Ability to generate images and videos with rich details and diverse content, while maintaining the consistency of character identities and costumes.
Can help generate long comics or videos with continuous plots.
Compared with methods such as IP-Adapter and PhotoMaker, StoryDiffusion maintains role consistency while better controlling text prompts and generates images and videos that better match the descriptions.
Key components:
Consistent Self-Attention is one of the core components of the StoryDiffusion framework. It enhances consistency between different images by introducing sample tokens for reference images during the generation process.
Semantic Motion Predictor is another key component in StoryDiffusion, which is specifically used for long-distance video generation.
The following content comes from the paper:
For recent diffusion-based generative models, maintaining consistent content across a range of generated images, especially those that contain themes and complex details, presents major challenges.
In this article, we propose a new self-attention calculation method called consistent self-attention, which significantly improves consistency between generated images and enhances the popular pre-training diffusion-based text-to-image model with zero-samples. To extend our method to remote video generation, we further introduced a novel semantic spatio-temporal motion prediction module called a semantic motion predictor. It is trained to estimate motion conditions between two provided images in semantic space. This module converts the generated image sequence into video with smooth transitions and consistent themes, which is more stable than modules based solely on potential space, especially if long video generation is generated.
By combining these two novel components, our framework (called StoryDiffusion) can describe text-based stories that contain consistent images or videos that contain rich content. The proposed StoryDiffusion covers the groundbreaking exploration of generating visual stories through the presentation of images and videos, which we hope will inspire more research in terms of architectural modifications. Our code is publicly available on this https URL.
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Thesis:https://arxiv.org/abs/2405.01434
Project address:https://storydiffusion.github.io
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