what is the need for num_samples parameter in inference?

[hwaseem04]

torchrun --nproc_per_node=2 --nnodes=1 ./gen_utils/generate.py \
model.name='bert-base-uncased' use_sentence_piece=True batch_size=128 \
exp.name=play2 load_step=10000 data.name=docedit \
tgt_len=90 max_pos_len=512 \
num_samples=1 intermediate_size=2048 num_attention_heads=8 dropout=0.2 \
in_channels=128 out_channels=128 time_channels=128 skip_sample=True gen_timesteps=1000 \
schedule_sampler='xy_uniform' time_att=False att_strategy='txl' load_from_ema=False prediction=True

anyway the for loop loads dev_dataloader which is just the same data. Then what is the point of num_sampes parameter

[wutong4012]

The value of random is different each time, so the sampling noise at the beginning of diffusion is different each time. So you can get different results every time you run it.