k-diffusion
Karras et al. (2022) diffusion models for PyTorch
Decision gist · record as of 2026-08-14
Yes, if you are actively researching diffusion models or need a flexible PyTorch-native training framework with advanced sampling algorithms. The aging maintenance status (no releases since 2023-12-07) and substantial dependency footprint make it less suitable for production systems or lightweight prototyping. No known security vulnerabilities. Best suited for GPU-equipped research environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch and torchvision; GPU strongly recommended for practical use.
- Full training scripts require repository clone, not available via PyPI install alone.
- Low install friction with a pure-Python wheel, but 17 runtime dependencies including torch, torchvision, and accelerate create a substantial environment footprint.
License · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions beyond attribution.
last release 2023-12-07 (981 days) · last repo commit 2026-02-12 · 2,600 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 120,076 downloads/mo, #12,043 on PyPI
Alternatives
Verify before relying
pip install k-diffusion
import k_diffusion
# For full training/inference scripts, clone the repository and run: pip install -e .- Whether the package's transformer diffusion enhancements are production-ready or experimental
- Current state of latent diffusion support (listed as 'To do' in the description)
- Compatibility with recent PyTorch and torchvision versions given the aging maintenance status
What it is and what it does
k-diffusion is a research-oriented PyTorch library that implements the diffusion model design space from Karras et al. (2022). It provides training and sampling infrastructure for diffusion-based generative models, with particular emphasis on improved sampling algorithms like DPM-Solver and support for transformer-based architectures. The library includes wrappers to use models from other frameworks with its own samplers, plus features like CLIP-guided sampling, FID and KID evaluation during training, and log-likelihood calculation.
The package is designed primarily for research and experimentation rather than production deployment. Installation via PyPI gives you the library code for use as a dependency, while full training and inference scripts require cloning the repository. The 17 runtime dependencies—including torch, torchvision, accelerate, and various image and math libraries—mean this is a heavyweight addition to any environment, intended for GPU-based machine learning workflows.
Use it for
- Train diffusion models on custom datasets with multi-GPU support via accelerate
- Experiment with transformer-based diffusion architectures as an alternative to convolutional designs
- Sample from diffusion models using DPM-Solver with adaptive step control for quality at low step counts
- Perform CLIP-guided generation from unconditional diffusion models
- Evaluate model quality during training using FID and KID metrics
- Wrap and sample from models trained with other frameworks using k-diffusion's samplers
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively researching diffusion models or need a flexible PyTorch-native training framework with advanced sampling algorithms.
The aging maintenance status (no releases since 2023-12-07) and substantial dependency footprint make it less suitable for production systems or lightweight prototyping. No known security vulnerabilities. Best suited for GPU-equipped research environments.
Install
k-diffusion on PyPI
Before you install
Low install friction with a pure-Python wheel, but 17 runtime dependencies including torch, torchvision, and accelerate create a substantial environment footprint. Maintenance status is aging—last release was 2023-12-07, though the repository remains unarchived.
Requires torch and torchvision; GPU strongly recommended for practical use. Full training scripts require repository clone, not available via PyPI install alone.
License in practice
MIT license is permissive, allowing commercial and private use with minimal restrictions beyond attribution.
Quickstart
pip install k-diffusion
import k_diffusion
# For full training/inference scripts, clone the repository and run: pip install -e .Verify before relying
- Whether the package's transformer diffusion enhancements are production-ready or experimental
- Current state of latent diffusion support (listed as 'To do' in the description)
- Compatibility with recent PyTorch and torchvision versions given the aging maintenance status
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 17 packagesaccelerateclean-fidclip-anytorchdctorcheinopsjsonmergekorniaPillowsafetensorsscikit-imagescipytorchtorchdiffeqtorchsdetorchvisiontqdmwandb |
| Maintenance | Aging 981 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 120,076 / month, #12,043 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: k_diffusion-0.1.1.post1-py3-none-any.whl
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