init

LICENSE

@@ -0,0 +1,71 @@
+The CogVideoX License
+
+1. Definitions
+
+“Licensor” means the CogVideoX Model Team that distributes its Software.
+
+“Software” means the CogVideoX model parameters made available under this license.
+
+2. License Grant
+
+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.
+
+3. Restriction
+
+You will not use, copy, modify, merge, publish, distribute, reproduce, or create derivative works of the Software, in whole or in part, for any military, or illegal purposes.
+
+You will not use the Software for any act that may undermine China's national security and national unity, harm the public interest of society, or infringe upon the rights and interests of human beings.
+
+4. Disclaimer
+
+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.
+
+5. Limitation of Liability
+
+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.
+
+6. Dispute Resolution
+
+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.
+
+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。
+
+2. 许可授予
+
+根据本许可的条款和条件，许可方特此授予您非排他性、全球性、不可转让、不可再许可、可撤销、免版税的版权许可。生成内容的知识产权所属，可根据适用当地法律的规定，在法律允许的范围内由用户享有生成内容的知识产权或其他权利。
+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。
+
+经过登记并获得基础商用授权的用户可以免费使用本模型进行商业活动，但必须遵守本许可的所有条款和条件。
+在本许可证下，您的商业活动的服务用户数量（访问量）不得超过100万人次访问 / 每月。如果超过，您需要与我们的商业团队联系以获得更多的商业许可。
+上述版权声明和本许可声明应包含在本软件的所有副本或重要部分中。
+
+3.限制
+
+您不得出于任何军事或非法目的使用、复制、修改、合并、发布、分发、复制或创建本软件的全部或部分衍生作品。
+
+您不得利用本软件从事任何危害国家安全和国家统一、危害社会公共利益、侵犯人身权益的行为。
+
+4.免责声明
+
+本软件“按原样”提供，不提供任何明示或暗示的保证，包括但不限于对适销性、特定用途的适用性和非侵权性的保证。
+在任何情况下，作者或版权持有人均不对任何索赔、损害或其他责任负责，无论是在合同诉讼、侵权行为还是其他方面，由软件或软件的使用或其他交易引起、由软件引起或与之相关 软件。
+
+5. 责任限制
+
+除适用��律禁止的范围外，在任何情况下且根据任何法律理论，无论是基于侵权行为、疏忽、合同、责任或其他原因，任何许可方均不对您承担任何直接、间接、特殊、偶然、示范性、 或间接损害，或任何其他商业损失，即使许可人已被告知此类损害的可能性。
+
+6.争议解决
+
+本许可受中华人民共和国法律管辖并按其解释。 因本许可引起的或与本许可有关的任何争议应提交北京市海淀区人民法院。
+
+请注意，许可证可能会更新到更全面的版本。 有关许可和版权的任何问题，请通过 [email protected] 与我们联系。

