Composable sampling functions for diffusion models
Project description
Skrample 0.5.1
Composable sampling functions for diffusion models
Status
Mostly complete for common models, superseding all diffusers features in quickdif
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 theBeta()schedule modifierbrownian-noise->torchsde: For theBrownian()noise generatorcdf-schedule->scipy: For theSigmoidCDF()schedulediffusers-wrapper->torch: For thediffusersintegration modulepytorch->torch: For thepytorchmodulepytorch.noise: Custom generators
all: All of the abovedev: For runningtests/
Samplers
- Euler
- DPM
- 1st order, 2nd order, 3rd order
- SDE
- Adams/IPNDM
- UniP & UniPC
- N order, limited to 9 for stability
- Custom solver via other SkrampleSampler types
- SPC
- Basic fully customizable midpoint corrector
Schedules
- Linear
- Scaled
uniformflag, AKA"trailing"in diffusers
- SigmaCDF
- ZSNR
Schedule modifiers
- Karras
- Exponential
- FlowShift
- Beta
- Hyper
Predictors
- Epsilon
- Velocity / vpred
- Flow
Noise generators
- Random
- Brownian
- Offset
- Pyramid
Integrations
Diffusers
- Compatibility for pipelines
- SD1
- SDXL
- SD3
- Flux
- Import from config
- Sampler
- Schedule
- Predictor
- Manage state
- Steps
- Higher order
- Generators
- Config as presented
Implementations
quickdif
My diffusers cli quickdif has full support for all major Skrample features, allowing extremely fine-grained customization.
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