Comments (4)
I can train the model on 1 3090 with bs=1. But when I use 2 or more GPUs, the memory is not enough.
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I can train the model on 1 3090 with bs=1. But when I use 2 or more GPUs, the memory is not enough.
The same question.
from seesr.
I can train the model on 1 3090 with bs=1. But when I use 2 or more GPUs, the memory is not enough.
+1
from seesr.
I can train the model on 1 3090 with bs=1. But when I use 2 or more GPUs, the memory is not enough.
Multi-GPU parallel training consumes additional memory compared to single-GPU training, which is likely the cause.
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Related Issues (20)
- compared to PASD HOT 1
- DAPE微调细节和收敛问题 HOT 1
- What's the difference between from_pretrained_orig() and from_pretrained()? Blurry turbo output? HOT 1
- SD-Turbo results look good, but have color banding/look like painting HOT 3
- Please verify your scheduler_config.json configuration file.
- Request: SeeSR with SUPIR
- Training strategy HOT 1
- Alternative to fix color?
- inference时cpu占用过高 HOT 1
- OSError: Can't load tokenizer for 'bert-base-uncased'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'bert-base-uncased' is the correct path to a directory containing all relevant files for a BertTokenizer tokenizer. HOT 3
- 多卡GPU使用 HOT 2
- 请问会有 'SeeSR-SDXL' 版本吗? HOT 5
- OSError: Error no file named diffusion_pytorch_model.bin found in directory preset/models/seesr HOT 1
- The PSNR and SSIM results HOT 4
- Training time and the total number of used datasset HOT 4
- 为什么一直出现baseball glove呢 HOT 2
- Question HOT 1
- 在自己的数据集上微调SD-Turbo模型
- The issue with training on multiple GPUs.
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