Diffusers BOINC AI docs
  • 🌍GET STARTED
    • Diffusers
    • Quicktour
    • Effective and efficient diffusion
    • Installation
  • 🌍TUTORIALS
    • Overview
    • Understanding models and schedulers
    • AutoPipeline
    • Train a diffusion model
  • 🌍USING DIFFUSERS
    • 🌍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
    • 🌍TASKS
      • Unconditional image generation
      • Text-to-image
      • Image-to-image
      • Inpainting
      • Depth-to-image
    • 🌍TECHNIQUES
      • Textual inversion
      • Distributed inference with multiple GPUs
      • Improve image quality with deterministic generation
      • Control image brightness
      • Prompt weighting
    • 🌍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
    • 🌍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
    • 🌍TAKING DIFFUSERS BEYOND IMAGES
      • Other Modalities
  • 🌍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
  • 🌍CONCEPTUAL GUIDES
    • Philosophy
    • Controlled generation
    • How to contribute?
    • Diffusers' Ethical Guidelines
    • Evaluating Diffusion Models
  • 🌍API
    • 🌍MAIN CLASSES
      • Attention Processor
      • Diffusion Pipeline
      • Logging
      • Configuration
      • Outputs
      • Loaders
      • Utilities
      • VAE Image Processor
    • 🌍MODELS
      • Overview
      • UNet1DModel
      • UNet2DModel
      • UNet2DConditionModel
      • UNet3DConditionModel
      • VQModel
      • AutoencoderKL
      • AsymmetricAutoencoderKL
      • Tiny AutoEncoder
      • Transformer2D
      • Transformer Temporal
      • Prior Transformer
      • ControlNet
    • 🌍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
      • 🌍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
    • 🌍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. TUTORIALS

Overview

PreviousTUTORIALSNextUnderstanding models and schedulers

Last updated 1 year ago

Overview

Welcome to 🧨 Diffusers! If you’re new to diffusion models and generative AI, and want to learn more, then you’ve come to the right place. These beginner-friendly tutorials are designed to provide a gentle introduction to diffusion models and help you understand the library fundamentals - the core components and how 🧨 Diffusers is meant to be used.

You’ll learn how to use a pipeline for inference to rapidly generate things, and then deconstruct that pipeline to really understand how to use the library as a modular toolbox for building your own diffusion systems. In the next lesson, you’ll learn how to train your own diffusion model to generate what you want.

After completing the tutorials, you’ll have gained the necessary skills to start exploring the library on your own and see how to use it for your own projects and applications.

Feel free to join our community on to connect and collaborate with other users and developers!

Let’s start diffusing! 🧨

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