpus-by-xdit)
- [Using Command Line](#using-command-line-1)
- [🔗 BibTeX](#-bibtex)
- [Acknowledgements](#acknowledgements)
---
## **HunyuanVideo-I2V Overall Architecture**
Leveraging the advanced video generation capabilities of [HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo), we have extended its application to image-to-video generation tasks. To achieve this, we employ a token replace technique to effectively reconstruct and incorporate reference image information into the video generation process.
Since we utilizes a pre-trained Multimodal Large Language Model (MLLM) with a Decoder-Only architecture as the text encoder, we can significantly enhance the model's ability to comprehend the semantic content of the input image and to seamlessly integrate information from both the image and its associated caption. Specifically, the input image is processed by the MLLM to generate semantic image tokens. These tokens are then concatenated with the video latent tokens, enabling comprehensive full-attention computation across the combined data.
The overall architecture of our system is designed to maximize the synergy between image and text modalities, ensuring a robust and coherent generation of video content from static images. This integration not only improves the fidelity of the generated videos but also enhances the model's ability to interpret and utilize complex multimodal inputs. The overall architecture is as follows.
<p align="center">
<img src="./assets/backbone.png" height=300>
</p>
## 📜 Requirements
The following table shows the requirements for running HunyuanVideo-I2V model (batch size = 1) to generate videos:
| Model | Resolution | GPU Peak Memory |
|:----------------:|:-----------:|:----------------:|
| HunyuanVideo-I2V | 720p | 60GB |
* 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 720p.
* **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-Hunyuan/HunyuanVideo-I2V
cd HunyuanVideo-I2V
```
### Installation Guide for Linux
We recommend CUDA versions 12.4 or 11.8 for the manual installation.
Conda's installation instructions are available [here](https://docs.anaconda.com/free/miniconda/index.html).
```shell
# 1. Create conda environment
conda create -n HunyuanVideo-I2V python==3.11.9
# 2. Activate the environment
conda activate HunyuanVideo-I2V
# 3. Install PyTorch and other dependencies using conda
# For CUDA 12.4
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
# 4. Install pip dependencies
python -m pip install -r requirements.txt
# 5. Install flash attention v2 for acceleration (requires CUDA 11.8 or above)
python -m pip install ninja
python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
# 6. Install xDiT for parallel inference (It is recommended to use torch 2.4.0 and flash-attn 2.6.3)
python -m pip install xfuser==0.4.0
```
In case of running into float point exception(core dump) on the specific GPU type, you may try the following solutions:
```shell
# Making sure you have installed CUDA 12.4, CUBLAS>=12.4.5.8, and CUDNN>=9.00 (or simply using our CUDA 12 docker image).
pip install nvidia-cublas-cu12==12.4.5.8
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/
```
Additionally, HunyuanVideo-I2V also provides a pre-built Docker image. Use the following command to pull and run the docker image.
```shell
# For CUDA 12.4 (updated to avoid float point exception)
docker pull hunyuanvideo/hunyuanvideo-i2v:cuda12
docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo-i2v --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo-i2v:cuda12
```
## 🧱 Download Pretrained Models
The details of download pretrained models are shown [here](ckpts/README.md).
## 🔑 Single-gpu Inference
Similar to [HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo), HunyuanVideo-I2V supports high-resolution video generation, with resolution up to 720P and video length up to 129 frames (5 seconds).
### Tips for Using Image-to-Video Models
- **Use Concise Prompts**: To effectively guide the model's generation, keep your prompts short and to the point.
- **Include Key Elements**: A well-structured prompt should cover:
- **Main Subject**: Specify the primary focus of the video.
- **Action**: Describe the main movement or activity taking place.
- **Background (Optional)**: Set the scene for the video.
- **Camera Angle (Optional)**: Indicate the perspective or viewpoint.
- **Avoid Overly Detailed Prompts**: Lengthy or highly detailed prompts can lead to unnecessary transitions in the video output.
