Model Detail
stable-video-diffusion-img2vid-xt
—stable-video-diffusion-img2vid-xt is an image generation model released by Stability. The model is registered under the image-to-video pipeline tag on Hugging Face.
stable-video-diffusion-img2vid-xt is published on Hugging Face but our pipeline has not yet captured architecture, license, or parameter-count metadata for this entry. The data is refreshed daily, so these fields typically populate within 24–48 hours of release.
stable-video-diffusion-img2vid-xt is best fit for text-to-image generation and creative iteration. It is a less obvious choice for production photography pipelines that need exact reproducibility. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Computing Evolutionarily Stable Strategies in Imperfect-Information Games
arXiv:2512.10279v5 Announce Type: replace-cross Abstract: We present an algorithm for computing evolutionarily stable strategies (ESSs) in symmetric perfect-recall extensive-form games of imperfect information. Our main algorithm is for two-player games, and we describe how it can be extended to mul
Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization
arXiv:2605.29547v2 Announce Type: replace-cross Abstract: Deep learning optimization relies heavily on the assumption of smooth loss landscapes, a condition systematically violated by modern architectures due to non-smooth components such as ReLU activations and quantization operators. In such non-s
FlowSonic: Stable Zero-Shot Music Editing via High-Order Trajectory Integration
arXiv:2607.17526v1 Announce Type: cross Abstract: Zero-shot text-guided editing of real-world music recordings requires balancing semantic modification with faithful preservation of the original musical structure. Although recent diffusion transformers trained with rectified flow have achieved remar
Stochastic Dimension Zeroth-Order Estimator: Stable and Memory-Efficient Training of PINNs
arXiv:2603.24002v3 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) for high-dimensional and high-order partial differential equations (PDEs) are primarily constrained by the $\mathcal{O}(d^k)$ spatial derivative complexity and the $\mathcal{O}(P)$ memory overhead of backpro
Retraining Seeks Stable Signals
arXiv:2607.15623v1 Announce Type: cross Abstract: Predictive models deployed at scale influence future data, a phenomenon called performativity. And there is always one way to cope: Train the model on new data, deploy it again, and repeat. This process, called retraining or repeated risk minimizatio
Sharp Stability Threshold and Certification for Designing Stable Residual Architectures
arXiv:2607.14576v1 Announce Type: new Abstract: We propose \emph{the sublinear-growth principle} for deep residual architectures -- a sharp stability threshold on the input-magnitude exponent of every residual block's velocity field: $$\|v(x, t)\| \leq c\,\|x\|^q + b, \qquad q \in [0, 1].$$ The thre
