# Customizing 360-Degree Panoramas through Text-to-Image Diffusion Models

arXiv: https://arxiv.org/abs/2310.18840 · HTML: https://mlsanity.com/paper/2310.18840/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 44 of its 57 references that are in the map.
- First posted on arXiv 28 Oct 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 30 of 44)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [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)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Multi-Concept Customization of Text-to-Image Diffusion](https://mlsanity.com/paper/2212.04488/) (2022)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021)
- [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://mlsanity.com/paper/2111.14822/) (2021)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Diffusion Models for Adversarial Purification](https://mlsanity.com/paper/2205.07460/) (2022)
- [ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation](https://mlsanity.com/paper/2302.13848/) (2023)
- [InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning](https://mlsanity.com/paper/2304.03411/) (2023)
- [SVDiff: Compact Parameter Space for Diffusion Fine-Tuning](https://mlsanity.com/paper/2303.11305/) (2023)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [P+: Extended Textual Conditioning in Text-to-Image Generation](https://mlsanity.com/paper/2303.09522/) (2023)
- [Diffusion models as plug-and-play priors](https://mlsanity.com/paper/2206.09012/) (2022)
- [Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models](https://mlsanity.com/paper/2302.12228/) (2023)
- [Key-Locked Rank One Editing for Text-to-Image Personalization](https://mlsanity.com/paper/2305.01644/) (2023)

## Built on by (most cited first, 30 of 47)

- [Controllable Generation with Text-to-Image Diffusion Models: A Survey](https://mlsanity.com/paper/2403.04279/) (2024)
- [DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting](https://mlsanity.com/paper/2404.06903/) (2024)
- [A Survey on LoRA of Large Language Models](https://mlsanity.com/paper/2407.11046/) (2024)
- [360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model](https://mlsanity.com/paper/2401.06578/) (2024)
- [LayerPano3D: Layered 3D Panorama for Hyper-Immersive Scene Generation](https://mlsanity.com/paper/2408.13252/) (2024)
- [Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion Models](https://mlsanity.com/paper/2311.13141/) (2023)
- [DiffPano: Scalable and Consistent Text to Panorama Generation with Spherical Epipolar-Aware Diffusion](https://mlsanity.com/paper/2410.24203/) (2024)
- [Low-Rank Adaptation for Foundation Models: A Comprehensive Review](https://mlsanity.com/paper/2501.00365/) (2024)
- [A Survey on Personalized Content Synthesis with Diffusion Models](https://mlsanity.com/paper/2405.05538/) (2024)
- [Imagine360: Immersive 360 Video Generation from Perspective Anchor](https://mlsanity.com/paper/2412.03552/) (2024)
- [FastScene: Text-Driven Fast 3D Indoor Scene Generation via Panoramic Gaussian Splatting](https://mlsanity.com/paper/2405.05768/) (2024)
- [HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions](https://mlsanity.com/paper/2407.15187/) (2024)
- [SceneDreamer360: Text-Driven 3D-Consistent Scene Generation with Panoramic Gaussian Splatting](https://mlsanity.com/paper/2408.13711/) (2024)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [DiT360: High-Fidelity Panoramic Image Generation via Hybrid Training](https://mlsanity.com/paper/2510.11712/) (2025)
- [Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models](https://mlsanity.com/paper/2411.07126/) (2024)
- [PanoWan: Lifting Diffusion Video Generation Models to 360deg with Latitude/Longitude-aware Mechanisms](https://mlsanity.com/paper/2505.22016/) (2025)
- [PanoFree: Tuning-Free Holistic Multi-view Image Generation with Cross-view Self-Guidance](https://mlsanity.com/paper/2408.02157/) (2024)
- [What Makes for Text to 360-degree Panorama Generation with Stable Diffusion?](https://mlsanity.com/paper/2505.22129/) (2025)
- [Taming Stable Diffusion for Text to 360deg Panorama Image Generation](https://mlsanity.com/paper/2404.07949/) (2024)
- [SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over Time](https://mlsanity.com/paper/2407.15507/) (2024)
- [DreamCube: 3D Panorama Generation via Multi-plane Synchronization](https://mlsanity.com/paper/2506.17206/) (2025)
- [Panorama Generation From NFoV Image Done Right](https://mlsanity.com/paper/2503.18420/) (2025)
- [Conditional Panoramic Image Generation via Masked Autoregressive Modeling](https://mlsanity.com/paper/2505.16862/) (2025)
- [ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment](https://mlsanity.com/paper/2507.19058/) (2025)
- [360Anything: Geometry-Free Lifting of Images and Videos to 360deg](https://mlsanity.com/paper/2601.16192/) (2026)
- [OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video Generation](https://mlsanity.com/paper/2412.09623/) (2024)
- [ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies](https://mlsanity.com/paper/2506.14315/) (2025)
- [360PanT: Training-Free Text-Driven 360-Degree Panorama-to-Panorama Translation](https://mlsanity.com/paper/2409.08397/) (2024)

## Abstract (arXiv)

Personalized text-to-image (T2I) synthesis based on diffusion models has attracted significant attention in recent research. However, existing methods primarily concentrate on customizing subjects or styles, neglecting the exploration of global geometry. In this study, we propose an approach that focuses on the customization of 360-degree panoramas, which inherently possess global geometric properties, using a T2I diffusion model. To achieve this, we curate a paired image-text dataset specifically designed for the task and subsequently employ it to fine-tune a pre-trained T2I diffusion model with LoRA. Nevertheless, the fine-tuned model alone does not ensure the continuity between the leftmost and rightmost sides of the synthesized images, a crucial characteristic of 360-degree panoramas. To address this issue, we propose a method called StitchDiffusion. Specifically, we perform pre-denoising operations twice at each time step of the denoising process on the stitch block consisting of the leftmost and rightmost image regions. Furthermore, a global cropping is adopted to synthesize seamless 360-degree panoramas. Experimental results demonstrate the effectiveness of our customized model combined with the proposed StitchDiffusion in generating high-quality 360-degree panoramic images. Moreover, our customized model exhibits exceptional generalization ability in producing scenes unseen in the fine-tuning dataset. Code is available at https://github.com/littlewhitesea/StitchDiffusion.