Update Weights

.gitattributes

@@ -33,3 +33,16 @@ 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
+google/umt5-xxl/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+xlm-roberta-large/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/comp_effic.png filter=lfs diff=lfs merge=lfs -text
+assets/data_for_diff_stage.jpg filter=lfs diff=lfs merge=lfs -text
+assets/i2v_res.png filter=lfs diff=lfs merge=lfs -text
+assets/logo.png filter=lfs diff=lfs merge=lfs -text
+assets/t2v_res.jpg filter=lfs diff=lfs merge=lfs -text
+assets/vben_vs_sota.png filter=lfs diff=lfs merge=lfs -text
+assets/vben_vs_sota_t2i.jpg filter=lfs diff=lfs merge=lfs -text
+assets/video_dit_arch.jpg filter=lfs diff=lfs merge=lfs -text
+assets/video_vae_res.jpg filter=lfs diff=lfs merge=lfs -text
+examples/i2v_input.JPG filter=lfs diff=lfs merge=lfs -text
+assets/vben_1.3b_vs_sota.png filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

@@ -0,0 +1,201 @@
+                                 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.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "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:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) 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
+
+      (d) 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.
+
+   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.
+
+   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.
+
+   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
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -1,3 +1,249 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-to-video
+library_name: diffusers
+tags:
+- video
+- video-generation
+---
+
+# Wan-Fun
+
+😊 Welcome!
+
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-yellow)](https://huggingface.co/spaces/alibaba-pai/Wan2.1-Fun-1.3B-InP)
+
+[![Github](https://img.shields.io/badge/🎬%20Code-Github-blue)](https://github.com/aigc-apps/VideoX-Fun)
+
+[English](./README_en.md) | [简体中文](./README.md)
+
+# 目录
+- [目录](#目录)
+- [模型地址](#模型地址)
+- [视频作品](#视频作品)
+- [快速启动](#快速启动)
+- [如何使用](#如何使用)
+- [参考文献](#参考文献)
+- [许可证](#许可证)
+
+# 模型地址
+V1.0:
+| 名称 | 存储空间 | Hugging Face | Model Scope | 描述 |
+|--|--|--|--|--|
+| Wan2.1-Fun-1.3B-InP | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | Wan2.1-Fun-1.3B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
+| Wan2.1-Fun-14B-InP | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | Wan2.1-Fun-14B文图生视频权重，以多分辨率训练，支持首尾图预测。 |
+| Wan2.1-Fun-1.3B-Control | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control)| Wan2.1-Fun-1.3B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
+| Wan2.1-Fun-14B-Control | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control)| Wan2.1-Fun-14B视频控制权重，支持不同的控制条件，如Canny、Depth、Pose、MLSD等，同时支持使用轨迹控制。支持多分辨率（512，768，1024）的视频预测，支持多分辨率（512，768，1024）的视频预测，以81帧、每秒16帧进行训练，支持多语言预测 |
+
+# 视频作品
+
+### Wan2.1-Fun-14B-InP && Wan2.1-Fun-1.3B-InP
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/bd72a276-e60e-4b5d-86c1-d0f67e7425b9" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/cb7aef09-52c2-4973-80b4-b2fb63425044" width="100%" controls autoplay loop></video>
+     </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/4e10d491-f1cf-4b08-a7c5-1e01e5418140" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/f7e363a9-be09-4b72-bccf-cce9c9ebeb9b" width="100%" controls autoplay loop></video>
+     </td>
+  </tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/28f3e720-8acc-4f22-a5d0-ec1c571e9466" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/fb6e4cb9-270d-47cd-8501-caf8f3e91b5c" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/989a4644-e33b-4f0c-b68e-2ff6ba37ac7e" width="100%" controls autoplay loop></video>
+     </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/9c604fa7-8657-49d1-8066-b5bb198b28b6" width="100%" controls autoplay loop></video>
+     </td>
+  </tr>
+</table>
+
+### Wan2.1-Fun-14B-Control && Wan2.1-Fun-1.3B-Control
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/f35602c4-9f0a-4105-9762-1e3a88abbac6" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/8b0f0e87-f1be-4915-bb35-2d53c852333e" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/972012c1-772b-427a-bce6-ba8b39edcfad" width="100%" controls autoplay loop></video>
+     </td>
+  <tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/53002ce2-dd18-4d4f-8135-b6f68364cabd" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/a1a07cf8-d86d-4cd2-831f-18a6c1ceee1d" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/3224804f-342d-4947-918d-d9fec8e3d273" width="100%" controls autoplay loop></video>
+     </td>
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/c6c5d557-9772-483e-ae47-863d8a26db4a" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/af617971-597c-4be4-beb5-f9e8aaca2d14" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/8411151e-f491-4264-8368-7fc3c5a6992b" width="100%" controls autoplay loop></video>
+     </td>
+  </tr>
+</table>
+
+# 快速启动
+### 1. 云使用: AliyunDSW/Docker
