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1a6a647e06
* part 2 of api fixes * move randn_tensor * add to toctree * apply feedback * more feedback
37 lines
1.5 KiB
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37 lines
1.5 KiB
Plaintext
<!--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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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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# Pipelines
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The [`DiffusionPipeline`] is the easiest way to load any pretrained diffusion pipeline from the [Hub](https://huggingface.co/models?library=diffusers) and use it for inference.
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<Tip>
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You shouldn't use the [`DiffusionPipeline`] class for training or finetuning a diffusion model. Individual
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components (for example, [`UNetModel`] and [`UNetConditionModel`]) of diffusion pipelines are usually trained individually, so we suggest directly working with instead.
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</Tip>
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The pipeline type (for example [`StableDiffusionPipeline`]) of any diffusion pipeline loaded with [`~DiffusionPipeline.from_pretrained`] is automatically
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detected and pipeline components are loaded and passed to the `__init__` function of the pipeline.
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Any pipeline object can be saved locally with [`~DiffusionPipeline.save_pretrained`].
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## DiffusionPipeline
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[[autodoc]] DiffusionPipeline
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- all
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- __call__
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- device
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- to
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- components
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