GLIGEN: Open-Set Grounded Text-to-Image Generation (2301.07093v2)

Abstract: Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs. To preserve the vast concept knowledge of the pre-trained model, we freeze all of its weights and inject the grounding information into new trainable layers via a gated mechanism. Our model achieves open-world grounded text2img generation with caption and bounding box condition inputs, and the grounding ability generalizes well to novel spatial configurations and concepts. GLIGEN's zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin.

• The paper introduces Gligen, enhancing latent diffusion models with additional grounding inputs like bounding boxes and keypoints for precise image control.
• It preserves pre-trained model knowledge by adding trainable layers for integrating spatial conditions without compromising generalization.
• Using scheduled sampling, the method balances grounding inputs and text prompts to achieve robust zero-shot performance on benchmarks such as COCO and LVIS.

Overview of "Gligen: Open-Set Grounded Text-to-Image Generation"

The paper "Gligen: Open-Set Grounded Text-to-Image Generation" presents a novel method to enhance pre-trained text-to-image diffusion models through the introduction of additional grounding inputs. This approach, termed Gligen, is capable of broadening the applicability of existing diffusion models by introducing controllability features that extend beyond the capabilities of relying solely on text prompts. In this manuscript, the authors propose the preservation of the foundational weights of a pre-trained diffusion model, allowing the model to retain its broad knowledge of concepts while extending its capabilities through additional grounding layers.

Core Contributions

1. Grounded Inputs Incorporation: Gligen extends upon latent diffusion models by allowing them to accept grounding conditions in addition to text prompts. These include bounding boxes, keypoints, depth maps, and other spatially aligned conditions, thereby providing a more robust framework for controlled image generation. The ability to utilize bounding boxes as a conditioning factor is highlighted as particularly beneficial for precise object localization, a task for which natural language descriptors often fall short.
2. Preservation and Expansion of Capabilities: A primary feature of Gligen is its ability to utilize the vast pre-existing concept knowledge within the frozen weights of previously trained text-to-image models. By introducing trainable layers on top of these frozen models, Gligen can introduce new conditional inputs without sacrificing the expansive general knowledge acquired during pre-training.
3. Scheduled Sampling: The methodology includes a novel inference technique termed scheduled sampling. This technique incorporates stages during the generation process that can prioritize either the newly added grounding inputs or the rich text-to-image generation capabilities borne from pre-training, balancing image quality and adherence to grounding conditions.
4. Experimental Validation: The authors provide quantitative evidence of Gligen's effectiveness through a variety of evaluation metrics. The zero-shot performance of Gligen on tasks beyond its training datasets, particularly on COCO and LVIS benchmarks, underscores its ability to generalize effectively. Notably, the method's YOLO score serves as an important metric for evaluating how well generated images adhere to the layout conditions specified.

Implications and Future Directions

The implications of integrating grounded inputs into text-to-image models are substantial. Practically, it increases the precision and usability of image synthesis models in applications where spatial configuration and recognizability of objects are critical, such as in design, virtual reality, and robotics. Theoretically, it marks a progression towards more nuanced interactions within AI-generated environments, presenting pathways for models to understand and manipulate objects in relation to their surroundings more effectively.

Future developments in AI will likely continue along the path of increasing model flexibility and control, with further exploration into multi-modal grounding inputs and interactive AI systems that can dynamically adapt to evolving conditions or instructions. The direction set forth by the integration technique introduced in Gligen provides a foundational framework from which the community can iteratively build more capable and adaptable AI-driven image synthesis tools.

Conclusion

The work embodied by Gligen represents a significant advancement in the field of text-to-image diffusion models, augmenting pre-trained systems with additional layers of control and input diversity. This paper is a testament to the potential of preserving foundational knowledge while seamlessly expanding model competencies, offering a rich vein of further inquiry for future research in grounded AI generation capabilities.