+#### a. 通过阿里云 DSW
+DSW 有免费 GPU 时间，用户可申请一次，申请后3个月内有效。
+
+阿里云在[Freetier](https://free.aliyun.com/?product=9602825&crowd=enterprise&spm=5176.28055625.J_5831864660.1.e939154aRgha4e&scm=20140722.M_9974135.P_110.MO_1806-ID_9974135-MID_9974135-CID_30683-ST_8512-V_1)提供免费GPU时间，获取并在阿里云PAI-DSW中使用，5分钟内即可启动CogVideoX-Fun。
+
+[![DSW Notebook](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/dsw.png)](https://gallery.pai-ml.com/#/preview/deepLearning/cv/cogvideox_fun)
+
+#### b. 通过ComfyUI
+我们的ComfyUI界面如下，具体查看[ComfyUI README](comfyui/README.md)。
+![workflow graph](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/cogvideoxfunv1_workflow_i2v.jpg)
+
+#### c. 通过docker
+使用docker的情况下，请保证机器中已经正确安装显卡驱动与CUDA环境，然后以此执行以下命令：
+
+```
+# pull image
+docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# enter image
+docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# clone code
+git clone https://github.com/aigc-apps/CogVideoX-Fun.git
+
+# enter CogVideoX-Fun's dir
+cd CogVideoX-Fun
+
+# download weights
+mkdir models/Diffusion_Transformer
+mkdir models/Personalized_Model
+
+# Please use the hugginface link or modelscope link to download the model.
+# CogVideoX-Fun
+# https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP
+# https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-InP
+
+# Wan
+# https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP
+# https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP
+```
+
+### 2. 本地安装: 环境检查/下载/安装
+#### a. 环境检查
+我们已验证该库可在以下环境中执行：
+
+Windows 的详细信息：
+- 操作系统 Windows 10
+- python: python3.10 & python3.11
+- pytorch: torch2.2.0
+- CUDA: 11.8 & 12.1
+- CUDNN: 8+
+- GPU： Nvidia-3060 12G & Nvidia-3090 24G
+
+Linux 的详细信息：
+- 操作系统 Ubuntu 20.04, CentOS
+- python: python3.10 & python3.11
+- pytorch: torch2.2.0
+- CUDA: 11.8 & 12.1
+- CUDNN: 8+
+- GPU：Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G
+
+我们需要大约 60GB 的可用磁盘空间，请检查！
+
+#### b. 权重放置
+我们最好将[权重](#model-zoo)按照指定路径进行放置：
+
+```
+📦 models/
+├── 📂 Diffusion_Transformer/
+│   ├── 📂 CogVideoX-Fun-V1.1-2b-InP/
+│   ├── 📂 CogVideoX-Fun-V1.1-5b-InP/
+│   ├── 📂 Wan2.1-Fun-14B-InP
+│   └── 📂 Wan2.1-Fun-1.3B-InP/
+├── 📂 Personalized_Model/
+│   └── your trained trainformer model / your trained lora model (for UI load)
+```
+
+# 如何使用
+
+<h3 id="video-gen">1. 生成 </h3>
+
+#### a、显存节省方案
+由于Wan2.1的参数非常大，我们需要考虑显存节省方案，以节省显存适应消费级显卡。我们给每个预测文件都提供了GPU_memory_mode，可以在model_cpu_offload，model_cpu_offload_and_qfloat8，sequential_cpu_offload中进行选择。该方案同样适用于CogVideoX-Fun的生成。
+
+- model_cpu_offload代表整个模型在使用后会进入cpu，可以节省部分显存。
+- model_cpu_offload_and_qfloat8代表整个模型在使用后会进入cpu，并且对transformer模型进行了float8的量化，可以节省更多的显存。
+- sequential_cpu_offload代表模型的每一层在使用后会进入cpu，速度较慢，节省大量显存。
+
+qfloat8会部分降低模型的性能，但可以节省更多的显存。如果显存足够，推荐使用model_cpu_offload。
+
+#### b、通过comfyui
+具体查看[ComfyUI README](comfyui/README.md)。
+
+#### c、运行python文件
+- 步骤1：下载对应[权重](#model-zoo)放入models文件夹。
+- 步骤2：根据不同的权重与预测目标使用不同的文件进行预测。当前该库支持CogVideoX-Fun、Wan2.1和Wan2.1-Fun，在examples文件夹下用文件夹名以区分，不同模型支持的功能不同，请视具体情况予以区分。以CogVideoX-Fun为例。
+  - 文生视频：
+    - 使用examples/cogvideox_fun/predict_t2v.py文件中修改prompt、neg_prompt、guidance_scale和seed。
+    - 而后运行examples/cogvideox_fun/predict_t2v.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos文件夹中。
+  - 图生视频：
+    - 使用examples/cogvideox_fun/predict_i2v.py文件中修改validation_image_start、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
+    - validation_image_start是视频的开始图片，validation_image_end是视频的结尾图片。
+    - 而后运行examples/cogvideox_fun/predict_i2v.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos_i2v文件夹中。
+  - 视频生视频：
+    - 使用examples/cogvideox_fun/predict_v2v.py文件中修改validation_video、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
+    - validation_video是视频生视频的参考视频。您可以使用以下视频运行演示：[演示视频](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/play_guitar.mp4)
+    - 而后运行examples/cogvideox_fun/predict_v2v.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos_v2v文件夹中。
+  - 普通控制生视频（Canny、Pose、Depth等）：
+    - 使用examples/cogvideox_fun/predict_v2v_control.py文件中修改control_video、validation_image_end、prompt、neg_prompt、guidance_scale和seed。
+    - control_video是控制生视频的控制视频，是使用Canny、Pose、Depth等算子提取后的视频。您可以使用以下视频运行演示：[演示视频](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1.1/pose.mp4)
+    - 而后运行examples/cogvideox_fun/predict_v2v_control.py文件，等待生成结果，结果保存在samples/cogvideox-fun-videos_v2v_control文件夹中。
+- 步骤3：如果想结合自己训练的其他backbone与Lora，则看情况修改examples/{model_name}/predict_t2v.py中的examples/{model_name}/predict_i2v.py和lora_path。
+
+#### d、通过ui界面
+
+webui支持文生视频、图生视频、视频生视频和普通控制生视频（Canny、Pose、Depth等）。当前该库支持CogVideoX-Fun、Wan2.1和Wan2.1-Fun，在examples文件夹下用文件夹名以区分，不同模型支持的功能不同，请视具体情况予以区分。以CogVideoX-Fun为例。
+
+- 步骤1：下载对应[权重](#model-zoo)放入models文件夹。
+- 步骤2：运行examples/cogvideox_fun/app.py文件，进入gradio页面。
+- 步骤3：根据页面选择生成模型，填入prompt、neg_prompt、guidance_scale和seed等，点击生成，等待生成结果，结果保存在sample文件夹中。
+
+# 参考文献
+- CogVideo: https://github.com/THUDM/CogVideo/
+- EasyAnimate: https://github.com/aigc-apps/EasyAnimate
+- Wan2.1: https://github.com/Wan-Video/Wan2.1/
+
+# 许可证
+本项目采用 [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
+

