nammeh
Maintainer: galleri5
1
| Property | Value |
|---|---|
| Model Link | View 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
nammeh is a SDXL LoRA model trained by galleri5 on SDXL generations with a "funky glitch aesthetic". According to the maintainer, the model was not trained on any artists' work. This model is similar to sdxl-allaprima which was trained on a blocky oil painting and still life, as well as glitch which is described as a "jumble-jam, a kerfuffle of kilobytes". The icons model by the same creator is also a SDXL finetune focused on generating slick icons and flat pop constructivist graphics.
Model inputs and outputs
nammeh is a text-to-image generation model that can take a text prompt and output one or more corresponding images. The model has a variety of input parameters that allow for fine-tuning the output, such as image size, number of outputs, guidance scale, and others. The output of the model is an array of image URLs.
Inputs
- Prompt: The text prompt describing the desired image
- Negative Prompt: Optional text to exclude from the image generation
- Image: Input image for img2img or inpaint mode
- Mask: Input mask for inpaint mode
- Width: Width of the output image
- Height: Height of the output image
- Seed: Random seed (leave blank to randomize)
- Scheduler: Scheduling algorithm to use
- Guidance Scale: Scale for classifier-free guidance
- Num Inference Steps: Number of denoising steps
- Refine: Refine style to use
- Lora Scale: LoRA additive scale
- Refine Steps: Number of refine steps
- High Noise Frac: Fraction of noise to use for expert_ensemble_refiner
- Apply Watermark: Whether to apply a watermark to the output
Outputs
- Array of image URLs: The generated images
Capabilities
nammeh is capable of generating high-quality, visually striking images from text prompts. The model seems to have a particular affinity for a "funky glitch aesthetic", producing outputs with a unique and distorted visual style. This could be useful for creative projects, experimental art, or generating images with a distinct digital/cyberpunk feel.
What can I use it for?
The nammeh model could be a great tool for designers, artists, and creatives looking to generate images with a glitch-inspired aesthetic. The model's ability to produce highly stylized and abstract visuals makes it well-suited for projects in the realms of digital art, music/album covers, and experimental video/film. Businesses in the tech or gaming industries may also find nammeh useful for generating graphics, illustrations, or promotional materials with a futuristic, cyberpunk-influenced look and feel.
Things to try
One interesting aspect of nammeh is its lack of artist references during training, which seems to have resulted in a unique and original visual style. Try experimenting with different prompts to see the range of outputs the model can produce, and see how the "funky glitch" aesthetic manifests in various contexts. You could also try combining nammeh with other Lora models or techniques to create even more striking and unexpected results.
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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The icons model is a fine-tuned version of the SDXL (Stable Diffusion XL) model, created by the Replicate user galleri5. It is trained to generate slick, flat, and constructivist-style icons and graphics with thick edges, drawing inspiration from Bing Generations. This model can be useful for quickly generating visually appealing icons and graphics for various applications, such as app development, web design, and digital marketing. Similar models that may be of interest include the sdxl-app-icons model, which is fine-tuned for generating app icons, and the sdxl-color model, which is trained for generating solid color images. Model inputs and outputs The icons model takes a text prompt as input and generates one or more images as output. The model can be used for both image generation and inpainting tasks, allowing users to either create new images from scratch or refine existing images. Inputs Prompt**: The text prompt that describes the desired image. This can be a general description or a more specific request for an icon or graphic. Image**: An optional input image for use in an inpainting task, where the model will refine the existing image based on the text prompt. Mask**: An optional input mask for the inpainting task, which specifies the areas of the image that should be preserved or inpainted. Seed**: An optional random seed value to ensure reproducible results. Width and Height**: The desired dimensions of the output image. Num Outputs**: The number of images to generate. Additional parameters**: The model also accepts various parameters to control the image generation process, such as guidance scale, number of inference steps, and refine settings. Outputs Output Images**: The model generates one or more images that match the input prompt and other specified parameters. Capabilities The icons model excels at generating high-quality, visually appealing icons and graphics with a distinct flat, constructivist style. The images produced have thick edges and a simplified, minimalist aesthetic, making them well-suited for use in a variety of digital applications. What can I use it for? The icons model can be used for a wide range of applications, including: App Development**: Generating custom icons and graphics for mobile app user interfaces. Web Design**: Creating visually striking icons and illustrations for websites and web applications. Digital Marketing**: Producing unique, branded graphics for social media, advertisements, and other marketing materials. Graphic Design**: Quickly prototyping and iterating on icon designs for various projects. Things to try To get the most out of the icons model, you can experiment with different prompts that describe the desired style, theme, or content of the icons or graphics. Try varying the level of detail in your prompts, as well as incorporating specific references to artistic movements or design styles (e.g., "constructivist", "flat design", "minimalist"). Additionally, you can explore the model's inpainting capabilities by providing an existing image and a mask or prompt to refine it, allowing you to seamlessly integrate generated elements into your existing designs.
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