[docs] Adapters #11331
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[docs] Adapters #11331
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
sayakpaul
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Apr 28, 2025
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| - sections: | ||
| - local: tutorials/using_peft_for_inference | ||
| title: LoRA | ||
| - local: using-diffusers/ip_adapter | ||
| title: IP-Adapter | ||
| - local: using-diffusers/dreambooth | ||
| title: DreamBooth | ||
| - local: using-diffusers/textual_inversion_inference | ||
| title: Textual inversion | ||
| title: Adapters |
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Looks crisp. Should we automatically fold this when a user lands on https://huggingface.co/docs/diffusers/main/en/index?
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| pipe_id = "stabilityai/stable-diffusion-xl-base-1.0" | ||
| pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda") | ||
| [torch.compile](../optimization/torch2.0#torchcompile) speeds up inference by compiling the PyTorch model to use optimized kernels. Before compiling, the LoRA weights need to be fused into the base model and unloaded first. |
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| [torch.compile](../optimization/torch2.0#torchcompile) speeds up inference by compiling the PyTorch model to use optimized kernels. Before compiling, the LoRA weights need to be fused into the base model and unloaded first. | |
| [torch.compile](../optimization/torch2.0#torchcompile) speeds up inference by compiling the PyTorch model to benefit from PyTorch's low-level hardware-dependent optimizations. Before compiling, the LoRA weights need to be fused into the base model and unloaded first to take the most advantage of `torch.compile()`. |
Wondering if we should also link to the hotswap docs?
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Refactors the adapter docs.