<!-- **For image-to-video models, we recommend using concise prompts to guide the model's generation process. A good prompt should include elements such as background, main subject, action, and camera angle. Overly long or excessively detailed prompts may introduce unnecessary transitions.** -->
### Using Command Line
<!-- ### Run a Gradio Server
```bash
python3 gradio_server.py --flow-reverse
# set SERVER_NAME and SERVER_PORT manually
# SERVER_NAME=0.0.0.0 SERVER_PORT=8081 python3 gradio_server.py --flow-reverse
``` -->
If you want to generate a more **stable** video, you can set `--i2v-stability` and `--flow-shift 7.0`. Execute the command as follows
```bash
cd HunyuanVideo-I2V
python3 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 7.0 \
--seed 0 \
--embedded-cfg-scale 6.0 \
--use-cpu-offload \
--save-path ./results
```
If you want to generate a more **high-dynamic** video, you can **unset** `--i2v-stability` and `--flow-shift 17.0`. Execute the command as follows
```bash
cd HunyuanVideo-I2V
python3 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 17.0 \
--embedded-cfg-scale 6.0 \
--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. |
| `--model` | HYVideo-T/2-cfgdistill | Here we use HYVideo-T/2 for I2V, HYVideo-T/2-cfgdistill is used for T2V mode. |
| `--i2v-mode` | False | Whether to open i2v mode. |
| `--i2v-image-path` | ./assets/demo/i2v/imgs/0.jpg | The reference image for video generation. |
| `--i2v-resolution` | 720p | The resolution for the generated video. |
| `--i2v-stability` | False | Whether to use stable mode for i2v inference. |
| `--video-length` | 129 | The length of the generated video. |
| `--infer-steps` | 50 | The number of steps for sampling. |
| `--flow-shift` | 7.0 | Shift factor for flow matching schedulers. We recommend 7 with `--i2v-stability` switch on for more stable video, 17 with `--i2v-stability` switch off for more dynamic video |
| `--flow-reverse` | False | If reverse, learning/sampling from t=1 -> t=0. |
| `--seed` | None | The random seed for generating video, if None, we init a random seed. |
| `--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. |
## 🎉 Customizable I2V LoRA effects training
### Requirements
The following table shows the requirements for training HunyuanVideo-I2V lora model (batch size = 1) to generate videos:
| Model | Resolution | GPU Peak Memory |
|:----------------:|:----------:|:---------------:|
| HunyuanVideo-I2V | 360p | 79GB |
* An NVIDIA GPU with CUDA support is required.
* The model is tested on a single 80G GPU.
* **Minimum**: The minimum GPU memory required is 79GB for 360p.
* **Recommended**: We recommend using a GPU with 80GB of memory for better generation quality.
* Tested operating system: Linux
* Note: You can train with 360p data and directly infer 720p videos
### Environment
```
pip install -r requirements.txt
```
### Training data construction
Prompt description: The trigger word is written directly in the video caption. It is recommended to use a phrase or short sentence.
For example, AI hair growth effect (trigger): rapid_hair_growth, The hair of the characters in the video is growing rapidly. + original prompt
After having the training video and prompt pair, refer to [here] (hyvideo/hyvae_extract/README.md) for training data construction.
### Training
```
cd HunyuanVideo-I2V
sh scripts/run_train_image2video_lora.sh
```
We list some training specific configurations for easy usage:
| Argument | Default | Description |
|:----------------:|:-------------------------------------------------------------:|:-----------------------------------------------------------:|
| `SAVE_BASE` | . | Root path for saving experimental results. |
| `EXP_NAME` | i2v_lora | Path suffix for saving experimental results. |
| `DATA_JSONS_DIR` | ./assets/demo/i2v_lora/train_dataset/processed_data/json_path | Data jsons dir generated by hyvideo/hyvae_extract/start.sh. |
| `CHIEF_IP` | 127.0.0.1 | Master node IP of the machine. |
After training, you can find `pytorch_lora_kohaya_weights.safetensors` in `{SAVE_BASE}/log_EXP/*_{EXP_NAME}/checkpoints/global_step{*}/pytorch_lora_kohaya_weights.safetensors` and set it in `--lora-path` to perform inference.
