for release

LICENSE

@@ -1,71 +1,201 @@
-The CogVideoX License
-
-1. Definitions
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-“Licensor” means the CogVideoX Model Team that distributes its Software.
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-“Software” means the CogVideoX model parameters made available under this license.
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-2. License Grant
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-Under the terms and conditions of this license, the licensor hereby grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty-free copyright license. The intellectual property rights of the generated content belong to the user to the extent permitted by applicable local laws.
-This license allows you to freely use all open-source models in this repository for academic research. Users who wish to use the models for commercial purposes must register and obtain a basic commercial license in https://open.bigmodel.cn/mla/form .
-Users who have registered and obtained the basic commercial license can use the models for commercial activities for free, but must comply with all terms and conditions of this license. Additionally, the number of service users (visits) for your commercial activities must not exceed 1 million visits per month.
-If the number of service users (visits) for your commercial activities exceeds 1 million visits per month, you need to contact our business team to obtain more commercial licenses.
-The above copyright statement and this license statement should be included in all copies or significant portions of this software.
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-EXCEPT TO THE EXTENT PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER BASED IN TORT, NEGLIGENCE, CONTRACT, LIABILITY, OR OTHERWISE WILL ANY LICENSOR BE LIABLE TO YOU FOR ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES, OR ANY OTHER COMMERCIAL LOSSES, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
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-This license shall be governed and construed in accordance with the laws of People’s Republic of China. Any dispute arising from or in connection with this License shall be submitted to Haidian District People's Court in Beijing.
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-Note that the license is subject to update to a more comprehensive version.  For any questions related to the license and copyright, please contact us at [email protected].
-
-1. 定义
-
-“许可方”是指分发其软件的 CogVideoX 模型团队。
-
-“软件”是指根据本许可提供的 CogVideoX 模型参数。
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-本许可允许您免费使用本仓库中的所有开源模型进行学术研究。对于希望将模型用于商业目的的用户，需在 https://open.bigmodel.cn/mla/form 完成登记并获得基础商用授权。
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README.md

@@ -20,6 +20,7 @@ inference: false
   </div>
   <p align="center">
   <a href="https://huggingface.co/THUDM/CogVideoX-2b/blob/main/README_zh.md">📄 中文阅读</a> | 
+  <a href="https://huggingface.co/spaces/THUDM/CogVideoX-2B-Space">🤗 Huggingface Space</a> |
   <a href="https://github.com/THUDM/CogVideo">🌐 Github </a> | 
   <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
 </p>
@@ -85,22 +86,91 @@ inference: false
 
