Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition (2402.15504v1)

Published 23 Feb 2024 in cs.CV and cs.AI

Abstract: Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examples for training. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models. First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e.g., LAION). Second, given an image containing multiple personalized concepts, there lacks a holistic metric that evaluates performance on not just the degree of resemblance of personalized concepts, but also whether all concepts are present in the image and whether the image accurately reflects the overall text description. To address these issues, we introduce Gen4Gen, a semi-automated dataset creation pipeline utilizing generative models to combine personalized concepts into complex compositions along with text-descriptions. Using this, we create a dataset called MyCanvas, that can be used to benchmark the task of multi-concept personalization. In addition, we design a comprehensive metric comprising two scores (CP-CLIP and TI-CLIP) for better quantifying the performance of multi-concept, personalized text-to-image diffusion methods. We provide a simple baseline built on top of Custom Diffusion with empirical prompting strategies for future researchers to evaluate on MyCanvas. We show that by improving data quality and prompting strategies, we can significantly increase multi-concept personalized image generation quality, without requiring any modifications to model architecture or training algorithms.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. A neural space-time representation for text-to-image personalization. arXiv preprint arXiv:2305.15391, 2023.
  2. Break-a-scene: Extracting multiple concepts from a single image. arXiv preprint arXiv:2305.16311, 2023.
  3. Hrs-bench: Holistic, reliable and scalable benchmark for text-to-image models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 20041–20053, 2023.
  4. Improving image generation with better captions. https://cdn.openai.com/papers/dall-e-3.pdf, 2023.
  5. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901, 2020.
  6. Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models. ACM Transactions on Graphics (TOG), 42(4):1–10, 2023.
  7. Disenbooth: Identity-preserving disentangled tuning for subject-driven text-to-image generation. arXiv preprint arXiv:2305.03374, 2023a.
  8. Photoverse: Tuning-free image customization with text-to-image diffusion models. arXiv preprint arXiv:2309.05793, 2023b.
  9. Subject-driven text-to-image generation via apprenticeship learning. arXiv preprint arXiv:2304.00186, 2023c.
  10. Dall-eval: Probing the reasoning skills and social biases of text-to-image generation models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3043–3054, 2023.
  11. Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023.
  12. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  13. Tise: Bag of metrics for text-to-image synthesis evaluation. In European Conference on Computer Vision, pages 594–609. Springer, 2022.
  14. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022.
  15. Vector quantized diffusion model for text-to-image synthesis, 2022.
  16. Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models. arXiv preprint arXiv:2305.18292, 2023.
  17. Svdiff: Compact parameter space for diffusion fine-tuning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 7323–7334, 2023.
  18. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. arXiv preprint arXiv:2307.06350, 2023.
  19. Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1931–1941, 2023.
  20. Llm-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models. arXiv preprint arXiv:2305.13655, 2023.
  21. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740–755. Springer, 2014.
  22. Visual instruction tuning. arXiv preprint arXiv:2304.08485, 2023a.
  23. Cones 2: Customizable image synthesis with multiple subjects. arXiv preprint arXiv:2305.19327, 2023b.
  24. Subject-diffusion: Open domain personalized text-to-image generation without test-time fine-tuning. arXiv preprint arXiv:2307.11410, 2023.
  25. Simple open-vocabulary object detection. In European Conference on Computer Vision, pages 728–755. Springer, 2022.
  26. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021.
  27. OpenAI. Gpt-4 technical report, 2023.
  28. Human evaluation of text-to-image models on a multi-task benchmark. arXiv preprint arXiv:2211.12112, 2022.
  29. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023.
  30. Highly accurate dichotomous image segmentation. In ECCV, 2022.
  31. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  32. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551, 2020.
  33. Hierarchical text-conditional image generation with clip latents, 2022.
  34. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022.
  35. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22500–22510, 2023.
  36. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  37. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35:25278–25294, 2022.
  38. A picture is worth a thousand words: Principled recaptioning improves image generation. arXiv preprint arXiv:2310.16656, 2023.
  39. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023.
  40. Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15943–15953, 2023.
  41. Singleinsert: Inserting new concepts from a single image into text-to-image models for flexible editing. arXiv preprint arXiv:2310.08094, 2023.
  42. Fastcomposer: Tuning-free multi-subject image generation with localized attention. arXiv preprint arXiv:2305.10431, 2023.
  43. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2(3):5, 2022.
  44. Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592, 2023.
Citations (3)
View on Semantic Scholar

Summary

  • The paper introduces Gen4Gen, a semi-automated pipeline that creates the MyCanvas benchmark for detailed multi-concept text-to-image personalization.
  • The paper proposes novel evaluation metrics—CP-CLIP and TI-CLIP—to quantitatively measure concept integration and text-image alignment.
  • The empirical results reveal significant improvements in generating complex, personalized images using advanced data synthesis and prompting strategies.

