Comments (2)
@maxin-cn
May i set the local bz=1 for training latte on my own dataset? I mean, I heard that the enough batchsize seems to be key for the training of diffusion.
Hi, you can set batchsize
as 1. But I'm not sure if this will slow down performance. You can try it first. Looking forward to your feedback later~
from latte.
I'm asking for the lowest amount of GPU video memory (VRAM) necessary to run latte video generation effectively? for both training and inference.
Hi, thanks for your interest. Inferencing one video on the A100 requires 20916MiB of GPU memory under fp16 precision mode. As for the GPU memory requirement of training, I think it may be dependent on your batch size.
from latte.
Related Issues (20)
- image_size = [256,512] HOT 4
- CUDA out of memory HOT 4
- Evaluate the FVD? HOT 5
- Some weights of AutoencoderKL were not initialized from the model checkpoint at /path/to/Latte/t2v_required_models/ and are newly initialized because the shapes did not match: HOT 2
- FaceForensics数据集 HOT 3
- No positional embeddings in LatteT2V?
- Is autoregression possible? HOT 3
- Questions about the *0.18215 and /0.18215 operation HOT 2
- About video VAE HOT 2
- 有关时长的问题。 HOT 4
- question on t2v model training HOT 2
- 视频帧率 HOT 2
- Question: model code and design choices HOT 2
- how to place and preprocess these datasets HOT 7
- 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
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