 ## Model Introduction
 
-CogVideoX is an open-source video generation model that shares the same origins as [清影](https://chatglm.cn/video).
-The table below provides a list of the video generation models we currently offer, along with their basic information.
+CogVideoX is an open-source version of the video generation model originating from [QingYing](https://chatglm.cn/video?fr=osm_cogvideo). The table below displays the list of video generation models we currently offer, along with their foundational information.
+
+<table style="border-collapse: collapse; width: 100%;">
+  <tr>
+    <th style="text-align: center;">Model Name</th>
+    <th style="text-align: center;">CogVideoX-2B (This Repository)</th>
+    <th style="text-align: center;">CogVideoX-5B </th>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Model Description</td>
+    <td style="text-align: center;">Entry-level model, balancing compatibility. Low cost for running and secondary development.</td>
+    <td style="text-align: center;">Larger model with higher video generation quality and better visual effects.</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Inference Precision</td>
+    <td style="text-align: center;"><b>FP16* (Recommended)</b>, BF16, FP32, FP8*, INT8, no support for INT4</td>
+    <td style="text-align: center;"><b>BF16 (Recommended)</b>, FP16, FP32, FP8*, INT8, no support for INT4</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Single GPU VRAM Consumption</td>
+    <td style="text-align: center;">FP16: 18GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>12.5GB* using diffusers</b><br><b>INT8: 7.8GB* using diffusers</b></td>
+    <td style="text-align: center;">BF16: 26GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>20.7GB* using diffusers</b><br><b>INT8: 11.4GB* using diffusers</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Multi-GPU Inference VRAM Consumption</td>
+    <td style="text-align: center;"><b>FP16: 10GB* using diffusers</b></td>
+    <td style="text-align: center;"><b>BF16: 15GB* using diffusers</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Inference Speed<br>(Step = 50, FP/BF16)</td>
+    <td style="text-align: center;">Single A100: ~90 seconds<br>Single H100: ~45 seconds</td>
+    <td style="text-align: center;">Single A100: ~180 seconds<br>Single H100: ~90 seconds</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Fine-tuning Precision</td>
+    <td style="text-align: center;"><b>FP16</b></td>
+    <td style="text-align: center;"><b>BF16</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Fine-tuning VRAM Consumption (per GPU)</td>
+    <td style="text-align: center;">47 GB (bs=1, LORA)<br> 61 GB (bs=2, LORA)<br> 62GB (bs=1, SFT)</td>
+    <td style="text-align: center;">63 GB (bs=1, LORA)<br> 80 GB (bs=2, LORA)<br> 75GB (bs=1, SFT)</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Prompt Language</td>
+    <td colspan="2" style="text-align: center;">English*</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Prompt Length Limit</td>
+    <td colspan="2" style="text-align: center;">226 Tokens</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Video Length</td>
+    <td colspan="2" style="text-align: center;">6 Seconds</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Frame Rate</td>
+    <td colspan="2" style="text-align: center;">8 Frames per Second</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Video Resolution</td>
+    <td colspan="2" style="text-align: center;">720 x 480, no support for other resolutions (including fine-tuning)</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Positional Encoding</td>
+    <td style="text-align: center;">3d_sincos_pos_embed</td>
+    <td style="text-align: center;">3d_rope_pos_embed</td>
+  </tr>
+</table>
+
+**Data Explanation**
+
++ When testing with the diffusers library, the `enable_model_cpu_offload()` option and `pipe.vae.enable_tiling()` optimization were enabled. This solution has not been tested on devices other than **NVIDIA A100 / H100**. Typically, this solution is adaptable to all devices above the **NVIDIA Ampere architecture**. If the optimization is disabled, memory usage will increase significantly, with peak memory being about 3 times the table value.
++ The CogVideoX-2B model was trained using `FP16` precision, so it is recommended to use `FP16` for inference.
++ For multi-GPU inference, the `enable_model_cpu_offload()` optimization needs to be disabled.
++ Using the INT8 model will lead to reduced inference speed. This is done to allow low-memory GPUs to perform inference while maintaining minimal video quality loss, though the inference speed will be significantly reduced.
++ Inference speed tests also used the memory optimization mentioned above. Without memory optimization, inference speed increases by approximately 10%. Only the `diffusers` version of the model supports quantization.
++ The model only supports English input; other languages can be translated to English for refinement by large models.
+
+**Note**
+
++ Using [SAT](https://github.com/THUDM/SwissArmyTransformer)  for inference and fine-tuning of SAT version
+  models. Feel free to visit our GitHub for more information.
 
-| Model Name                                | CogVideoX-2B                         | 
-|-------------------------------------------|--------------------------------------|
-| Prompt Language                           | English                              | 
-| Single GPU  Inference (FP16)              | 23.9GB                               | 
-| Multi GPUs Inference (FP16)               | 20GB minimum per GPU using diffusers |
-| GPU Memory Required for Fine-tuning(bs=1) | 40GB                                 |
-| Prompt Max  Length                        | 226 Tokens                           |
-| Video Length                              | 6 seconds                            | 
-| Frames Per Second                         | 8 frames                             | 
-| Resolution                                | 720 * 480                            |
-| Quantized Inference                       | Not Supported                        |          
 
-**Note** Using [SAT](https://github.com/THUDM/SwissArmyTransformer) model cost 18GB for inference. Check our github.
 
