Grounding Text-to-Image Diffusion Models for Controlled High-Quality Image Generation (2501.09194v2)

Abstract: Text-to-image (T2I) generative diffusion models have demonstrated outstanding performance in synthesizing diverse, high-quality visuals from text captions. Several layout-to-image models have been developed to control the generation process by utilizing a wide range of layouts, such as segmentation maps, edges, and human keypoints. In this work, we propose ObjectDiffusion, a model that conditions T2I diffusion models on semantic and spatial grounding information, enabling the precise rendering and placement of desired objects in specific locations defined by bounding boxes. To achieve this, we make substantial modifications to the network architecture introduced in ControlNet to integrate it with the grounding method proposed in GLIGEN. We fine-tune ObjectDiffusion on the COCO2017 training dataset and evaluate it on the COCO2017 validation dataset. Our model improves the precision and quality of controllable image generation, achieving an AP$_{\text{50}}$ of 46.6, an AR of 44.5, and an FID of 19.8, outperforming the current SOTA model trained on open-source datasets across all three metrics. ObjectDiffusion demonstrates a distinctive capability in synthesizing diverse, high-quality, high-fidelity images that seamlessly conform to the semantic and spatial control layout. Evaluated in qualitative and quantitative tests, ObjectDiffusion exhibits remarkable grounding capabilities in closed-set and open-set vocabulary settings across a wide variety of contexts. The qualitative assessment verifies the ability of ObjectDiffusion to generate multiple detailed objects in varying sizes, forms, and locations.

• The paper introduces ObjectDiffusion, a model that enhances text-to-image synthesis by integrating object labels and bounding boxes for precise spatial and semantic control.
• It innovatively adapts components from ControlNet and GLIGEN, combining Stable Diffusion with GroundNet using multi-scale gated self-attention to overcome knowledge forgetting.
• ObjectDiffusion achieves strong performance metrics (AP₅₀ of 46.6 and FID of 19.8) and paves the way for future improvements in handling fine-grained image details.

Critical Analysis of "Grounding Text-To-Image Diffusion Models For Controlled High-Quality Image Generation"

The field of text-to-image (T2I) synthesis has gained considerable traction due to its potential for creating diverse and high-quality visuals from textual inputs. The paper "Grounding Text-To-Image Diffusion Models For Controlled High-Quality Image Generation" presents a novel approach aimed at enhancing the controllability of T2I models through the introduction of ObjectDiffusion. This model leverages bounding box capabilities to provide spatial and semantic control over image generation, marking a significant stride in model precision and control.

Model Framework and Innovations

ObjectDiffusion enhances text-to-image models by conditioning them with object labels and their corresponding bounding boxes. The authors innovatively integrate components from existing architectures, specifically making substantial modifications to ControlNet and GLIGEN to suit their purpose. ObjectDiffusion's framework initiates with pre-trained parameters to harness existing generative knowledge and is fine-tuned using the COCO2017 dataset.

Two primary networks constitute ObjectDiffusion:

1. Stable Diffusion (SD): A locked, pre-trained model that synthesizes high-resolution images.
2. GroundNet: A parallel trainable network that incorporates pretrained encoding blocks from GLIGEN for processing the spatial and semantic grounding tokens.

The interaction between these networks occurs through a multi-scale injection of conditional features, facilitated by gated self-attention layers. This design choice ensures that the problematic 'knowledge forgetting' often observed during model fine-tuning is circumvented.

Quantitative and Qualitative Outcomes

ObjectDiffusion exhibits impressive performance metrics, achieving an AP$_{50}$ of 46.6, an AP of 27.4, and an FID of 19.8, surpassing previous state-of-the-art models across these critical metrics. Such statistical results confirm the model's effective grounding abilities and high-fidelity image synthesis.

The qualitative assessments further underline the model’s capability to generate diverse images that are faithful to the spatial and semantic conditions. ObjectDiffusion adeptly handled closed-set settings where it used known COCO categories and open-set settings with novel entities introduced during testing. The images synthesized demonstrate the model's strength in rendering complex scenes with variable object placement and size.

Limitations and Future Directions

Despite its successes, ObjectDiffusion displays certain limitations, notably in generating legible text and occasionally rendering faces and hands with imperfections. These challenges underscore the intricate task of capturing fine-grained details and suggest avenues for future enhancements.

Further research could focus on expanding pre-training datasets to include a broader spectrum of textual descriptions, enhancing the model's adaptability to novel inputs. Improvements in GPU infrastructure would also facilitate the scaling of batch sizes and precision, potentially amplifying the model’s performance capabilities.

Conclusion

ObjectDiffusion represents a notable advancement in controlled image generation, illustrating how effective integration of bounding box conditioning can meaningfully improve model output in terms of both quality and precision. Its architecture and methodological innovations could serve as a reference point for subsequent exploration in the domain of T2I models, paving the way for systems that are not only accurate but also increasingly customizable to user specifications.