Discrete Distribution Network
Exploration into Discrete Distribution Network, by Lei Yang out of Beijing
Besides the split-and-prune, may also throw in an option for crossover (mixing of top 2 nodes to replace the pruned)
Install
$ pip install discrete-distribution-network
Usage
import torch
from discrete_distribution_network import DDN
ddn = DDN(
dim = 32,
image_size = 256
)
images = torch.randn(2, 3, 256, 256)
loss = ddn(images)
loss.backward()
# after much training
sampled = ddn.sample(batch_size = 1)
assert sampled.shape == (1, 3, 256, 256)
The proposed GuidedSampler in the paper
import torch
from discrete_distribution_network import GuidedSampler
sampler = GuidedSampler(
dim = 16, # feature dimension
dim_query = 3, # the query image dimension
codebook_size = 10, # the codebook size K in the paper, which is K separate projections of the features, which is then measured distance wise to the query image guide
)
features = torch.randn(20, 16, 32, 32)
query_image = torch.randn(20, 3, 32, 32)
out, codes, commit_loss = sampler(features, query_image)
# (20, 3, 32, 32), (20,), ()
assert torch.allclose(sampler.forward_for_codes(features, codes), out, atol = 1e-5)
# after optimizer step, this needs to be called
# there is also a helper function by the same name that can take in your module and will invoke all of the guided samplers
sampler.split_and_prune_()
Oxford flowers
Install uv, which will probably become the default in the near future
$ pip install uv
Then
$ uv run train_oxford_flowers.py
Citations
@misc{yang2025discretedistributionnetworks,
title = {Discrete Distribution Networks},
author = {Lei Yang},
year = {2025},
eprint = {2401.00036},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2401.00036},
}
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