爆圈Sora横空出世，AGI通用人工智能时代真的要来了吗？一键Run带你体验扩散模型的魅力！

在视频创作领域，画面的稳定性至关重要。如果要呈现出优质的效果，创作者需要具备高超的视频剪辑技能和相关基础。然而，SORA这次的表现真是逆天！通过简单的文字描述，它能生成画面稳定、理解能力强的长视频。

SORA的技术思路与众不同，完全碾压了传统方法。它不再仅关注二维像素的变化，而是专注于语义理解的变化。从以往的视频画面生成，转变为故事逻辑的生成。这种创新思路让人瞠目结舌，展示了技术的无限可能性

   

二、SORA背后原理的推测

  根据OpenAI最新发布的技术报告，Sora背后的“text-to-video”模型基于Diffusion Transformer Model。这种模型结合了Transformer架构和扩散模型，用于生成图像、视频和其他数据。

实际上，Sora是一个基于Transformer的扩散模型。这类模型不仅在理论上具有创新性，而且在实际应用中也显示出了强大的潜力。例如，DiT模型（Sora的基础）和GenTron模型在图像和视频生成等领域都已经取得了巨大的成功，这些创新性的模型为我们展示了技术的无限可能性。目前Sora技术没有公开，大家对它都有不同猜测。DIT提出人谢赛宁：

1）Sora应该是建立在DiT这个扩散Transformer之上的 。

2）Sora可能有大约30亿个参数,(引用论文模型0.13B, 32X算力)。
3）训练数据是Sora 成功的最关键因素。
4）主要的挑战是如何解决错误累积问题并随着时间的推移保持质量/一致 。

DiT模型：Meta提出的完全基于transformer架构的扩散模型，不仅将transformer成功应用在扩散模型，还探究了transformer架构在扩散模型上的scalability能力。

GenTron模型：一种基于Transformer的扩散模型，在针对SDXL的人类评估中，GenTron在视觉质量方面取得了51.1%的胜率（19.8%的平局率），在文本对齐方面取得了42.3%的胜率（42.9%的平局率）。 

DiT模型
Scalable Diffusion Models with Transformers ---- 基于transformer的扩散模型，称为Diffusion Transformers（DiTs） ，Diffusion Transformer Model（DiT）的设计空间、扩展行为、网络复杂度和样本质量之间的关系。这些研究结果表明，通过简单地扩展DiT并使用高容量的骨干网络，可以在类条件256x256 ImageNet生成基准测试中实现最新的2.27 FID。与像素空间扩散模型相比，DiTs在使用的Gflops只是其一小部分，因此具有较高的计算效率。此外，DiTs还可以应用于像素空间，使得图像生成流程成为混合方法，使用现成的卷积VAEs和基于transformer的DDPMs。

 扩散模型中引入了transformer类的标准设计，以取代传统的U-Net设计，从而提供了一种新的架构选择。

引入了潜在扩散模型（LDMs），通过将图像压缩为较小的空间表示，并在这些表示上训练扩散模型，从而解决了在高分辨率像素空间中直接训练扩散模型的计算问题。 

那对于我们开发者用户想要强烈体验文生视频的乐趣，那里可以体验呢？今天给大家介绍下Stable Video Diffusion (SVD)，一起在华为云一键Run体验其中的乐趣：

三、Stable Video Diffusion (SVD) 扩散模型的图像生成视频的体验

1. 案例简介

Stable Video Diffusion (SVD) 是一种扩散模型，它将静止图像作为条件帧，并从中生成视频。

🔹 本案例需使用 Pytorch-1.8 GPU-V100 及以上规格运行

🔹 点击Run in ModelArts，将会进入到ModelArts CodeLab中，这时需要你登录华为云账号，如果没有账号，则需要注册一个，且要进行实名认证，参考《ModelArts准备工作_简易版》 即可完成账号注册和实名认证。 登录之后，等待片刻，即可进入到CodeLab的运行环境

🔹 出现 Out Of Memory ，请检查是否为您的参数配置过高导致，修改参数配置，重启kernel或更换更高规格资源进行规避❗❗❗

2. 下载代码和模型

!git clone https://github.com/Stability-AI/generative-models.git

Cloning into 'generative-models'...
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remote: Enumerating objects: 860, done.•[K
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remote: Counting objects: 100% (489/489), done.•[K
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remote: Compressing objects: 100% (222/222), done.•[K
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remote: Total 860 (delta 368), reused 267 (delta 267), pack-reused 371•[K
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Receiving objects: 100% (860/860), 42.67 MiB | 462.00 KiB/s, done.
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Resolving deltas: 100% (445/445), done.

