sd

README

stable-diffusion

pure go for stable-diffusion and support cross-platform.

sd.go is a wrapper around stable-diffusion-cpp, which is an adaption of ggml.cpp.

Installation

go get github.com/seasonjs/stable-diffusion

AutoModel Compatibility

See deps folder for dylib compatibility, push request is welcome.

platform	x32	x64	arm
windows	not support	support avx/avx2/avx512	not support
linux	not support	support	not support
darwin	not support	support (no test)	support

AutoModel Dynamic Libraries Disclaimer

The Source of dynamic Libraries

These dynamic libraries come from stable-diffusion.cpp-build release, The dynamic library version can be obtained by viewing stable-diffusion.version file Anyone can check the consistency of the file by checksum the md5 of the file.

The Security Of Dynamic Libraries

All I can say is that the creation of the dynamic library is public and does not contain any subjective malicious logic. If you are worried about the security of the dynamic library during the use process, you can build it yourself.

I and any author related to dynamic libraries do not assume any problems, responsibilities or legal liability during use.

Usage

This stable-diffusion golang library provide two api Predict and ImagePredict. You must instantiate each separately and set the correct options for the corresponding model.

You don't need to download stable-diffusion dynamic library.

package main

import (
	"os"
	"github.com/seasonjs/stable-diffusion"
)

func main() {
	options := sd.DefaultStableDiffusionOptions
	//It's important to set image size,different model support different size
	options.Width = 256
	options.Height = 256
	model, err := sd.NewStableDiffusionAutoModel(options)
	if err != nil {
		panic(err)
	}
	defer model.Close()
	err = model.LoadFromFile("./data/miniSD-ggml-model-q5_0.bin")
	if err != nil {
		panic(err)
	}
	file, err := os.Create("./data/output2.png")
	defer file.Close()
	if err != nil {
		panic(err)
	}
	err = model.Predict("A lovely cat, high quality", file)
	if err != nil {
		panic(err)
	}
}

package main
import (
	"os"
	"github.com/seasonjs/stable-diffusion"
)

func main()  {
	options := sd.DefaultStableDiffusionOptions
	//It's important to set image size,different model support different size
	options.Width = 256
	options.Height = 256
	//ImagePredict model need set VaeDecodeOnly false
	options.VaeDecodeOnly = false
	model, err := sd.NewStableDiffusionAutoModel(options)
	if err != nil {
		panic(err)
	}
	defer model.Close()
	err = model.LoadFromFile("./data/miniSD-ggml-model-q5_0.bin")
	if err != nil {
		panic(err)
	}
	inFile, err := os.Open("./data/output2.png")
	defer inFile.Close()
	if err != nil {
		panic(err)
	}
	outfile, err := os.Create("./data/output3.png")
	if err != nil {
		panic(err)
	}
	defer outfile.Close()
	err = model.ImagePredict(inFile, "the cat that wears shoes", outfile)
}

If NewStableDiffusionAutoModel can't automatic loading of dynamic library, please use NewStableDiffusionModel method load manually.

package main

import (
	"fmt"
	"runtime"
	"github.com/seasonjs/stable-diffusion"
)

func getLibrary() string {
	switch runtime.GOOS {
	case "darwin":
		return "./deps/darwin/libstable-diffusion_arm64.dylib"
	case "linux":
		return "./deps/linux/libstable-diffusion.so"
	case "windows":
		return "./deps/windows/stable-diffusion_avx2_x64.dll"
	default:
		panic(fmt.Errorf("GOOS=%s is not supported", runtime.GOOS))
	}
}
func main()  {
	options := sd.DefaultStableDiffusionOptions
	//It's important to set image size,different model support different size
	options.Width = 256
	options.Height = 256
	model, err := sd.NewStableDiffusionModel(getLibrary(),options)
	print(model,err)
	//.... the usage is same as `NewStableDiffusionAutoModel`. they both return `StableDiffusionModel` struct

}

Packaging

To ship a working program that includes this AI, you will need to include the following files:

• libstable-diffusion.dylib / libstable-diffusion.so / stable-diffusion.dll (buildin)
• the model file
• the tokenizer file (buildin)

Low level API

This package also provide low level Api which is same as stable-diffusion-cpp. See detail at stable-diffusion-doc.

Thanks

• stable-diffusion-cpp
• ggml.cpp
• purego

Successful Examples

License

Copyright (c) seasonjs. All rights reserved. Licensed under the MIT License. See License.txt in the project root for license information.

Documentation

Index

• Constants
• Variables
• type CSDCtx
  • func (c *CSDCtx) Close()
  • func (c *CSDCtx) StableDiffusionImageToImage(initImg []byte, prompt string, negativePrompt string, cfgScale float32, ...) ([]byte, error)
  • func (c *CSDCtx) StableDiffusionLoadFromFile(path string, schedule Schedule)
  • func (c *CSDCtx) StableDiffusionTextToImage(prompt string, negativePrompt string, cfgScale float32, width int, height int, ...) ([]byte, error)
• type CStableDiffusion
  • func NewCStableDiffusion(libraryPath string) (*CStableDiffusion, error)
  • func (cSD *CStableDiffusion) NewStableDiffusionCtx(nThreads int, vaeDecodeOnly bool, freeParamsImmediately bool, ...) *CSDCtx
  • func (cSD *CStableDiffusion) StableDiffusionGetSystemInfo() string
  • func (cSD *CStableDiffusion) StableDiffusionSetLogLevel(level SDLogLevel)
• type OutputsImageType
• type RNGType
• type SDLogLevel
• type SampleMethod
• type Schedule
• type StableDiffusionModel
  • func NewStableDiffusionAutoModel(options StableDiffusionOptions) (*StableDiffusionModel, error)
  • func NewStableDiffusionModel(dylibPath string, options StableDiffusionOptions) (*StableDiffusionModel, error)
  • func (sd *StableDiffusionModel) Close() error
  • func (sd *StableDiffusionModel) ImagePredict(reader io.Reader, prompt string, writer io.Writer) error
  • func (sd *StableDiffusionModel) LoadFromFile(path string) error
  • func (sd *StableDiffusionModel) Predict(prompt string, writer io.Writer) error
• type StableDiffusionOptions

