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..
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image Manipulation
Authors: Sihan Xu, Ziqiao Ma, Yidong Huang, Honglak Lee, Joyce Chai
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical studies show that Cycle Net is superior in translation consistency and quality, and can generate high-quality images for out-of-domain distributions with a simple change of the textual prompt. The empirical results demonstrate that compared to previous approaches, Cycle Net is superior in translation faithfulness, cycle consistency, and image quality. |
| Researcher Affiliation | Collaboration | 1University of Michigan, 2LG AI Research |
| Pseudocode | Yes | The pseudocode for training is given in Algo. 1. |
| Open Source Code | Yes | Our code is available at https://github.com/sled-group/CycleNet. |
| Open Datasets | Yes | Additionally, we introduce Mani Cups1, a dataset of state-level image manipulation that tasks models to manipulate cups by filling or emptying liquid to/from containers... Our data is available at https://huggingface.co/datasets/sled-umich/ManiCups. |
| Dataset Splits | No | Table 3: The statistics of the Mani Cups dataset, with 3 abundant domains and 2 lowresource domains. Table 4: The statistics of the Yosemite summer winter, horse zebra, and apple orange datasets. (These tables show Train and Test splits, but no explicit Validation split details beyond a monitor metric) |
| Hardware Specification | Yes | We train our model with a batch size of 4 on only one single A40 GPU. |
| Software Dependencies | No | The paper lists versions for specific models (e.g., Stable Diffusion v1.5, v2.1) but does not provide specific version numbers for general ancillary software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | In the training of Cycle Net, the weights of our three loss functions are respectively set as λ1 = 1, λ2 = 0.1, and λ3 = 0.01. We train the model for 50k steps. ... Our configuration is as follows: model: params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 timesteps: 1000 image_size: 64 channels: 4 cond_stage_trainable: false monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False only_mid_control: False recon_weight: 1 #lambda1 disc_weight: 0.1 #lambda2 cycle_weight: 0.01 #lambda3 disc_mode: eps consis_weight: 0.1 |