Stable Video Diffusion Training Video Models

Stable Video Diffusion Image Pretraining

For image pretraining, the paper discusses initially pretraining a Diffuse Transformer on a large-scale semantic segmentation dataset called CC-12M. This was done using self-supervised learning to acquire strong image representation capabilities. The pretraining enabled the model to recognize visual details and structures in images such as faces, objects etc. Subsequently, the image pretrained model provided an excellent starting point for the video generation task by providing pixel-level representations of individual frames. This step laid the groundwork for the model to effectively understand and process visual content at its most granular level.

Stable Video Diffusion Video Pretraining

For the video pretraining stage, the paper introduces a temporal model to capture inter-frame dependencies in video sequences. This model is jointly pretrained along with the Diffuse Transformer on a large animation tutorial dataset called HowTo100M. By utilizing this video data source, the temporal component learned to recognize and model temporal patterns and dynamics such as motion patterns that appear across subsequent frames in videos. This allowed the model to better understand the flow of time within videos. Pretraining on HowTo100M equipped the system with the ability to generate more natural and coherent multi-frame video outputs by taking into account temporal relationships between frames in an unlabeled, self-supervised manner.

Stable Video Diffusion Temporal Models

It compares 3D convolutional neural networks and Transformer blocks for modeling inter-frame relationships over time. Results showed that using self-attention in both the spatial and temporal dimensions within a Transformer architecture made it easier for the model to capture long-term dependencies between video frames. This is because the attention mechanism allowed for more direct information flow across wider contexts in both the visual and temporal sense. The Transformer-based approach led to improved video generation quality by facilitating a more thorough understanding of frame order and dynamics. Overall, Transformer blocks were found to be well-suited as the temporal modeling approach within the Stable Video Diffusion framework.

Stable Video Diffusion Finetuning for Quality

This dataset contains content at a finer 600x600 resolution compared to earlier datasets. Finetuning on LAION-5B helped refine the models' ability to produce sharper and more finely detailed videos at a higher definition. This is because the models could leverage richer visual examples at a 600x600 pixel scale during finetuning. As a result, when evaluated, the finetuned models demonstrated significantly improved rendering capabilities, with videos exhibiting clearer textures, lines and features. Overall, finetuning on LAION-5B enhanced the visual fidelity and quality of generations from the Stable Video Diffusion framework.

Stable Video Diffusion Controllable Generation

It leverages Learned Object Representations for Animation (LoRA) to enable spatial and class-level conditional generation. LoRA embeddings are used to induce object-level constraints, which the model can follow to insert certain elements into the scenes. Additionally, class conditionality is applied by priming the model with different semantic labels or image inputs. This provides control over high-level attributes like scene type, objects, actions etc. The paper shows these methods help produce more targetted and diverse videos according to different user specifications. The controllable generation capabilities demonstrate the potential for guide video synthesis through high-level textual or visual context cues.

Stable Video Diffusion Evaluation Metrics

To assess video generation quality both automatically and manually, the paper employs various objective and subjective metrics. On the automatic side, it calculates the Fréchet Video Distance (FVD) to evaluate statistical similarities between generated and real videos. Other quantitative measures like LPIPS distance and Inception Score are also used. In addition, the authors conduct human evaluation studies where users perform pairwise A/B tests and preference judgments. This helps rank models based on perceived human realism, coherence, diversity and other quality aspects. The evaluation metrics provide a multipronged analysis of how effectively the models can synthesize photorealistic videos under different conditions and prompts. They present key insights into the models' generation prowess both numerically and from a person's perspective.

For more details, please refer to Stable Video Diffusion: How to Use It and What It Is?