Image Generation On Lsun Churches 256 X 256
Metriken
FID
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | FID |
|---|---|
| unleashing-transformers-parallel-token | 4.07 |
| wavelet-diffusion-models-are-fast-and | 5.06 |
| tackling-the-generative-learning-trilemma-1 | 5.25 |
| adversarial-generation-of-continuous-images | 4.04 |
| diffusion-gan-training-gans-with-diffusion | 1.85 |
| swagan-a-style-based-wavelet-driven | 4.97 |
| truncated-diffusion-probabilistic-models | 3.98 |
| bellman-optimal-step-size-straightening-of | - |
| flow-matching-in-latent-space | 5.54 |
| Modell 10 | 2.66 |
| a-style-based-generator-architecture-for | 4.21 |
| denoising-diffusion-probabilistic-models | 7.89 |
| diffusion-gan-training-gans-with-diffusion | 3.17 |
| pseudo-numerical-methods-for-diffusion-models-1 | 8.69 |
| gotta-go-fast-when-generating-data-with-score | 26.46 |
| clr-gan-improving-gans-stability-and-quality | 3.43 |
| ensembling-off-the-shelf-models-for-gan | 1.72 |
| analyzing-and-improving-the-image-quality-of | 3.86 |
| polarity-sampling-quality-and-diversity | 3.92 |
| image-generators-with-conditionally | 2.92 |
| stylenat-giving-each-head-a-new-perspective | 3.4 |
| transgan-two-transformers-can-make-one-strong | 8.94 |
| styleswin-transformer-based-gan-for-high-1 | 2.95 |
| projected-gans-converge-faster | 1.59 |
| gotta-go-fast-when-generating-data-with-score | 26.46 |
| progressive-growing-of-gans-for-improved | 6.42 |
| msg-gan-multi-scale-gradients-gan-for-more | 5.2 |