README.md

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+---
+license: other
+license_link: https://huggingface.co/THUDM/CogVideoX-5b/blob/main/LICENSE
+language:
+  - en
+tags:
+  - cogvideox
+  - video-generation
+  - thudm
+  - text-to-video
+inference: false
+---
+
+# CogVideoX-5B
+
+<p style="text-align: center;">
+  <div align="center">
+  <img src=https://github.com/THUDM/CogVideo/raw/main/resources/logo.svg width="50%"/>
+  </div>
+  <p align="center">
+  <a href="README_zh.md">📄 中文阅读</a> | 
+  <a href="https://github.com/THUDM/CogVideo">🌐 Github </a> | 
+  <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
+</p>
+
+## Demo Show
+
+## 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.
+
+<table style="border-collapse: collapse; width: 100%;">
+  <tr>
+    <th style="text-align: center;">Model Name</th>
+    <th style="text-align: center;">CogVideoX-2B</th>
+    <th style="text-align: center;">CogVideoX-5B (Current Repository)</th>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Model Description</td>
+    <td style="text-align: center;">Entry-level model, balancing compatibility, operation, and low cost of secondary development.</td>
+    <td style="text-align: center;">A larger model that generates higher-quality videos with better visual effects.</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Inference Precision</td>
+    <td style="text-align: center;">FP16, FP32, does not support BF16.<br>Can run on mainstream NVIDIA GPUs.</td>
+    <td style="text-align: center;">BF16, FP32, does not support FP16.<br>Requires NVIDIA GPUs with Ampere architecture or higher (e.g., A100, H100).</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Inference Speed<br>(Single A100, Step = 50)</td>
+    <td style="text-align: center;">FP16: ~90 s</td>
+    <td style="text-align: center;">BF16: ~200 s</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Single GPU Inference Memory Usage</td>
+    <td style="text-align: center;">18GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a><br>12GB (with tied VAE) using diffusers<br>24GB (without tied VAE) using diffusers</td>
+    <td style="text-align: center;">26GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a><br>21GB (with tied VAE) using diffusers<br>41GB (without tied VAE) using diffusers</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Multi-GPU Inference Memory Usage</td>
+    <td colspan="2" style="text-align: center;">20GB minimum per GPU using diffusers</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Fine-tuning Memory Usage (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)<br></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;">Maximum Prompt Length</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/second</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Video Resolution</td>
+    <td colspan="2" style="text-align: center;">720 x 480, does not support other resolutions (including fine-tuning)</td>
+  </tr>
+</table>
+
+**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.
+
+## Quick Start 🤗
+
+This model supports deployment using the huggingface diffusers library. You can deploy it by following these steps.
+
+**We recommend that you visit our [GitHub](https://github.com/THUDM/CogVideo) and check out the relevant prompt
+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
+```
+
+2. Run the code
+
+```python
+import gc
+import torch
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
+
+prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
+
+pipe = CogVideoXPipeline.from_pretrained(
+    "THUDM/CogVideoX-5b",
+    torch_dtype=torch.bfloat16
+)
+
+pipe.enable_model_cpu_offload()
+
+gc.collect()
+torch.cuda.empty_cache()
+torch.cuda.reset_accumulated_memory_stats()
+torch.cuda.reset_peak_memory_stats()
+pipe.vae.enable_tiling()
+
+video = pipe(
+    prompt=prompt,
+    num_videos_per_prompt=1,
+    num_inference_steps=50,
+    num_frames=49,
+    guidance_scale=6,
+    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 200 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:
+
+1. More detailed technical details and code explanation.
+2. Optimization and conversion of prompt words.
+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.
+
+## Model License
+
+This model is released under the [CogVideoX LICENSE](LICENSE).
+
+## Citation
+
+```
+@article{yang2024cogvideox,
+      title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer}, 
+      author={Zhuoyi Yang and Jiayan Teng and Wendi Zheng and Ming Ding and Shiyu Huang and JiaZheng Xu and Yuanming Yang and Xiaohan Zhang and Xiaotao Gu and Guanyu Feng and Da Yin and Wenyi Hong and Weihan Wang and Yean Cheng and Yuxuan Zhang and Ting Liu and Bin Xu and Yuxiao Dong and Jie Tang},
+      year={2024},
+}
+```

