Composable sampling functions for diffusion models

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  • License Expression: Apache-2.0
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  • Author: Beinsezii
  • Requires: Python >=3.12
  • Provides-Extra: all , beta-schedule , brownian-noise , cdf-schedule , dev , diffusers-wrapper , pytorch , scripts , test
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Project description

Skrample 0.6.0

Composable sampling functions for diffusion models

Status

Production-tested on all popular diffusion models. The library has significantly matured since 0.5

Quickstart

Fastest way to jump in is examples. The classes and functions themselves have docstrings and type hints, so it's recommended to make liberal use of your IDE or python help()

Feature Flags

  • beta-schedule -> scipy : For the Beta() schedule modifier
  • brownian-noise -> torchsde : For the Brownian() noise generator
  • cdf-schedule -> scipy : For the Probit() schedule
  • diffusers-wrapper -> torch : For the diffusers integration module
  • pytorch -> torch : For the pytorch module
    • pytorch.noise : Custom generators
  • all : All of the above
  • dev : For running tests/

Structured Samplers

These samplers are written inside-out to be compatible with Diffusers and similar frameworks

  • Euler
    • Stochastic
  • DPM
    • Order 1-3
    • Stochastic
  • Adams/IPNDM
    • Order 1-9
    • Stochastic
  • UniP & UniPC
    • Order 1-9
    • Stochastic
    • Custom predictor via other SkrampleSampler types
  • SPC
    • Basic fully customizable midpoint corrector

Functional Samplers

These samplers are written using closures similar to ksampler

  • RKUltra
    • Arbitrary Runge-Kutta solver
    • Order 1-15, customizable through tableaux system
    • Stochastic
  • DynasauRK
    • Procedural Runge-Kutta solver
    • Order 2-4
    • Stochastic
  • RKMoire
    • Experimental
    • Embedded Runge-Kutta solver
    • Order 2-6, customizable through tableaux system

Schedules

  • Linear
    • Flow-matching default
  • Scaled
    • Variance-preserving default
  • ZSNR

Subschedules

Replaces sigmas on an existing schedule

  • Karras
  • Exponential
  • Beta
  • Probit

Schedule Modifiers

Modifies timestep spacing of a schedule

  • FlowShift
  • Hyper
  • Sinner

Models

  • Data / Sample / X-Pred
  • Noise / Epsilon / Ε-Pred
  • Velocity / V-Pred
  • Flow / U-pred

Noise generators

  • Random
  • Brownian
  • Offset
  • Pyramid

Integrations

Diffusers

  • Compatibile with DiffusionPipeline
  • Import from config
    • Sampler
    • Schedule
    • Predictor
  • Structured sampler wrapper
  • Functional sampler wrappers
    • RKUltra
    • DynasauRK

Implementations

quickdif

My diffusers cli quickdif has full support for all major Diffusers-compatible Skrample features, allowing extremely fine-grained customization.

Project details

Verified details

These details have been verified by PyPI
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Avatar for beinsezii from gravatar.com beinsezii

Unverified details

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Meta
  • License Expression: Apache-2.0
    SPDX License Expression
  • Author: Beinsezii
  • Requires: Python >=3.12
  • Provides-Extra: all , beta-schedule , brownian-noise , cdf-schedule , dev , diffusers-wrapper , pytorch , scripts , test

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