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..
On Mechanistic Knowledge Localization in Text-to-Image Generative Models
Authors: Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda, Vlad I Morariu, Nanxuan Zhao, Ryan A. Rossi, Varun Manjunatha, Soheil Feizi
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically observe the effectiveness of causal tracing to models beyond Stable-Diffusion-v15. ... In this section, we provide empirical results highlighting the localized layers across various open-source text-to-image generative models: ... Human-Study Results. We run a human-study to verify that LOCOGEN can effectively identify controlling layers for different visual attributes. ... In Fig 57 we provide a comprehensive comparison and analysis of how LOCOEDIT compares to other methods. |
| Researcher Affiliation | Collaboration | 1University of Maryland 2Adobe Research. |
| Pseudocode | Yes | Algorithm 1 provides the pseudocode to ๏ฌnd the best candidate. |
| Open Source Code | Yes | Code will be available at https://github.com/samyadeepbasu/LocoGen. |
| Open Datasets | Yes | We use the benchmark dataset from (Basu et al., 2023) and (Kumari et al., 2023) for obtaining prompts for objects , style and facts . ... In particular, we curate a set of 320 prompts from MS-COCO with 80 objects and 4 locations ( beach , forest , city , house ) for each. |
| Dataset Splits | No | The paper discusses the use of prompts for generating and evaluating images but does not specify training, validation, or test dataset splits with explicit percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names, framework versions) used for replicating the experiments. |
| Experiment Setup | Yes | We set the following hyper-parameters for ฮปK and ฮปV in LOCOEDIT as 0.01 for all the text-to-image models, as it led to the best editing results. ... To select the cardinality of the set C , we run an iterative hyper-parameter search with m [1, M]... |