Upload 12 files

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/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。

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/README.md

@@ -0,0 +1,280 @@
+---
+license: other
+license_link: https://huggingface.co/THUDM/CogVideoX-5b-I2V/blob/main/LICENSE
+language:
+  - en
+tags:
+  - cogvideox
+  - video-generation
+  - thudm
+  - image-to-video
+inference: false
+---
+
+# CogVideoX-5B-I2V
+
+<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="https://huggingface.co/THUDM//CogVideoX-5b-I2V/blob/main/README.md">📄 Read in English</a> | 
+  <a href="https://huggingface.co/spaces/THUDM/CogVideoX-5B-Space">🤗 Huggingface Space</a> |
+  <a href="https://github.com/THUDM/CogVideo">🌐 Github </a> | 
+  <a href="https://arxiv.org/pdf/2408.06072">📜 arxiv </a>
+</p>
+<p align="center">
+📍 Visit <a href="https://chatglm.cn/video?fr=osm_cogvideox">Qingying</a> and <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9">API Platform</a> for the commercial version of the video generation model
+</p>
+
+## Model Introduction
+
+CogVideoX is an open-source video generation model originating
+from [Qingying](https://chatglm.cn/video?fr=osm_cogvideo). The table below presents information related to the video
+generation models we offer in this version.
+
+<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</th>
+    <th style="text-align: center;">CogVideoX-5B-I2V (This Repository)</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>
+    <td style="text-align: center;">CogVideoX-5B image-to-video version.</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Inference Precision</td>
+    <td style="text-align: center;"><b>FP16*(recommended)</b>, BF16, FP32, FP8*, INT8, not supported: INT4</td>
+    <td colspan="2" style="text-align: center;"><b>BF16 (recommended)</b>, FP16, FP32, FP8*, INT8, not supported: INT4</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Single GPU Memory Usage<br></td>
+    <td style="text-align: center;"><a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> FP16: 18GB <br><b>diffusers FP16: from 4GB* </b><br><b>diffusers INT8 (torchao): from 3.6GB*</b></td>
+    <td colspan="2" style="text-align: center;"><a href="https://github.com/THUDM/SwissArmyTransformer">SAT</a> BF16: 26GB <br><b>diffusers BF16: from 5GB* </b><br><b>diffusers INT8 (torchao): from 4.4GB*</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Multi-GPU Inference Memory Usage</td>
+    <td style="text-align: center;"><b>FP16: 10GB* using diffusers</b><br></td>
+    <td colspan="2" style="text-align: center;"><b>BF16: 15GB* using diffusers</b><br></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 colspan="2" 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 colspan="2" style="text-align: center;"><b>BF16</b></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Fine-tuning Memory Usage</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>
+    <td style="text-align: center;">78 GB (bs=1, LORA)<br> 75GB (bs=1, SFT, 16GPU)<br></td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Prompt Language</td>
+    <td colspan="3" style="text-align: center;">English*</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Maximum Prompt Length</td>
+    <td colspan="3" style="text-align: center;">226 Tokens</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Video Length</td>
+    <td colspan="3" style="text-align: center;">6 Seconds</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Frame Rate</td>
+    <td colspan="3" style="text-align: center;">8 Frames / Second</td>
+  </tr>
+  <tr>
+    <td style="text-align: center;">Video Resolution</td>
+    <td colspan="3" style="text-align: center;">720 x 480, no support for other resolutions (including fine-tuning)</td>
+  </tr>
+    <tr>
+    <td style="text-align: center;">Position Embedding</td>
+    <td style="text-align: center;">3d_sincos_pos_embed</td>
+    <td style="text-align: center;">3d_rope_pos_embed</td>
+    <td style="text-align: center;">3d_rope_pos_embed + learnable_pos_embed</td>
+  </tr>
+</table>
+
+**Data Explanation**
+
++ While testing using the diffusers library, all optimizations included in the diffusers library were enabled. This
+  scheme has not been tested for actual memory usage on devices outside of **NVIDIA A100 / H100** architectures.
+  Generally, this scheme can be adapted to all **NVIDIA Ampere architecture** and above devices. If optimizations are
+  disabled, memory consumption will multiply, with peak memory usage being about 3 times the value in the table.
+  However, speed will increase by about 3-4 times. You can selectively disable some optimizations, including:
+
+```
+pipe.enable_sequential_cpu_offload()
+pipe.vae.enable_slicing()
+pipe.vae.enable_tiling()
+```
+
++ For multi-GPU inference, the `enable_sequential_cpu_offload()` optimization needs to be disabled.
++ Using INT8 models will slow down inference, which is done to accommodate lower-memory GPUs while maintaining minimal
+  video quality loss, though inference speed will significantly decrease.
++ The CogVideoX-2B model was trained in `FP16` precision, and all CogVideoX-5B models were trained in `BF16` precision.
+  We recommend using the precision in which the model was trained for inference.
++ [PytorchAO](https://github.com/pytorch/ao) and [Optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be
