98730c5dd7
* Fix typos
* Trim trailing whitespaces
* Remove a trailing whitespace
* chore: Update MarigoldDepthPipeline checkpoint to prs-eth/marigold-lcm-v1-0
* Revert "chore: Update MarigoldDepthPipeline checkpoint to prs-eth/marigold-lcm-v1-0"
This reverts commit fd742b30b4.
* pokemon -> naruto
* `DPMSolverMultistep` -> `DPMSolverMultistepScheduler`
* Improve Markdown stylization
* Improve style
* Improve style
* Refactor pipeline variable names for consistency
* up style
3.3 KiB
LEDITS++
LEDITS++ was proposed in LEDITS++: Limitless Image Editing using Text-to-Image Models by Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos.
The abstract from the paper is:
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 .
You can find additional information about LEDITS++ on the project page and try it out in a demo.
Due to some backward compatability issues with the current diffusers implementation of [`~schedulers.DPMSolverMultistepScheduler`] this implementation of LEdits++ can no longer guarantee perfect inversion. 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).We provide two distinct pipelines based on different pre-trained models.
LEditsPPPipelineStableDiffusion
autodoc pipelines.ledits_pp.LEditsPPPipelineStableDiffusion - all - call - invert
LEditsPPPipelineStableDiffusionXL
autodoc pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL - all - call - invert
LEditsPPDiffusionPipelineOutput
autodoc pipelines.ledits_pp.pipeline_output.LEditsPPDiffusionPipelineOutput - all
LEditsPPInversionPipelineOutput
autodoc pipelines.ledits_pp.pipeline_output.LEditsPPInversionPipelineOutput - all