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Diffusers
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🧨 Diffusers Quicktour Effective and efficient diffusion Installation
Tutorials
Overview Understanding pipelines, models and schedulers AutoPipeline Train a diffusion model Load LoRAs for inference Accelerate inference of text-to-image diffusion models Working with big models
Load pipelines and adapters
Load pipelines Load community pipelines and components Load schedulers and models Model files and layouts Load adapters Push files to the Hub
Generative tasks
Unconditional image generation Text-to-image Image-to-image Inpainting Text or image-to-video Depth-to-image
Inference techniques
Overview Distributed inference Merge LoRAs Scheduler features Pipeline callbacks Reproducible pipelines Controlling image quality Prompt techniques
Advanced inference
Specific pipeline examples
Stable Diffusion XL SDXL Turbo Kandinsky IP-Adapter PAG ControlNet T2I-Adapter Latent Consistency Model Textual inversion Shap-E DiffEdit Trajectory Consistency Distillation-LoRA Stable Video Diffusion Marigold Computer Vision
Training
Overview Create a dataset for training Adapt a model to a new task
Models
Methods
Accelerate inference and reduce memory
Speed up inference Reduce memory usage PyTorch 2.0 xFormers Token merging DeepCache TGATE xDiT
Optimized model formats
JAX/Flax ONNX OpenVINO Core ML
Optimized hardware
Metal Performance Shaders (MPS) Habana Gaudi
Conceptual Guides
Philosophy Controlled generation How to contribute? Diffusers' Ethical Guidelines Evaluating Diffusion Models
Community Projects
API
Main Classes
Loaders
Models
Pipelines
Schedulers
Internal classes