sdxl-money

Maintainer: cbh123

Total Score

5

Last updated 6/11/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

sdxl-money is a Stable Diffusion XL (SDXL) model fine-tuned on currencies by the maintainer cbh123. This model is similar to other SDXL models like [object Object], [object Object], [object Object], and the base [object Object] model, all of which have been fine-tuned or enhanced for various use cases.

Model inputs and outputs

The sdxl-money model takes a variety of inputs including a prompt, an optional image for image-to-image or inpainting tasks, a seed, and parameters like width, height, guidance scale, and number of inference steps. The model outputs one or more images based on the inputs.

Inputs

  • Prompt: The text prompt describing the image to be generated
  • Image: An optional input image for image-to-image or inpainting tasks
  • Seed: A random seed to control image generation
  • Width/Height: The desired dimensions of the output image
  • Guidance Scale: A parameter to control the strength of the text prompt
  • Num Inference Steps: The number of denoising steps for the image generation

Outputs

  • Images: One or more output images based on the provided inputs

Capabilities

The sdxl-money model is capable of generating high-quality images of various financial and currency-related scenes, such as banknotes, coins, and financial transactions. The fine-tuning on currencies allows the model to produce realistic and detailed depictions of monetary subjects.

What can I use it for?

You can use sdxl-money to generate images for financial applications, educational materials, or artistic projects involving currencies and money. The model could be useful for designers, illustrators, or anyone needing currency-themed visuals.

Things to try

Try providing the model with prompts related to different currencies, financial instruments, or economic concepts. Experiment with the input parameters to see how they affect the output, such as adjusting the guidance scale to change the level of detail in the generated images.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

AI model preview image

sdxl-davinci

cbh123

Total Score

5

sdxl-davinci is a fine-tuned version of the SDXL model, created by cbh123, that has been trained on Davinci drawings. This model is similar to other SDXL models like sdxl-allaprima, sdxl-shining, sdxl-money, sdxl-victorian-illustrations, and sdxl-2004, which have been fine-tuned on specific datasets to capture unique artistic styles and visual characteristics. Model inputs and outputs The sdxl-davinci model accepts a variety of inputs, including an image, prompt, and various parameters to control the output. The model can generate images based on the provided prompt, or perform tasks like image inpainting and refinement. The output is an array of one or more generated images. Inputs Prompt**: The text prompt that describes the desired image Image**: An input image to be used for tasks like img2img or inpainting Mask**: An input mask for the inpaint mode, where black areas will be preserved and white areas will be inpainted Width/Height**: The desired dimensions of the output image Seed**: A random seed value to control the image generation Refine**: The type of refinement to apply to the generated image Scheduler**: The scheduler algorithm to use for image generation LoRA Scale**: The scale to apply to any LoRA components Num Outputs**: The number of images to generate Refine Steps**: The number of refinement steps to apply Guidance Scale**: The scale for classifier-free guidance Apply Watermark**: Whether to apply a watermark to the generated image High Noise Frac**: The fraction of high noise to use for the expert_ensemble_refiner Negative Prompt**: An optional negative prompt to guide the image generation Outputs An array of one or more generated images Capabilities sdxl-davinci can generate a variety of artistic and illustrative images based on the provided prompt. The model's fine-tuning on Davinci drawings allows it to capture a unique and expressive style in the generated outputs. The model can also perform image inpainting and refinement tasks, allowing users to modify or enhance existing images. What can I use it for? The sdxl-davinci model can be used for a range of creative and artistic applications, such as generating illustrations, concept art, and digital paintings. Its ability to work with input images and masks makes it suitable for tasks like image editing, restoration, and enhancement. Additionally, the model's varied capabilities allow for experimentation and exploration of different artistic styles and compositions. Things to try One interesting aspect of the sdxl-davinci model is its ability to capture the expressive and dynamic qualities of Davinci's drawing style. Users can experiment with different prompts and input parameters to see how the model interprets and translates these artistic elements into unique and visually striking outputs. Additionally, the model's inpainting and refinement capabilities can be used to transform or enhance existing images, opening up opportunities for creative image manipulation and editing.

Read more

Updated Invalid Date

AI model preview image

sdxl-lightning-4step

bytedance

Total Score

106.3K

sdxl-lightning-4step is a fast text-to-image model developed by ByteDance that can generate high-quality images in just 4 steps. It is similar to other fast diffusion models like AnimateDiff-Lightning and Instant-ID MultiControlNet, which also aim to speed up the image generation process. Unlike the original Stable Diffusion model, these fast models sacrifice some flexibility and control to achieve faster generation times. Model inputs and outputs The sdxl-lightning-4step model takes in a text prompt and various parameters to control the output image, such as the width, height, number of images, and guidance scale. The model can output up to 4 images at a time, with a recommended image size of 1024x1024 or 1280x1280 pixels. Inputs Prompt**: The text prompt describing the desired image Negative prompt**: A prompt that describes what the model should not generate Width**: The width of the output image Height**: The height of the output image Num outputs**: The number of images to generate (up to 4) Scheduler**: The algorithm used to sample the latent space Guidance scale**: The scale for classifier-free guidance, which controls the trade-off between fidelity to the prompt and sample diversity Num inference steps**: The number of denoising steps, with 4 recommended for best results Seed**: A random seed to control the output image Outputs Image(s)**: One or more images generated based on the input prompt and parameters Capabilities The sdxl-lightning-4step model is capable of generating a wide variety of images based on text prompts, from realistic scenes to imaginative and creative compositions. The model's 4-step generation process allows it to produce high-quality results quickly, making it suitable for applications that require fast image generation. What can I use it for? The sdxl-lightning-4step model could be useful for applications that need to generate images in real-time, such as video game asset generation, interactive storytelling, or augmented reality experiences. Businesses could also use the model to quickly generate product visualization, marketing imagery, or custom artwork based on client prompts. Creatives may find the model helpful for ideation, concept development, or rapid prototyping. Things to try One interesting thing to try with the sdxl-lightning-4step model is to experiment with the guidance scale parameter. By adjusting the guidance scale, you can control the balance between fidelity to the prompt and diversity of the output. Lower guidance scales may result in more unexpected and imaginative images, while higher scales will produce outputs that are closer to the specified prompt.

