| --- |
| library_name: pytorch |
| license: mit |
| tags: [meanflow, multi-trace, image-generation, class-conditional, cifar10] |
| --- |
| |
| # Multi-Trace U-Net — 14,992,182 parameters |
|
|
| U-Net adapted from the user-provided Untitled0.ipynb and widened to approximately |
| 22M parameters. Trained for 50,000 optimizer updates on CIFAR-10. |
| Training samples K uniformly from 1..5 and a grid interval j/K; |
| the same u head fits the interval and diagonal, with a detached directional JVP. |
| Default inference weights are **ema**. Model input/output pixels |
| are 32x32 RGB; sampling maps the final [-1,1] image into [0,1]. |
|
|
| This is a custom PyTorch model. Download with huggingface_hub.snapshot_download, |
| install the included requirements.txt, then run: |
|
|
| ```bash |
| python sample.py --model-dir . --nfe 1 --output samples_1nfe.png |
| python sample.py --model-dir . --nfe 5 --output samples_5nfe.png |
| ``` |
|
|
| config.json describes architecture/objective; training_config.json records the |
| effective batch, accumulation, LR schedule, clipping and EMA decay. |
| Full optimizer/EMA/RNG resume checkpoints remain on the Modal Volume and, when |
| the Modal notebook backup option is enabled, are also stored under |
| training-checkpoints/<run-name>/ in this Hub repository. |
| best_validation.safetensors contains the selected EMA validation snapshot; |
| its step and fixed held-out losses are recorded in best_validation.json. |
| Diagnostics include gradient RMS, actual optimizer updates, losses by K, |
| clipping frequency, samples, throughput, ETA and TensorBoard events. |
| Plateau flags are heuristics, not proof of convergence or image quality. |
| |
| When present, fid.json reports pytorch-fid pool3 FID against 50,000 unaugmented |
| CIFAR-10 training images, with the generated sample count and each NFE explicitly |
| recorded. This protocol differs from the reference notebook's ImageNet-feature |
| FID; the values must not be compared directly. No quality claim is made here. |
| The test split was used as fixed validation during training. |
| |
| Labels: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. |
| The U-Net and interval schedule follow the user-provided notebook; repository |
| training flow and DiT baseline are adapted from haidog-yaqub/MeanFlow (MIT). |
| See third_party/ for attribution. |
|
|
