mlsanityData to 14 Oct 2026

ObjectAdd: Adding Objects into Image via a Training-Free Diffusion Modification Fashion

arXiv 2404.17230 ↗First posted 26 Apr 2024cs.CVcited by 7

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Abstract (arXiv)

We introduce ObjectAdd, a training-free diffusion modification method to add user-expected objects into user-specified area. The motive of ObjectAdd stems from: first, describing everything in one prompt can be difficult, and second, users often need to add objects into the generated image. To accommodate with real world, our ObjectAdd maintains accurate image consistency after adding objects with technical innovations in: (1) embedding-level concatenation to ensure correct text embedding coalesce; (2) object-driven layout control with latent and attention injection to ensure objects accessing user-specified area; (3) prompted image inpainting in an attention refocusing & object expansion fashion to ensure rest of the image stays the same. With a text-prompted image, our ObjectAdd allows users to specify a box and an object, and achieves: (1) adding object inside the box area; (2) exact content outside the box area; (3) flawless fusion between the two areas

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 26 Apr 2024.

Builds on

24 of 45 references.

References · 24

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
  6. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,322
  7. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
  8. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,611
  9. 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,431
  10. 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 781
  11. 2023MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editingcited by 727
  12. 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Modelscited by 702
12 more
  1. 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 647
  2. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  3. 2022Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesiscited by 388
  4. 2023Training-Free Layout Control with Cross-Attention Guidancecited by 313
  5. 2023DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editingcited by 294
  6. 2023BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusioncited by 282
  7. 2024Diffusion Model-Based Image Editing: A Surveycited by 212
  8. 2023Dense Text-to-Image Generation with Attention Modulationcited by 163
  9. 2023Localizing Object-level Shape Variations with Text-to-Image Diffusion Modelscited by 142
  10. 2024ReNoise: Real Image Inversion Through Iterative Noisingcited by 128
  11. 2023Effective Real Image Editing with Accelerated Iterative Diffusion Inversioncited by 79
  12. 2023ITI-GEN: Inclusive Text-to-Image Generationcited by 64

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 4.

7 papers.