TaylorSeer-DiT.md

TaylorSeer-DiT

1. Prepare Environment

cd TaylorSeer-DiT
conda env create -f environment.yml
conda activate DiT
pip install flash-attention

2. Download Checkpoints

Simply follow the official documentation to download the necessary checkpoints.

3. Run Samples

Single-Batch Inference

Set your desired class ID in TaylorSeer-DiT/sample.py.

Recommended Parameter Settings

It is recommended to use interval=4 and max_order=4, as this configuration achieves 3.57× computation compression while only increasing FID by 0.17. In contrast, previous methods at the same compression level resulted in an FID increase of at least 1.10.
You can also experiment with different parameter configurations to suit your needs. For example:

• interval=3, max_order=3: FID is 2.34, compared to 2.32 without acceleration, showing almost no change.

Run the inference with the recommended settings:

python sample.py --ddim-sample --num-sampling-steps 50 --interval 4 --max-order 4 

Distributed Data Parallel (DDP) Inference

torchrun --nnodes=1 --nproc_per_node=8 sample_ddp.py \
  --model DiT-XL/2 \
  --per-proc-batch-size 50 \
  --image-size 256 \
  --cfg-scale 1.5 \
  --ddim-sample \
  --num-sampling-steps 50 \
  --interval 4 \
  --max-order 4 \
  --num-fid-samples 50000