FBSDiff: Plug-and-Play Frequency Band Substitution of Diffusion Features for Highly Controllable Text-Driven Image Translation

• URL: http://arxiv.org/abs/2408.00998v2
• Date: Tue, 6 Aug 2024 12:01:17 GMT
• Title: FBSDiff: Plug-and-Play Frequency Band Substitution of Diffusion Features for Highly Controllable Text-Driven Image Translation
• Authors: Xiang Gao, Jiaying Liu,
• Abstract summary: This paper contributes a novel, concise, and efficient approach that adapts pre-trained large-scale text-to-image (T2I) diffusion model to the image-to-image (I2I) paradigm in a plug-and-play manner. Our method allows flexible control over both guiding factor and guiding intensity of the reference image simply by tuning the type and bandwidth of the substituted frequency band.
• Score: 19.65838242227773
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale text-to-image diffusion models have been a revolutionary milestone in the evolution of generative AI and multimodal technology, allowing wonderful image generation with natural-language text prompt. However, the issue of lacking controllability of such models restricts their practical applicability for real-life content creation. Thus, attention has been focused on leveraging a reference image to control text-to-image synthesis, which is also regarded as manipulating (or editing) a reference image as per a text prompt, namely, text-driven image-to-image translation. This paper contributes a novel, concise, and efficient approach that adapts pre-trained large-scale text-to-image (T2I) diffusion model to the image-to-image (I2I) paradigm in a plug-and-play manner, realizing high-quality and versatile text-driven I2I translation without any model training, model fine-tuning, or online optimization process. To guide T2I generation with a reference image, we propose to decompose diverse guiding factors with different frequency bands of diffusion features in the DCT spectral space, and accordingly devise a novel frequency band substitution layer which realizes dynamic control of the reference image to the T2I generation result in a plug-and-play manner. We demonstrate that our method allows flexible control over both guiding factor and guiding intensity of the reference image simply by tuning the type and bandwidth of the substituted frequency band, respectively. Extensive qualitative and quantitative experiments verify superiority of our approach over related methods in I2I translation visual quality, versatility, and controllability. The code is publicly available at: https://github.com/XiangGao1102/FBSDiff.

Related papers

• Frequency-Controlled Diffusion Model for Versatile Text-Guided Image-to-Image Translation [17.30877810859863]
  Large-scale text-to-image (T2I) diffusion models have emerged as a powerful tool for image-to-image translation (I2I) This paper proposes frequency-controlled diffusion model (FCDiffusion), an end-to-end diffusion-based framework.
  arXiv  Detail & Related papers  (2024-07-03T11:05:19Z)
• DreamDistribution: Prompt Distribution Learning for Text-to-Image Diffusion Models [53.17454737232668]
  We introduce a solution that allows a pretrained T2I diffusion model to learn a set of soft prompts. These prompts offer text-guided editing capabilities and additional flexibility in controlling variation and mixing between multiple distributions. We also show the adaptability of the learned prompt distribution to other tasks, such as text-to-3D.
  arXiv  Detail & Related papers  (2023-12-21T12:11:00Z)
• UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion Models [25.219960711604728]
  This paper proposes a novel approach for text image generation, utilizing a pre-trained diffusion model. Our approach involves the design and training of a light-weight character-level text encoder, which replaces the original CLIP encoder. By employing an inference stage refinement process, we achieve a notably high sequence accuracy when synthesizing text in arbitrarily given images.
  arXiv  Detail & Related papers  (2023-12-08T07:47:46Z)
• Controlling Text-to-Image Diffusion by Orthogonal Finetuning [74.21549380288631]
  We introduce a principled finetuning method -- Orthogonal Finetuning (OFT) for adapting text-to-image diffusion models to downstream tasks. Unlike existing methods, OFT can provably preserve hyperspherical energy which characterizes the pairwise neuron relationship on the unit hypersphere. We empirically show that our OFT framework outperforms existing methods in generation quality and convergence speed.
  arXiv  Detail & Related papers  (2023-06-12T17:59:23Z)
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding [53.170767750244366]
  Imagen is a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. To assess text-to-image models in greater depth, we introduce DrawBench, a comprehensive and challenging benchmark for text-to-image models.
  arXiv  Detail & Related papers  (2022-05-23T17:42:53Z)
• Cycle-Consistent Inverse GAN for Text-to-Image Synthesis [101.97397967958722]
  We propose a novel unified framework of Cycle-consistent Inverse GAN for both text-to-image generation and text-guided image manipulation tasks. We learn a GAN inversion model to convert the images back to the GAN latent space and obtain the inverted latent codes for each image. In the text-guided optimization module, we generate images with the desired semantic attributes by optimizing the inverted latent codes.
  arXiv  Detail & Related papers  (2021-08-03T08:38:16Z)
• Towards Open-World Text-Guided Face Image Generation and Manipulation [52.83401421019309]
  We propose a unified framework for both face image generation and manipulation. Our method supports open-world scenarios, including both image and text, without any re-training, fine-tuning, or post-processing.
  arXiv  Detail & Related papers  (2021-04-18T16:56:07Z)
• Text to Image Generation with Semantic-Spatial Aware GAN [41.73685713621705]
  A text to image generation (T2I) model aims to generate photo-realistic images which are semantically consistent with the text descriptions. We propose a novel framework Semantic-Spatial Aware GAN, which is trained in an end-to-end fashion so that the text encoder can exploit better text information.
  arXiv  Detail & Related papers  (2021-04-01T15:48:01Z)
• TIME: Text and Image Mutual-Translation Adversarial Networks [55.1298552773457]
  We propose Text and Image Mutual-Translation Adversarial Networks (TIME) TIME learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework. In experiments, TIME achieves state-of-the-art (SOTA) performance on the CUB and MS-COCO dataset.
  arXiv  Detail & Related papers  (2020-05-27T06:40:12Z)

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.