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..
RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance
Authors: Zhicheng Sun, Zhenhao Yang, Yang Jin, Haozhe Chi, Kun Xu, Liwei Chen, Hao Jiang, Yang Song, Kun Gai, Yadong Mu
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on these tasks clearly validate the effectiveness of our approach. |
| Researcher Affiliation | Collaboration | 1Peking University, 2Kuaishou Technology, 3University of Electronic Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 Anchored Classifier Guidance |
| Open Source Code | Yes | Code is available at https://github.com/feifeiobama/Rectif ID. [...] The code to reproduce all results is available at this anonymized link https: //github.com/feifeiobama/Rectif ID, with sufficient instructions in the README file. |
| Open Datasets | Yes | For face-centric evaluation, we follow Pang et al. (2024) to evaluate on 20 prompts with the first 200 images from Celeb A-HQ (Liu et al., 2015; Karras et al., 2018) as reference images. For subject-driven generation, we conduct qualitative studies on a subset of examples from the Dream Booth dataset (Ruiz et al., 2023b). |
| Dataset Splits | No | The paper describes its method as 'training-free' and evaluates it on specific subsets of datasets (Celeb A-HQ, Dream Booth) rather than defining traditional train/validation/test splits for its own experimental setup. |
| Hardware Specification | Yes | The inference time is measured in seconds on an NVIDIA A800. [...] our method takes less than 0.5s per iteration on an NVIDIA A800 GPU and fits on consumer-grade GPUs such as NVIDIA RTX 4080 |
| Software Dependencies | No | The paper mentions specific models and frameworks like 'Stable Diffusion 1.5' and 'Insight Face library', but does not provide version numbers for general software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We experiment with a frozen piecewise rectified flow (Yan et al., 2024) finetuned from Stable Diffusion 1.5 (Rombach et al., 2022) with 4 equally divided time windows. The number of sampling steps is set to a minimum K = 4 [...] The guidance scale is fixed to s = 1 in quantitative evaluation. [...] The number of iterations is set to N = 100. |