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@@ -11,7 +11,7 @@ This is the Official Repository of "[Learning Few-Step Diffusion Models by Traje
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  ## User Study Time!
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- ![user_study](assets/user_study.jpg)
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  Which one do you think is better? Some images are generated by Pixart-α (50 NFE). Some images are generated by **TDM (4 NFE)**, distilling from Pixart-α in a data-free way with merely 500 training iterations and 2 A800 hours.
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  <details>
@@ -27,8 +27,8 @@ Which one do you think is better? Some images are generated by Pixart-α (50 NFE
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  Our proposed TDM can be easily extended to text-to-video.
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  <p align="center">
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- <img src="assets/teacher.gif" alt="Teacher" width="45%">
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- <img src="assets/student.gif" alt="Student" width="45%">
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  </p>
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  The video on the left was generated by CogVideoX-2B (100 NFE). In the same amount of time, **TDM (4NFE)** can generate 25 videos, as shown on the right, achieving an impressive **25 times speedup without performance degradation**. (Note: The noise in the GIF is due to compression.)
 
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  ## User Study Time!
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+ ![user_study](user_study.jpg)
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  Which one do you think is better? Some images are generated by Pixart-α (50 NFE). Some images are generated by **TDM (4 NFE)**, distilling from Pixart-α in a data-free way with merely 500 training iterations and 2 A800 hours.
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  <details>
 
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  Our proposed TDM can be easily extended to text-to-video.
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  <p align="center">
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+ <img src="teacher.gif" alt="Teacher" width="45%">
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+ <img src="student.gif" alt="Student" width="45%">
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  </p>
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  The video on the left was generated by CogVideoX-2B (100 NFE). In the same amount of time, **TDM (4NFE)** can generate 25 videos, as shown on the right, achieving an impressive **25 times speedup without performance degradation**. (Note: The noise in the GIF is due to compression.)