Comments (1)
No the demo samples are not from training set, but all generated.
You can check some examples, which are generated by other people, such as https://twitter.com/multimodalart/status/1513947558913187843?s=21&t=Ofu8oiHTE5_3keSX_IDAiQ on Twitter.
You can adjust the topk and topk parameters according to the text prompt, and the performance could be different according to the texts, since 3.9B params model trained on 30M is still smaller size than DALL-E.
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Related Issues (20)
- Checkpoints URL ERROR HOT 3
- how about training VQ transformer code? HOT 1
- using MSE loss to update cookbook? HOT 1
- Minimum GPU memory size for training RQ-Transformer HOT 2
- The usage of soft codes HOT 1
- Possible to share your training logs? HOT 3
- What is the reason for using cosine annealing scheduler for stage1 if min_lr = optimizer lr HOT 1
- Is it available on CPU? HOT 1
- Could you release the code for your new paper? Hierarchical VQ-VAE HOT 4
- How to use pretrained model to reconstruct?
- train rvq-vae using ffhq dataset:checkpointing is not compatible with .grad() or when an inputs parameter is passed to .backward() HOT 2
- Train rq-vae on FFHQ failed when using the default ffhq256-rqvae-8x8x4.yaml HOT 1
- question about target of compute_loss function
- Confused by this LogitMask
- Checkpointing is not compatible with .grad() or when an `inputs` parameter is passed to .backward(). Please use .backward() and do not pass its `inputs` argument.
- About the feature statistics of datasets HOT 4
- Are there any plans to release pre-trained RQ-VAEs with different shapes? HOT 1
- gpu memory enough for inference HOT 1
- ImageNet model has missing keys HOT 1
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from rq-vae-transformer.