Compositional Text-to-Image Generation with Dense Blob Representations (2405.08246v1)

Abstract: Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose a scene into visual primitives - denoted as dense blob representations - that contain fine-grained details of the scene while being modular, human-interpretable, and easy-to-construct. Based on blob representations, we develop a blob-grounded text-to-image diffusion model, termed BlobGEN, for compositional generation. Particularly, we introduce a new masked cross-attention module to disentangle the fusion between blob representations and visual features. To leverage the compositionality of LLMs, we introduce a new in-context learning approach to generate blob representations from text prompts. Our extensive experiments show that BlobGEN achieves superior zero-shot generation quality and better layout-guided controllability on MS-COCO. When augmented by LLMs, our method exhibits superior numerical and spatial correctness on compositional image generation benchmarks. Project page: https://blobgen-2d.github.io.

• The paper introduces BlobGEN, a diffusion-based model that uses dense blob representations to enhance accuracy in compositional text-to-image generation.
• It employs masked cross-attention to align blob features with visual cues, reducing text leakage and ensuring fine-grained control over image details.
• BlobGEN achieves superior zero-shot FID (8.61 on MS-COCO) and improved layout guidance, enabling precise editing and compositional scene creation.

Compositional Text-to-Image Generation with Dense Blob Representations

Introduction to the Problem

Text-to-image models have made remarkable strides, but they still struggle with comprehending complex prompts. This often leads to images that don't quite match the fine-grained details described in the text. Traditionally, model enhancements involve conditioning on external inputs like bounding boxes or semantic maps, which have their own trade-offs between usability and detail.

The paper "Compositional Text-to-Image Generation with Dense Blob Representations" explores a fresh approach by employing dense blob representations. This method decomposes a scene into modular, detailed, and user-friendly visual primitives.

Key Concepts and Methods

Blob Representations

The idea revolves around dense blob representations, composed of:

1. Blob Parameters: Specifies position, size, and orientation using tilted ellipses.
2. Blob Descriptions: Rich text descriptions detailing the appearance and attributes of objects.

Blob-Grounded Model (BlobGEN)

Using blob representations, the researchers developed a method called BlobGEN, which integrates these representations into a diffusion model for text-to-image generation. This involves:

• Masked Cross-Attention: Aligns blob features with visual features for disentangled fusion, reducing text leakage and ensuring each blob influences only its corresponding region.
• In-Context Learning for LLMs: LLMs are trained to generate blob representations from text prompts, leveraging their compositionality.

Strong Numerical Results

The paper reports superior zero-shot generation performance on the MS-COCO dataset:

• Zero-shot FID: Improved from 10.40 (base model) to 8.61.
• Controllability: Exhibited better layout-guided performance reflected in Region-level CLIP scores.

Moreover, BlobGEN outperformed existing models in compositional image generation tasks with significant advances in spatial and numerical correctness.

Practical Implications

BlobGEN exhibits several practical advances:

• Fine-Grained Control: Users can create and manipulate image content more precisely, such as changing object colors or repositioning without affecting other regions.
• Image Editing: Small edits to specific blobs can alter the image significantly, shown by altering an object's description or position.
• Compositional Generation: The model aptly handles complex scenes with multiple objects, making it useful for creating detailed illustrations or layout-based designs.

Theoretical Implications

Theoretically, this approach pushes the boundary of modular generative models. By decomposing scenes into interpretable units, it also simplifies the task of incorporating human feedback and corrections. This modularity could inspire future frameworks that further improve generalized generation tasks.

Speculating Future Developments

Looking ahead, a few areas could see advancements:

• Enhanced Editing Techniques: Incorporating sophisticated editing mechanisms could make local transformations even more reliable.
• Integration with Inversion Methods: Combining blob representations with methods like inversion could bridge gaps in perfect image recovery.
• Improved LLM Integration: Advanced integrations could fine-tune blob generation, addressing the occasional failures in handling complex composite images.

Conclusion

This paper offers a promising leap forward in text-to-image generation by utilizing dense blob representations. While the approach improves generation quality and layout control significantly, the research hints at broader application potentials and room for future enhancements. The work serves as a foundational step, paving the way for even more interactive and controllable AI-driven image generation tools.