A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Python
Category
Tutorials
Subcategory
ML/AI fundamentals
Keywords
Image production
Stars
1,464
Forks
304
Contributors
1
Created date
2022-06-07
Updated date
2026-10-15
Stars in 1 day
0
Stars in 7 days
-1
Rank
10,254
Previous rank
10,262
Rank change
8
7-day stars rank
17,141
Previous 7-day stars rank
12,602
7-day stars rank change
-4,539
Total stars rank
8,244
Previous total stars rank
8,235
Total stars rank change
-9
Country
Germany
README summary
Diffusion Models This is an easy-to-understand implementation of diffusion models within 100 lines of code. Different from other implementations, this code doesn't use the lower-bound formulation for sampling and strictly follows Algorithm 1 from the DDPM paper, which makes it extremely short and easy to follow. Furthermore, the conditional code also implements Classifier-Free-Guidance (CFG) and Exponential-Moving-Average (EMA). Below you can find two explanation videos for the theory behind diffusion models and the implementation
. Train a Diffusion Model on your own data: Unconditional Training 1. (optional) Configure Hyperparameters in 2. (optional) Configure Hyperparameters in 2. Sampling The following examples show how to sample images using the models trained in the video on the Landscape Dataset. You can download the checkpoints for the models here. Unconditional Model Conditional Model This model was trained on CIFAR-10 64x64 with 10 classes A more advanced version of this code can be found here by @tcapelle. It introduces
better logging, faster & more efficient training and other nice features and is also being followed by a nice write-up.
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dome272/Diffusion-Models-pytorch has 1,464 stars and 304 forks.
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dome272/Diffusion-Models-pytorch has 1,464 stars, uses Python and belongs to Tutorials.
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README
Diffusion Models
This is an easy-to-understand implementation of diffusion models within 100 lines of code. Different from other implementations, this code doesn't use the lower-bound formulation for sampling and strictly follows Algorithm 1 from the DDPM paper, which makes it extremely short and easy to follow. There are two implementations: conditional and unconditional. Furthermore, the conditional code also implements Classifier-Free-Guidance (CFG) and Exponential-Moving-Average (EMA). Below you can find two explanation videos for the theory behind diffusion models and the implementation.
Train a Diffusion Model on your own data:
Unconditional Training
(optional) Configure Hyperparameters in ddpm.py
Set path to dataset in ddpm.py
python ddpm.py
Conditional Training
(optional) Configure Hyperparameters in ddpm_conditional.py
Set path to dataset in ddpm_conditional.py
python ddpm_conditional.py
Sampling
The following examples show how to sample images using the models trained in the video on the Landscape Dataset. You can download the checkpoints for the models here.
Unconditional Model
device = "cuda"
model = UNet().to(device)
ckpt = torch.load("unconditional_ckpt.pt")
model.load_state_dict(ckpt)
diffusion = Diffusion(img_size=64, device=device)
x = diffusion.sample(model, n=16)
plot_images(x)
Conditional Model
This model was trained on CIFAR-10 64x64 with 10 classes airplane:0, auto:1, bird:2, cat:3, deer:4, dog:5, frog:6, horse:7, ship:8, truck:9
n = 10
device = "cuda"
model = UNet_conditional(num_classes=10).to(device)
ckpt = torch.load("conditional_ema_ckpt.pt")
model.load_state_dict(ckpt)
diffusion = Diffusion(img_size=64, device=device)
y = torch.Tensor([6] * n).long().to(device)
x = diffusion.sample(model, n, y, cfg_scale=3)
plot_images(x)
A more advanced version of this code can be found here by @tcapelle. It introduces better logging, faster & more efficient training and other nice features and is also being followed by a nice write-up.