### Inference
```bash
cd HunyuanVideo-I2V
python3 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "Two people hugged tightly, In the video, two people are standing apart from each other. They then move closer to each other and begin to hug tightly. The hug is very affectionate, with the two people holding each other tightly and looking into each other's eyes. The interaction is very emotional and heartwarming, with the two people expressing their love and affection for each other." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v_lora/imgs/embrace.png \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 5.0 \
--embedded-cfg-scale 6.0 \
--seed 0 \
--use-cpu-offload \
--save-path ./results \
--use-lora \
--lora-scale 1.0 \
--lora-path ./ckpts/hunyuan-video-i2v-720p/lora/embrace_kohaya_weights.safetensors
```
We list some lora specific configurations for easy usage:
| Argument | Default | Description |
|:-------------------:|:-------:|:----------------------------:|
| `--use-lora` | False | Whether to open lora mode. |
| `--lora-scale` | 1.0 | Fusion scale for lora model. |
| `--lora-path` | "" | Weight path for lora model. |
## 🚀 Parallel Inference on Multiple GPUs by xDiT
[xDiT](https://github.com/xdit-project/xDiT) is a Scalable Inference Engine for Diffusion Transformers (DiTs) on multi-GPU Clusters.
It has successfully provided low-latency parallel inference solutions for a variety of DiTs models, including mochi-1, CogVideoX, Flux.1, SD3, etc. This repo adopted the [Unified Sequence Parallelism (USP)](https://arxiv.org/abs/2405.07719) APIs for parallel inference of the HunyuanVideo-I2V model.
### Using Command Line
For example, to generate a video with 8 GPUs, you can use the following command:
```bash
cd HunyuanVideo-I2V
ALLOW_RESIZE_FOR_SP=1 torchrun --nproc_per_node=8 \
sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 7.0 \
--seed 0 \
--embedded-cfg-scale 6.0 \
--save-path ./results \
--ulysses-degree 8 \
--ring-degree 1
```
The number of GPUs equals the product of `--ulysses-degree` and `--ring-degree.` Feel free to adjust these parallel configurations to optimize performance.
xDiT divides the video in the latent space based on either the height or width dimension, depending on which one is divisible by the number of GPUs. Enabling `ALLOW_RESIZE_FOR_SP=1` permits xDiT to slightly adjust the input image size so that the height or width is divisible by the number of GPUs.
The speedup of parallel inference is shown as follows.
<p align="center">
<table align="center">
<thead>
<tr>
<th colspan="4">Latency (Sec) for 1280x720 (129 frames 50 steps) on 8xGPU</th>
</tr>
<tr>
<th>1</th>
<th>2</th>
<th>4</th>
<th>8</th>
</tr>
</thead>
<tbody>
<tr>
<th>1904.08</th>
<th>934.09 (2.04x)</th>
<th>514.08 (3.70x)</th>
<th>337.58 (5.64x)</th>
</tr>
</tbody>
</table>
</p>
## 🔗 BibTeX
If you find [HunyuanVideo](https://arxiv.org/abs/2412.03603) useful for your research and applications, please cite using this BibTeX:
```BibTeX
@article{kong2024hunyuanvideo,
title={Hunyuanvideo: A systematic framework for large video generative models},
author={Kong, Weijie and Tian, Qi and Zhang, Zijian and Min, Rox and Dai, Zuozhuo and Zhou, Jin and Xiong, Jiangfeng and Li, Xin and Wu, Bo and Zhang, Jianwei and others},
