Comments (1)
Translation is achieved by affine transformation of input fourier features. Samples from NVIDIA also contains slight variations, and this is came from perturbation to latent vectors. (Random walking) I don't know how they did "randomly walk" latent spaces, but there are many known recipes for this kind of problem...
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Related Issues (20)
- Scale affine transformation [WIP] HOT 7
- Questions on Filter parameters HOT 4
- Training Speed HOT 1
- Required GPU Memory HOT 1
- GPU Scalability Bug (GPU 0 has 4x the vram of all others) HOT 2
- Internal Representations HOT 2
- Any tips or code for generating images and movies from the checkpoints? HOT 6
- Training on 1024 resolution HOT 5
- colab demo
- Jinc function HOT 1
- Artifacts HOT 3
- about input fourier features.
- Train script issue HOT 5
- any body can give a pretrained model?
- Issues regarding the LMDB dataset creating. HOT 1
- Are there still differences to the official implementation?
- Is `conv2d_gradfix` really incompatible with PyTorch > 1.8?
- About train 1024 img
- Augment set to true causes RuntimeError
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