StepDrop

Stochastic Step Skipping in Tiny Diffusion Models

Table of Contents

1. About The Project
2. Built With
3. Getting Started
4. Quick Start
5. Pipeline Script
6. Training
7. Sampling
8. Evaluation & Benchmarking
9. Interpreting Metrics
10. StepDrop Skip Strategies
11. Visualization Utilities
12. Project Structure
13. Roadmap
14. Contributing
15. License
16. Contact
17. Acknowledgments

About The Project

StepDrop is a novel sampling method designed to accelerate inference in diffusion models, particularly tiny ones. By introducing a stochastic step skipping technique, it significantly reduces the number of required sampling steps while maintaining high-quality image generation.

This repository contains the official implementation, experiments, and demo notebooks for the StepDrop project.

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Built With

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Getting Started

Prerequisites

• Python 3.8+
• pip or conda
• CUDA-compatible GPU (recommended)

Installation

1. Clone the repository

   git clone https://github.com/Tonic4to/stepdrop-tiny-diffusion.git
   cd stepdrop-tiny-diffusion

2. Create a virtual environment (recommended)

   python -m venv venv
   source venv/bin/activate # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Verify installation

   python scripts/checklibs.py

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Quick Start

Interactive Menu

The easiest way to get started is with the interactive quick start menu:

./scripts/quick_start.sh

This provides a menu-driven interface for common tasks like training, sampling, and benchmarking.

One-Command Pipeline

Run the full pipeline (train → sample → evaluate) with a single command:

chmod +x scripts/pipeline.sh
./scripts/pipeline.sh --all --dataset cifar10 --epochs 10 --eval-samples 1000

Quick Test

For a fast sanity check on MNIST:

./scripts/pipeline.sh --all --dataset mnist --epochs 5 --n-samples 16 --eval-samples 100

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Pipeline Script

The main automation tool is scripts/pipeline.sh. It orchestrates training, sampling, and evaluation.

Usage

./scripts/pipeline.sh [OPTIONS]

Pipeline Stages

Flag	Description
--train	Run training stage
--sample	Run sampling stage
--evaluate	Run evaluation/benchmarking
--all	Run all stages (train → sample → evaluate)
--clean	Clean generated files

Common Options

Option	Default	Description
--dataset	cifar10	Dataset: mnist, cifar10, custom
--epochs	50	Training epochs
--batch-size	128	Training batch size
--base-channels	64	U-Net base channels
--checkpoint	auto	Path to model checkpoint
--n-samples	64	Number of samples to generate
--method	ddim	Sampling method: ddpm, ddim, stepdrop
--eval-samples	1000	Samples for FID/IS evaluation
--device	cuda	Device: cuda or cpu

Examples

# Full CIFAR-10 training with evaluation
./scripts/pipeline.sh --all --dataset cifar10 --epochs 100 --base-channels 128 --eval-samples 5000
# Train only on MNIST
./scripts/pipeline.sh --train --dataset mnist --epochs 20
# Sample with DDIM from existing checkpoint
./scripts/pipeline.sh --sample --checkpoint checkpoints/model.pt --method ddim --ddim-steps 50 --n-samples 64
# Sample with StepDrop
./scripts/pipeline.sh --sample --checkpoint checkpoints/model.pt --method stepdrop --skip-prob 0.3 --skip-strategy linear
# Evaluate with full metrics
./scripts/pipeline.sh --evaluate --checkpoint checkpoints/model.pt --eval-samples 5000 --full-metrics
# Compare StepDrop strategies against DDIM baselines
./scripts/pipeline.sh --evaluate --checkpoint checkpoints/model.pt --compare-stepdrop --eval-samples 1000
# Dry run (show commands without executing)
./scripts/pipeline.sh --all --dataset mnist --epochs 5 --dry-run

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Training

Direct Training Script

python src/train.py --dataset cifar10 --epochs 50 --batch_size 128

Training Options

Argument	Default	Description
--dataset	mnist	Dataset: mnist, cifar10, custom
--custom_data_dir	None	Path to custom images folder
--img_size	28	Image size
--channels	1	Number of image channels
--batch_size	128	Training batch size
--epochs	20	Number of epochs
--lr	2e-4	Learning rate
--n_timesteps	1000	Diffusion timesteps
--schedule_type	cosine	Noise schedule: linear, cosine
--base_channels	64	U-Net base channels
--save_path	checkpoints/model.pt	Model save path
--resume	None	Resume from checkpoint

