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* Fix typos, improve, update * Change to trending and apply some Grammarly fixes * Grammarly fixes * Update loading_adapters.md * Update loading_adapters.md * Update other-formats.md * Update push_to_hub.md * Update loading_adapters.md * Update loading.md * Update docs/source/en/using-diffusers/push_to_hub.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update schedulers.md * Update docs/source/en/using-diffusers/loading.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/using-diffusers/loading_adapters.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update A1111 LoRA files part * Update other-formats.md --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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18 lines
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Markdown
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# Overview
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🧨 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 Hugging Face [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) 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.
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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. You'll also learn how to load schedulers and compare the speed and quality trade-offs of using different schedulers. Finally, you'll see how to convert and load KerasCV checkpoints so you can use them in PyTorch with 🧨 Diffusers.
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