MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization

News

• [24/11] We release the ComfyUI plugin for MixDQ, check it out at ./ComfyUI/README.md for usage!
• [24/08] We release the CUDA kernels for MixDQ hardware acceleration, please check out ./kernels/README.md
• [24/07] MixDQ is accepted by ECCV2024.
• [24/05] We release the MixDQ hardware acceleration pipline (with INT8 GPU kernel) at https://huggingface.co/nics-efc/MixDQ.
• [24/05] We release the MixDQ algorithm-level quantization simulation code at https://github.com/A-suozhang/MixDQ.

This repo contains the official code of our ECCV2024 paper: MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization

We design MixDQ, a mixed-precision quantization framework that successfully tackles the challenging few-step text-to-image diffusion model quantization. With negligible visual quality degradation and content change, MixDQ could achieve W4A8, with equivalent 3.4x memory compression and 1.5x latency speedup.

• 🤗 Open-Source Huggingface Pipeline 🤗: We implement efficient INT8 GPU kernel to achieve actual GPU acceleration (1.45x) and memory savings (2x) for W8A8. The pipeline is released at: https://huggingface.co/nics-efc/MixDQ. It could be easily implemented with just a few lines of code.

• Open-source CUDA Kernels: We provide open-sourced CUDA kernels for practical hardware savings in ./kernels, for more details for the CUDA development, please refer to the ./kernels/README.md

  • Memory Savings

    Memory Cost (MB)	Static (Weight)	Dynamic (Act)	Peak Memory
    FP16 version	4998	240.88	5239
    Quantized version	2575	55.77	2631
    Savings	1.94x	4.36x	1.99x

  • Latency Speedup

    UNet Latency (ms)	RTX3090	RT4080	A100
    FP16 version	43.6	36.1	30.7
    Quantized version	34.2	24.9	28.8
    Speedup	1.27x	1.45x	1.07x

• For more information, please refer to our Project Page: https://a-suozhang.xyz/mixdq.github.io/

Usage

EnvSetup

We recommend using conda for environment management.

cd quant_utils
conda env create -f environment.yml
conda activate mixdq
python -m pip install --upgrade --user ortools
pip install accelerate

Data Preparation

The stable diffusion checkpoints are automatically downloaded with the diffusers pipeline, we also provide manual download scripts in ./scripts/utils/download_huggingface_model.py. For text-to-image generation on COCO annotations, we provide the captions_val2014.json with Google Drive, please put it in the ./scripts/utils.

0. FP text-to-image generation

Run the main_fp_infer.sh to generate images based on coco annotation or given prompt. (When deleting --prompt xxx, using coco annotations as the default prompts.) The images could be found in $BASE_PATH/generated_images.

## SDXL_Turbo FP Inference
config_name='sdxl_turbo.yaml' # quant config, but only model names are used
BASE_PATH='./logs/debug_fp'   # save image path

CUDA_VISIBLE_DEVICES=$1 python scripts/txt2img.py \
		--config ./configs/stable-diffusion/$config_name \
		--base_path $BASE_PATH --batch_size 2 --num_imgs 8  --prompt  "a vanilla and chocolate mixing icecream cone, ice background" \
		--fp16

1. Normal Quantization

We provide the shell script main.sh for the whole quantization process. The quantization process consists of 3 steps: (1) generating the calibration data. (2) conduct PTQ process. (3) conduct quantized model inference. We also provide the scripts for each of the 3 processes (main_calib_data.sh,main_ptq.sh,main_quant_infer.sh). You could run the main.sh to finish the whole quantization process, or run three steps respectively.

1.1 Generate Calibration Data

Run the main_calib_data.sh $GPU_ID to generate the FP activation calibration data. The output path of calib data is specified in the quant config.yaml. the --save_image_path saves the FP generated reference images. (We provide the pre-generated calib data at Google Drive, you could replace it with "/share/public/diffusion_quant/calib_dataset/bs1024_t1_sdxl.pt" in mixdq_open_source/MixDQ/configs/stable-diffusion/sdxl_turbo.yaml. Noted that the calib_data in the google drive contains 1024 samples, so you may increase the n_samples in the sdxl_turbo.yaml up to 1024.)

