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      • Unconditional image generation
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    • 🌍TECHNIQUES
      • Textual inversion
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      • Create a dataset for training
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      • Unconditional image generation
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      • Other Modalities
  • 🌍OPTIMIZATION/SPECIAL HARDWARE
    • Overview
    • Memory and Speed
    • Torch2.0 support
    • Stable Diffusion in JAX/Flax
    • xFormers
    • ONNX
    • OpenVINO
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    • Token Merging
  • 🌍CONCEPTUAL GUIDES
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      • UNet1DModel
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      • Pix2Pix Zero
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      • Self-Attention Guidance
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      • Shap-E
      • Spectrogram Diffusion
      • 🌍STABLE DIFFUSION
        • Overview
        • Text-to-image
        • Image-to-image
        • Inpainting
        • Depth-to-image
        • Image variation
        • Safe Stable Diffusion
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        • Stable Diffusion T2I-adapter
        • GLIGEN (Grounded Language-to-Image Generation)
      • Stable unCLIP
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      • 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. OPTIMIZATION/SPECIAL HARDWARE

xFormers

PreviousStable Diffusion in JAX/FlaxNextONNX

Last updated 1 year ago

Installing xFormers

We recommend the use of for both inference and training. In our tests, the optimizations performed in the attention blocks allow for both faster speed and reduced memory consumption.

Starting from version 0.0.16 of xFormers, released on January 2023, installation can be easily performed using pre-built pip wheels:

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pip install xformers

The xFormers PIP package requires the latest version of PyTorch (1.13.1 as of xFormers 0.0.16). If you need to use a previous version of PyTorch, then we recommend you install xFormers from source using .

After xFormers is installed, you can use enable_xformers_memory_efficient_attention() for faster inference and reduced memory consumption, as discussed .

According to , xFormers v0.0.16 cannot be used for training (fine-tune or Dreambooth) in some GPUs. If you observe that problem, please install a development version as indicated in that comment.

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xFormers
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