StoryDiffusion

Preview：

Introduce：

StoryDiffusion is an open source image and video generation model that generates coherent long sequences of images and videos through a consistent self-attention mechanism and motion predictor. The main advantage of this model is its ability to generate images with character consistency, and it can be extended to video generation, giving users a new way to create long videos. The model has a positive impact on the field of AI-driven image and video generation and encourages users to use the tool responsibly.

Stakeholders:

Usage Scenario Examples:

• Use StoryDiffusion to generate a series of manga style images.
• Create a long video based on text prompts that show a coherent story.
• Use StoryDiffusion for character design and pre-visualization of scene layout.

The features of the tool:

• Consistent self-attention mechanism: generating consistent images of characters in a growth sequence.
• Motion Predictor: Predicts motion in a compressed image semantic space for greater motion prediction.
• Comic Generation: Create videos with seamless transitions from images generated by consistent self-attention mechanisms.
• Image-to-video generation: Provide a conditional image sequence of user input to generate video.
• Two-stage Long video generation: Combine two parts to generate very long and high quality AIGC video.
• Conditional image use: The image-to-video model can generate video by providing a series of conditional images for user input.
• Short video generation: Provides fast video generation results.

Steps for Use:

• Step 1: Visit StoryDiffusion’s GitHub page and download the source code.
• Step 2: Make sure you have Python 3.8 or later installed on your computer, as well as PyTorch 2.0.0 or later.
• Step 3: Generate the comic by running the provided Juliyter notebook or launching a local gradio demo.
• Step 4: Provide at least 3 text prompts to the Consistent Self-attention module as needed to generate a consistent image of the character.
• Step 5: Using the generated image as the conditional image, generate the video through the StoryDiffusion image to the video model.
• Step 6: Adjust and optimize the generated images and videos to meet specific creative needs.

Tool’s Tabs: AI generation, image generation