 ## Quick Start 🤗
 
@@ -112,7 +182,11 @@ optimizations and conversions to get a better experience.**
 1. Install the required dependencies
 
 ```shell
-pip install --upgrade opencv-python transformers diffusers # Must using diffusers>=0.30.0
+# diffusers>=0.30.1
+# transformers>=0.44.0
+# accelerate>=0.33.0 (suggest install from source)
+# imageio-ffmpeg>=0.5.1
+pip install --upgrade transformers accelerate diffusers imageio-ffmpeg 
 ```
 
 2. Run the code
@@ -130,30 +204,20 @@ pipe = CogVideoXPipeline.from_pretrained(
 )
 
 pipe.enable_model_cpu_offload()
+pipe.vae.enable_tiling()
 
-prompt_embeds, _ = pipe.encode_prompt(
+video = pipe(
     prompt=prompt,
-    do_classifier_free_guidance=True,
     num_videos_per_prompt=1,
-    max_sequence_length=226,
-    device="cuda",
-    dtype=torch.float16,
-)
-
-video = pipe(
     num_inference_steps=50,
+    num_frames=49,
     guidance_scale=6,
-    prompt_embeds=prompt_embeds,
+    generator=torch.Generator(device="cuda").manual_seed(42),
 ).frames[0]
 
