Comments (3)
Yes,as your suggestion above, i can use the distributed training and the generation is as good as one GPU
from text-to-video-finetuning.
will it be difficult to modify the code to support multi-gpu training?
I've never tried multiple GPU training, but you may be able to do it naively with accelerate.
accelerate config
You should be prompted to configure your setup, including multiple GPU training.
Then it should be as simple as:
accelerate launch train.py --config ./configs/my_config_hq.yaml
Let us know how it goes if you decide to try! If it doesn't I could try to implement it, but I don't have multiple GPUs and would probably need to rent out a server to do so.
from text-to-video-finetuning.
Yes,as your suggestion above, i can use the distributed training and the generation is as good as one GPU
What do you mean with "as good as one gpu"? You mean, if you had a single gpu as powerful as both of the gpus that you are using combined, there would be no difference? Also, are you connecting them through nvlink, or its just pcie?
from text-to-video-finetuning.
Related Issues (20)
- webui Lora Might be causing errors in checkpoint models. HOT 3
- How to train with folder video HOT 1
- Which paper? HOT 1
- RuntimeError: cannot reshape tensor of 0 elements into shape [0, -1, 1, 512] because the unspecified dimension size -1 can be any value and is ambiguous HOT 3
- Does this code support native finetune for damo text to video model? HOT 2
- AttributeError: 'Tensor' object has no attribute 'config' HOT 5
- How can I run the fine-tuning on a GPU with <= 16GB of VRAM? HOT 3
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- A typo
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- wrong norm method HOT 1
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- Lora on ResnetBlock2D in modelscope model HOT 1
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from text-to-video-finetuning.