README_en.md

@@ -0,0 +1,249 @@
+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-to-video
+library_name: diffusers
+tags:
+- video
+- video-generation
+---
+
+# Wan-Fun
+
+😊 Welcome!
+
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-yellow)](https://huggingface.co/spaces/alibaba-pai/Wan2.1-Fun-1.3B-InP)
+
+[![Github](https://img.shields.io/badge/🎬%20Code-Github-blue)](https://github.com/aigc-apps/VideoX-Fun)
+
+[English](./README_en.md) | [简体中文](./README.md)
+
+# Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Model zoo](#model-zoo)
+- [Video Result](#video-result)
+- [Quick Start](#quick-start)
+- [How to use](#how-to-use)
+- [Reference](#reference)
+- [License](#license)
+
+# Model zoo
+V1.0:
+| Name | Storage Space | Hugging Face | Model Scope | Description |
+|--|--|--|--|--|
+| Wan2.1-Fun-1.3B-InP | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-InP) | Wan2.1-Fun-1.3B text-to-video weights, trained at multiple resolutions, supporting start and end frame prediction. |
+| Wan2.1-Fun-14B-InP | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP) | Wan2.1-Fun-14B text-to-video weights, trained at multiple resolutions, supporting start and end frame prediction. |
+| Wan2.1-Fun-1.3B-Control | 19.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-1.3B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-1.3B-Control) | Wan2.1-Fun-1.3B video control weights, supporting various control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports multi-resolution (512, 768, 1024) video prediction at 81 frames, trained at 16 frames per second, with multilingual prediction support. |
+| Wan2.1-Fun-14B-Control | 47.0 GB | [🤗Link](https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-Control) | [😄Link](https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-Control) | Wan2.1-Fun-14B video control weights, supporting various control conditions such as Canny, Depth, Pose, MLSD, etc., and trajectory control. Supports multi-resolution (512, 768, 1024) video prediction at 81 frames, trained at 16 frames per second, with multilingual prediction support. |
+
+# Video Result
+
+### Wan2.1-Fun-14B-InP && Wan2.1-Fun-1.3B-InP
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/bd72a276-e60e-4b5d-86c1-d0f67e7425b9" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/cb7aef09-52c2-4973-80b4-b2fb63425044" width="100%" controls autoplay loop></video>
+     </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/4e10d491-f1cf-4b08-a7c5-1e01e5418140" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/f7e363a9-be09-4b72-bccf-cce9c9ebeb9b" width="100%" controls autoplay loop></video>
+     </td>
+  </tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/28f3e720-8acc-4f22-a5d0-ec1c571e9466" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/fb6e4cb9-270d-47cd-8501-caf8f3e91b5c" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/989a4644-e33b-4f0c-b68e-2ff6ba37ac7e" width="100%" controls autoplay loop></video>
+     </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/9c604fa7-8657-49d1-8066-b5bb198b28b6" width="100%" controls autoplay loop></video>
+     </td>
+  </tr>
+</table>
+
+### Wan2.1-Fun-14B-Control && Wan2.1-Fun-1.3B-Control
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/f35602c4-9f0a-4105-9762-1e3a88abbac6" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/8b0f0e87-f1be-4915-bb35-2d53c852333e" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/972012c1-772b-427a-bce6-ba8b39edcfad" width="100%" controls autoplay loop></video>
+     </td>
+  <tr>
+</table>
+
+<table border="0" style="width: 100%; text-align: left; margin-top: 20px;">
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/53002ce2-dd18-4d4f-8135-b6f68364cabd" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/a1a07cf8-d86d-4cd2-831f-18a6c1ceee1d" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/3224804f-342d-4947-918d-d9fec8e3d273" width="100%" controls autoplay loop></video>
+     </td>
+  <tr>
+      <td>