+  used to quantize the text encoder, transformer, and VAE modules to reduce the memory requirements of CogVideoX. This
+  allows the model to run on free T4 Colabs or GPUs with smaller memory! Also, note that TorchAO quantization is fully
+  compatible with `torch.compile`, which can significantly improve inference speed. FP8 precision must be used on
+  devices with NVIDIA H100 and above, requiring source installation of `torch`, `torchao`, `diffusers`, and `accelerate`
+  Python packages. CUDA 12.4 is recommended.
++ The inference speed tests also used the above memory optimization scheme. Without memory optimization, inference speed
+  increases by about 10%. Only the `diffusers` version of the model supports quantization.
++ The model only supports English input; other languages can be translated into English for use via large model
+  refinement.
++ The memory usage of model fine-tuning is tested in an `8 * H100` environment, and the program automatically
+  uses `Zero 2` optimization. If a specific number of GPUs is marked in the table, that number or more GPUs must be used
+  for fine-tuning.
+
+**Reminders**
+
++ Use [SAT](https://github.com/THUDM/SwissArmyTransformer) for inference and fine-tuning SAT version models. Feel free
+  to visit our GitHub for more details.
+
+## Getting Started Quickly 🤗
+
+This model supports deployment using the Hugging Face diffusers library. You can follow the steps below to get started.
+
+**We recommend that you visit our [GitHub](https://github.com/THUDM/CogVideo) to check out prompt optimization and
+conversion to get a better experience.**
+
+1. Install the required dependencies
+
+```shell
+# diffusers>=0.30.3
+# transformers>=0.44.2
+# accelerate>=0.34.0
+# imageio-ffmpeg>=0.5.1
+pip install --upgrade transformers accelerate diffusers imageio-ffmpeg 
+```
+
+2. Run the code
+
+```python
+import torch
+from diffusers import CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+
+prompt = "A little girl is riding a bicycle at high speed. Focused, detailed, realistic."
+image = load_image(image="input.jpg")
+pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+    "THUDM/CogVideoX-5b-I2V",
+    torch_dtype=torch.bfloat16
+)
+
+pipe.enable_sequential_cpu_offload()
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+
+video = pipe(
+    prompt=prompt,
+    image=image,
+    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)
+```
+
+## Quantized Inference
+
+[PytorchAO](https://github.com/pytorch/ao) and [Optimum-quanto](https://github.com/huggingface/optimum-quanto/) can be
+used to quantize the text encoder, transformer, and VAE modules to reduce CogVideoX's memory requirements. This allows
+the model to run on free T4 Colab or GPUs with lower VRAM! Also, note that TorchAO quantization is fully compatible
+with `torch.compile`, which can significantly accelerate inference.
+
+```
+# To get started, PytorchAO needs to be installed from the GitHub source and PyTorch Nightly.
+# Source and nightly installation is only required until the next release.
+
+import torch
+from diffusers import AutoencoderKLCogVideoX, CogVideoXTransformer3DModel, CogVideoXImageToVideoPipeline
+from diffusers.utils import export_to_video, load_image
+from transformers import T5EncoderModel
+from torchao.quantization import quantize_, int8_weight_only
+
+quantization = int8_weight_only
+
+text_encoder = T5EncoderModel.from_pretrained("THUDM/CogVideoX-5b-I2V", subfolder="text_encoder", torch_dtype=torch.bfloat16)
+quantize_(text_encoder, quantization())
+
+transformer = CogVideoXTransformer3DModel.from_pretrained("THUDM/CogVideoX-5b-I2V",subfolder="transformer", torch_dtype=torch.bfloat16)
+quantize_(transformer, quantization())
+
+vae = AutoencoderKLCogVideoX.from_pretrained("THUDM/CogVideoX-5b-I2V", subfolder="vae", torch_dtype=torch.bfloat16)
+quantize_(vae, quantization())
+
+# Create pipeline and run inference
+pipe = CogVideoXImageToVideoPipeline.from_pretrained(
+    "THUDM/CogVideoX-5b-I2V",
+    text_encoder=text_encoder,
+    transformer=transformer,
+    vae=vae,
+    torch_dtype=torch.bfloat16,
+)
+
+pipe.enable_model_cpu_offload()
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+
+prompt = "A little girl is riding a bicycle at high speed. Focused, detailed, realistic."
+image = load_image(image="input.jpg")
+video = pipe(
+    prompt=prompt,
+    image=image,
+    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)
+```
+
+Additionally, these models can be serialized and stored using PytorchAO in quantized data types to save disk space. You
+can find examples and benchmarks at the following links:
+
+- [torchao](https://gist.github.com/a-r-r-o-w/4d9732d17412888c885480c6521a9897)
+- [quanto](https://gist.github.com/a-r-r-o-w/31be62828b00a9292821b85c1017effa)
+
+## Further Exploration
+
+Feel free to enter our [GitHub](https://github.com/THUDM/CogVideo), where you'll find:
+
+1. More detailed technical explanations and code.
+2. Optimized prompt examples and conversions.
+3. Detailed code for model inference and fine-tuning.
+4. Project update logs and more interactive opportunities.
+5. CogVideoX toolchain to help you better use the model.
+6. INT8 model inference code.
+
+## 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={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}
+}
+```