import moxing as mox
mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/Stable_Video_Diffusion/file/modify_file/generative-models/sgm/modules/encoders','generative-models/sgm/modules/encoders')
mox.file.copy_parallel('obs://modelarts-labs-bj4-v2/case_zoo/Stable_Video_Diffusion/file/models','generative-models/models')
mox.file.copy_parallel(,'obs://modelarts-labs-bj4-v2/case_zoo/Stable_Video_Diffusion/file/checkpoints','generative-models/checkpoints')

INFO:root:Using MoXing-v2.1.0.5d9c87c8-5d9c87c8
​
INFO:root:Using OBS-Python-SDK-3.20.9.1

3. 配置运行环境

本案例依赖Python3.10.10及以上环境，因此我们首先创建虚拟环境：

!/home/ma-user/anaconda3/bin/conda create -n python-3.10.10 python=3.10.10 -y --override-channels --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
!/home/ma-user/anaconda3/envs/python-3.10.10/bin/pip install ipykernel

/home/ma-user/anaconda3/lib/python3.7/site-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (3.0.4) doesn't match a supported version!
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  RequestsDependencyWarning)
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Collecting package metadata (current_repodata.json): done
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Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
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Collecting package metadata (repodata.json): done
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Solving environment: done

import json
import os
​
data = {
   "display_name": "python-3.10.10",
   "env": {
      "PATH": "/home/ma-user/anaconda3/envs/python-3.10.10/bin:/home/ma-user/anaconda3/envs/python-3.7.10/bin:/modelarts/authoring/notebook-conda/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/home/ma-user/modelarts/ma-cli/bin:/home/ma-user/modelarts/ma-cli/bin:/home/ma-user/anaconda3/envs/PyTorch-1.8/bin"
   },
   "language": "python",
   "argv": [
      "/home/ma-user/anaconda3/envs/python-3.10.10/bin/python",
      "-m",
      "ipykernel",
      "-f",
      "{connection_file}"
   ]
}
​
if not os.path.exists("/home/ma-user/anaconda3/share/jupyter/kernels/python-3.10.10/"):
    os.mkdir("/home/ma-user/anaconda3/share/jupyter/kernels/python-3.10.10/")
​
with open('/home/ma-user/anaconda3/share/jupyter/kernels/python-3.10.10/kernel.json', 'w') as f:
    json.dump(data, f, indent=4)

创建完成后，稍等片刻，或刷新页面，点击右上角kernel选择python-3.10.10

!pip install torch==2.0.1 torchvision==0.15.2
!pip install MoviePy

Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple
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Collecting torch==2.0.1
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  Downloading http://repo.myhuaweicloud.com/repository/pypi/packages/8c/4d/17e07377c9c3d1a0c4eb3fde1c7c16b5a0ce6133ddbabc08ceef6b7f2645/torch-2.0.1-cp310-cp310-manylinux1_x86_64.whl (619.9 MB)
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•[2K     •[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━•[0m •[32m619.9/619.9 MB•[0m •[31m5.6 MB/s•[0m eta •[36m0:00:00•[0m00:01•[0m00:01•[0m
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......
    Uninstalling decorator-5.1.1:
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      Successfully uninstalled decorator-5.1.1
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Successfully installed MoviePy-1.0.3 decorator-4.4.2 imageio-2.34.0 imageio_ffmpeg-0.4.9 proglog-0.1.10 tqdm-4.66.2

%cd generative-models

/home/ma-user/work/stable-video-diffusion/generative-models

/home/ma-user/anaconda3/envs/python-3.10.10/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
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  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]

