免费使用支持离线部署使用的 txt2video 文本生成视频大模型(Text-to-Video-Synthesis Model)-Toy模板网

这篇具有很好参考价值的文章主要介绍了免费使用支持离线部署使用的 txt2video 文本生成视频大模型(Text-to-Video-Synthesis Model)。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

免费使用支持离线部署使用的 txt2video 文本生成视频大模型(Text-to-Video-Synthesis Model)。

文本生成视频大模型(Text-to-Video-Synthesis Model)是一种基于深度学习技术的人工智能模型，它可以将自然语言文本描述转换为相应的视频。即通过输入文本描述，自动生成符合描述内容的视频。

text2video插件,智能工具,音视频,离线,部署,大模型,生成视频,NLP,AIGC

这些模型使用深度学习技术，并结合计算机视觉和自然语言处理领域的知识，以实现自动生成视频的目的。该技术可以广泛应用于电影、游戏、虚拟现实等领域，具有巨大的商业价值和创新潜力。

这个开源的txt2video模型是一个非常有趣和有用的项目，它为文本到视频生成技术的进一步发展和推广提供了很好的契机。我们可以期待随着这种技术的不断完善和推广，会有更多的创新应用出现。

text2video插件,智能工具,音视频,离线,部署,大模型,生成视频,NLP,AIGC

txt2video文本生成视频大模型的发布具有重要意义，主要表现在以下几个方面。

首先，该模型的发布为文本到视频生成技术的进一步发展和推广提供了很好的契机。随着人工智能技术的不断发展和普及，文本到视频生成技术已经成为了一个热门研究领域，并且在电影、游戏、虚拟现实等领域具有广泛的应用前景。

然而，由于技术门槛较高，很少有开源的文本到视频生成模型供普通开发者或研究者使用。因此，这个开源项目的发布可以让更多的人参与到这个领域的研究和应用中，推动技术的快速发展和应用落地。

其次，该模型的发布为电影、游戏、虚拟现实等领域带来了更多可能性和价值。文本到视频生成技术可以极大地降低电影、游戏、虚拟现实等领域的制作成本和时间，提高制作效率和质量。通过文本描述生成视频，可以避免漫长的拍摄和后期制作过程，同时还可以实现更加灵活和自由的创作方式，从而为这些领域带来更多的可能性和价值。

最后，该模型的发布也对开源社区和AI产业发展具有积极的促进作用。开源社区是一个充满活力和创新的社区，通过开源项目的发布，可以让更多的人参与到共同的研发和创新中来，从而推动技术进步和社区发展。

同时，AI产业也是一个蓬勃发展的行业，在这个行业中，开源项目的共享和合作是非常重要的，可以促进技术的跨界融合和互相学习。因此，该模型的发布也为开源社区和AI产业的发展提供了积极的促进作用。

总之，txt2video文本生成视频大模型的发布为文本到视频生成技术的发展和推广提供了很好的契机，同时也为电影、游戏、虚拟现实等领域带来了更多可能性和价值。通过开源社区和AI产业的共同努力，我们可以期待这种技术的不断完善和应用，为人类的生产和生活带来更多的便利和创新。

代码：

!pip install modelscope==1.4.2
!pip install open_clip_torch
!pip install pytorch-lightning
!pip install modelscope==1.4.2
!pip install open_clip_torch
!pip install pytorch-lightning
account_circle
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Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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[ ]
!nvidia-smi
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Tue Mar 21 14:35:47 2023       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   69C    P0    31W /  70W |      0MiB / 15360MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
[ ]
from modelscope.pipelines import pipeline
from modelscope.outputs import OutputKeys

p = pipeline('text-to-video-synthesis', 'damo/text-to-video-synthesis')
test_text = {
        'text': 'A panda eating bamboo on a rock.',
    }
output_video_path = p(test_text,)[OutputKeys.OUTPUT_VIDEO]
print('output_video_path:', output_video_path)
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2023-03-20 15:24:10,130 - modelscope - INFO - Model revision not specified, use default: master in development mode
2023-03-20 15:24:10,133 - modelscope - INFO - Development mode use revision: master
Downloading: 100%|██████████| 7.58k/7.58k [00:00<00:00, 1.73MB/s]
Downloading: 100%|██████████| 760k/760k [00:02<00:00, 312kB/s]
Downloading: 100%|██████████| 5.26G/5.26G [07:35<00:00, 12.4MB/s]
Downloading: 100%|██████████| 4.86G/4.86G [07:36<00:00, 11.4MB/s]
2023-03-20 15:40:18,525 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/text-to-video-synthesis
2023-03-20 15:40:18,526 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/text-to-video-synthesis.
2023-03-20 15:40:18,530 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/text-to-video-synthesis
2023-03-20 15:41:56,677 - modelscope - WARNING - No preprocessor field found in cfg.
2023-03-20 15:41:56,685 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2023-03-20 15:41:56,686 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/text-to-video-synthesis'}. trying to build by task and model information.
2023-03-20 15:41:56,688 - modelscope - WARNING - No preprocessor key ('latent-text-to-video-synthesis', 'text-to-video-synthesis') found in PREPROCESSOR_MAP, skip building preprocessor.
2023-03-20 15:41:56,696 - modelscope - WARNING - task text-to-video-synthesis input definition is missing
2023-03-20 15:43:01,717 - modelscope - WARNING - task text-to-video-synthesis output keys are missing
output_video_path: /tmp/tmp737yow52.mp4
[ ]
# Download result
from google.colab import files
files.download(output_video_path)
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The above code will display the save path of the output mp4 video, and the current encoding format can be played normally with VLC player. Some other media players may not view it normally.