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/configuration.json

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

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/model_index.json

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

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/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
+}

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/transformer/config.json

@@ -0,0 +1,29 @@
+{
+  "_class_name": "CogVideoXTransformer3DModel",
+  "_diffusers_version": "0.31.0.dev0",
+  "activation_fn": "gelu-approximate",
+  "attention_bias": true,
+  "attention_head_dim": 64,
+  "dropout": 0.0,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 32,
+  "max_text_seq_length": 226,
+  "norm_elementwise_affine": true,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 48,
+  "num_layers": 42,
+  "out_channels": 16,
+  "patch_size": 2,
+  "sample_frames": 49,
+  "sample_height": 60,
+  "sample_width": 90,
+  "spatial_interpolation_scale": 1.875,
+  "temporal_compression_ratio": 4,
+  "temporal_interpolation_scale": 1.0,
+  "text_embed_dim": 4096,
+  "time_embed_dim": 512,
+  "timestep_activation_fn": "silu",
+  "use_learned_positional_embeddings": true,
+  "use_rotary_positional_embeddings": true
+}

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/transformer/diffusion_pytorch_model-00001-of-00003.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2e3060199c34a0d18892a19d687455f938b0ac3d2ea7d48f37cb4090e141965
+size 4992465072

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/transformer/diffusion_pytorch_model-00002-of-00003.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e8d0c62d366b0d9cc3476d2b21ca54afbecea154d54d923da120b2ec174c7e7
+size 4985800640

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/transformer/diffusion_pytorch_model-00003-of-00003.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da91a0051da3f39caf10944b7c9aa66b14ddeffb37a25b087c49fc1692c1a361
+size 1272025856

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/vae/config.json

@@ -0,0 +1,39 @@
+{
+  "_class_name": "AutoencoderKLCogVideoX",
+  "_diffusers_version": "0.31.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    256,
+    512
+  ],
+  "down_block_types": [
+    "CogVideoXDownBlock3D",
+    "CogVideoXDownBlock3D",
+    "CogVideoXDownBlock3D",
+    "CogVideoXDownBlock3D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 3,
+  "norm_eps": 1e-06,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_height": 480,
+  "sample_width": 720,
+  "scaling_factor": 0.7,
+  "shift_factor": null,
+  "temporal_compression_ratio": 4,
+  "up_block_types": [
+    "CogVideoXUpBlock3D",
+    "CogVideoXUpBlock3D",
+    "CogVideoXUpBlock3D",
+    "CogVideoXUpBlock3D"
+  ],
+  "use_post_quant_conv": false,
+  "use_quant_conv": false
+}

Generation/Video/CogVideo-based/CogVideoX-5b-I2V/vae/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd47d57ad948ff80da0af0cb2e4dcdef65073aba59bccfd383ada9a7d1c02024
+size 431221142