Diffusers documentation
Overview
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Load pipelines and adapters
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Adapters
Generative tasks
Inference techniques
OverviewCreate a serverDistributed inferenceScheduler featuresPipeline callbacksReproducible pipelinesControlling image qualityPrompt techniques
Advanced inference
Hybrid Inference
Specific pipeline examples
ConsisIDStable Diffusion XLSDXL TurboKandinskyOmniGenPAGLatent Consistency ModelShap-EDiffEditTrajectory Consistency Distillation-LoRAStable Video DiffusionMarigold Computer Vision
Training
Quantization Methods
Accelerate inference and reduce memory
Accelerate inferenceCachingReduce memory usageCompile and offloading quantized modelsPrunaxFormersToken mergingDeepCacheTGATExDiTParaAttention
Optimized model formats
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Conceptual Guides
PhilosophyControlled generationHow to contribute?Diffusers' Ethical GuidelinesEvaluating Diffusion Models
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API
Main Classes
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Internal classes
You are viewing v0.34.0 version. A newer version v0.38.0 is available.
Overview
The inference pipeline supports and enables a wide range of techniques that are divided into two categories:
- Pipeline functionality: these techniques modify the pipeline or extend it for other applications. For example, pipeline callbacks add new features to a pipeline and a pipeline can also be extended for distributed inference.
- Improve inference quality: these techniques increase the visual quality of the generated images. For example, you can enhance your prompts with GPT2 to create better images with lower effort.