bytedance--lance/benchmarks/image_gen/DPG

Chinese Version

DPG Image Generation Evaluation

Benchmark evaluation scripts for DPG based on the Lance model.

Files

• sample_DPG.py - Python inference script
• sample_DPG.sh - Launch script
• DPG.jsonl - Evaluation dataset

Quick Start

Basic Usage

bash benchmarks/image_gen/DPG/sample_DPG.sh

Before running, edit the "Inference Parameters" section at the top of benchmarks/image_gen/DPG/sample_DPG.sh.

Parameters

Parameter	Default	Description
TASK_NAME	t2i	Task type. DPG is fixed to image generation.
VALIDATION_NUM_TIMESTEPS	50	Number of inference steps.
VALIDATION_TIMESTEP_SHIFT	3.5	Timestep shift.
EVALUATION_SEED	42	Random seed.
CFG_TEXT_SCALE	4.0	CFG scale.
CFG_INTERVAL_START	0.4	Start of the CFG interval.
CFG_INTERVAL_END	1.0	End of the CFG interval.
SAMPLE_NUM_PER_PROMPT	4	Number of images generated per case for the final grid.
USE_KVCACHE	true	Whether to enable KV cache.
NUM_GPUS	8	Number of GPUs.
VIDEO_HEIGHT/VIDEO_WIDTH	768	Image resolution.
MODEL_PATH	downloads/Lance_3B	Path to the Lance checkpoint.
VAL_DATASET_CONFIG_FILE	benchmarks/image_gen/DPG/DPG.jsonl	Path to the evaluation data.

How To Modify

• Edit the "Inference Parameters" section at the top of benchmarks/image_gen/DPG/sample_DPG.sh.
• After updating the parameters, run bash benchmarks/image_gen/DPG/sample_DPG.sh directly.
• SAVE_PATH_GEN is generated automatically from the script parameters and does not need to be set manually.

Output Format

Results are saved in a structure like this:

results/DPG_ts50_tss3.5_seed42_cfg4.0_kvcache_20260507_120000/
├── 0.png
├── 1.png
├── 2.png
└── ...