CUDA_VISIBLE_DEVICES=$1 python scripts/gen_calib_data.py --config ./configs/stable-diffusion/$config_name --save_image_path ./debug_imgs

1.2 Post Training Quantization (PTQ) Process

Run the main_ptq.sh $LOG_NAME $GPU_ID to conduct PTQ to determine quant parameters, the quant parameters are saved as ckpt.pth in the log path. (We provide the ckpt.pth quant_params checkpoint for sdxl_turbo at Google Drive, you may put it under the ./logs/$log_name folder. It contains the quant_parmas for 2/4/8 bit, so you could use it with differnt mixed-precision configurations. )

CUDA_VISIBLE_DEVICES=$2 python scripts/ptq.py --config ./configs/stable-diffusion/${cfg_name} --outdir ./logs/$1 --seed 42

1.3 Inference Quantized Model

1.3.1 Normal

We provide the quantized inference example in the latter part of main.sh, and the main_quant_infer.sh (the commented part). The --num_imgs denotes how many images to generate, when no --prompt is given, the COCO annotations are used as the default prompts. By default, 1 image is generated for each prompt. When a user-defined prompt is given, "#num_imgs" of images are generated following this prompt.

CUDA_VISIBLE_DEVICES=$1 python scripts/quant_txt2img.py --base_path $CKPT_PATH --batch_size 2 --num_imgs 8 

1.3.2 Mixed Precision

For simplicity, we provide MixDQ acquired mixed precision configurations in ./mixed_precision_scripts/mixed_percision_config/sdxl_turbo/final_config/, the example of mixed precision inference is shown in main_quant_infer.sh. The "act protect" represents layers that are preserved as FP16. (It's also worth noting that the mixed_precision_scripts/quant_inference_mp.py are used for mixed precision search, for infering the mixed precision quant model, use scripts/quant_txt2img.py)

# Mixed Precision Quant Inference
WEIGHT_MP_CFG="./mixed_precision_scripts/mixed_percision_config/sdxl_turbo/final_config/weight/weight_8.00.yaml"  # [weight_5.02.yaml, weight_8.00.yaml]
ACT_MP_CFG="./mixed_precision_scripts/mixed_percision_config/sdxl_turbo/final_config/act/act_7.77.yaml "
ACT_PROTECT="./mixed_precision_scripts/mixed_percision_config/sdxl_turbo/final_config/act/act_sensitivie_a8_1%.pt"

CUDA_VISIBLE_DEVICES=$1 python scripts/quant_txt2img.py \
  --base_path $CKPT_PATH --batch_size 2 --num_imgs 8  --prompt"a vanilla and chocolate mixing icecream cone, ice background"\
  --config_weight_mp $WEIGHT_MP_CFG \
  --config_act_mp  $ACT_MP_CFG \
  --act_protect  $ACT_PROTECT \
  --fp16

Using the example prompt, the generated W8A8 with mixed precision should be like:

3. Mixed Precision Search

Please download the util_files from Google Drive, and unzip it in the repository root directory. Please refer to the ./mixed_precision_scripts/mixed_precision_search.md for detailed process of the mixed precision search process.

Acknowledgements

Our code is developed based on Q-Diffusion, and the Diffusers Libraray.

Citation

If you find our work helpful, please consider citing:

@misc{zhao2024mixdq,
      title={MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization}, 
      author={Tianchen Zhao and Xuefei Ning and Tongcheng Fang and Enshu Liu and Guyue Huang and Zinan Lin and Shengen Yan and Guohao Dai and Yu Wang},
      year={2024},
      eprint={2405.17873},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
    }

TODOs

• the evaluation scripts (FID, ClipScore, ImageReward)
• the efficient INT8 GPU kernels implementation

Contact

If you have any questions, feel free to contact:

Tianchen Zhao: [email protected]

Xuefei Ning: [email protected]