Diffusers BOINC AI docs
  • ๐ŸŒGET STARTED
    • Diffusers
    • Quicktour
    • Effective and efficient diffusion
    • Installation
  • ๐ŸŒTUTORIALS
    • Overview
    • Understanding models and schedulers
    • AutoPipeline
    • Train a diffusion model
  • ๐ŸŒUSING DIFFUSERS
    • ๐ŸŒLOADING & HUB
      • Overview
      • Load pipelines, models, and schedulers
      • Load and compare different schedulers
      • Load community pipelines
      • Load safetensors
      • Load different Stable Diffusion formats
      • Push files to the Hub
    • ๐ŸŒTASKS
      • Unconditional image generation
      • Text-to-image
      • Image-to-image
      • Inpainting
      • Depth-to-image
    • ๐ŸŒTECHNIQUES
      • Textual inversion
      • Distributed inference with multiple GPUs
      • Improve image quality with deterministic generation
      • Control image brightness
      • Prompt weighting
    • ๐ŸŒPIPELINES FOR INFERENCE
      • Overview
      • Stable Diffusion XL
      • ControlNet
      • Shap-E
      • DiffEdit
      • Distilled Stable Diffusion inference
      • Create reproducible pipelines
      • Community pipelines
      • How to contribute a community pipeline
    • ๐ŸŒTRAINING
      • Overview
      • Create a dataset for training
      • Adapt a model to a new task
      • Unconditional image generation
      • Textual Inversion
      • DreamBooth
      • Text-to-image
      • Low-Rank Adaptation of Large Language Models (LoRA)
      • ControlNet
      • InstructPix2Pix Training
      • Custom Diffusion
      • T2I-Adapters
    • ๐ŸŒTAKING DIFFUSERS BEYOND IMAGES
      • Other Modalities
  • ๐ŸŒOPTIMIZATION/SPECIAL HARDWARE
    • Overview
    • Memory and Speed
    • Torch2.0 support
    • Stable Diffusion in JAX/Flax
    • xFormers
    • ONNX
    • OpenVINO
    • Core ML
    • MPS
    • Habana Gaudi
    • Token Merging
  • ๐ŸŒCONCEPTUAL GUIDES
    • Philosophy
    • Controlled generation
    • How to contribute?
    • Diffusers' Ethical Guidelines
    • Evaluating Diffusion Models
  • ๐ŸŒAPI
    • ๐ŸŒMAIN CLASSES
      • Attention Processor
      • Diffusion Pipeline
      • Logging
      • Configuration
      • Outputs
      • Loaders
      • Utilities
      • VAE Image Processor
    • ๐ŸŒMODELS
      • Overview
      • UNet1DModel
      • UNet2DModel
      • UNet2DConditionModel
      • UNet3DConditionModel
      • VQModel
      • AutoencoderKL
      • AsymmetricAutoencoderKL
      • Tiny AutoEncoder
      • Transformer2D
      • Transformer Temporal
      • Prior Transformer
      • ControlNet
    • ๐ŸŒPIPELINES
      • Overview
      • AltDiffusion
      • Attend-and-Excite
      • Audio Diffusion
      • AudioLDM
      • AudioLDM 2
      • AutoPipeline
      • Consistency Models
      • ControlNet
      • ControlNet with Stable Diffusion XL
      • Cycle Diffusion
      • Dance Diffusion
      • DDIM
      • DDPM
      • DeepFloyd IF
      • DiffEdit
      • DiT
      • IF
      • PaInstructPix2Pix
      • Kandinsky
      • Kandinsky 2.2
      • Latent Diffusionge
      • MultiDiffusion
      • MusicLDM
      • PaintByExample
      • Parallel Sampling of Diffusion Models
      • Pix2Pix Zero
      • PNDM
      • RePaint
      • Score SDE VE
      • Self-Attention Guidance
      • Semantic Guidance
      • Shap-E
      • Spectrogram Diffusion
      • ๐ŸŒSTABLE DIFFUSION
        • Overview
        • Text-to-image
        • Image-to-image
        • Inpainting
        • Depth-to-image
        • Image variation
        • Safe Stable Diffusion
        • Stable Diffusion 2
        • Stable Diffusion XL
        • Latent upscaler
        • Super-resolution
        • LDM3D Text-to-(RGB, Depth)
        • Stable Diffusion T2I-adapter
        • GLIGEN (Grounded Language-to-Image Generation)
      • Stable unCLIP
      • Stochastic Karras VE
      • Text-to-image model editing
      • Text-to-video
      • Text2Video-Zero
      • UnCLIP
      • Unconditional Latent Diffusion
      • UniDiffuser
      • Value-guided sampling
      • Versatile Diffusion
      • VQ Diffusion
      • Wuerstchen
    • ๐ŸŒSCHEDULERS
      • Overview
      • CMStochasticIterativeScheduler
      • DDIMInverseScheduler
      • DDIMScheduler
      • DDPMScheduler
      • DEISMultistepScheduler
      • DPMSolverMultistepInverse
      • DPMSolverMultistepScheduler
      • DPMSolverSDEScheduler
      • DPMSolverSinglestepScheduler
      • EulerAncestralDiscreteScheduler
      • EulerDiscreteScheduler
      • HeunDiscreteScheduler
      • IPNDMScheduler
      • KarrasVeScheduler
      • KDPM2AncestralDiscreteScheduler
      • KDPM2DiscreteScheduler
      • LMSDiscreteScheduler
      • PNDMScheduler
      • RePaintScheduler
      • ScoreSdeVeScheduler
      • ScoreSdeVpScheduler
      • UniPCMultistepScheduler
      • VQDiffusionScheduler
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  1. USING DIFFUSERS
  2. LOADING & HUB

Overview

PreviousLOADING & HUBNextLoad pipelines, models, and schedulers

Last updated 1 year ago

Overview

๐Ÿงจ Diffusers offers many pipelines, models, and schedulers for generative tasks. To make loading these components as simple as possible, we provide a single and unified method - from_pretrained() - that loads any of these components from either the BOINC AI or your local machine. Whenever you load a pipeline or model, the latest files are automatically downloaded and cached so you can quickly reuse them next time without redownloading the files.

This section will show you everything you need to know about loading pipelines, how to load different components in a pipeline, how to load checkpoint variants, and how to load community pipelines. Youโ€™ll also learn how to load schedulers and compare the speed and quality trade-offs of using different schedulers. Finally, youโ€™ll see how to convert and load KerasCV checkpoints so you can use them in PyTorch with ๐Ÿงจ Diffusers.

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