# General Image-to-Image Translation with One-Shot Image Guidance

arXiv: https://arxiv.org/abs/2307.14352 · HTML: https://mlsanity.com/paper/2307.14352/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 54 of its 60 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 30 of 54)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://mlsanity.com/paper/2211.13227/) (2022)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Inversion-Based Style Transfer with Diffusion Models](https://mlsanity.com/paper/2211.13203/) (2022)
- [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis](https://mlsanity.com/paper/1701.02096/) (2017)
- [Dual Diffusion Implicit Bridges for Image-to-Image Translation](https://mlsanity.com/paper/2203.08382/) (2022)

## Built on by (most cited first, 30 of 31)

- [Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code](https://mlsanity.com/paper/2310.01506/) (2023)
- [CSGO: Content-Style Composition in Text-to-Image Generation](https://mlsanity.com/paper/2408.16766/) (2024)
- [BrushEdit: All-In-One Image Inpainting and Editing](https://mlsanity.com/paper/2412.10316/) (2024)
- [UniGS: Unified Representation for Image Generation and Segmentation](https://mlsanity.com/paper/2312.01985/) (2023)
- [StyleBooth: Image Style Editing with Multimodal Instruction](https://mlsanity.com/paper/2404.12154/) (2024)
- [IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models](https://mlsanity.com/paper/2311.06792/) (2023)
- [Z^*: Zero-shot Style Transfer via Attention Rearrangement](https://mlsanity.com/paper/2311.16491/) (2023)
- [TiNO-Edit: Timestep and Noise Optimization for Robust Diffusion-Based Image Editing](https://mlsanity.com/paper/2404.11120/) (2024)
- [Style-NeRF2NeRF: 3D Style Transfer From Style-Aligned Multi-View Images](https://mlsanity.com/paper/2406.13393/) (2024)
- [Portrait Diffusion: Training-free Face Stylization with Chain-of-Painting](https://mlsanity.com/paper/2312.02212/) (2023)
- [TF-TI2I: Training-Free Text-and-Image-to-Image Generation via Multi-Modal Implicit-Context Learning in Text-to-Image Models](https://mlsanity.com/paper/2503.15283/) (2025)
- [Compositional Neural Textures](https://mlsanity.com/paper/2404.12509/) (2024)
- [DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer](https://mlsanity.com/paper/2410.15007/) (2024)
- [SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer](https://mlsanity.com/paper/2503.15934/) (2025)
- [Latent Representation Matters: Human-like Sketches in One-shot Drawing Tasks](https://mlsanity.com/paper/2406.06079/) (2024)
- [SPAST: Arbitrary Style Transfer with Style Priors via Pre-trained Large-scale Model](https://mlsanity.com/paper/2505.08695/) (2025)
- [Difference Inversion: Interpolate and Isolate the Difference with Token Consistency for Image Analogy Generation](https://mlsanity.com/paper/2506.07750/) (2025)
- [ZePo: Zero-Shot Portrait Stylization with Faster Sampling](https://mlsanity.com/paper/2408.05492/) (2024)
- [Gorgeous: Create Your Desired Character Facial Makeup from Any Ideas](https://mlsanity.com/paper/2404.13944/) (2024)
- [FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models](https://mlsanity.com/paper/2410.14429/) (2024)
- [WikiStyle+: A Multimodal Approach to Content-Style Representation Disentanglement for Artistic Image Stylization](https://mlsanity.com/paper/2412.14496/) (2024)
- [Improved 3D Scene Stylization via Text-Guided Generative Image Editing with Region-Based Control](https://mlsanity.com/paper/2509.05285/) (2025)
- [Unsupervised Intrinsic Image Decomposition with LiDAR Intensity Enhanced Training](https://mlsanity.com/paper/2403.14089/) (2024)
- [Garment Animation NeRF with Color Editing](https://mlsanity.com/paper/2407.19774/) (2024)
- [Zero-Shot Visual Concept Blending Without Text Guidance](https://mlsanity.com/paper/2503.21277/) (2025)
- [Advancing Facial Stylization through Semantic Preservation Constraint and Pseudo-Paired Supervision](https://mlsanity.com/paper/2506.22022/) (2025)
- [Styleclone: Face Stylization with Diffusion Based Data Augmentation](https://mlsanity.com/paper/2508.17045/) (2025)
- [Inversion-Free Style Transfer with Dual Rectified Flows](https://mlsanity.com/paper/2511.20986/) (2025)
- [LAMS-Edit: Latent and Attention Mixing with Schedulers for Improved Content Preservation in Diffusion-Based Image and Style Editing](https://mlsanity.com/paper/2601.02987/) (2026)
- [Sissi: Zero-shot Style-guided Image Synthesis via Semantic-style Integration](https://mlsanity.com/paper/2601.06605/) (2026)

## Abstract (arXiv)

Large-scale text-to-image models pre-trained on massive text-image pairs show excellent performance in image synthesis recently. However, image can provide more intuitive visual concepts than plain text. People may ask: how can we integrate the desired visual concept into an existing image, such as our portrait? Current methods are inadequate in meeting this demand as they lack the ability to preserve content or translate visual concepts effectively. Inspired by this, we propose a novel framework named visual concept translator (VCT) with the ability to preserve content in the source image and translate the visual concepts guided by a single reference image. The proposed VCT contains a content-concept inversion (CCI) process to extract contents and concepts, and a content-concept fusion (CCF) process to gather the extracted information to obtain the target image. Given only one reference image, the proposed VCT can complete a wide range of general image-to-image translation tasks with excellent results. Extensive experiments are conducted to prove the superiority and effectiveness of the proposed methods. Codes are available at https://github.com/CrystalNeuro/visual-concept-translator.