Resume Training

python src/train.py --resume checkpoints/checkpoint_epoch_50.pt --epochs 100

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Sampling

Direct Sampling Script

python src/sample.py --checkpoint checkpoints/model.pt --method ddim --ddim_steps 50 --n_samples 16

Sampling Methods

Method	Command	Description
DDPM	--method ddpm	Full 1000 steps, highest quality
DDIM	--method ddim --ddim_steps 50	Accelerated, deterministic
StepDrop	--method stepdrop --skip_prob 0.3	Stochastic step skipping
Adaptive StepDrop	--method adaptive_stepdrop	Error-based dynamic skipping

Sampling Options

Argument	Default	Description
--checkpoint	required	Path to trained model
--method	ddpm	Sampling method
--n_samples	16	Number of samples
--ddim_steps	50	DDIM inference steps
--ddim_eta	0.0	DDIM stochasticity (0 = deterministic)
--skip_prob	0.3	StepDrop skip probability
--skip_strategy	linear	StepDrop strategy
--output_dir	samples	Output directory
--save_grid	True	Save as image grid
--save_individual	False	Save individual images

Examples

# DDPM (best quality, slow)
python src/sample.py --checkpoint checkpoints/model.pt --method ddpm --n_samples 16
# DDIM (fast)
python src/sample.py --checkpoint checkpoints/model.pt --method ddim --ddim_steps 25 --n_samples 64
# StepDrop with linear strategy
python src/sample.py --checkpoint checkpoints/model.pt --method stepdrop --skip_prob 0.3 --skip_strategy linear
# StepDrop with quadratic strategy (more aggressive)
python src/sample.py --checkpoint checkpoints/model.pt --method stepdrop --skip_prob 0.5 --skip_strategy quadratic
# Adaptive StepDrop
python src/sample.py --checkpoint checkpoints/model.pt --method adaptive_stepdrop

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Evaluation & Benchmarking

Benchmark Script

Run comprehensive benchmarks comparing different sampling strategies:

# Quick test with dummy model
python scripts/benchmark_strategies.py --dummy --samples 10
# Full benchmark with trained model
python scripts/benchmark_strategies.py --checkpoint checkpoints/model.pt --samples 5000
# With full metrics (FID, KID, IS, Precision, Recall, LPIPS, SSIM, PSNR, Vendi)
python scripts/benchmark_strategies.py --checkpoint checkpoints/model.pt --samples 5000 --full-metrics

Via Pipeline

# Basic evaluation
./scripts/pipeline.sh --evaluate --checkpoint checkpoints/model.pt --eval-samples 1000
# Full metrics
./scripts/pipeline.sh --evaluate --checkpoint checkpoints/model.pt --eval-samples 5000 --full-metrics
# Compare all StepDrop strategies vs DDIM
./scripts/pipeline.sh --evaluate --checkpoint checkpoints/model.pt --compare-stepdrop
# Evaluate only StepDrop variants
./scripts/pipeline.sh --evaluate --checkpoint checkpoints/model.pt --stepdrop-only
# Specific strategies only
./scripts/pipeline.sh --evaluate --checkpoint checkpoints/model.pt \
--strategies "DDIM_50,StepDrop_Linear_0.3,StepDrop_Quadratic_0.3"

Output

Results are saved to results/<timestamp>/:

• report.json - Full metrics data
• report.csv - Summary for Excel/Sheets
• *.png - Auto-generated plots (Pareto frontier, radar charts, etc.)
• samples/ - Generated sample images per strategy

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Interpreting Metrics

Metric	Full Name	Goal	Description
FID	Fréchet Inception Distance	📉 Lower is better	Similarity to real dataset. <10: excellent, 10-30: good, >50: poor
IS	Inception Score	📈 Higher is better	Clarity and diversity. CIFAR-10 real data ≈ 11.0
KID	Kernel Inception Distance	📉 Lower is better	Similar to FID, less biased for small samples
Precision	-	📈 Higher is better	Quality: are generated images realistic?
Recall	-	📈 Higher is better	Diversity: does the model cover the data distribution?
LPIPS	Perceptual Similarity	📉 Lower is better	Perceptual distance (diversity among samples)
Throughput	Images/Second	📈 Higher is better	Generation speed
NFE	Number of Function Evaluations	📉 Lower is better	U-Net forward passes per image

