Diffusers documentation
Activation functions
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OverviewUnderstanding pipelines, models and schedulersAutoPipelineTrain a diffusion modelLoad LoRAs for inferenceAccelerate inference of text-to-image diffusion models
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OverviewLoad pipelines, models, and schedulersLoad and compare different schedulersLoad community pipelines and componentsLoad safetensorsLoad different Stable Diffusion formatsLoad adaptersPush files to the Hub
Tasks
OverviewUnconditional image generationText-to-imageImage-to-imageInpaintingText or image-to-videoDepth-to-image
Techniques
Textual inversionIP-AdapterMerge LoRAsDistributed inference with multiple GPUsImprove image quality with deterministic generationControl image brightnessPrompt weightingImprove generation quality with FreeU
Specific pipeline examples
OverviewStable Diffusion XLSDXL TurboKandinskyControlNetShap-EDiffEditDistilled Stable Diffusion inferencePipeline callbacksCreate reproducible pipelinesCommunity pipelinesContribute a community pipelineLatent Consistency Model-LoRALatent Consistency ModelTrajectory Consistency Distillation-LoRAStable Video Diffusion
Training
OverviewCreate a dataset for trainingAdapt a model to a new task
Models
Unconditional image generationText-to-imageStable Diffusion XLKandinsky 2.2WuerstchenControlNetT2I-AdaptersInstructPix2Pix
Methods
Taking Diffusers Beyond Images
Optimization
Conceptual Guides
PhilosophyControlled generationHow to contribute?Diffusers' Ethical GuidelinesEvaluating Diffusion Models
API
Main Classes
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Models
OverviewUNet1DModelUNet2DModelUNet2DConditionModelUNet3DConditionModelUNetMotionModelUViT2DModelVQModelAutoencoderKLAsymmetricAutoencoderKLTiny AutoEncoderConsistencyDecoderVAETransformer2DTransformer TemporalPrior TransformerControlNet
Pipelines
OverviewaMUSEdAnimateDiffAttend-and-ExciteAudioLDMAudioLDM 2AutoPipelineBLIP-DiffusionConsistency ModelsControlNetControlNet with Stable Diffusion XLDance DiffusionDDIMDDPMDeepFloyd IFDiffEditDiTI2VGen-XLInstructPix2PixKandinsky 2.1Kandinsky 2.2Kandinsky 3Latent Consistency ModelsLatent DiffusionLEDITS++MultiDiffusionMusicLDMPaint by ExamplePersonalized Image Animator (PIA)PixArt-αSelf-Attention GuidanceSemantic GuidanceShap-EStable Cascade
Stable Diffusion
OverviewText-to-imageImage-to-imageImage-to-videoInpaintingDepth-to-imageImage variationSafe Stable DiffusionStable Diffusion 2Stable Diffusion XLSDXL TurboLatent upscalerSuper-resolutionK-DiffusionLDM3D Text-to-(RGB, Depth), Text-to-(RGB-pano, Depth-pano), LDM3D UpscalerStable Diffusion T2I-AdapterGLIGEN (Grounded Language-to-Image Generation)
Stable unCLIPText-to-videoText2Video-ZerounCLIPUniDiffuserValue-guided samplingWuerstchenSchedulers
OverviewCMStochasticIterativeSchedulerConsistencyDecoderSchedulerDDIMInverseSchedulerDDIMSchedulerDDPMSchedulerDEISMultistepSchedulerDPMSolverMultistepInverseDPMSolverMultistepSchedulerDPMSolverSDESchedulerDPMSolverSinglestepSchedulerEulerAncestralDiscreteSchedulerEulerDiscreteSchedulerEDMEulerSchedulerEDMDPMSolverMultistepSchedulerHeunDiscreteSchedulerIPNDMSchedulerKarrasVeSchedulerKDPM2AncestralDiscreteSchedulerKDPM2DiscreteSchedulerLCMSchedulerLMSDiscreteSchedulerPNDMSchedulerRePaintSchedulerScoreSdeVeSchedulerScoreSdeVpSchedulerTCDSchedulerUniPCMultistepSchedulerVQDiffusionScheduler
Internal classes
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Activation functions
Customized activation functions for supporting various models in 🤗 Diffusers.
GELU
class diffusers.models.activations.GELU
< source >( dim_in: int dim_out: int approximate: str = 'none' bias: bool = True )
GELU activation function with tanh approximation support with approximate="tanh".
GEGLU
class diffusers.models.activations.GEGLU
< source >( dim_in: int dim_out: int bias: bool = True )
A variant of the gated linear unit activation function.
ApproximateGELU
class diffusers.models.activations.ApproximateGELU
< source >( dim_in: int dim_out: int bias: bool = True )
The approximate form of the Gaussian Error Linear Unit (GELU). For more details, see section 2 of this paper.