 export_to_video(video, "output.mp4", fps=8)
 ```
 
-**Using a single A100 GPU, generating a video with the above configuration takes approximately 90 seconds**
-
-If the generated model appears “all green” and not viewable in the default MAC player, it is a normal phenomenon (due to
-OpenCV saving video issues). Simply use a different player to view the video.
-
 ## Explore the Model
 
 Welcome to our [github](https://github.com/THUDM/CogVideo), where you will find:
@@ -163,11 +227,21 @@ Welcome to our [github](https://github.com/THUDM/CogVideo), where you will find:
 3. Reasoning and fine-tuning of SAT version models, and even pre-release.
 4. Project update log dynamics, more interactive opportunities.
 5. CogVideoX toolchain to help you better use the model.
+6. INT8 model inference code support.
 
 ## Model License
 
-This model is released under the [CogVideoX LICENSE](LICENSE).
+The CogVideoX-2B model (including its corresponding Transformers module and VAE module) is released under the [Apache 2.0 License](LICENSE).
+
+The CogVideoX-5B model (Transformers module) is released under the [CogVideoX LICENSE](https://huggingface.co/THUDM/CogVideoX-5b/blob/main/LICENSE).
 
 ## Citation
 
-The technical report is still being written, stay tuned.
+```
+@article{yang2024cogvideox,
+  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
+  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
+  journal={arXiv preprint arXiv:2408.06072},
+  year={2024}
+}
+```

README_zh.md

@@ -6,8 +6,9 @@
   </div>
   <p align="center">
   <a href="https://huggingface.co/THUDM/CogVideoX-2b/blob/main/README.md">📄 Read in English</a> | 
-  <a href="https://github.com/THUDM/CogVideo">🌐 Github(包含PDF论文)</a> | 
-  <a href="#">📜 arxiv (即将发布) </a>
+  <a href="https://huggingface.co/spaces/THUDM/CogVideoX-2B-Space">🤗 Huggingface Space</a> |
+  <a href="https://github.com/THUDM/CogVideo">🌐 Github </a> | 
+  <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
 </p>
 
 ## 作品案例
@@ -71,21 +72,91 @@
 
 ## 模型介绍
 
-CogVideoX是 [清影](https://chatglm.cn/video) 同源的开源版本视频生成模型。下表展示目前我们提供的视频生成模型列表，以及相关基础信息。
-
-| 模型名                 | CogVideoX-2B                         | 
-|---------------------|--------------------------------------|
-| 提示词语言               | English                              | 
-| 单GPU推理 (FP-16) 显存消耗 | 23.9GB                               | 
-| 多GPU推理 (FP-16) 显存消耗 | 20GB minimum per GPU using diffusers |                                                                                                            
-| 微调显存消耗 (bs=1)       | 42GB                                 |
-| 提示词长度上限             | 226 Tokens                           |
-| 视频长度                | 6 seconds                            | 
-| 帧率（每秒）              | 8 frames                             | 
-| 视频分辨率               | 720 * 480                            |
-| 量化推理                | 不支持                                  |          
-
-**Note** 使用 [SAT](https://github.com/THUDM/SwissArmyTransformer) 推理SAT版本模型仅需18G显存。欢迎前往我们的github查看。
+CogVideoX是 [清影](https://chatglm.cn/video?fr=osm_cogvideo) 同源的开源版本视频生成模型。下表展示目前我们提供的视频生成模型列表，以及相关基础信息。
+
+<table  style="border-collapse: collapse; width: 100%;">
+  <tr>
+    <th style="text-align: center;">模型名</th>
+    <th style="text-align: center;">CogVideoX-2B (本仓库)</th>
+    <th style="text-align: center;">CogVideoX-5B </th>
+  </tr>
+  <tr>
+    <td style="text-align: center;">模型介绍</td>
+    <td style="text-align: center;">入门级模型，兼顾兼容性。运行，二次开发成本低。</td>
+    <td style="text-align: center;">视频生成质量更高，视觉效果更好的更大尺寸模型。</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">推理精度</td>
+    <td style="text-align: center;"><b>FP16*(推荐)</b>, BF16, FP32，FP8*，INT8，不支持INT4</td>
+    <td style="text-align: center;"><b>BF16(推荐)</b>, FP16, FP32，FP8*，INT8，不支持INT4</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">单GPU显存消耗<br></td>
+    <td style="text-align: center;">FP16: 18GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>12.5GB* using diffusers</b><br><b>INT8: 7.8GB* using diffusers</b></td>
+    <td style="text-align: center;">BF16: 26GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> / <b>20.7GB* using diffusers</b><br><b>INT8: 11.4GB* using diffusers</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">多GPU推理显存消耗</td>
+    <td style="text-align: center;"><b>FP16: 10GB* using diffusers</b><br></td>
+    <td style="text-align: center;"><b>BF16: 15GB* using diffusers</b><br></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">推理速度<br>(Step = 50, FP/BF16)</td>
+    <td style="text-align: center;">单卡A100: ~90秒<br>单卡H100: ~45秒</td>
+    <td style="text-align: center;">单卡A100: ~180秒<br>单卡H100: ~90秒</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">微调精度</td>
+    <td style="text-align: center;"><b>FP16</b></td>
+    <td style="text-align: center;"><b>BF16</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">微调显存消耗(每卡)</td>
+    <td style="text-align: center;">47 GB (bs=1, LORA)<br> 61 GB (bs=2, LORA)<br> 62GB (bs=1, SFT)</td>
+    <td style="text-align: center;">63 GB (bs=1, LORA)<br> 80 GB (bs=2, LORA)<br> 75GB (bs=1, SFT)<br></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">提示词语言</td>
+    <td colspan="2" style="text-align: center;">English*</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">提示词长度上限</td>
+    <td colspan="2" style="text-align: center;">226 Tokens</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">视频长度</td>
+    <td colspan="2" style="text-align: center;">6 秒</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">帧率</td>
+    <td colspan="2" style="text-align: center;">8 帧 / 秒 </td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">视频分辨率</td>
+    <td colspan="2" style="text-align: center;">720 * 480，不支持其他分辨率(含微调)</td>
+  </tr>
+    <tr>
+    <td style="text-align: center;">位置编码</td>
+    <td style="text-align: center;">3d_sincos_pos_embed</td>
+    <td style="text-align: center;">3d_rope_pos_embed<br></td>
+  </tr>
+</table>
+
+**数据解释**
+
++ 使用 diffusers 库进行测试时，启用了 `enable_model_cpu_offload()` 选项 和 `pipe.vae.enable_tiling()` 优化，该方案未测试在非
+  **NVIDIA A100 / H100** 外的设备上的实际显存 / 内存占用。通常，该方案可以适配于所有 **NVIDIA 安培架构**
+  以上的设备。若关闭优化，显存占用会成倍增加，峰值显存约为表格的3倍。
++ 多GPU推理时，需要关闭 `enable_model_cpu_offload()` 优化。
++ 使用 INT8 模型会导致推理速度降低，此举是为了满足显存较低的显卡能正常推理并保持较少的视频质量损失，推理速度大幅降低。
++ 2B 模型采用 `FP16` 精度训练， 5B模型采用 `BF16` 精度训练。我们推荐使用模型训练的精度进行推理。
++ `FP8` 精度必须在`NVIDIA H100` 及以上的设备上使用，需要源代码安装`torch`,`torchao`,`diffusers`,`accelerate` python包，推荐使用 `CUDA 12.4`。
++ 推理速度测试同样采用了上述显存优化方案，不采用显存优化的情况下，推理速度提升约10%。 只有`diffusers`版本模型支持量化。
++ 模型仅支持英语输入，其他语言可以通过大模型润色时翻译为英语。
+
+**提醒**
+
++ 使用 [SAT](https://github.com/THUDM/SwissArmyTransformer) 推理和微调SAT版本模型。欢迎前往我们的github查看。
 
 ## 快速上手 🤗
 
@@ -96,10 +167,14 @@ CogVideoX是 [清影](https://chatglm.cn/video) 同源的开源版本视频生
 1. 安装对应的依赖
 