+          <video src="https://github.com/user-attachments/assets/c6c5d557-9772-483e-ae47-863d8a26db4a" width="100%" controls autoplay loop></video>
+      </td>
+      <td>
+          <video src="https://github.com/user-attachments/assets/af617971-597c-4be4-beb5-f9e8aaca2d14" width="100%" controls autoplay loop></video>
+      </td>
+       <td>
+          <video src="https://github.com/user-attachments/assets/8411151e-f491-4264-8368-7fc3c5a6992b" width="100%" controls autoplay loop></video>
+     </td>
+  </tr>
+</table>
+
+# Quick Start
+### 1. Cloud usage: AliyunDSW/Docker
+#### a. From AliyunDSW
+DSW has free GPU time, which can be applied once by a user and is valid for 3 months after applying.
+
+Aliyun provide free GPU time in [Freetier](https://free.aliyun.com/?product=9602825&crowd=enterprise&spm=5176.28055625.J_5831864660.1.e939154aRgha4e&scm=20140722.M_9974135.P_110.MO_1806-ID_9974135-MID_9974135-CID_30683-ST_8512-V_1), get it and use in Aliyun PAI-DSW to start CogVideoX-Fun within 5min!
+
+[![DSW Notebook](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/easyanimate/asset/dsw.png)](https://gallery.pai-ml.com/#/preview/deepLearning/cv/cogvideox_fun)
+
+#### b. From ComfyUI
+Our ComfyUI is as follows, please refer to [ComfyUI README](comfyui/README.md) for details.
+![workflow graph](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/cogvideoxfunv1_workflow_i2v.jpg)
+
+#### c. From docker
+If you are using docker, please make sure that the graphics card driver and CUDA environment have been installed correctly in your machine.
+
+Then execute the following commands in this way:
+
+```
+# pull image
+docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# enter image
+docker run -it -p 7860:7860 --network host --gpus all --security-opt seccomp:unconfined --shm-size 200g mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easycv/torch_cuda:cogvideox_fun
+
+# clone code
+git clone https://github.com/aigc-apps/CogVideoX-Fun.git
+
+# enter CogVideoX-Fun's dir
+cd CogVideoX-Fun
+
+# download weights
+mkdir models/Diffusion_Transformer
+mkdir models/Personalized_Model
+
+# Please use the hugginface link or modelscope link to download the model.
+# CogVideoX-Fun
+# https://huggingface.co/alibaba-pai/CogVideoX-Fun-V1.1-5b-InP
+# https://modelscope.cn/models/PAI/CogVideoX-Fun-V1.1-5b-InP
+
+# Wan
+# https://huggingface.co/alibaba-pai/Wan2.1-Fun-14B-InP
+# https://modelscope.cn/models/PAI/Wan2.1-Fun-14B-InP
+```
+
+### 2. Local install: Environment Check/Downloading/Installation
+#### a. Environment Check
+We have verified this repo execution on the following environment:
+
+The detailed of Windows:
+- OS: Windows 10
+- python: python3.10 & python3.11
+- pytorch: torch2.2.0
+- CUDA: 11.8 & 12.1
+- CUDNN: 8+
+- GPU： Nvidia-3060 12G & Nvidia-3090 24G
+
+The detailed of Linux:
+- OS: Ubuntu 20.04, CentOS
+- python: python3.10 & python3.11
+- pytorch: torch2.2.0
+- CUDA: 11.8 & 12.1
+- CUDNN: 8+
+- GPU：Nvidia-V100 16G & Nvidia-A10 24G & Nvidia-A100 40G & Nvidia-A100 80G
+
+We need about 60GB available on disk (for saving weights), please check!
+
+#### b. Weights
+We'd better place the [weights](#model-zoo) along the specified path:
+
+```
+📦 models/
+├── 📂 Diffusion_Transformer/
+│   ├── 📂 CogVideoX-Fun-V1.1-2b-InP/
+│   ├── 📂 CogVideoX-Fun-V1.1-5b-InP/
+│   ├── 📂 Wan2.1-Fun-14B-InP
+│   └── 📂 Wan2.1-Fun-1.3B-InP/
+├── 📂 Personalized_Model/
+│   └── your trained trainformer model / your trained lora model (for UI load)
+```
+
+# How to Use
+
+<h3 id="video-gen">1. Generation</h3>
+
+#### a. GPU Memory Optimization
+Since Wan2.1 has a very large number of parameters, we need to consider memory optimization strategies to adapt to consumer-grade GPUs. We provide `GPU_memory_mode` for each prediction file, allowing you to choose between `model_cpu_offload`, `model_cpu_offload_and_qfloat8`, and `sequential_cpu_offload`. This solution is also applicable to CogVideoX-Fun generation.
+
+- `model_cpu_offload`: The entire model is moved to the CPU after use, saving some GPU memory.
