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Note: Stable Video Diffusion XT 1.1 weights are released under a Stability AI Non-Commercial Research Community License, and cannot be utilized for commercial purposes. Please read the license to verify if your use case is permitted.
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Stable Video Diffusion XT 1.1 is a latent diffusion model developed by Stability AI that generates 25-frame video sequences at 1024x576 resolution from single input images. The model employs a three-stage training process including image pretraining, video training on curated datasets, and high-resolution finetuning, enabling motion synthesis with configurable camera controls and temporal consistency for image-to-video transformation applications.
Stable Video Diffusion XT 1.1 (often abbreviated as SVD-XT 1.1) is a generative image-to-video latent diffusion model developed by Stability AI. Built as a fine-tuned extension of previous Stable Video Diffusion (SVD) variants, SVD-XT 1.1 enables the transformation of a single input image into a coherent short video sequence. The model synthesizes 25 video frames at a resolution of 1024x576 pixels, facilitating a wide range of applications in content generation, creative media, and research. SVD-XT 1.1 employs a combination of advanced conditioning techniques and fine-tuning strategies to allow for output consistency, motion representation, and flexibility across diverse image domains.
A collection of sample outputs demonstrating Stable Video Diffusion XT 1.1's ability to generate videos from diverse images including landscapes, food, autumn scenery, and underwater environments.
SVD-XT 1.1 is built upon the latent video diffusion framework, operating in a lower-dimensional latent space to efficiently generate high-resolution videos. Its architecture incorporates both spatial and temporal processing, with temporal convolution and attention layers inserted into a U-Net backbone derived from Stable Diffusion 2. This design improves motion representation and video consistency. The model’s training is conducted in three progressive stages. Initially, it undergoes image pretraining using a large-scale image dataset to leverage strong spatial representations from Stable Diffusion 2. Next, the model is further trained on a vast collection of videos at moderate resolutions to acquire generalized motion dynamics. The final stage involves high-resolution finetuning with carefully curated video data, enabling SVD-XT 1.1 to synthesize sharp and temporally consistent video sequences.
The noise conditioning strategy utilizes the EDM framework with a schedule shifted toward higher noise values, which benefits high-resolution video synthesis. Input conditioning mechanisms incorporate frame rate and motion control parameters, allowing for adjustable motion intensity and dynamics during inference. In image-to-video tasks, the model uses CLIP image embeddings for the input frame and appends a channel-wise, noise-augmented version of the same frame to its neural architecture.
SVD-XT 1.1’s effectiveness stems from the extensive dataset curation pipeline implemented during its development. Stability AI initiated the process with a large-scale collection of approximately 580 million annotated video clips, which amounts to over 212 years of video content. This raw data underwent multiple processing stages, including cut detection with PySceneDetect to eliminate frame transitions and maintain temporal consistency, as well as scoring for motion using optical flow techniques implemented with OpenCV. Each clip was annotated using advanced captioning tools such as VideoBLIP and LLM-based summarization, along with comprehensive visual and text-image similarity metrics computed through CLIP embeddings.
Following the filtering procedures—ranking for motion, aesthetic quality, and semantic alignment—the curated dataset was reduced to about 152 million high-quality training examples. For the high-resolution finetuning phase, a separate dataset of approximately 1 million video clips was assembled, characterized by pronounced object and camera motion and high captioning accuracy. Specialized datasets, including synthetic videos rendered from Objaverse 3D objects and the multi-view MVImgNet collection, were used to further bolster the model’s capabilities in multi-view and three-dimensional synthesis.
SVD-XT 1.1 enables nuanced image-to-video transformation, synthesizing video sequences from a single still image at 1024x576 resolution and 25 frames per sequence. The model supports advanced camera motion control via parameters fine-tuned during training, including horizontal panning and zooming, as well as static camera views. The temporal attention blocks are enhanced through Low-Rank Adaptation (LoRA) modules, allowing subtle and complex camera manipulations without explicit user intervention.
The architecture supports a form of classifier-free guidance, which offers control over the conditioning strength applied during inference. SVD-XT 1.1 deploys a linear, framewise scaling approach to guidance—rather than a constant value—to reduce artifacts such as temporal inconsistency and color oversaturation. The model demonstrates an ability to partially disentangle motion and content, making it possible to prompt spatial and temporal characteristics independently in some scenarios.
In addition to standard image-to-video generation, SVD-XT 1.1 can be fine-tuned as a frame interpolation model, predicting intermediate frames between designated conditions to produce smoother, higher-frame-rate outputs. Its design also facilitates multi-view synthesis, supporting consistent rendering of objects from various perspectives, a feature of particular interest for virtual reality and 3D reconstruction.
The efficacy of SVD-XT 1.1 has been measured using both quantitative benchmarks and human preference evaluations. In human studies, the model’s outputs were generally rated higher than those from competitive systems such as GEN-2 and PikaLabs, for both visual fidelity and prompt alignment, as documented in the original research publication. On the UCF-101 zero-shot text-to-video benchmark, the base SVD variant achieves a Fréchet Video Distance (FVD) score of 242.02—lower than competing frameworks including CogVideo, Make-A-Video, and MagicVideo, where lower values indicate better generation quality.
In multi-view synthesis tasks, SVD-MV (the multi-view finetuned version) outperformed other methods such as SyncDreamer, Zero123XL, and Stable Diffusion 2.1-MV across metrics like LPIPS (0.14, lower is better), PSNR (16.83, higher is better), and CLIP-S (0.89, higher is better). Notably, SVD-MV demonstrated a faster convergence rate during training, achieving better CLIP-S and PSNR after only a fraction of the training iterations required by comparison models.
Stable Video Diffusion XT 1.1 is applicable in a broad range of creative and technical domains. The principal use involves generating dynamic video scenes from still images, relevant for content creation, animation, and simulation workflows. The underlying SVD framework also supports text-to-video generation, though SVD-XT 1.1 itself is optimized for image-to-video transformations. Its multi-view synthesis abilities are valuable for 3D modeling, object recognition, and immersive media applications.
Notable limitations include the relatively short temporal span of generated videos—25 frames at 6 FPS, resulting in outputs typically below four seconds in duration. Although SVD-XT 1.1 provides configurable motion parameters, some generated videos may display minimal movement or limited camera manipulation unless explicitly directed. The current model’s architecture does not reliably render human faces or legible textual elements within generated scenes. Furthermore, the lossy nature of its autoencoding pipeline results in some fidelity loss during the video reconstruction process. Due to the computational demands of diffusion-based synthesis, generation speed and memory usage remain constraining factors. While the risk of misuse for misinformation exists, Stability AI advocates for responsible deployment and evaluation of generative video technologies, as outlined in their Acceptable Use Policy.
The Stable Video Diffusion XT 1.1 model is distributed under the Stability AI Community License Agreement, permitting free academic, research, and personal uses, along with limited commercial applications for organizations with less than $1,000,000 in annual revenue. Registration is required for commercial deployment within these bounds, and the license mandates appropriate attribution and display of the "Powered by Stability AI" notice in derivative outputs. Usage must adhere to applicable laws and the company’s Acceptable Use Policy, with direct model improvement or foundational generative model construction restricted to Stability AI’s products and derivatives.
Within the Stable Video Diffusion family, SVD-XT 1.1 is a specialized image-to-video variant finetuned for enhanced output consistency and motion control. Adjacent models include SVD (supporting text-to-video and alternative priors), SVD-MV (multi-view synthesis), and models finetuned from either image or randomized priors such as SD2.1-MV and Scratch-MV, with SVD-based variants consistently exhibiting better multi-view coherence and image-to-video performance.