journal={arXiv preprint arXiv:2412.03603},
year={2024}
}
```
## 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.
<!-- ## Github Star History
<a href="https://star-history.com/#Tencent-Hunyuan/HunyuanVideo&Date">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Tencent-Hunyuan/HunyuanVideo&type=Date&theme=dark" />
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</a> -->
README_zh.md 0 → 100644
View file @ e2ec88b4
<!-- ## **HunyuanVideo** -->
[English Version](./README.md)
<p align="center">
<img src="./assets/logo.png" height=100>
</p>
# **HunyuanVideo-I2V** 🌅
<div align="center">
<a href="https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-I2V 代码&message=Github&color=blue"></a> &ensp;
<a href="https://aivideo.hunyuan.tencent.com"><img src="https://img.shields.io/static/v1?label=项目主页&message=Web&color=green"></a> &ensp;
<a href="https://video.hunyuan.tencent.com"><img src="https://img.shields.io/static/v1?label=在线体验&message=Web&color=green"></a>
</div>
<div align="center">
<a href="https://arxiv.org/abs/2412.03603"><img src="https://img.shields.io/static/v1?label=技术报告&message=Arxiv&color=red"></a> &ensp;
<a href="https://aivideo.hunyuan.tencent.com/hunyuanvideo.pdf"><img src="https://img.shields.io/static/v1?label=技术报告&message=高清版本 (~350M)&color=red"></a>
</div>
<div align="center">
<a href="https://huggingface.co/tencent/HunyuanVideo-I2V"><img src="https://img.shields.io/static/v1?label=HunyuanVideo-I2V&message=HuggingFace&color=yellow"></a> &ensp;
</div>
<p align="center">
👋 加入我们的<a href="assets/WECHAT.md" target="_blank">微信社区</a>和<a href="https://discord.gg/tv7FkG4Nwf" target="_blank">Discord</a>
</p>
-----
继我们成功开源[HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo)后，我们很高兴推出[HunyuanVideo-I2V](https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V)，一个新的图像到视频生成框架，加速开源社区的探索！
本仓库包含官方PyTorch模型定义、预训练权重及推理/采样代码。更多可视化效果请访问[项目主页](https://aivideo.hunyuan.tencent.com)。同时，我们发布了LoRA训练代码，用于定制化特效生成，可创建更有趣的视频效果。
> [**HunyuanVideo: A Systematic Framework For Large Video Generation Model**](https://arxiv.org/abs/2412.03603)
## 🔥🔥🔥 最新动态
* 2025年03月13日: 🚀 开源 HunyuanVideo-I2V 多卡并行推理代码，由[xDiT](https://github.com/xdit-project/xDiT)提供。
* 2025年03月11日: 🎉 在修复bug后我们更新了lora的训练和推理代码。
* 2025年03月07日: 🔥 我们已经修复了开源版本中导致ID变化的bug，请尝试[HunyuanVideo-I2V](https://huggingface.co/tencent/HunyuanVideo-I2V)新的模型权重，以确保首帧完全视觉一致性，并制作更高质量的视频。
* 2025年03月06日: 👋 发布HunyuanVideo-I2V的推理代码和模型权重。[下载地址](https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V/blob/main/ckpts/README.md)
## 🎥 演示
### I2V 示例
<div align="center">
<video src="https://github.com/user-attachments/assets/442afb73-3092-454f-bc46-02361c285930" width="80%" poster="./assets/video_poster.jpg"> </video>
<p>联合创作 @D-aiY 导演 丁一</p>
</div>
### 首帧一致性示例
| 参考图 | 生成视频 |
|:----------------:|:----------------:|
| <img src="https://github.com/user-attachments/assets/83e7a097-ffca-40db-9c72-be01d866aa7d" width="80%"> | <video src="https://github.com/user-attachments/assets/f81d2c88-bb1a-43f8-b40f-1ccc20774563" width="100%"> </video> |