+- `model_cpu_offload_and_qfloat8`: The entire model is moved to the CPU after use, and the transformer model is quantized to float8, saving more GPU memory.
+- `sequential_cpu_offload`: Each layer of the model is moved to the CPU after use. It is slower but saves a significant amount of GPU memory.
+
+`qfloat8` may slightly reduce model performance but saves more GPU memory. If you have sufficient GPU memory, it is recommended to use `model_cpu_offload`.
+
+#### b. Using ComfyUI
+For details, refer to [ComfyUI README](comfyui/README.md).
+
+#### c. Running Python Files
+- **Step 1**: Download the corresponding [weights](#model-zoo) and place them in the `models` folder.
+- **Step 2**: Use different files for prediction based on the weights and prediction goals. This library currently supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun. Different models are distinguished by folder names under the `examples` folder, and their supported features vary. Use them accordingly. Below is an example using CogVideoX-Fun:
+  - **Text-to-Video**:
+    - Modify `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_t2v.py`.
+    - Run the file `examples/cogvideox_fun/predict_t2v.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos`.
+  - **Image-to-Video**:
+    - Modify `validation_image_start`, `validation_image_end`, `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_i2v.py`.
+    - `validation_image_start` is the starting image of the video, and `validation_image_end` is the ending image of the video.
+    - Run the file `examples/cogvideox_fun/predict_i2v.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos_i2v`.
+  - **Video-to-Video**:
+    - Modify `validation_video`, `validation_image_end`, `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_v2v.py`.
+    - `validation_video` is the reference video for video-to-video generation. You can use the following demo video: [Demo Video](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1/play_guitar.mp4).
+    - Run the file `examples/cogvideox_fun/predict_v2v.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos_v2v`.
+  - **Controlled Video Generation (Canny, Pose, Depth, etc.)**:
+    - Modify `control_video`, `validation_image_end`, `prompt`, `neg_prompt`, `guidance_scale`, and `seed` in the file `examples/cogvideox_fun/predict_v2v_control.py`.
+    - `control_video` is the control video extracted using operators such as Canny, Pose, or Depth. You can use the following demo video: [Demo Video](https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/cogvideox_fun/asset/v1.1/pose.mp4).
+    - Run the file `examples/cogvideox_fun/predict_v2v_control.py` and wait for the results. The generated videos will be saved in the folder `samples/cogvideox-fun-videos_v2v_control`.
+- **Step 3**: If you want to integrate other backbones or Loras trained by yourself, modify `lora_path` and relevant paths in `examples/{model_name}/predict_t2v.py` or `examples/{model_name}/predict_i2v.py` as needed.
+
+#### d. Using the Web UI
+The web UI supports text-to-video, image-to-video, video-to-video, and controlled video generation (Canny, Pose, Depth, etc.). This library currently supports CogVideoX-Fun, Wan2.1, and Wan2.1-Fun. Different models are distinguished by folder names under the `examples` folder, and their supported features vary. Use them accordingly. Below is an example using CogVideoX-Fun:
+
+- **Step 1**: Download the corresponding [weights](#model-zoo) and place them in the `models` folder.
+- **Step 2**: Run the file `examples/cogvideox_fun/app.py` to access the Gradio interface.
+- **Step 3**: Select the generation model on the page, fill in `prompt`, `neg_prompt`, `guidance_scale`, and `seed`, click "Generate," and wait for the results. The generated videos will be saved in the `sample` folder.
+
+# Reference
+- CogVideo: https://github.com/THUDM/CogVideo/
+- EasyAnimate: https://github.com/aigc-apps/EasyAnimate
+- Wan2.1: https://github.com/Wan-Video/Wan2.1/
+
+# License
+This project is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).

