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..
FineStyle: Fine-grained Controllable Style Personalization for Text-to-image Models
Authors: Gong Zhang, Kihyuk Sohn, Meera Hahn, Humphrey Shi, Irfan Essa
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the effectiveness of Fine Style at following fine-grained text prompts and delivering visual quality faithful to the specified style, measured by CLIP scores and human raters. ... 5 Experiment |
| Researcher Affiliation | Collaboration | Gong Zhang1,2 Kihyuk Sohn3 Meera Hahn2 Humphrey Shi1 Irfan Essa1,2 1Georgia Tech 2Google Deep Mind 3Meta Reality Labs |
| Pseudocode | Yes | A.5 Derivation of Concept Attention Map ... 2 import jax.numpy as jnp ... def aggregate_xattn_by_phrase ( |
| Open Source Code | Yes | Visit https://github.com/SHI-Labs/Fine Style for code and more examples. ... Justification: We will release the codes to public. |
| Open Datasets | Yes | We adopt the evaluation set from [41] containing 24 styles encompassing fine-art oil painting, 3D rendering, and sculpture. ... We synthesize images by combining a filtered version of Parti [50] prompts and 10 styles from the evaluation set, details in Appendix A.2. ... We utilize CLIP [35] to calculate Text (text-image) and Style (image-image) scores. |
| Dataset Splits | No | The paper mentions training models and evaluating on a 'Parti prompts' set, but it does not provide specific train/validation/test dataset splits or percentages for these processes. |
| Hardware Specification | Yes | We train Fine Stlye using Adam optimizer [23] on TPUv4 with a batch size of 8. |
| Software Dependencies | No | The paper mentions using Adam optimizer [23] but does not provide specific software names with version numbers for other dependencies (e.g., Python, PyTorch, TensorFlow, CUDA). |
| Experiment Setup | Yes | A.3 Implementation Details ... We train Fine Stlye using Adam optimizer [23] on TPUv4 with a batch size of 8. See Tab. 3 for detailed hyperparamters. Table 3: Hyperparameters for optimizer, adapter architecture, and synthesis. |