Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators

The evolution of generative models has seen significant breakthroughs with GANs, transformers, and diffusion models. Text-to-image synthesis methods like DALL-E, Imagen, and Stable Diffusion have achieved high-quality outputs, transforming creative workflows. However, extending these models to video introduces challenges such as maintaining temporal consistency and object identity over sequences. Existing solutions like Tune-A-Video and Video Diffusion require extensive training on large-scale video datasets, which is costly and limits scalability. Recent efforts focus on fine-tuning or optimizing pre-trained models, but these still demand significant computational resources. The need for a low-cost, flexible approach that can generate videos from text without additional training remains unmet. This paper addresses this gap by leveraging the capabilities of pre-trained image diffusion models, proposing a novel method that encodes motion and employs cross-frame attention to produce coherent videos efficiently.

The core challenge is to generate temporally consistent videos directly from textual prompts without any training or fine-tuning. Existing methods rely heavily on large-scale video datasets and complex optimization, which are resource-intensive and not scalable. Ensuring object identity, scene continuity, and motion coherence over long sequences remains difficult, especially when models are trained solely on static images. The problem is compounded by the probabilistic nature of diffusion models, which tend to produce inconsistent frames when naively extended to videos. Developing a method that can utilize pre-trained image models for video synthesis, maintaining high quality and temporal coherence, is crucial for democratizing video generation technology.

This work introduces several key innovations: 1) embedding motion dynamics into latent codes to encode scene and background continuity; 2) designing a cross-frame attention mechanism that replaces self-attention layers, linking each frame to the first frame to preserve object identity; 3) enabling zero-shot video generation without additional training, leveraging existing pre-trained diffusion models. These innovations collectively address the limitations of prior methods by reducing computational costs, simplifying workflows, and improving temporal consistency. The approach supports multiple conditional inputs, such as pose and edge maps, broadening its applicability. It also introduces a novel way to manipulate latent codes via motion warping and probabilistic forward/backward diffusion steps, creating a flexible framework that can generate diverse and coherent videos efficiently.

• �� Start with a randomly sampled latent code for the first frame, x1T, using the pre-trained SD model.
• �� Perform ∆t DDIM backward steps to refine x1T into x1T′, capturing initial scene details.
• �� Define a motion vector δ for scene and camera movement, typically diagonal.
• �� For each subsequent frame k, compute a translation δk = λ·(k−1)δ, where λ controls motion magnitude.
• �� Warp the initial latent x1T′ with δk to produce a sequence of warped latent codes ˜xkT′.
• �� Apply forward diffusion (DDPM) steps on these warped codes to generate latent codes xkT for each frame.
• �� Pass these codes through a modified SD model with cross-frame attention replacing self-attention, where each frame attends to the first frame’s features.
• �� Optionally, perform background smoothing by blending warped background latent codes with the first frame’s background to enhance temporal coherence.
• �� Decode the final latent codes to generate the video frames, ensuring scene and object consistency.

The experiments use 8 frames at 512×512 resolution, with prompts covering diverse scenes. The model’s performance is evaluated via CLIP scores, comparing against baselines like Tune-A-Video and CogVideo. Ablation studies test the impact of motion encoding and cross-frame attention. The dataset includes various prompts with different conditions, such as pose and edge guidance. Hyperparameters like the number of backward steps ∆t and motion scale λ are tuned for optimal results. The evaluation metrics focus on semantic alignment, temporal consistency, and visual quality. Additional tests demonstrate robustness across different scene complexities and conditions, validating the method’s versatility and effectiveness.

The approach achieves a CLIP score of 31.19, outperforming some supervised models. Generated videos exhibit high fidelity, object identity preservation, and scene continuity across diverse prompts, including complex motions. Ablation results confirm that motion encoding and cross-frame attention are critical for temporal coherence. The method maintains high visual quality even in challenging scenarios, demonstrating its robustness and generalization. Comparisons show that it produces more semantically aligned videos than baselines, with fewer computational resources and no training required.

This method can be used for rapid content creation, virtual scene generation, and personalized video editing, especially where training data is scarce. It supports various conditional inputs, enabling content customization for entertainment, advertising, and virtual reality. Its low-cost nature makes it accessible for individual creators and small studios, democratizing high-quality video synthesis. Future integration with multi-modal controls could further enhance its usability in industry workflows.

The current model struggles with scenes involving rapid or complex motions, which can cause temporal artifacts. Resolution is limited by the underlying diffusion model, and high-resolution or long-duration videos are challenging. Extreme scene changes or occlusions may lead to object identity loss. Additionally, the probabilistic nature of diffusion introduces some randomness, affecting reproducibility. Addressing these issues requires more sophisticated motion modeling, multi-scale techniques, and possibly fine-tuning for specific tasks.