Comments (5)
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Hi everybody. We just added a brand new DiT PyTorch training script (train.py
) to the repo. Note that you'll need to update to the latest version of the repo to use it. Sorry for the delay!
The script is not super well-tested currently; we only tried training a 256x256 DiT-XL/2 model from scratch for 90K steps on an A100 node (8x GPUs), but the loss curve looks correct (at least up to that point), and FID-50K at 50K steps is very similar to the JAX version's. If you encounter any bugs, please open a new issue and I'll try my best to take a look.
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Update: since the training script was released, I've trained a few XL/2 and B/4 models. In all experiments the PyTorch-trained models perform very closely compared to the JAX ones (sometimes better actually). I added a bunch of info to the README here. Just make sure you update to the latest version of the repo.
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Following this post.
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@wpeebles Thanks for sharing your incredible work.
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Related Issues (20)
- How do you calculate flops? HOT 4
- image generation label doesn't match validation label
- How to condition on an image? HOT 2
- sample_ddp failed (CUDA error: device-side assert triggered) HOT 5
- Request to adapt to Ascend NPU
- possible bug for sampling script: y_null = torch.tensor([1000] * n) HOT 1
- time embedding use cat[cos, sin]
- Bugs Fixing and Supporting for Multi-nodes HOT 5
- Green image during inference.
- Clarification on Zero Initialization in FinalLayer of DiT Model HOT 2
- Do the pre-trained DiT chekpoints contain EMA weights? HOT 1
- DiT results on CIFAR10 HOT 3
- training batch
- Performance for patch size = 1
- about fused_attention
- The model could not be fitted if not predict xstart HOT 4
- Question on Evaluation
- Visualing q-k attention of ViT layers
- high cfg scale,low image diverse
- Does input expects the image to be between 0 to 1?
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