README_zh.md

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+# CogVideoX-5B
+
+<p style="text-align: center;">
+  <div align="center">
+  <img src=https://github.com/THUDM/CogVideo/raw/main/resources/logo.svg width="50%"/>
+  </div>
+  <p align="center">
+  <a href="README.md">📄 Read in English</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
+
+## 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。
+
+<table  style="border-collapse: collapse; width: 100%;">
+  <tr>
+    <th style="text-align: center;">模型名</th>
+    <th style="text-align: center;">CogVideoX-2B</th>
+    <th style="text-align: center;">CogVideoX-5B (当前仓库)</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;">FP16, FP32, 不支持 BF16。<br> 可以在主流的NVIDIA显卡上运行</td>
+    <td style="text-align: center;">BF16, FP32, 不支持 FP16。 <br> 需要在安培架构以上(例如 A100，H100) 的 NVIDIA显卡运行</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">推理速度<br>(Single A100, Step = 50)</td>
+    <td style="text-align: center;">FP16: ~90 s</td>
+    <td style="text-align: center;">BF16: ~200 s</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">单GPU推理显存消耗</td>
+    <td style="text-align: center;">18GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a><br>12GB (with tied VAE) using diffusers<br>24GB (without tied VAE) using diffusers</td>
+    <td style="text-align: center;">26GB using <a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a><br>21GB (with tied VAE) using diffusers<br>41GB (without tied VAE) using diffusers</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">多GPU推理显存消耗</td>
+    <td colspan="2" style="text-align: center;">20GB minimum per GPU using diffusers</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 seconds</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>
+</table>
+
+**Note** 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 🤗
+
+本模型已经支持使用 huggingface 的 diffusers 库进行部署，你可以按照以下步骤进行部署。
+
+**我们推荐您进入我们的 [github](https://github.com/THUDM/CogVideo) 并查看相关的提示词优化和转换，以获得更好的体验。**
+
+1. 安装对应的依赖
+
+```shell
+pip install --upgrade opencv-python transformers accelerate diffusers # Must using diffusers>=0.30.0
+```
+
+2. 运行代码
+
+```python
+import gc
+import torch
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
+
+prompt = "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical atmosphere of this unique musical performance."
+
+pipe = CogVideoXPipeline.from_pretrained(
+    "THUDM/CogVideoX-5b",
+    torch_dtype=torch.bfloat16
+)
+
+pipe.enable_model_cpu_offload()
+
+gc.collect()
+torch.cuda.empty_cache()
+torch.cuda.reset_accumulated_memory_stats()
+torch.cuda.reset_peak_memory_stats()
+pipe.vae.enable_tiling()
+
+video = pipe(
+    prompt=prompt,
+    num_videos_per_prompt=1,
+    num_inference_steps=50,
+    num_frames=49,
+    guidance_scale=6,
+    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200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：
+
+1. 更加详细的技术细节介绍和代码解释。
+2. 提示词的优化和转换。
+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。
+
+## 模型协议
+
+该模型根据 [CogVideoX LICENSE](LICENSE) 许可证发布。
+
+## 引用
+
+```
+@article{yang2024cogvideox,
+      title={CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer}, 
+      author={Zhuoyi Yang and Jiayan Teng and Wendi Zheng and Ming Ding and Shiyu Huang and JiaZheng Xu and Yuanming Yang and Xiaohan Zhang and Xiaotao Gu and Guanyu Feng and Da Yin and Wenyi Hong and Weihan Wang and Yean Cheng and Yuxuan Zhang and Ting Liu and Bin Xu and Yuxiao Dong and Jie Tang},
+      year={2024},
+}
+```

configuration.json

@@ -0,0 +1 @@
+{"framework":"Pytorch","task":"text-to-video-synthesis"}

model_index.json

@@ -0,0 +1,24 @@
+{
+  "_class_name": "CogVideoXPipeline",
+  "_diffusers_version": "0.31.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "CogVideoXDDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5Tokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "CogVideoXTransformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLCogVideoX"
+  ]
+}

scheduler/scheduler_config.json

@@ -0,0 +1,18 @@
+{
+  "_class_name": "CogVideoXDDIMScheduler",
+  "_diffusers_version": "0.31.0.dev0",
+  "beta_end": 0.012,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.00085,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "num_train_timesteps": 1000,
+  "prediction_type": "v_prediction",
+  "rescale_betas_zero_snr": true,
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": true,
+  "snr_shift_scale": 1.0,
+  "steps_offset": 0,
+  "timestep_spacing": "trailing",
+  "trained_betas": null
+}

text_encoder/config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "/share/official_pretrains/hf_home/t5-v1_1-xxl",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
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tokenizer/added_tokens.json

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+}

tokenizer/special_tokens_map.json

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tokenizer/spiece.model

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+oid sha256:d60acb128cf7b7f2536e8f38a5b18a05535c9e14c7a355904270e15b0945ea86
+size 791656

tokenizer/tokenizer_config.json

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