Conditional Score Guidance for Text-Driven Image-to-Image Translation
Authors: Hyunsoo Lee, Minsoo Kang, Bohyung Han
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {philip21, kminsoo, bhhan}@snu.ac.kr |
| 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]. |