｜ <img src="https://github.com/user-attachments/assets/c385a11f-60c7-4919-b0f1-bc5e715f673c" width="80%"> | <video src="https://github.com/user-attachments/assets/0c29ede9-0481-4d40-9c67-a4b6267fdc2d" width="100%"> </video> |
｜ <img src="https://github.com/user-attachments/assets/5763f5eb-0be5-4b36-866a-5199e31c5802" width="95%"> | <video src="https://github.com/user-attachments/assets/a8da0a1b-ba7d-45a4-a901-5d213ceaf50e" width="100%"> </video> |
### 定制化I2V LoRA效果演示
| 特效类型 | 参考图像 | 生成视频 |
|:---------------:|:--------------------------------:|:----------------:|
| 头发生长 | <img src="./assets/demo/i2v_lora/imgs/hair_growth.png" width="40%"> | <video src="https://github.com/user-attachments/assets/06b998ae-bbde-4c1f-96cb-a25a9197d5cb" width="100%"> </video> |
| 拥抱 | <img src="./assets/demo/i2v_lora/imgs/embrace.png" width="40%"> | <video src="https://github.com/user-attachments/assets/f8c99eb1-2a43-489a-ba02-6bd50a6dd260" width="100%" > </video> |
## 🧩 社区贡献
如果您的项目中有开发或使用 HunyuanVideo-I2V，欢迎告知我们。
- ComfyUI (支持FP8推理、V2V和IP2V生成): [ComfyUI-HunyuanVideoWrapper](https://github.com/kijai/ComfyUI-HunyuanVideoWrapper) by [Kijai](https://github.com/kijai)
- HunyuanVideoGP (针对低性能GPU的版本): [HunyuanVideoGP](https://github.com/deepbeepmeep/HunyuanVideoGP) by [DeepBeepMeep](https://github.com/deepbeepmeep)
- xDiT 兼容性改进: [兼容性改进](https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V/issues/36#issuecomment-2728068507) by [pftq](https://github.com/pftq) and [xibosun](https://github.com/xibosun)
## 📑 开源计划
- HunyuanVideo-I2V（图像到视频模型）
- [x] 推理代码
- [x] 模型权重
- [x] ComfyUI支持
- [x] LoRA训练脚本
- [x] 多GPU序列并行推理（提升多卡推理速度）
- [ ] Diffusers集成
## 目录
- [**HunyuanVideo-I2V** 🌅](#hunyuanvideo-i2v-)
- [🔥🔥🔥 最新动态](#-最新动态)
- [🎥 演示](#-演示)
- [I2V 示例](#i2v-示例)
- [首帧一致性示例](#首帧一致性示例)
- [定制化I2V LoRA效果演示](#定制化i2v-lora效果演示)
- [🧩 社区贡献](#-社区贡献)
- [📑 开源计划](#-开源计划)
- [目录](#目录)
- [**HunyuanVideo-I2V 整体架构**](#hunyuanvideo-i2v-整体架构)
- [📜 运行要求](#-运行要求)
- [🛠️ 依赖安装](#️-依赖安装)
- [Linux 安装指引](#linux-安装指引)
- [🧱 下载预训练模型](#-下载预训练模型)
- [🔑 单 GPU 推理](#-单-gpu-推理)
- [使用图生视频模型的建议](#使用图生视频模型的建议)
- [使用命令行](#使用命令行)
- [更多配置](#更多配置)
- [🎉自定义 I2V LoRA 效果训练](#自定义-i2v-lora-效果训练)
- [要求](#要求)
- [训练环境](#训练环境)
- [训练数据构建](#训练数据构建)
- [开始训练](#开始训练)
- [推理](#推理)
- [🚀 使用 xDiT 实现多卡并行推理](#-使用-xdit-实现多卡并行推理)
- [使用命令行](#使用命令行-1)
- [🔗 BibTeX](#-bibtex)
- [致谢](#致谢)
---
## **HunyuanVideo-I2V 整体架构**
基于[HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo)强大的视频生成能力，我们将其扩展至图像到视频生成任务。为此，我们采用首帧Token替换方案，有效重构并融合参考图像信息至视频生成流程中。
由于我们使用预训练的Decoder-Only架构多模态大语言模型（MLLM）作为文本编码器，可用于显著增强模型对输入图像语义内容的理解能力，并实现图像与文本描述信息的深度融合。具体而言，输入图像经MLLM处理后生成语义图像tokens，这些tokens与视频隐空间tokens拼接，实现跨模态的全注意力计算。
我们的系统架构旨在最大化图像与文本模态的协同效应，确保从静态图像生成连贯的视频内容。该集成不仅提升了生成视频的保真度，还增强了模型对复杂多模态输入的解析能力。整体架构如下图所示：
<p align="center">
<img src="./assets/backbone.png" height=300>
</p>
## 📜 运行要求
下表展示了运行HunyuanVideo-I2V模型（batch size=1）生成视频的硬件要求：
| 模型 | 分辨率 | GPU显存峰值 |
|:---------------:|:-------:|:-----------:|
| HunyuanVideo-I2V | 720p | 60GB |
* 需配备支持CUDA的NVIDIA GPU
* 测试环境为单卡80G GPU
* **最低要求**: 720p分辨率需至少60GB显存
* **推荐配置**: 建议使用80GB显存GPU以获得更佳生成质量
* 测试操作系统：Linux
## 🛠️ 依赖安装
首先克隆仓库：
```shell
git clone https://github.com/Tencent-Hunyuan/HunyuanVideo-I2V
cd HunyuanVideo-I2V
```
### Linux 安装指引
我们推荐使用 CUDA 12.4 或 11.8 的版本。
Conda 的安装指南可以参考[这里](https://docs.anaconda.com/free/miniconda/index.html)。
