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 | Conference PDF | Archive PDF | Plain Text | 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. |