 ```shell
-pip install --upgrade opencv-python transformers accelerate diffusers # Must using diffusers>=0.30.0
+# diffusers>=0.30.1
+# transformers>=0.44.0
+# accelerate>=0.33.0 (suggest install from source)
+# imageio-ffmpeg>=0.5.1
+pip install --upgrade transformers accelerate diffusers imageio-ffmpeg 
 ```
 
-2. 运行代码
+2. 运行代码 (BF16 / FP16)
 
 ```python
 import torch
@@ -114,29 +189,20 @@ pipe = CogVideoXPipeline.from_pretrained(
 )
 
 pipe.enable_model_cpu_offload()
+pipe.vae.enable_tiling()
 
-prompt_embeds, _ = pipe.encode_prompt(
+video = pipe(
     prompt=prompt,
-    do_classifier_free_guidance=True,
     num_videos_per_prompt=1,
-    max_sequence_length=226,
-    device="cuda",
-    dtype=torch.float16,
-)
-
-video = pipe(
     num_inference_steps=50,
+    num_frames=49,
     guidance_scale=6,
-    prompt_embeds=prompt_embeds,
+    generator=torch.Generator(device="cuda").manual_seed(42),
 ).frames[0]
 
 export_to_video(video, "output.mp4", fps=8)
 ```
 
-**使用单卡A100按照上述配置生成一次视频大约需要90秒**。
-
-如果您生成的模型在 MAC 默认播放器上表现为 "全绿" 无法正常观看，属于正常现象 (OpenCV保存视频问题)，仅需更换一个播放器观看。
-
 ## 深入研究
 
 欢迎进入我们的 [github](https://github.com/THUDM/CogVideo)，你将获得：
@@ -146,11 +212,22 @@ export_to_video(video, "output.mp4", fps=8)
 3. SAT版本模型进行推理和微调，甚至预发布。
 4. 项目更新日志动态，更多互动机会。
 5. CogVideoX 工具链，帮助您更好的使用模型。
+6. INT8 模型推理代码。
 
 ## 模型协议
 
-该模型根据 [CogVideoX LICENSE](LICENSE) 许可证发布。
+CogVideoX-2B 模型 (包括其对应的Transformers模块，VAE模块) 根据 [Apache 2.0 License](LICENSE) 许可证发布。
+
+CogVideoX-5B 模型 (Transformers 模块) 根据 [CogVideoX LICENSE](https://huggingface.co/THUDM/CogVideoX-5b/blob/main/LICENSE)
+许可证发布。
 
 ## 引用
 
-技术报告仍在撰写中，敬请期待
+```
+@article{yang2024cogvideox,
+  title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer},
+  author={Yang, Zhuoyi and Teng, Jiayan and Zheng, Wendi and Ding, Ming and Huang, Shiyu and Xu, Jiazheng and Yang, Yuanming and Hong, Wenyi and Zhang, Xiaohan and Feng, Guanyu and others},
+  journal={arXiv preprint arXiv:2408.06072},
+  year={2024}
+}
+```

text_encoder/config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/share/home/zyx/Models/t5-v1_1-xxl_1",
+  "_name_or_path": "google/t5-v1_1-xxl_1",
   "architectures": [
     "T5EncoderModel"
   ],