instant-style
Maintainer: jyoung105
1
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
instant-style is a general framework developed by the InstantX team that employs two straightforward yet potent techniques for achieving an effective disentanglement of style and content from reference images. The key insights are separating content from the image by subtracting the content text features from the image features, and injecting the style into specific attention layers. This strategy is quite effective in mitigating content leakage compared to previous works like StyleMC and StyleCLIP.
Model inputs and outputs
instant-style takes in a text prompt, an optional style image, and various configuration options to generate images that preserve the style of the reference image while following the content of the text prompt. The model outputs one or more generated images.
Inputs
- Prompt: The text prompt describing the desired image content.
- Style Image: An optional reference image to guide the style of the generated image.
- Seed: A random seed for reproducibility.
- Width/Height: The desired dimensions of the output image.
- Num Outputs: The number of images to generate.
- Guidance Scale: The scale for classifier-free guidance.
- Num Inference Steps: The number of denoising steps.
- Block Mode: The mode to reference the image: original, style with or without layout.
- Adapter Mode: The mode to reference the image: high flexibility but low fidelity or low flexibility but high fidelity.
- Style Strength: The conditioning scale for the IP-Adapter.
- Negative Prompt: The text prompt for content to exclude.
- Negative Content: The text prompt for style to exclude.
- Negative Content Strength: The conditioning scale for content to exclude.
Outputs
- Generated Images: One or more images generated based on the input prompt and style.
Capabilities
instant-style can generate images that preserve the style of a reference image while following the content of a text prompt. It is particularly effective at maintaining the color, material, atmosphere, and spatial layout of the reference image. The model can also selectively control the style and layout components, allowing for fine-grained stylization.
What can I use it for?
instant-style can be useful for a variety of applications, such as:
- Artistic Image Generation: Create visually striking images by combining a text prompt with a reference style image.
- Stylized Product Visualization: Generate product images with a desired aesthetic by providing a reference style.
- Augmented Reality and Virtual Try-On: Quickly generate stylized images of products or avatars for immersive experiences.
Things to try
Some interesting things to try with instant-style include:
- Experimenting with different combinations of text prompts and style images to see how the model handles various types of content and styles.
- Trying different block and adapter modes to find the right balance between style preservation and content fidelity.
- Leveraging the selective style and layout control to create unique hybrid styles.
- Exploring the use of negative prompts to exclude certain style or content elements.
Overall, instant-style provides a powerful and flexible framework for generating visually compelling images that preserve the desired style while following the provided text prompt.
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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