Wan2.1_VAE.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38071ab59bd94681c686fa51d75a1968f64e470262043be31f7a094e442fd981
+size 507609880

config.json

@@ -0,0 +1,29 @@
+{
+  "_class_name": "WanTransformer3DModel",
+  "_diffusers_version": "0.31.0",
+  "cross_attn_norm": true,
+  "dim": 5120,
+  "eps": 1e-06,
+  "ffn_dim": 13824,
+  "freq_dim": 256,
+  "hidden_size": 2048,
+  "in_channels": 36,
+  "in_dim": 48,
+  "model_type": "i2v",
+  "num_heads": 40,
+  "num_layers": 40,
+  "out_dim": 16,
+  "patch_size": [
+    1,
+    2,
+    2
+  ],
+  "qk_norm": true,
+  "text_dim": 4096,
+  "text_len": 512,
+  "window_size": [
+    -1,
+    -1
+  ],
+  "add_ref_conv": true
+}

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "others", "allow_remote": true}

models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:628c9998b613391f193eb67ff68da9667d75f492911e4eb3decf23460a158c38
+size 4772359047

models_t5_umt5-xxl-enc-bf16.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7cace0da2b446bbbbc57d031ab6cf163a3d59b366da94e5afe36745b746fd81d
+size 11361920418