```shell
# 1. 创建conda环境
conda create -n HunyuanVideo-I2V python==3.11.9
# 2. 激活环境
conda activate HunyuanVideo-I2V
# 3. 通过conda安装PyTorch等依赖
# CUDA 12.4版本
conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=12.4 -c pytorch -c nvidia
# 4. 安装pip依赖
python -m pip install -r requirements.txt
# 5. 安装flash attention v2加速（需CUDA 11.8及以上）
python -m pip install ninja
python -m pip install git+https://github.com/Dao-AILab/flash-attention.git@v2.6.3
# 6. Install xDiT for parallel inference (It is recommended to use torch 2.4.0 and flash-attn 2.6.3)
python -m pip install xfuser==0.4.0
```
如果在特定 GPU 型号上遭遇 float point exception(core dump) 问题，可尝试以下方案修复：
```shell
# 确保已安装CUDA 12.4、CUBLAS>=12.4.5.8和CUDNN>=9.00（或直接使用我们的CUDA 12 docker镜像）
pip install nvidia-cublas-cu12==12.4.5.8
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/nvidia/cublas/lib/
```
另外，我们提供了一个预构建的 Docker 镜像，可以使用如下命令进行拉取和运行。
```shell
# CUDA 12.4镜像（避免浮点异常）
docker pull hunyuanvideo/hunyuanvideo-i2v:cuda12
docker run -itd --gpus all --init --net=host --uts=host --ipc=host --name hunyuanvideo-i2v --security-opt=seccomp=unconfined --ulimit=stack=67108864 --ulimit=memlock=-1 --privileged hunyuanvideo/hunyuanvideo-i2v:cuda12
```
## 🧱 下载预训练模型
下载预训练模型的详细信息请参见 [here](ckpts/README.md)。
## 🔑 单 GPU 推理
类似于 [HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo)，HunyuanVideo-I2V 支持高分辨率视频生成，分辨率最高可达 720P，视频长度最高可达 129 帧（5 秒）。
### 使用图生视频模型的建议
- **使用简短的提示**：为了有效地引导模型的生成，请保持提示简短且直截了当。
- **包含关键元素**：一个结构良好的提示应包括：
- **主体**：指定视频的主要焦点。
- **动作**：描述正在发生的运动或活动。
- **背景（可选）**：设置视频的场景。
- **镜头（可选）**：指示视角或视点。
- **避免过于详细的提示**：冗长或高度详细的提示可能会导致视频输出中出现不必要的转场。
### 使用命令行
如果想生成更**稳定**的视频，可以设置`--i2v-stability`和`--flow-shift 7.0`。执行命令如下
```bash
cd HunyuanVideo-I2V
python3 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 7.0 \
--seed 0 \
--embedded-cfg-scale 6.0 \
--use-cpu-offload \
--save-path ./results
```
如果想要生成更**高动态**的视频，可以**取消设置**`--i2v-stability`和`--flow-shift 17.0`。执行命令如下
```bash
cd HunyuanVideo-I2V
python3 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 17.0 \
--embedded-cfg-scale 6.0 \
--seed 0 \
--use-cpu-offload \
--save-path ./results
```
<!-- # ### 运行gradio服务
# ```bash
# python3 gradio_server.py --flow-reverse
# # set SERVER_NAME and SERVER_PORT manually
# # SERVER_NAME=0.0.0.0 SERVER_PORT=8081 python3 gradio_server.py --flow-reverse
# ``` -->
### 更多配置
我们列出了一些常用的配置以方便使用：
| 参数 | 默认 | 描述 |
|:----------------------:|:-----------------------------:|:----------------------------------------------------------------------------------------------------------------------------------:|
| `--prompt` | None | 用于视频生成的文本提示。 |
| `--model` | HYVideo-T/2-cfgdistill | 这里我们使用 HYVideo-T/2 用于 I2V，HYVideo-T/2-cfgdistill 用于 T2V 模式。 |
| `--i2v-mode` | False | 是否开启 I2V 模式。 |
| `--i2v-image-path` | ./assets/demo/i2v/imgs/0.png | 用于视频生成的参考图像。 |
| `--i2v-resolution` | 720p | 生成视频的分辨率。 |
| `--i2v-stability` | False | 是否使用稳定模式进行 i2v 推理。 |
| `--video-length` | 129 | 生成视频的长度。 |
| `--infer-steps` | 50 | 采样步骤的数量。 |
| `--flow-shift` | 7.0 | 流匹配调度器的偏移因子。我们建议开启`--i2v-stability`时设置为 7，以获得更稳定的视频；关闭`--i2v-stability`时设置为 17，以获得更动态的视频 |
| `--flow-reverse` | False | 如果反转，从 t=1 学习/采样到 t=0。 |
| `--seed` | None | 生成视频的随机种子，如果为 None，则初始化一个随机种子。 |
