Blended Latent Diffusion
- URL: http://arxiv.org/abs/2206.02779v2
- Date: Sun, 30 Apr 2023 17:43:11 GMT
- Title: Blended Latent Diffusion
- Authors: Omri Avrahami, Ohad Fried, Dani Lischinski
- Abstract summary: We present an accelerated solution to the task of local text-driven editing of generic images, where the desired edits are confined to a user-provided mask.
Our solution leverages a recent text-to-image Latent Diffusion Model (LDM), which speeds up diffusion by operating in a lower-dimensional latent space.
- Score: 18.043090347648157
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The tremendous progress in neural image generation, coupled with the
emergence of seemingly omnipotent vision-language models has finally enabled
text-based interfaces for creating and editing images. Handling generic images
requires a diverse underlying generative model, hence the latest works utilize
diffusion models, which were shown to surpass GANs in terms of diversity. One
major drawback of diffusion models, however, is their relatively slow inference
time. In this paper, we present an accelerated solution to the task of local
text-driven editing of generic images, where the desired edits are confined to
a user-provided mask. Our solution leverages a recent text-to-image Latent
Diffusion Model (LDM), which speeds up diffusion by operating in a
lower-dimensional latent space. We first convert the LDM into a local image
editor by incorporating Blended Diffusion into it. Next we propose an
optimization-based solution for the inherent inability of this LDM to
accurately reconstruct images. Finally, we address the scenario of performing
local edits using thin masks. We evaluate our method against the available
baselines both qualitatively and quantitatively and demonstrate that in
addition to being faster, our method achieves better precision than the
baselines while mitigating some of their artifacts.
Related papers
- Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation [54.96563068182733]
We propose Modality Adaptation with text-to-image Diffusion Models (MADM) for semantic segmentation task.
MADM utilizes text-to-image diffusion models pre-trained on extensive image-text pairs to enhance the model's cross-modality capabilities.
We show that MADM achieves state-of-the-art adaptation performance across various modality tasks, including images to depth, infrared, and event modalities.
arXiv Detail & Related papers (2024-10-29T03:49:40Z) - AdaptiveDrag: Semantic-Driven Dragging on Diffusion-Based Image Editing [14.543341303789445]
We propose a novel mask-free point-based image editing method, AdaptiveDrag, which generates images that better align with user intent.
To ensure a comprehensive connection between the input image and the drag process, we have developed a semantic-driven optimization.
Building on these effective designs, our method delivers superior generation results using only the single input image and the handle-target point pairs.
arXiv Detail & Related papers (2024-10-16T15:59:02Z) - Coherent and Multi-modality Image Inpainting via Latent Space Optimization [61.99406669027195]
PILOT (intextbfPainting vtextbfIa textbfLatent textbfOptextbfTimization) is an optimization approach grounded on a novel textitsemantic centralization and textitbackground preservation loss.
Our method searches latent spaces capable of generating inpainted regions that exhibit high fidelity to user-provided prompts while maintaining coherence with the background.
arXiv Detail & Related papers (2024-07-10T19:58:04Z) - Eta Inversion: Designing an Optimal Eta Function for Diffusion-based Real Image Editing [2.5602836891933074]
A commonly adopted strategy for editing real images involves inverting the diffusion process to obtain a noisy representation of the original image.
Current methods for diffusion inversion often struggle to produce edits that are both faithful to the specified text prompt and closely resemble the source image.
We introduce a novel and adaptable diffusion inversion technique for real image editing, which is grounded in a theoretical analysis of the role of $eta$ in the DDIM sampling equation for enhanced editability.
arXiv Detail & Related papers (2024-03-14T15:07:36Z) - Adversarial Supervision Makes Layout-to-Image Diffusion Models Thrive [21.49096276631859]
Current L2I models either suffer from poor editability via text or weak alignment between the generated image and the input layout.
We propose to integrate adversarial supervision into the conventional training pipeline of L2I diffusion models (ALDM)
Specifically, we employ a segmentation-based discriminator which provides explicit feedback to the diffusion generator on the pixel-level alignment between the denoised image and the input layout.
arXiv Detail & Related papers (2024-01-16T20:31:46Z) - LIME: Localized Image Editing via Attention Regularization in Diffusion
Models [74.3811832586391]
This paper introduces LIME for localized image editing in diffusion models that do not require user-specified regions of interest (RoI) or additional text input.
Our method employs features from pre-trained methods and a simple clustering technique to obtain precise semantic segmentation maps.
We propose a novel cross-attention regularization technique that penalizes unrelated cross-attention scores in the RoI during the denoising steps, ensuring localized edits.
arXiv Detail & Related papers (2023-12-14T18:59:59Z) - MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask [84.84034179136458]
A crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning.
We propose an adaptive mask, which is conditioned on the attention maps and the prompt embeddings, to dynamically adjust the contribution of each text token to the image features.
Our method, termed MaskDiffusion, is training-free and hot-pluggable for popular pre-trained diffusion models.
arXiv Detail & Related papers (2023-09-08T15:53:37Z) - DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing [94.24479528298252]
DragGAN is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision.
By harnessing large-scale pretrained diffusion models, we greatly enhance the applicability of interactive point-based editing on both real and diffusion-generated images.
We present a challenging benchmark dataset called DragBench to evaluate the performance of interactive point-based image editing methods.
arXiv Detail & Related papers (2023-06-26T06:04:09Z) - Improving Diffusion-based Image Translation using Asymmetric Gradient
Guidance [51.188396199083336]
We present an approach that guides the reverse process of diffusion sampling by applying asymmetric gradient guidance.
Our model's adaptability allows it to be implemented with both image-fusion and latent-dif models.
Experiments show that our method outperforms various state-of-the-art models in image translation tasks.
arXiv Detail & Related papers (2023-06-07T12:56:56Z) - DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models [33.79188588182528]
We present a novel DiffusionCLIP which performs text-driven image manipulation with diffusion models using Contrastive Language-Image Pre-training (CLIP) loss.
Our method has a performance comparable to that of the modern GAN-based image processing methods for in and out-of-domain image processing tasks.
Our method can be easily used for various novel applications, enabling image translation from an unseen domain to another unseen domain or stroke-conditioned image generation in an unseen domain.
arXiv Detail & Related papers (2021-10-06T12:59:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.