Enhancing Multi-Concept Personalization in Text-to-Image Generation with Gen4Gen

Introduction

The evolution of text-to-image diffusion models has unlocked new doors in the creation of personalized images, combining multiple user-defined concepts into a single coherent scene. Despite remarkable advancements, perfecting multi-concept personalization remains a significant challenge. Traditional personalization methods struggle with complex scene compositions, often due to a mismatch between simplistic text descriptions and the desired intricate visual outputs. Addressing these challenges, this paper introduces Gen4Gen, a semi-automated dataset creation pipeline, and MyCanvas, a dataset designed for benchmarking multi-concept personalization. Furthermore, a novel evaluation metric comprising CP-CLIP and TI-CLIP scores is proposed to quantitatively assess the models' capability in generating personalized multi-concept images.

MyCanvas: Proposing a New Benchmark for Personalized Text-to-Image Generation

MyCanvas emerges as a response to the inadequacies of current datasets in accommodating the intricacies of multi-concept personalization. Leveraging advancements in foundation models, Gen4Gen synthesizes realistic, custom images with corresponding densely detailed text descriptions. This dataset not only improves upon the existing datasets' quality but also introduces more challenging scenarios for text-to-image models by including images with multiple, semantically similar objects in complex compositions.

Dataset Design Principles

The design of MyCanvas is guided by three principles:

  • Detailed Text-Image Alignment: Every image is paired with a comprehensive text description, ensuring a precise match between the visual content and the textual narrative.
  • Logical Object Layout with Reasonable Backgrounds: The pipeline ensures realistic object coexistence and positioning, providing images that surpass the simplistic 'cut-and-paste' appearance of traditional datasets.
  • High Resolution: Maintaining high resolution is pivotal to support the generation of detailed, high-quality personalized images.

Gen4Gen Pipeline

Gen4Gen streamlines the creation of the MyCanvas dataset through a three-stage process:

  1. Object Association and Foreground Segmentation: Combining objects likely to appear in real-world scenes, applying object segmentation to generate foregrounds.
  2. LLM-Guided Object Composition: Utilizing LLMs to dictate the composition layout and background scenario suggestions.
  3. Background Repainting and Image Recaptioning: Enhancing foreground objects with suitable backgrounds followed by detailed recaptioning to ensure text-image alignment.

Novel Evaluation Metrics: CP-CLIP and TI-CLIP

Evaluating the effectiveness of text-to-image models in the context of personalized multi-concept images necessitates metrics that can capture both the accuracy of concept representation and the alignment with textual descriptions. The CP-CLIP score evaluates how well a model generates images that incorporate all personalized concepts with high fidelity, whereas the TI-CLIP score measures the alignment between the generated image and the entire text description, serving as a means to detect potential overfitting to training backgrounds.

Empirical Results and Findings

Empirical tests on the MyCanvas dataset reveal significant improvements in generating realistic multi-concept images using existing diffusion models with enhanced data quality and prompting strategies. Specifically, the paper outlines how Custom Diffusion benefits from the quality and complexity of the MyCanvas dataset, achieving a notable boost in the generation of personalized images as measured by the CP-CLIP and TI-CLIP scores.

Future Directions and Conclusion

This research underscores the critical role of high-quality datasets and innovative evaluation metrics in advancing personalized text-to-image generation. As AI models continue to evolve, the integration of foundation models into dataset creation processes like Gen4Gen offers promising avenues for crafting tailored datasets that address specific challenges within computer vision tasks. The introduction of MyCanvas sets a new benchmark for evaluating and improving multi-concept personalization in generative models, potentially stimulating further research in dataset quality enhancement and model evaluation methodologies.

This paper represents a significant stride towards understanding and perfecting the generation of personalized, multi-concept text-to-image models. Through the Gen4Gen pipeline, the research community gains access to a sophisticated tool for developing datasets that better align with the nuanced requirements of personalized image generation. As we look to the future, it is anticipated that these innovations will not only refine the capabilities of generative models but also unlock new potentials in creating deeply personalized and contextually rich visual content.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown