Top Tweets for #VideoCrafter2
We also find this strategy has broader applications. For example in T2I, FreeInit can help SDXL generate very dark/bright images with better text alignment (see below). Further support for #VideoCrafter2 and #SDXL will be updated soon.
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![_tianxing's tweet photo. We also find this strategy has broader applications. For example in T2I, FreeInit can help SDXL generate very dark/bright images with better text alignment (see below). Further support for #VideoCrafter2 and #SDXL will be updated soon.
[2/3] https://t.co/8JnmbjyqOV](https://pbs.twimg.com/media/GRg9NHTb0AEwUt8.jpg)
📽 VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models 🔥 Text2Video 📹 Image2Video 🎥 Jupyter Notebook 🥳
Thanks to @haoxin_chen ❤ @Norris29973102 ❤ @shadocun ❤ Menghan Xia ❤ @xinntao ❤ @cweng6 ❤ @yshan2u ❤
🌐page: https://t.co/fpSmyc07iG
📄paper: https://t.co/WnROEQXve8
🧬code: https://t.co/9TQ1v2CrlX
🍊jupyter by https://t.co/uUxzMCZb53: please try it 🐣 https://t.co/4fgcb9Oaxw
#VideoCrafter2 で生成した月面の宇宙飛行士
#月面着陸 もっとリアタイで着陸時の動画見れると思ってた🤭
センサ類のデータだけで確認も簡単じゃない🇯🇵の #SLIM ちゃん、着陸しててね
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
https://t.co/22pmuT4laX
Tencent just released VideoCrafter2 demo on Hugging Face
high quality text to video model
demo: https://t.co/wFKLQf8EnO
Overcoming Data Limitations for High-Quality Video Diffusion Models
code, models and data are distributed under Apache 2.0 License
Thanks to @_akhaliq for sharing! #VideoCrafter2 is NOW open-sourced - featuring improved visual quality, motion and concept combos! Feel free to try it out!🌐📷
Project Page: https://t.co/vFZzglp3vD
Thanks to the team:
@haoxin_chen @Norris29973102 @shadocun @RichardXia101 @xinntao @cweng6
Tencent just released VideoCrafter2 demo on Hugging Face
high quality text to video model
demo: https://t.co/wFKLQf8EnO
Overcoming Data Limitations for High-Quality Video Diffusion Models
code, models and data are distributed under Apache 2.0 License
🥳 #VideoCrafter2 Large improvements over VideoCrafter1 with limited data!
Better Motion, Better Concept Combination!
📷 Demo: https://t.co/lR3wGKeWd5
Project: https://t.co/p0EkRnaWqr
Thanks to co-authors @haoxin_chen @Norris29973102
@shadocun @RichardXia101 @cweng6 @yshan2u
🥳 #VideoCrafter2 Large improvements over VideoCrafter1 with limited data
Better Motion, Better Concept Combination!
🤗 Demo https://t.co/8viRm1j0I5
Project Page https://t.co/GzdFvhT2OH
Thanks to co-authors @haoxin_chen @Norris29973102 @shadocun @RichardXia101 @cweng6 @yshan2u
Tencent presents VideoCrafter2
Overcoming Data Limitations for High-Quality Video Diffusion Models
paper page: https://t.co/vBYXsGZFHl
Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition.

Thanks to @_akhaliq for sharing! #VideoCrafter2 is NOW open-sourced - featuring improved visual quality, motion and concept combos! Feel free to try it out!🌐✨
Demo: https://t.co/ecMzEUeNEC
Project Page: https://t.co/vFZzglp3vD
Thanks to the team: @haoxin_chen @Norris29973102 @shadocun @RichardXia101 @xinntao @cweng6
Tencent presents VideoCrafter2
Overcoming Data Limitations for High-Quality Video Diffusion Models
paper page: https://t.co/vBYXsGZFHl
Text-to-video generation aims to produce a video based on a given prompt. Recently, several commercial video models have been able to generate plausible videos with minimal noise, excellent details, and high aesthetic scores. However, these models rely on large-scale, well-filtered, high-quality videos that are not accessible to the community. Many existing research works, which train models using the low-quality WebVid-10M dataset, struggle to generate high-quality videos because the models are optimized to fit WebVid-10M. In this work, we explore the training scheme of video models extended from Stable Diffusion and investigate the feasibility of leveraging low-quality videos and synthesized high-quality images to obtain a high-quality video model. We first analyze the connection between the spatial and temporal modules of video models and the distribution shift to low-quality videos. We observe that full training of all modules results in a stronger coupling between spatial and temporal modules than only training temporal modules. Based on this stronger coupling, we shift the distribution to higher quality without motion degradation by finetuning spatial modules with high-quality images, resulting in a generic high-quality video model. Evaluations are conducted to demonstrate the superiority of the proposed method, particularly in picture quality, motion, and concept composition.

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