Constants

const (
	DEBUG SDLogLevel = "DEBUG"
	INFO             = "INFO"
	WARN             = "WARN"
	ERROR            = "ERROR"
)

const (
	EULER_A          SampleMethod = "EULER_A"
	EULER                         = "EULER"
	HEUN                          = "HEUN"
	DPM2                          = "DPM2"
	DPMPP2S_A                     = "DPMPP2S_A"
	DPMPP2M                       = "DPMPP2M"
	DPMPP2Mv2                     = "DPMPP2Mv2"
	LCM                           = "LCM"
	N_SAMPLE_METHODS              = "N_SAMPLE_METHODS"
)

const (
	DEFAULT     Schedule = "DEFAULT"
	DISCRETE             = "DISCRETE"
	KARRAS               = "KARRAS"
	N_SCHEDULES          = "N_SCHEDULES"
)

Variables

var DefaultStableDiffusionOptions = StableDiffusionOptions{
	Threads:               -1,
	VaeDecodeOnly:         true,
	FreeParamsImmediately: true,
	LoraModelDir:          "",
	RngType:               CUDA_RNG,

	Schedule: DEFAULT,

	NegativePrompt:   "out of frame, lowers, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature",
	CfgScale:         7.0,
	Width:            500,
	Height:           500,
	SampleMethod:     EULER_A,
	SampleSteps:      20,
	Strength:         0.4,
	Seed:             42,
	OutputsImageType: PNG,
}

Types

type CSDCtx

type CSDCtx struct {
	// contains filtered or unexported fields
}

func (*CSDCtx) Close

func (c *CSDCtx) Close()

func (*CSDCtx) StableDiffusionImageToImage

func (c *CSDCtx) StableDiffusionImageToImage(initImg []byte, prompt string, negativePrompt string, cfgScale float32, width int, height int, sampleMethod SampleMethod, sampleSteps int, strength float32, seed int64) ([]byte, error)

func (*CSDCtx) StableDiffusionLoadFromFile

func (c *CSDCtx) StableDiffusionLoadFromFile(path string, schedule Schedule)

func (*CSDCtx) StableDiffusionTextToImage

func (c *CSDCtx) StableDiffusionTextToImage(prompt string, negativePrompt string, cfgScale float32, width int, height int, sampleMethod SampleMethod, sampleSteps int, seed int64) ([]byte, error)

type CStableDiffusion

type CStableDiffusion struct {
	// contains filtered or unexported fields
}

func NewCStableDiffusion

func NewCStableDiffusion(libraryPath string) (*CStableDiffusion, error)

func (*CStableDiffusion) NewStableDiffusionCtx

func (cSD *CStableDiffusion) NewStableDiffusionCtx(nThreads int, vaeDecodeOnly bool, freeParamsImmediately bool, loraModelDir string, rngType RNGType) *CSDCtx

func (*CStableDiffusion) StableDiffusionGetSystemInfo

func (cSD *CStableDiffusion) StableDiffusionGetSystemInfo() string

func (*CStableDiffusion) StableDiffusionSetLogLevel

func (cSD *CStableDiffusion) StableDiffusionSetLogLevel(level SDLogLevel)

type OutputsImageType

type OutputsImageType string

const (
	PNG  OutputsImageType = "PNG"
	JPEG                  = "JPEG"
)

type RNGType

type RNGType string

const (
	STD_DEFAULT_RNG RNGType = "STD_DEFAULT_RNG"
	CUDA_RNG                = "CUDA_RNG"
)

type SDLogLevel

type SDLogLevel string

type SampleMethod

type SampleMethod string

type Schedule

type Schedule string

type StableDiffusionModel

type StableDiffusionModel struct {
	// contains filtered or unexported fields
}

func NewStableDiffusionAutoModel

func NewStableDiffusionAutoModel(options StableDiffusionOptions) (*StableDiffusionModel, error)

func NewStableDiffusionModel

func NewStableDiffusionModel(dylibPath string, options StableDiffusionOptions) (*StableDiffusionModel, error)

func (*StableDiffusionModel) Close

func (sd *StableDiffusionModel) Close() error

func (*StableDiffusionModel) ImagePredict

func (sd *StableDiffusionModel) ImagePredict(reader io.Reader, prompt string, writer io.Writer) error

func (*StableDiffusionModel) LoadFromFile

func (sd *StableDiffusionModel) LoadFromFile(path string) error

func (*StableDiffusionModel) Predict

func (sd *StableDiffusionModel) Predict(prompt string, writer io.Writer) error

type StableDiffusionOptions

type StableDiffusionOptions struct {
	Threads               int
	VaeDecodeOnly         bool
	FreeParamsImmediately bool
	LoraModelDir          string
	RngType               RNGType

	Schedule Schedule

	NegativePrompt   string
	CfgScale         float32
	Width            int
	Height           int
	SampleMethod     SampleMethod
	SampleSteps      int
	Strength         float32
	Seed             int64
	OutputsImageType OutputsImageType
}