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..
Frequency-Controlled Diffusion Model for Versatile Text-Guided Image-to-Image Translation
Authors: Xiang Gao, Zhengbo Xu, Junhan Zhao, Jiaying Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness and superiority of our method for text-guided I2I are demonstrated with extensive experiments both qualitatively and quantitatively. |
| Researcher Affiliation | Academia | Wangxuan Institute of Computer Technology, Peking University, Beijing, China EMAIL |
| Pseudocode | No | The paper provides architectural diagrams and schematics (Figure 2) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our project is publicly available at: https://xianggao1102.github.io/FCDiffusion/. |
| Open Datasets | Yes | We use Stable Diffusion v2-1-base as the pre-trained LDM in our model, and use LAION-Aesthetics 6.5+ which contains 625K image-text pairs as our dataset |
| Dataset Splits | No | The paper mentions partitioning the dataset into a training set and a test set at a ratio of 9:1, but does not explicitly provide details for a validation set split. |
| Hardware Specification | Yes | Each frequency control branch in our model is separately finetuned for 100K iterations with batch size 4 on a single RTX 3090 Ti GPU. |
| Software Dependencies | No | The paper mentions software components like Stable Diffusion v2-1-base, LDM, Control Net, and Open CLIP, but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | We train at 512 512 image resolution, i.e., H = W = 512, h = w = 64. We set the initial learning rate as 1e-5. Each frequency control branch in our model is separately finetuned for 100K iterations with batch size 4 on a single RTX 3090 Ti GPU. All the results in this paper are generated using the DDIM (Song, Meng, and Ermon 2020) sampler with 50 steps. |