Diffusion Self-Distillation for Zero-Shot Customized Image Generation
On the map: Diffusion models → Image generation, text-to-image and editing
Abstract (arXiv)
Text-to-image diffusion models produce impressive results but are frustrating tools for artists who desire fine-grained control. For example, a common use case is to create images of a specific instance in novel contexts, i.e., "identity-preserving generation". This setting, along with many other tasks (e.g., relighting), is a natural fit for image+text-conditional generative models. However, there is insufficient high-quality paired data to train such a model directly. We propose Diffusion Self-Distillation, a method for using a pre-trained text-to-image model to generate its own dataset for text-conditioned image-to-image tasks. We first leverage a text-to-image diffusion model's in-context generation ability to create grids of images and curate a large paired dataset with the help of a Visual-Language Model. We then fine-tune the text-to-image model into a text+image-to-image model using the curated paired dataset. We demonstrate that Diffusion Self-Distillation outperforms existing zero-shot methods and is competitive with per-instance tuning techniques on a wide range of identity-preservation generation tasks, without requiring test-time optimization.
Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 48 of its 51 references that are in the map. First posted on arXiv 27 Nov 2024.
Builds on
48 of 51 references.
Closest ancestors · 18
Same direction, newest first
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- 2024BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models25 Jan
- 2024InstantID: Zero-shot Identity-Preserving Generation in Seconds15 Jan
- 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models13 Aug
- 2023Subject-Diffusion:Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning21 Jul
- 2023Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models25 May
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- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding23 May
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latents13 Apr
- 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models20 Dec
Other references · 30
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
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- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
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- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,152
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,581
- 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,261
- 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,245
- 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,937
- 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,433
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- 2023MVDream: Multi-view Diffusion for 3D Generationcited by 938
- 2024WorldSimBench: Towards Video Generation Models as World Simulatorscited by 881
- 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animationcited by 688
- 2023Generative Multimodal Models are In-Context Learnerscited by 446
- 2023BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editingcited by 442
- 2022Latent Video Diffusion Models for High-Fidelity Long Video Generationcited by 388
- 2023Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Modelcited by 378
- 2024OminiControl: Minimal and Universal Control for Diffusion Transformercited by 340
- 2023DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Modelcited by 214
- 2023Subject-driven Text-to-Image Generation via Apprenticeship Learningcited by 212
- 2024DreamBench++: A Human-Aligned Benchmark for Personalized Image Generationcited by 131
- 2023State of the Art on Diffusion Models for Visual Computingcited by 127
- 2024Collaborative Video Diffusion: Consistent Multi-video Generation with Camera Controlcited by 117
- 2024Human4DiT: 360-degree Human Video Generation with 4D Diffusion Transformercited by 61
- 2022DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Modelscited by 51
- 2023Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Modelscited by 30
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Citing papers per year: 2024: 1; 2025: 28; 2026: 18.
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- 2025Less-to-More Generalization: Unlocking More Controllability by In-Context Generationcited by 154
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- 2025IdentityStory: Taming Your Identity-Preserving Generator for Human-Centric Story Generation29 Dec 2025
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