| `--use-cpu-offload` | False | 使用 CPU 卸载模型加载以节省更多内存，对于高分辨率视频生成是必要的。 |
| `--save-path` | ./results | 保存生成视频的路径。 |
## 🎉自定义 I2V LoRA 效果训练
### 要求
下表显示了训练 HunyuanVideo-I2V lora 模型（批量大小 = 1）以生成视频的要求：
| 模型 | 分辨率 | GPU 峰值内存 |
|:----------------:|:----------:|:---------------:|
| HunyuanVideo-I2V | 360p | 79GB |
* 需要支持 CUDA 的 NVIDIA GPU。
* 该模型在单个 80G GPU 上进行了测试。
* **最低要求**: 生成 360p 视频所需的最小 GPU 内存为 79GB。
* **推荐**: 建议使用 80GB 内存的 GPU 以获得更好的生成质量。
* 测试操作系统: Linux
* 注意: 您可以使用 360p 数据进行训练，并直接推理 720p 视频
### 训练环境
```
pip install -r requirements.txt
```
### 训练数据构建
提示描述：触发词直接写在视频说明中。建议使用短语或简短句子。
例如，AI 头发生长效果（触发词）：rapid_hair_growth, The hair of the characters in the video is growing rapidly. + 原始提示
准备好训练视频和提示对后，参考 [这里](hyvideo/hyvae_extract/README.md) 进行训练数据构建。
### 开始训练
```
cd HunyuanVideo-I2V
sh scripts/run_train_image2video_lora.sh
```
我们列出了一些训练特定配置以方便使用：
| 参数 | 默认 | 描述 |
|:----------------:|:-------------------------------------------------------------:|:-----------------------------------------------------------:|
| `SAVE_BASE` | . | 保存实验结果的根路径。 |
| `EXP_NAME` | i2v_lora | 保存实验结果的路径后缀。 |
| `DATA_JSONS_DIR` | ./assets/demo/i2v_lora/train_dataset/processed_data/json_path | 由 hyvideo/hyvae_extract/start.sh 生成的数据 jsons 目录。 |
| `CHIEF_IP` | 127.0.0.1 | 主节点 IP 地址。 |
### 推理
```bash
cd HunyuanVideo-I2V
python3 sample_image2video.py \
--model HYVideo-T/2 \
--prompt "Two people hugged tightly, In the video, two people are standing apart from each other. They then move closer to each other and begin to hug tightly. The hug is very affectionate, with the two people holding each other tightly and looking into each other's eyes. The interaction is very emotional and heartwarming, with the two people expressing their love and affection for each other." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v_lora/imgs/embrace.png \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 5.0 \
--embedded-cfg-scale 6.0 \
--seed 0 \
--use-cpu-offload \
--save-path ./results \
--use-lora \
--lora-scale 1.0 \
--lora-path ./ckpts/hunyuan-video-i2v-720p/lora/embrace_kohaya_weights.safetensors
```
我们列出了一些 LoRA 特定配置以方便使用：
| 参数 | 默认 | 描述 |
|:-------------------:|:-------:|:----------------------------:|
| `--use-lora` | None | 是否开启 LoRA 模式。 |
| `--lora-scale` | 1.0 | LoRA 模型的融合比例。 |
| `--lora-path` | "" | LoRA 模型的权重路径。 |
## 🚀 使用 xDiT 实现多卡并行推理
[xDiT](https://github.com/xdit-project/xDiT) 是一个针对多 GPU 集群的扩展推理引擎，用于扩展 Transformers（DiTs）。
它成功为各种 DiT 模型（包括 mochi-1、CogVideoX、Flux.1、SD3 等）提供了低延迟的并行推理解决方案。该存储库采用了 [Unified Sequence Parallelism (USP)](https://arxiv.org/abs/2405.07719) API 用于混元视频模型的并行推理。
### 使用命令行
例如，可用如下命令使用8张GPU卡完成推理
```bash
cd HunyuanVideo-I2V
ALLOW_RESIZE_FOR_SP=1 torchrun --nproc_per_node=8 \
sample_image2video.py \
--model HYVideo-T/2 \
--prompt "An Asian man with short hair in black tactical uniform and white clothes waves a firework stick." \
--i2v-mode \
--i2v-image-path ./assets/demo/i2v/imgs/0.jpg \
--i2v-resolution 720p \
--i2v-stability \
--infer-steps 50 \
--video-length 129 \
--flow-reverse \
--flow-shift 7.0 \
--seed 0 \
--embedded-cfg-scale 6.0 \
--save-path ./results \
--ulysses-degree 8 \
--ring-degree 1
```
GPU 的数量等于 `--ulysses-degree` 与 `--ring-degree` 的乘积。您可以更改这些并行配置以获得最佳性能。
