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55 lines
3.4 KiB
Markdown
55 lines
3.4 KiB
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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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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# LEDITS++
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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</div>
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LEDITS++ was proposed in [LEDITS++: Limitless Image Editing using Text-to-Image Models](https://huggingface.co/papers/2311.16711) by Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos.
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The abstract from the paper is:
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*Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming fine-tuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. The project page is available at https://leditsplusplus-project.static.hf.space .*
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> [!TIP]
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> You can find additional information about LEDITS++ on the [project page](https://leditsplusplus-project.static.hf.space/index.html) and try it out in a [demo](https://huggingface.co/spaces/editing-images/leditsplusplus).
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> [!WARNING]
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> Due to some backward compatibility issues with the current diffusers implementation of [`~schedulers.DPMSolverMultistepScheduler`] this implementation of LEdits++ can no longer guarantee perfect inversion.
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> This issue is unlikely to have any noticeable effects on applied use-cases. However, we provide an alternative implementation that guarantees perfect inversion in a dedicated [GitHub repo](https://github.com/ml-research/ledits_pp).
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We provide two distinct pipelines based on different pre-trained models.
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## LEditsPPPipelineStableDiffusion
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[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusion
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- all
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- __call__
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- invert
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## LEditsPPPipelineStableDiffusionXL
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[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL
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- all
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- __call__
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- invert
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## LEditsPPDiffusionPipelineOutput
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[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPDiffusionPipelineOutput
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- all
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## LEditsPPInversionPipelineOutput
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[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPInversionPipelineOutput
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- all
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