Read more

Updated Invalid Date

AI model preview image

sdxl-shining

cbh123

Total Score

2

sdxl-shining is a Stable Diffusion XL (SDXL) model fine-tuned on the horror classic "The Shining". This model is maintained by cbh123 and is part of a growing ecosystem of SDXL-based models. Similar models include animagine-xl-3.1, an anime-themed SDXL model, and sdxl-custom-model, which incorporates Callback Adjust and other enhancements. Model inputs and outputs sdxl-shining is a text-to-image AI model that takes a text prompt as input and generates an image based on that prompt. The model also accepts additional parameters like image size, seed, and scheduler. Inputs Prompt**: The text prompt to generate the image from Negative Prompt**: Additional text to guide the image generation away from certain elements Image**: An input image for img2img or inpaint mode Mask**: An input mask for inpaint mode, where black areas will be preserved and white areas will be inpainted Seed**: A random seed, left blank to randomize Width**: The width of the output image Height**: The height of the output image Refine**: The refine style to use Scheduler**: The scheduler to use Lora Scale**: The LoRA additive scale, only applicable on trained models Num Outputs**: The number of images to output Refine Steps**: The number of steps to refine for the base_image_refiner Guidance Scale**: The scale for classifier-free guidance Apply Watermark**: Whether to apply a watermark to the output image High Noise Frac**: The fraction of noise to use for the expert_ensemble_refiner Replicate Weights**: The LoRA weights to use, left blank to use the default Outputs An array of image URIs representing the generated images Capabilities sdxl-shining can generate highly detailed and atmospheric images inspired by the iconic horror film "The Shining". The model is capable of producing a wide range of unsettling, eerie, and surreal imagery that captures the dark and foreboding tone of the source material. What can I use it for? With sdxl-shining, you can create custom horror-themed artwork, illustrations, and assets for a variety of projects, such as video games, book covers, album art, and more. The model's fine-tuning on "The Shining" allows it to generate highly specific and recognizable imagery that can be used to evoke a particular mood or aesthetic. Additionally, the model's versatility with different input parameters makes it a powerful tool for experimentation and creative expression. Things to try One interesting aspect of sdxl-shining is its ability to blend elements from the original "The Shining" with more contemporary or unexpected themes. For example, you could try generating images that juxtapose the classic horror setting of the Overlook Hotel with futuristic or sci-fi elements, or combine the ominous atmosphere of the film with more whimsical or surreal imagery. The model's flexibility allows for a wide range of creative possibilities and can help you push the boundaries of what is possible with text-to-image AI technology.

Read more

Updated Invalid Date

AI model preview image

sdxl

stability-ai

Total Score

53.9K

sdxl is a text-to-image generative AI model created by Stability AI, the same company behind the popular Stable Diffusion model. Like Stable Diffusion, sdxl can generate beautiful, photorealistic images from text prompts. However, sdxl has been designed to create even higher-quality images with additional capabilities such as inpainting and image refinement. Model inputs and outputs sdxl takes a variety of inputs to generate and refine images, including text prompts, existing images, and masks. The model can output multiple images per input, allowing users to explore different variations. The specific inputs and outputs are: Inputs Prompt**: A text description of the desired image Negative Prompt**: Text that specifies elements to exclude from the image Image**: An existing image to use as a starting point for img2img or inpainting Mask**: A black and white image indicating which parts of the input image should be preserved or inpainted Seed**: A random number to control the image generation process Refine**: The type of refinement to apply to the generated image Scheduler**: The algorithm used to generate the image Guidance Scale**: The strength of the text guidance during image generation Num Inference Steps**: The number of denoising steps to perform during generation Lora Scale**: The additive scale for any LoRA (Low-Rank Adaptation) weights used Refine Steps**: The number of refinement steps to perform (for certain refinement methods) High Noise Frac**: The fraction of noise to use (for certain refinement methods) Apply Watermark**: Whether to apply a watermark to the generated image Outputs One or more generated images, returned as image URLs Capabilities sdxl can generate a wide range of high-quality images from text prompts, including scenes, objects, and creative visualizations. The model also supports inpainting, where you can provide an existing image and a mask, and sdxl will fill in the masked areas with new content. Additionally, sdxl offers several refinement options to further improve the generated images. What can I use it for? sdxl is a versatile model that can be used for a variety of creative and commercial applications. For example, you could use it to: Generate concept art or illustrations for games, books, or other media Create custom product images or visualizations for e-commerce or marketing Produce unique, personalized art and design assets Experiment with different artistic styles and visual ideas Things to try One interesting aspect of sdxl is its ability to refine and enhance generated images. You can try using different refinement methods, such as the base_image_refiner or expert_ensemble_refiner, to see how they affect the output quality and style. Additionally, you can play with the Lora Scale parameter to adjust the influence of any LoRA weights used by the model.

Read more

Updated Invalid Date