Comments (1)
dfaker
commented on September 27, 2024
Yes this has a good deal larger VRAM requirement, both because of the larger 128x128 ouput size, the extra masking channel being generated in the output and the extra residual blocks in the decoders, there's no one-shot method to get it down to what you'd need but removing some of the decoder res_blocks removing and reducing ENCODER_DIM are both options.
Bringing the output size back down is possible but would need multiple changes to be made in a number of locations during data generation, training and merge.
from df.
Related Issues (20)
- Merging process HOT 1
- Adding this repo as deepfakes/faceswap plugin HOT 12
- Enable multiple gpus HOT 5
- Why not utilize blur/kernel size HOT 2
- minImages Training Variable HOT 6
- Models breaking after several hours HOT 3
- Need for alignments.json during training HOT 1
- Consider using face_segmentation library
- Alignment with Cuda fails HOT 1
- Out of VRAM HOT 3
- Face area in conversion
- how to apply a swap on a particular part
- Empty 'merged' folder? HOT 1
- merge_faces_larger.py TypeError: unhashable type: 'slice' HOT 1
- What do I write in the command line with align_images_masked.py? HOT 2
- My model is fully trained but now I can't swap out the faces HOT 1
- How much size supported most?
- Need for facial alignment HOT 1
- the converted image is the same as the previous one. HOT 2
- There is no “load_images ” in utils HOT 1
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from df.