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StepDrop Skip Strategies

Probability-Based Strategies (StepDropSampler)

Strategy	Formula	Description
constant	$P(t) = p$	Fixed skip probability
linear	$P(t) = p \cdot 4t(1-t)$	Parabolic peak at middle
cosine_sq	$P(t) = p \cdot \sin^2(\pi t)$	Smooth cosine curve
quadratic	$P(t) = p \cdot 16t^2(1-t)^2$	Sharper middle peak
early_skip	$P(t) = p \cdot t$	Skip more at high noise
late_skip	$P(t) = p \cdot (1-t)$	Skip more at low noise
critical_preserve	Variable	Protect [0.3, 0.7] interval

Adaptive Strategy (AdaptiveStepDropSampler)

Dynamically adjusts skipping based on reconstruction error:

• Low error → skip more aggressively
• High error → force denoising steps

Target NFE Strategy (TargetNFEStepDropSampler)

Targets a specific step budget:

• uniform - Evenly spaced (like DDIM)
• importance - More steps at start/end
• stochastic - Random with boundary protection

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Visualization Utilities

Generate Comparison Grid

python scripts/generate_grid.py

Output: results/comparison_grid.png - Side-by-side DDPM vs DDIM vs StepDrop

Visualize Schedules

python scripts/plot_schedules.py --save_path results/schedules.png

Output: Probability curves and step sizes for different strategies

Benchmark Plots

python scripts/plot_results.py --results results/2025-12-07_12-00-00/

Output: Pareto frontiers, radar charts, metric comparisons

Denoising Evolution

python scripts/plot_denoising_evolution.py

Output: results/plot_denoising_evolution.png - Film strip showing denoising progression

Efficiency Plots

python scripts/plot_efficiency.py --results results/

Output: FLOPs/Memory analysis

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Project Structure

stepdrop-tiny-diffusion/
├── src/
│ ├── config.py # Configuration management
│ ├── dataset.py # Data loading (MNIST, CIFAR-10, custom)
│ ├── modules.py # U-Net architecture
│ ├── scheduler.py # Noise schedules
│ ├── train.py # Training script
│ ├── sample.py # Sampling script
│ ├── sampler/ # Sampler implementations
│ │ ├── DDPM.py
│ │ ├── DDIM.py
│ │ ├── StepDrop.py
│ │ └── AdaptiveStepDrop.py
│ └── eval/ # Evaluation metrics
│ └── metrics_utils.py
├── scripts/
│ ├── pipeline.sh # Main automation script
│ ├── quick_start.sh # Interactive menu
│ ├── benchmark_strategies.py
│ ├── plot_results.py
│ ├── plot_schedules.py
│ ├── plot_denoising_evolution.py
│ └── generate_grid.py
├── notebooks/ # Jupyter notebooks
├── checkpoints/ # Saved models
├── samples/ # Generated samples
├── results/ # Benchmark results
└── docs/ # Documentation

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HPC / SLURM Support

For cluster environments:

# Submit job to SLURM
sbatch scripts/run_pipeline.slurm
# With custom arguments
sbatch scripts/run_pipeline.slurm --train --dataset cifar10 --epochs 100

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Roadmap

• Core StepDrop sampler implementation
• Pipeline automation script
• Comprehensive benchmarking suite
• Multiple skip strategies
• Example notebook for Tiny Diffusion
• Example notebook for Stable Diffusion 1.5
• Package as pip-installable library
• Integration with HuggingFace Diffusers
• Support for more diffusion schedulers

See open issues for proposed features and known issues.

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Contributing

Contributions are welcome!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the GNU General Public License v3.0. See LICENSE for more information.

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Contact

Wanghley Soares Martins - @wanghley - me@wanghley.com

Nicolas Vasilescu - @NicolasVasilescu

Project Link: https://github.com/Tonic4to/stepdrop-tiny-diffusion

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Acknowledgments

• Hugging Face Diffusers
• Choose an Open Source License
• Img Shields

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