Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models
Authors: Ruichen Wang, Zekang Chen, Chen Chen, Jian Ma, Haonan Lu, Xiaodong Lin
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on the publicly available COCO and open-domain datasets, and the results show that our method generates images that are more closely aligned with the given descriptions, thereby improving fidelity and faithfulness. |
| Researcher Affiliation | Collaboration | 1OPPO Research Institute 2South China University of Technology 3Rutgers University |
| Pseudocode | Yes | Algorithm 1: Denoising Process of Our Method Input: A text prompt p, a trained Box Net B, sets of each parsed entity s token indices {s1, s2, ..., s N}, a trained diffusion model SD Output: Denoised latent z0. 1: for t T, T 1, ..., 1 do 2: boxes B(SD, zt, p, t) 3: for (cx, cy, h, w) in boxes do 4: Convert box to zero-one masks mn 5: Gn Gaussian distribution 2D((cx, cy), h, w) 6: M argmax(Gn) 7: m n (M = n) mn, n = 1, 2..., N unique masks 8: SD SD 9: for each cross attention layer in SD do cross attention mask control 10: Obtain Cross Attention Map C 11: Ci Ci m n i sn, n = 1, 2..., N 12: for each self attention layer in SD do self attention mask control 13: Obtain Self Attention Map S 14: Si Si flatten(m n) i {i|flatten(m n)i = 1}, n = 1, 2..., N 15: zt 1 SD (zt, p, t) |
| Open Source Code | Yes | Please refer to https://github.com/OPPOMente-Lab/attention-mask-control. |
| Open Datasets | Yes | Specifically, we first train a Box Net applied to the forward process of SD on the COCO dataset (Lin et al. 2014) to predict object boxes for entities with attributes parsed by a constituency parser (Honnibal et al. 2020)." and "We conduct comprehensive experiments on the publicly available COCO and open-domain datasets |
| Dataset Splits | No | For evaluation, we construct a new benchmark dataset to evaluate all methods with respect to semantic infidelity issues in T2I synthesis." and "We conduct comprehensive experiments on the publicly available COCO and open-domain datasets". However, it does not explicitly provide training/validation/test splits for their custom benchmark dataset, nor for the COCO dataset beyond mentioning "test split of COCO dataset". |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like spaCy, U-Net, and Stable Diffusion, but it does not specify their version numbers. |
| Experiment Setup | No | All the training details and hyper-parameter determination are presented in Appendix A.2. (This indicates that the specific experimental setup details are not in the main text). |