Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Conditional Score Guidance for Text-Driven Image-to-Image Translation
Authors: Hyunsoo Lee, Minsoo Kang, Bohyung Han
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,670 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,347 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through comprehensive experiments, we demonstrate that our approach achieves outstanding image-to-image translation performance on various tasks. |
| Researcher Affiliation | Academia | Hyunsoo Lee1 Minsoo Kang1 Bohyung Han1,2 1ECE & 2IPAI, Seoul National University EMAIL |
| Pseudocode | Yes | Algorithm 1 shows the detailed procedure of CSG. |
| Open Source Code | Yes | Code is available at https://github.com/Hleephilip/CSG. |
| Open Datasets | Yes | For quantitative evaluation, we select 250 images with the highest CLIP similarity from the LAION 5B dataset [20]. |
| Dataset Splits | No | The paper mentions using a subset of the LAION 5B dataset for evaluation but does not specify explicit train/validation/test splits, percentages, or predefined partitions. |
| Hardware Specification | Yes | Our method is implemented based on the publicly available official code of Pix2Pix-Zero [35] in Py Torch [44] and tested on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number for it or any other software dependencies. |
| Experiment Setup | Yes | For faster generation, we adopt 50 forward steps using Eq. (1) and 50 reverse steps for target image generation. For translating target images, all methods employ the classifier-free guidance [46]. |