Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
By: Mayssam Naji
The large-scale multimodal dataset, which consists of billions of text-image pairs from the Internet, has revolutionized Text-to-Image (T2I) generation. To replicate this success in Text-to-Video (T2V) generation, the spatial-only T2I generation models have been extended to the spatiotemporal domain. However, this requires extensive training on large hardware accelerators, which is costly and time-consuming. A new concept of One-Shot Video Tuning is introduced, which aims to train a T2V generator using only a single text-video pair.The key to successful video generation is maintaining consistent objects' continuous motion. Observations on state-of-the-art T2I diffusion models revealed that they could generate images that align well with the text, and extending spatial self-attention to multiple images results in consistent content across frames.The findings were used to develop a Tune-A-Video method, which inflates state-of-the-art T2I models over the spatiotemporal dimension. The method introduces a sparse spatiotemporal attention mechanism and an efficient tuning strategy to tackle the computational challenge and prevent the loss of existing knowledge from T2I models. The proposed method produces temporally-coherent videos featuring smooth movement, and it is compatible with existing personalized and conditional pre trained T2I models. Tune-A-Video showcases impressive results in text-driven video generation and outperforms state-of-the-art baselines in qualitative and quantitative experiments.
This paper presents a new setting for text-to-video generation, where the model only needs one text-video pair as input. This is different from previous works that require large-scale video datasets to train a video generator. The model is a state-of-the-art text-to-image diffusion models that are pre-trained on massive image data.
This paper introduces a new approach to text-to-video (T2V) generation, proposing a unique setting where only one text-video pair is needed as input. The model is fundamentally built on denoising diffusion probabilistic models (DDPMs) and latent diffusion models (LDMs), pre-trained on large-scale image data and capable of generating static images from text.
The DDPMs are generative models that recreate a fixed forward Markov chain using a Gaussian distribution. The parameters within these models are strategically trained, ensuring the generated reverse process closely aligns with the forward process. On the other hand, LDMs are variants of DDPMs operating in the latent space of an autoencoder. These models comprise two key components: an autoencoder that transforms images into latent representations and a DDPM that eliminates noise from the sampled data.
In the proposed T2V model, named "Tune-A-Video", these models are incorporated within a sophisticated neural network architecture. This architecture integrates spatial downsampling, and upsampling passes with convolutional residual blocks and transformer blocks. The transformer blocks are characterized by spatial self-attention, cross-attention, and a feed-forward network.
The architecture is further improved with a spatio-temporal self-attention mechanism designed to handle the temporal aspect of video frames while maintaining computational efficiency. Fine-tuning of the model is performed on specific input videos to ensure temporal consistency and refine the text-video alignment.
The fine-tuned model uses a structural guidance approach known as DDIM inversion to enhance control over pixel shifts during inference. This method begins with extracting a latent noise from the source video through DDIM inversion, which serves as a starting point for subsequent DDIM sampling directed by an edited prompt.
"Tune-A-Video" signifies an innovative blend of DDPMs and LDMs, spatial and temporal self-attention mechanisms, within a complex neural network architecture. It creates natural and diverse videos from text across various applications like editing, captioning, and synthesis. It leverages existing image diffusion models without needing large-scale video data, thus ensuring computational efficiency. The model's strategic fine-tuning on specific video inputs and DDIM inversion during inference significantly improve the video generation process.
- Introduces a new setting for text-to-video (T2V) generation, where only one text-video pair is needed as input, instead of using large-scale video datasets.
- The paper leverages image diffusion models that are pre-trained on large-scale image data and can generate still images from text, and extends them to generate videos by using a spatio-temporal attention mechanism and a one-shot tuning strategy.
- Uses DDIM inversion to guide the sampling process at inference time, which provides structure guidance and improves the quality and diversity of the generated videos.
- The paper demonstrates that the proposed method can generate realistic and diverse videos from text across various applications, such as video editing, video captioning, and video synthesis, and outperforms existing methods in terms of visual quality and semantic consistency.
- The paper claims that the proposed method is computationally efficient and can leverage existing image diffusion models without requiring large-scale video data.
The "Tune-A-Video" model displays distinct advantages when compared to existing text-to-video (T2V) generation methods like CogVideo, Plug-and-play, and Text2LIVE.
CogVideo, while capable of generating videos reflecting general text concepts, suffers from inconsistent video quality and doesn't allow for video inputs.
Plug-and-play can edit each video frame individually, but it falls short in maintaining frame consistency as it neglects temporal context, leading to inconsistencies in elements like the appearance of objects across frames.
While Text2LIVE manages to produce temporally smooth videos, it struggles to accurately represent the edited prompt.
The proposed "Tune-A-Video" method, however, can generate realistic, diverse videos that align closely with given text descriptions and video contexts. It outperforms the aforementioned methods in several aspects: visual quality, content consistency, motion smoothness, and diversity. Additionally, it can handle complex scenes with multiple objects and movements and generate videos in different styles and moods, providing users with a range of customizable options.
The paper demonstrates that each component and technique in the "Tune-A-Video" method contributes to its overall performance and the diversity of generated videos. Removing or replacing any of these components or techniques would result in a decrease in the quality of the output. Thus, the proposed method offers a comprehensive and efficient solution for text-driven video generation and editing.
The paper is significant as it introduces a novel task for T2V generation, One-Shot Video Tuning. Training a T2V generator using a single text-video pair and pre-trained T2I models necessitates training. This approach reduces the computational cost and data requirements for T2V generation, enabling more flexible and creative applications.
In Figure 3, the method showcases a limitation when dealing with input videos with multiple objects and occlusion. This could be attributed to the T2I model's inherent limitations when handling multiple objects and their interactions. To address this issue as suggested, one potential solution would be to incorporate additional, conditional information, such as depth, to aid the model in distinguishing between different objects and their interactions. However, this will require further research and development in the future.
Researchers propose an effective method to adapt image diffusion models for video generation using a spatiotemporal attention mechanism and a one-shot tuning strategy. They also introduce an efficient tuning strategy and structural inversion for generating temporally-coherent videos. Through extensive experimentation, the paper demonstrates that the proposed method can generate accurate and diverse videos from text across various applications such as video editing, captioning, and synthesis. The paper also opens up new possibilities for future research and development in T2V generation. For instance, the proposed method can be extended to other modalities, such as audio or text. It could be improved by incorporating more advanced techniques like contrastive or self-supervised learning. However, it also acknowledges the challenges and limitations of T2V generation, such as handling complex scenes or long-term dependencies.
-  Zhangjie, Jay Wu, et al. (2023). "Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation." ArXiv, cs.CV. Retrieved from https://arxiv.org/abs/2212.11565