• Denoising Diffusion Probabilistic Model, in MindSpore
• • Results
  • Install
  • • From PyPI
    • From Source code
  • Usage
  • Citations

本项目是使用以下这位大佬的mindspore代码
https://github.com/lvyufeng/denoising-diffusion-mindspore
增加了一些数据copy的操作，适配启智的训练作业环境，训练afhq动物数据集图像的生成，demo.py是训练作业中的启动文件
经测试，将参数设置为image_size=128，train_batch_size=32，能够得到比较好的训练效果，如训练作业train_cat_data中版本V0010中的参数设置：


以下是原readme中的内容

Denoising Diffusion Probabilistic Model, in MindSpore

Implementation of Denoising Diffusion Probabilistic Model in MindSpore. The implementation refers to lucidrains's denoising-diffusion-pytorch.

Results

Training 50k steps with EMA.

Install

From PyPI

pip install denoising-diffusion-mindspore

From Source code

# Github repo(oversea)
pip install git+https://github.com/lvyufeng/denoising-diffusion-mindspore
# From OpenI repo(in China)
pip install git+https://openi.pcl.ac.cn/lvyufeng/denoising-diffusion-mindspore

Usage

from ddm import Unet, GaussianDiffusion, value_and_grad
from ddm.ops import randn

model = Unet(
    dim = 64,
    dim_mults = (1, 2, 4, 8)
)

diffusion = GaussianDiffusion(
    model,
    image_size = 128,
    timesteps = 1000,   # number of steps
    loss_type = 'l1'    # L1 or L2
)

training_images = randn((1, 3, 128, 128)) # images are normalized from 0 to 1
grad_fn = value_and_grad(diffusion, None, diffusion.trainable_params())

loss, grads = grad_fn(training_images)
# after a lot of training

sampled_images = diffusion.sample(batch_size = 1)
print(sampled_images.shape) # (4, 3, 128, 128)

Or, if you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model.

from download import download
from ddm import Unet, GaussianDiffusion, Trainer

url = 'https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz'
path = download(url, './102flowers', 'tar.gz')

model = Unet(
    dim = 64,
    dim_mults = (1, 2, 4, 8)
)

diffusion = GaussianDiffusion(
    model,
    image_size = 64,
    timesteps = 10,             # number of steps
    sampling_timesteps = 5,     # number of sampling timesteps (using ddim for faster inference [see citation for ddim paper])
    loss_type = 'l1'            # L1 or L2
)

trainer = Trainer(
    diffusion,
    path,
    train_batch_size = 1,
    train_lr = 8e-5,
    train_num_steps = 1000,         # total training steps
    gradient_accumulate_every = 2,    # gradient accumulation steps
    ema_decay = 0.995,                # exponential moving average decay
    amp_level = 'O1',                        # turn on mixed precision
)

trainer.train()

amp_level of Trainer will automaticlly set to O1 on Ascend.

Citations

@inproceedings{NEURIPS2020_4c5bcfec,
    author      = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
    booktitle   = {Advances in Neural Information Processing Systems},
    editor      = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
    pages       = {6840--6851},
    publisher   = {Curran Associates, Inc.},
    title       = {Denoising Diffusion Probabilistic Models},
    url         = {https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
    volume      = {33},
    year        = {2020}
}