xDiT 在潜在空间中根据高度或宽度维度对视频进行分割，具体取决于哪个维度可以被 GPU 数量整除。设置 `ALLOW_RESIZE_FOR_SP=1` 允许 xDiT 稍微调整输入图像的大小，以使高度或宽度可以被GPU数量整除。
xDiT 并行推理加速如下表所示。
<p align="center">
<table align="center">
<thead>
<tr>
<th colspan="4">在 8xGPU上生成1280x720 (129 帧 50 步)的时耗 (秒) </th>
</tr>
<tr>
<th>1</th>
<th>2</th>
<th>4</th>
<th>8</th>
</tr>
</thead>
<tbody>
<tr>
<th>1904.08</th>
<th>934.09 (2.04x)</th>
<th>514.08 (3.70x)</th>
<th>337.58 (5.64x)</th>
</tr>
</tbody>
</table>
</p>
## 🔗 BibTeX
如果您发现 [HunyuanVideo](https://arxiv.org/abs/2412.03603) 对您的研究和应用有所帮助，请使用以下 BibTeX 引用：
```BibTeX
@article{kong2024hunyuanvideo,
title={Hunyuanvideo: A systematic framework for large video generative models},
author={Kong, Weijie and Tian, Qi and Zhang, Zijian and Min, Rox and Dai, Zuozhuo and Zhou, Jin and Xiong, Jiangfeng and Li, Xin and Wu, Bo and Zhang, Jianwei and others},
journal={arXiv preprint arXiv:2412.03603},
year={2024}
}
```
## 致谢
HunyuanVideo 的开源离不开诸多开源工作，这里我们特别感谢 [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) 的开源工作和探索。另外，我们也感谢腾讯混元多模态团队对 HunyuanVideo 适配多种文本编码器的支持。
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<!-- # I2V + lora
## 训练环境
```
pip install -r requirements.txt
```
## 训练数据构造
prompt说明: trigger词直接写在video caption里面，建议用短语或短句, 比如
比如ai生发特效：rapid_hair_growth, The hair of the characters in the video is growing rapidly. + 原始prompt
有了训练视频和prompt对后，训练数据构造参考[这里](hyvideo/hyvae_extract/README.md)。
## 启动训练
```
sh scripts/run_train_image2video_lora.sh
# 重要参数
# --data-jsons-path 训练数据路径
# --model 训练底模
# --output-dir lora存放位置
```
## 推理
```
sh scripts/run_sample_image2video.sh
# 重要参数
# --prompt 推理prompt
# --i2v-image-path 输入图片位置
# --lora-path 待加载lora位置
# --lora-scale lora加载权重
``` -->
assets/WECHAT.md 0 → 100644
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<div align="center">
<img src=wechat.jpg width="60%"/>
<p> 扫码关注混元系列工作，加入「 Hunyuan Video 交流群」 </p>
<p> Scan the QR code to join the "Hunyuan Discussion Group" </p>
</div>
assets/demo/i2v_lora/train_dataset/meta_data.json 0 → 100644
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{
"video_path": "/path/to/video.mp4",
"raw_caption": {
"long caption": "Detailed description text of the video"
}
}
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assets/demo/i2v_lora/train_dataset/meta_file.list 0 → 100644
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./assets/demo/i2v_lora/train_dataset/meta_data.json
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assets/demo/i2v_lora/train_dataset/processed_data/json_path/embrace.json 0 → 100644
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{"video_id": "embrace", "latent_shape": [1, 16, 33, 52, 68], "video_path": ".", "prompt": "Two people hugged tightly, In the video, two people are standing apart from each other. They then move closer to each other and begin to hug tightly. The hug is very affectionate, with the two people holding each other tightly and looking into each other's eyes. The interaction is very emotional and heartwarming, with the two people expressing their love and affection for each other.", "npy_save_path": "./assets/demo/i2v_lora/train_dataset/processed_data/embrace.npy"}
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