shows these results. CODEFUSION performs com-
parable to transformer based models and better than
other diffusion based text generation approaches
for top-1 accuracy. For top-3 accuracy, CODEFU-
SION outperforms all baselines. This is consistent
with the results in Table 1 and show that CODEFU-
SION produces better and more diverse candidate
programs for a variety of tasks.

C.2 Visualizing Diffusion

CODEFUSION iteratively denoises the latent repre-
sentation to construct the final target. This can be
visualized by mapping the representation at each
time step to discrete tokens. We follow the setup
as explained in Section-5.3. Figure 4 shows a suc-
cess example from the Python benchmarks where
CODEFUSION is able to generate the correct code.
The figures shows the input query, the target code
CODEFUSION