Comments (3)
Thanks so much for pointing this out! What a terrible bug. I've been able to fix it so that gradients are synchronized properly across GPUs, but it uses more memory for some reason (up to 16GB from 10GB before the change).
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Hi! Does this bug effect any of the findings in the paper?
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@bhattg no, fortunately the results in the paper all used the original Jax codebase.
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Related Issues (20)
- About the training with prompt_image_alignment configuration which uses llava_bertscore reward function HOT 5
- Question about the optimized objective. HOT 1
- Hello, when I trained an aesthetic model using the default configuration on 8 A800 cards, I found that the training process got stuck after completing one epoch, but it worked fine when using a single A800 card. May I ask what could be the cause of this situation? HOT 8
- Support for other schedulers HOT 1
- prompt-dependent value function optimization
- SDXL Support? HOT 1
- unet keeps producing nan during training HOT 2
- Code logics, thanks
- On reproducibility and LoRA HOT 1
- Batch size unrecogonized HOT 1
- Finetuning on google colab
- Questions about the reward curve and bert. HOT 1
- OOM when using "stabilityai/stable-diffusion-2-1" with batch size of 2 HOT 1
- reproducing the aesthetic experiment HOT 7
- OOM despite using A100-80GB GPU and 128GB CPU memory (+16 CPUs per task) HOT 2
- On reproducing LLaVA alignment experiments. HOT 2
- Prompt Alignment with LLaVA-server: Client-side prompt and image doesn't match server side reward HOT 4
- About the large dataset and Unet Training HOT 3
- fp16 only if using lora? HOT 2
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