Comments (3)
Thanks for your answer
from latte.
As the title.
Does it mean the current t2v model is not trained on other frame lengths and cannot generalize to other frame length?
Hi, producing videos directly with more than 16 frames can lead to low-quality output. To generate videos longer than 16 frames, you might consider using the autoregressive mode for better results.
from latte.
Hi, how can I turn on the autoregressive mode?
from latte.
Related Issues (20)
- T2V training and evaluation HOT 7
- Results & ckpts of different sized Latte on UCF-101 HOT 3
- 训练模型后的效果评估 HOT 4
- FFS evaluation HOT 5
- Some problems with reimplementing the training process HOT 20
- the code of variant 4 HOT 1
- Question: evaluate the FVD HOT 6
- Error once speed up training HOT 2
- How to get preprocessed_ffs HOT 4
- Any plan to implement Latte in HuggingFace's diffusers library? HOT 3
- 模型在ucf101上无法收敛 HOT 5
- Can Latte train for I2V tasks? HOT 2
- Batch Size Ablations HOT 1
- what the param <input_sq_size> stands for? HOT 2
- Can we use batch_size>1 in sample_t2x.py HOT 7
- Evaluate the `FVD` on FFS HOT 4
- How can I utilize the weights of pre-trained PixArt-α to initialize the parameters of the spatial Transformer block in the Latte T2V model? HOT 2
- FVD on UCF-101 HOT 15
- Is there tutorial on transfering t2i to t2v model? HOT 5
- inference memory with torch.set_grad_enabled(True) HOT 2
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