illusion
Maintainer: andreasjansson
248
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | No paper link provided |
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Model overview
The illusion model is an implementation of Monster Labs' QR code control net on top of Stable Diffusion 1.5, created by maintainer andreasjansson. It is designed to generate creative yet scannable QR codes. This model builds upon previous ControlNet models like illusion-diffusion-hq, controlnet_2-1, controlnet_1-1, and control_v1p_sd15_qrcode_monster to provide further improvements in scannability and creativity.
Model inputs and outputs
The illusion model takes in a variety of inputs to guide the QR code generation process, including a prompt, seed, image, width, height, number of outputs, guidance scale, negative prompt, QR code content, background color, number of inference steps, and conditioning scale. The model then generates one or more QR codes that can be scanned and link to the specified content.
Inputs
- Prompt: The prompt to guide QR code generation
- Seed: The seed to use for reproducible results
- Image: An input image, if provided (otherwise a QR code will be generated)
- Width: The width of the output image
- Height: The height of the output image
- Number of outputs: The number of QR codes to generate
- Guidance scale: The scale for classifier-free guidance
- Negative prompt: The negative prompt to guide image generation
- QR code content: The website/content the QR code will point to
- QR code background: The background color of the raw QR code
- Number of inference steps: The number of diffusion steps
- ControlNet conditioning scale: The scaling factor for the ControlNet outputs
Outputs
- Output images: One or more generated QR code images
Capabilities
The illusion model is capable of generating creative yet scannable QR codes that can seamlessly blend the image by using a gray-colored background. It provides an upgraded version of the previous Monster Labs QR code ControlNet model, with improved scannability and creativity. Users can experiment with different prompts, parameters, and the image-to-image feature to achieve their desired QR code output.
What can I use it for?
The illusion model can be used to generate unique and visually appealing QR codes for a variety of applications, such as marketing, branding, and artistic projects. The ability to create scannable QR codes with creative designs can make them more engaging and memorable for users. Additionally, the model's flexibility in allowing users to specify the QR code content and customize various parameters can be useful for both personal and professional projects.
Things to try
One interesting aspect of the illusion model is the ability to balance scannability and creativity by adjusting the ControlNet conditioning scale. Higher values will result in more readable QR codes, while lower values will yield more creative and unique designs. Users can experiment with this setting, as well as the other input parameters, to find the right balance for their specific needs. Additionally, the image-to-image feature can be leveraged to improve the readability of generated QR codes by decreasing the denoising strength and increasing the ControlNet guidance scale.
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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