Diffusers documentation
Normalization layers
Get started
Tutorials
OverviewUnderstanding pipelines, models and schedulersAutoPipelineTrain a diffusion modelInference with PEFT
Using Diffusers
Loading & Hub
OverviewLoad pipelines, models, and schedulersLoad and compare different schedulersLoad community pipelines and componentsLoad safetensorsLoad different Stable Diffusion formatsLoad adaptersPush files to the Hub
Tasks
Techniques
Textual inversionDistributed inference with multiple GPUsImprove image quality with deterministic generationControl image brightnessPrompt weightingImprove generation quality with FreeU
Specific pipeline examples
OverviewStable Diffusion XLKandinskyControlNetCallbackShap-EDiffEditDistilled Stable Diffusion inferenceCreate reproducible pipelinesCommunity pipelinesContribute a community pipeline
Training
OverviewCreate a dataset for trainingAdapt a model to a new taskUnconditional image generationTextual InversionDreamBoothText-to-imageLow-Rank Adaptation of Large Language Models (LoRA)ControlNetInstructPix2Pix TrainingCustom DiffusionT2I-AdaptersReinforcement learning training with DDPO
Taking Diffusers Beyond Images
Optimization
Conceptual Guides
PhilosophyControlled generationHow to contribute?Diffusers' Ethical GuidelinesEvaluating Diffusion Models
API
Main Classes
Models
OverviewUNet1DModelUNet2DModelUNet2DConditionModelUNet3DConditionModelUNetMotionModelVQModelAutoencoderKLAsymmetricAutoencoderKLTiny AutoEncoderTransformer2DTransformer TemporalPrior TransformerControlNet
Pipelines
OverviewAltDiffusionAnimateDiffAttend-and-ExciteAudio DiffusionAudioLDMAudioLDM 2AutoPipelineBLIP DiffusionConsistency ModelsControlNetControlNet with Stable Diffusion XLCycle DiffusionDance DiffusionDDIMDDPMDeepFloyd IFDiffEditDiTInstructPix2PixKandinsky 2.1Kandinsky 2.2Latent Consistency ModelsLatent DiffusionMultiDiffusionMusicLDMPaint By ExampleParallel Sampling of Diffusion ModelsPix2Pix ZeroPixArtPNDMRePaintScore SDE VESelf-Attention GuidanceSemantic GuidanceShap-ESpectrogram Diffusion
Stable Diffusion
OverviewText-to-imageImage-to-imageInpaintingDepth-to-imageImage variationSafe Stable DiffusionStable Diffusion 2Stable Diffusion XLLatent upscalerSuper-resolutionLDM3D Text-to-(RGB, Depth)Stable Diffusion T2I-AdapterGLIGEN (Grounded Language-to-Image Generation)
Stable unCLIPStochastic Karras VEText-to-image model editingText-to-videoText2Video-ZerounCLIPUnconditional Latent DiffusionUniDiffuserValue-guided samplingVersatile DiffusionVQ DiffusionWuerstchenSchedulers
OverviewCMStochasticIterativeSchedulerDDIMInverseSchedulerDDIMSchedulerDDPMSchedulerDEISMultistepSchedulerDPMSolverMultistepInverseDPMSolverMultistepSchedulerDPMSolverSDESchedulerDPMSolverSinglestepSchedulerEulerAncestralDiscreteSchedulerEulerDiscreteSchedulerHeunDiscreteSchedulerIPNDMSchedulerKarrasVeSchedulerKDPM2AncestralDiscreteSchedulerKDPM2DiscreteSchedulerLCMSchedulerLMSDiscreteSchedulerPNDMSchedulerRePaintSchedulerScoreSdeVeSchedulerScoreSdeVpSchedulerUniPCMultistepSchedulerVQDiffusionScheduler
Internal classes
You are viewing v0.22.1 version. A newer version v0.38.0 is available.
Normalization layers
Customized normalization layers for supporting various models in 🤗 Diffusers.
AdaLayerNorm
class diffusers.models.normalization.AdaLayerNorm
< source >( embedding_dim: int num_embeddings: int )
Norm layer modified to incorporate timestep embeddings.
AdaLayerNormZero
class diffusers.models.normalization.AdaLayerNormZero
< source >( embedding_dim: int num_embeddings: int )
Norm layer adaptive layer norm zero (adaLN-Zero).
AdaGroupNorm
class diffusers.models.normalization.AdaGroupNorm
< source >( embedding_dim: int out_dim: int num_groups: int act_fn: typing.Optional[str] = None eps: float = 1e-05 )
Parameters
- embedding_dim (
int) — The size of each embedding vector. - num_embeddings (
int) — The size of the embeddings dictionary. - num_groups (
int) — The number of groups to separate the channels into. - act_fn (
str, optional, defaults toNone) — The activation function to use. - eps (
float, optional, defaults to1e-5) — The epsilon value to use for numerical stability.
GroupNorm layer modified to incorporate timestep embeddings.