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Roopal Garg

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ICLR Conference 2024 Conference Paper

Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation

Jaemin Cho 0001
Yushi Hu
Jason M. Baldridge
Roopal Garg
Peter Anderson
Ranjay Krishna
Mohit Bansal
Jordi Pont-Tuset

Evaluating text-to-image models is notoriously difficult. A strong recent approach for assessing text-image faithfulness is based on QG/A (question generation and answering), which uses pre-trained foundational models to automatically generate a set of questions and answers from the prompt, and output images are scored based on whether these answers extracted with a visual question answering model are consistent with the prompt-based answers. This kind of evaluation is naturally dependent on the quality of the underlying QG and VQA models. We identify and address several reliability challenges in existing QG/A work: (a) QG questions should respect the prompt (avoiding hallucinations, duplications, and omissions) and (b) VQA answers should be consistent (not asserting that there is no motorcycle in an image while also claiming the motorcycle is blue). We address these issues with Davidsonian Scene Graph (DSG), an empirically grounded evaluation framework inspired by formal semantics, which is adaptable to any QG/A frameworks. DSG produces atomic and unique questions organized in dependency graphs, which (i) ensure appropriate semantic coverage and (ii) sidestep inconsistent answers. With extensive experimentation and human evaluation on a range of model configurations (LLM, VQA, and T2I), we empirically demonstrate that DSG addresses the challenges noted above. Finally, we present DSG-1k, an open-sourced evaluation benchmark that includes 1,060 prompts, covering a wide range of fine-grained semantic categories with a balanced distribution. We release the DSG-1k prompts and the corresponding DSG questions.

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TMLR Journal 2024 Journal Article

Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models

Cristina Nader Vasconcelos
Abdullah Rashwan
Austin Waters
Trevor Walker
Keyang Xu
Jimmy Yan
Rui Qian
Yeqing Li

We address the long-standing problem of how to learn effective pixel-based image diffusion models at scale, introducing a remarkably simple greedy method for stable training of large-scale, high-resolution models. without the needs for cascaded super-resolution components.The key insight stems from careful pre-training of core components, namely, those responsible for text-to-image alignment vs. high resolution rendering. We first demonstrate the benefits of scaling a Shallow UNet, with no down(up)-sampling enc(dec)oder. Scaling its deep core layers is shown to improve alignment, object structure, and composition. Building on this core model, we propose a greedy algorithm that grows the architecture into high resolution end-to-end models, while preserving the integrity of the pre-trained representation,stabilizing training, and reducing the need for large high-resolution datasets. This enables a single stage model capable of generating high-resolution images without the need of a super-resolution cascade. Our key results rely on public datasets and show that we are able to train non-cascaded models up to 8B parameters with no further regularization schemes.Vermeer, our full pipeline model trained with internal datasets to produce 1024×1024 images, without cascades, is preferred by 44.0% vs. 21.4% human evaluators over SDXL.

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