mo-di-diffusion
Maintainer: tstramer
46
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
mo-di-diffusion is a diffusion model that can be used to generate videos by interpolating the latent space of Stable Diffusion. It was created by tstramer, who has also developed other video-focused diffusion models like Stable Diffusion Videos. The model is similar to MultiDiffusion, which also explores fusing diffusion paths for controlled image generation.
Model inputs and outputs
The mo-di-diffusion model takes in a text prompt, an optional init image, and various parameters to control the output. The inputs include the prompt, a random seed, image size, number of outputs, guidance scale, and the number of inference steps. The model then generates one or more images based on the input.
Inputs
- Prompt: The text prompt describing the desired output image
- Seed: A random seed value to control the output
- Width: The width of the output image (max 1024x768)
- Height: The height of the output image (max 1024x768)
- Negative Prompt: Specify things not to include in the output
- Num Outputs: The number of images to generate (up to 4)
- Prompt Strength: Controls how much the prompt influences an init image
- Guidance Scale: Scales the influence of the classifier-free guidance
- Num Inference Steps: The number of denoising steps to perform
Outputs
- Array of image URIs: The generated image(s) as a list of URIs
Capabilities
The mo-di-diffusion model can generate high-quality, photorealistic images from text prompts, similar to the capabilities of Stable Diffusion. However, the unique capability of this model is its ability to generate videos by interpolating the latent space of Stable Diffusion. This allows for the creation of dynamic, moving imagery that evolves over time based on the input prompt.
What can I use it for?
The mo-di-diffusion model could be used for a variety of creative and commercial applications, such as generating animated visuals for videos, making interactive art installations, or creating dynamic product visualizations. The ability to control the output through detailed prompts and parameters also opens up possibilities for use in film, gaming, or other media production. Additionally, as with other text-to-image models, the mo-di-diffusion model could be leveraged for content creation, visual marketing, and prototyping.
Things to try
One interesting aspect of the mo-di-diffusion model is its potential for generating dynamic, transformative imagery. By playing with the prompt, seed, and other parameters, users could experiment with creating videos that morph and evolve over time, leading to surreal and unexpected visual narratives. Additionally, combining the model's video capabilities with other tools for audio, 3D modeling, or animation could result in highly immersive and engaging multimedia experiences.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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