ParaTAA-Diffusion

This is the official repo for the paper "Accelerating Parallel Sampling of Diffusion Models" Tang et al. ICML 2024 [paper].

Environment

Here we provide the conda environment file for the code.

conda env create -f environment.yml
conda activate paraTAA

Use cases

Remark: In the following implementation, we use accelerate package from HuggingFace to implement DDP (Distributed Data Parallelism) for spliting the batch inference across 8 GPUs evenly.

1. ParaTAA with DiT

The used DiT models can be found here [DiT].

# Running ParaTAA with DiT on 8 GPUs, key parameters below
accelerate launch --num_processes 8 parallel_dit.py \
--timesteps <> \ # number of timesteps for generation
--cfg_scale <> \ # classifier-free guidance
--eta <> \ # eta for DDIM, eta=0 is ODE sampler, eta>0 is SDE sampler
--seed <> \ # random seed
--num_samples <> \ # number of samples to generate 
--window_size <> \ # equivalent to effect batch size
--order <> \ # order of used nonlinear equations
--memory_size <> \ # Parameter for Triangular Anderson Acceleration, determines the number of previous iteration to be used, recommended to be set between 2-5. If set to 1, no Triangular Anderson Acceleration will be used and it reduces to the naive Fixed-Point iteration.
--max_steps <> \ # maximum number of steps for the fixed-point iteration
--model_path <> \ # path to the pretrained DiT model
--vae_path <> \ # path to the pretrained VAE model
--output_path <> \ # path to save the generated samples
--fp16 # whether to use fp16, store_true action

# Example
accelerate launch --num_processes 8 parallel_dit.py --max_steps 10 --fp16

After running the above command, you will get the generated samples in the output_path. This command will store all the intermediate samples during the generation, which can help you see how the sample evolves after each step of the fixed-point iteration.

Sample output are provided in the output folder.

2. ParaTAA with SD v1.5

The used SD models can be found here [SD 1.5]. Most of the parameters are the same as the ones used in the paper, in the following command, we only list the key parameters.

accelerate launch --num_processes 8 parallel_sd.py \
--prompt <> \ # The prompt use for text-to-image generation
--model_path <> \ # The path to the diffuser pipeline of the SD 1.5 model

# Example
accelerate launch --num_processes 8 parallel_sd.py --max_steps 15 --fp16

The output will be the same as the one in the case 1.

3. ParaTAA with SD v1.5, initializing from existing samples

As we discussed in the paper, the logic behind parallel sampling of diffusion models is to refine an existing sample trajectory in parallel. With this observation, a natural idea for further speeding up the the parallel sampling is to initialize from an existing sample trajectory, rather than starting from scratch.

We consider this scenario: Suppose that we have done the generation for prompt A and stored the generation trajectory. Then later, some user decides to do the the generation for prompt B, which is a different but related prompt to prompt A. We note that this scenario is very common in practice especially during the prompt engineering. In this scenario, we can employ the existing sample trajectory of prompt A to initialize the fix-point iteration for prompt B, thus speeding up the generation process.

The code example is as follows:

accelerate launch --num_processes 8 parallel_sd_winit.py \
--prompt1 <> \ # The prompt use for generating a initial trajectory for initializing the generation with prompt2
--prompt2 <> \ # The prompt use for generation with initialization from prompt1
--variation_steps <> \ # The numbers of timestep to be updated
                                            for prompt 2 when initializing from prompt 1. E.g., if timesteps=50 and variation_steps=10, then only the last 10 timesteps will be updated when doing the generation for prompt 2.
                                            """)

# Example
accelerate launch --num_processes 8 parallel_sd_winit.py --fp16 --variation_steps 30 --prompt1 "a cute dog" --prompt2 "a cute cat"

The output will be two folders, one for prompt1 and the other for prompt2. For the prompt 1, the output is the same as above, showing the generation trajectory from scratch. For the prompt 2, the output will show how the trajectory from prompt 1 evolves during the generation for prompt 2.

If you find this code useful, please cite our paper:

@inproceedings{
tang2024accelerating,
title={Accelerating Parallel Sampling of Diffusion Models},
author={Zhiwei Tang and Jiasheng Tang and Hao Luo and Fan Wang and Tsung-Hui Chang},
booktitle={Forty-first International Conference on Machine Learning},
year={2024},
url={https://openreview.net/forum?id=CjVWen8aJL}
}

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