!pip install -r requirements/pt2.txt

Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple
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Collecting clip@ git+https://github.com/openai/CLIP.git (from -r requirements/pt2.txt (line 3))
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  Cloning https://github.com/openai/CLIP.git to /tmp/pip-install-_vzv4vq_/clip_4273bc4d2cba4d6486a222a5093fbe4b
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 conda3/envs/python-3.10.10/lib/python3.10/site-packages (from -r requirements/pt2.txt (line 32)) (4.66.2)
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Collecting transformers==4.19.1 (from -r requirements/pt2.txt (line 33))
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      Successfully uninstalled urllib3-2.2.1
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Successfully installed PyWavelets-1.5.0 aiohttp-3.9.3 aiosignal-1.3.1 altair-5.2.0 antlr4-python3-runtime-4.9.3 appdirs-1.4.4 async-timeout-4.0.3 attrs-23.2.0 black-23.7.0 blinker-1.7.0 braceexpand-0.1.7 cachetools-5.3.2 chardet-5.1.0 click-8.1.7 clip-1.0 contourpy-1.2.0 cycler-0.12.1 docker-pycreds-0.4.0 einops-0.7.0 fairscale-0.4.13 fire-0.5.0 fonttools-4.49.0 frozenlist-1.4.1 fsspec-2024.2.0 ftfy-6.1.3 gitdb-4.0.11 gitpython-3.1.42 huggingface-hub-0.20.3 importlib-metadata-7.0.1 invisible-watermark-0.2.0 jsonschema-4.21.1 jsonschema-specifications-2023.12.1 kiwisolver-1.4.5 kornia-0.6.9 lightning-utilities-0.10.1 markdown-it-py-3.0.0 matplotlib-3.8.3 mdurl-0.1.2 multidict-6.0.5 mypy-extensions-1.0.0 natsort-8.4.0 ninja-1.11.1.1 omegaconf-2.3.0 open-clip-torch-2.24.0 opencv-python-4.6.0.66 pandas-2.2.0 pathspec-0.12.1 protobuf-3.20.3 pudb-2024.1 pyarrow-15.0.0 pydeck-0.8.1b0 pyparsing-3.1.1 pytorch-lightning-2.0.1 pytz-2024.1 pyyaml-6.0.1 referencing-0.33.0 regex-2023.12.25 rich-13.7.0 rpds-py-0.18.0 safetensors-0.4.2 scipy-1.12.0 sentencepiece-0.2.0 sentry-sdk-1.40.5 setproctitle-1.3.3 smmap-5.0.1 streamlit-1.31.1 streamlit-keyup-0.2.0 tenacity-8.2.3 tensorboardx-2.6 termcolor-2.4.0 timm-0.9.16 tokenizers-0.12.1 toml-0.10.2 tomli-2.0.1 toolz-0.12.1 torchaudio-2.0.2 torchdata-0.6.1 torchmetrics-1.3.1 transformers-4.19.1 tzdata-2024.1 tzlocal-5.2 urllib3-1.26.18 urwid-2.6.4 urwid-readline-0.13 validators-0.22.0 wandb-0.16.3 watchdog-4.0.0 webdataset-0.2.86 xformers-0.0.22 yarl-1.9.4 zipp-3.17.0

!pip install .

Looking in indexes: http://repo.myhuaweicloud.com/repository/pypi/simple
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Processing /home/ma-user/work/stable-video-diffusion/generative-models
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  Installing build dependencies ... •[?25ldone
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•[?25h  Getting requirements to build wheel ... •[?25ldone
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•[?25h  Preparing metadata (pyproject.toml) ... •[?25ldone
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•[?25hBuilding wheels for collected packages: sgm
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  Building wheel for sgm (pyproject.toml) ... •[?25ldone
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•[?25h  Created wheel for sgm: filename=sgm-0.1.0-py3-none-any.whl size=127368 sha256=0f9ff6913b03b2e0354cd1962ecb2fc03df36dea90d14b27dc46620e6eafc9a0
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  Stored in directory: /home/ma-user/.cache/pip/wheels/a9/b8/f4/e84140beaf1762b37f5268788964d58d91394ee17de04b3f9a
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Successfully built sgm
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Installing collected packages: sgm
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Successfully installed sgm-0.1.0

4. 生成视频

视频默认生成到outputs文件夹内

!python scripts/sampling/simple_video_sample.py --decoding_t 1 --input_path 'assets/test_image.png'

/home/ma-user/work/stable-video-diffusion/generative-models
​
VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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VideoTransformerBlock is using checkpointing
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Initialized embedder #0: FrozenOpenCLIPImagePredictionEmbedder with 683800065 params. Trainable: False
​
Initialized embedder #1: ConcatTimestepEmbedderND with 0 params. Trainable: False
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Initialized embedder #2: ConcatTimestepEmbedderND with 0 params. Trainable: False
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Initialized embedder #3: VideoPredictionEmbedderWithEncoder with 83653863 params. Trainable: False
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Initialized embedder #4: ConcatTimestepEmbedderND with 0 params. Trainable: False
​
Restored from checkpoints/svd.safetensors with 0 missing and 0 unexpected keys
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100%|███████████████████████████████████████| 890M/890M [00:50<00:00, 18.5MiB/s]
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/home/ma-user/anaconda3/envs/python-3.10.10/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
​
  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")

#将视频文件转成动图显示
from moviepy.editor import *
 
# 指定输入视频路径
video_path = "outputs/simple_video_sample/svd/000000.mp4"
 
# 加载视频
clip = VideoFileClip(video_path)
 
# 设置保存GIF的参数（如分辨率、持续时间等）
output_file = "output_animation.gif"
fps = 10 # GIF每秒显示的帧数
 
# 生成并保存GIF
clip.write_gif(output_file, fps=fps)

MoviePy - Building file output_animation.gif with imageio.

from IPython.display import Image
Image(open('output_animation.gif','rb').read())

大家赶紧来体验文生视频的乐趣吧！