We lifted 12 functions out of this paper's own repositories and ran 9 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Gsunshine/meanflow | canonical | 0 of 1 |
| andypinxinliu/GestureLSM | pwc_unofficial | 9 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| exponential_pdf | Ran | andypinxinliu/GestureLSM/models/LSM.py code served (permissive licence) · get_code("f3c44f47824302dc") |
| find_attention_modules | Ran | andypinxinliu/GestureLSM/models/MeanFlow.py code served (permissive licence) · get_code("1b12f7f17b973eee") |
| get_loss_func | Ran | andypinxinliu/GestureLSM/optimizers/loss_factory.py code served (permissive licence) · get_code("4f043bf1bec12652") |
| get_module_config | Ran | andypinxinliu/GestureLSM/models/config.py code served (permissive licence) · get_code("2057a8b48b0d5cf3") |
| get_obj_from_str | Ran | andypinxinliu/GestureLSM/models/config.py code served (permissive licence) · get_code("6f951d437f9f5c5f") |
| mean_flat | Ran | andypinxinliu/GestureLSM/models/MeanFlow.py code served (permissive licence) · get_code("dd96bc6c07c85dae") |
| sample_beta_distribution | Ran | andypinxinliu/GestureLSM/models/LSM.py code served (permissive licence) · get_code("1ba44760ad133e99") |
| sample_t | Ran | andypinxinliu/GestureLSM/models/LSM.py code served (permissive licence) · get_code("f9b3244bb6e382cd") |
| update_lr_warm_up | Ran | andypinxinliu/GestureLSM/rvq_beatx_train.py code served (permissive licence) · get_code("b3786bbc856619f6") |
| generate | Not yet run | Gsunshine/meanflow/meanflow.py code served (permissive licence) · get_code("15cb9cb0de4aadad") |
| instantiate_from_config | Not yet run | andypinxinliu/GestureLSM/models/config.py code served (permissive licence) · get_code("16e41676220876a4") |
| reshape_coefs | Not yet run | andypinxinliu/GestureLSM/models/MeanFlow.py code served (permissive licence) · get_code("24486901843b5f5c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256x256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2505.13447")
get_code_for_paper("2505.13447")
have("2505.13447")
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