Comments (1)
madebyollin
commented on August 17, 2024
I checked with Saining and confirmed it's just a bug left in to allow exact reproducibility of the DiT results, not a design choice (specifically, it looks like the DiT forward_with_cfg code was based on GLIDE - which only guides the first 3 channels - and they forgot to change the CFG code when switching to the 4 channel latent representation).
It looks like this CFG bug is also present in SiT, MDT, and PixArt, but it was fixed in LaViE and some other projects... so I guess it doesn't matter much either way.
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Related Issues (20)
- [Question] Why DiT-XL/2 takes 119 GFlops to generate 256x256 images? HOT 3
- Giving Prompt instead of classes HOT 1
- Prompt-conditioning model instead of class-conditioning HOT 5
- RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling cublasLtMatmul
- 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
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