sdxl-akira

Maintainer: doriandarko

Total Score

1

Last updated 6/21/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The sdxl-akira model is a text-to-image generation AI trained on the cult classic anime film Akira. It is one of several specialized SDXL models created by doriandarko. Similar SDXL models include those trained on Hiroshi Nagai's illustrations, blocky oil paintings, and Blade Runner 2049 stills. The sdxl and sdxl-niji-se models, created by lucataco, provide a more general text-to-image generation capability.

Model inputs and outputs

The sdxl-akira model takes a text prompt as input and generates one or more related images as output. The input prompt can describe the desired image in natural language, and the model will attempt to create a matching visual representation. The input schema also allows for optional parameters like image size, guidance scale, and seed values to tailor the output.

Inputs

  • Prompt: The text prompt describing the desired image
  • Negative Prompt: An optional prompt specifying content to exclude from the generated image
  • Image: An input image for use in img2img or inpaint mode
  • Mask: An input mask for inpaint mode, with black areas preserved and white areas inpainted
  • Seed: A random seed value to control image generation
  • Width/Height: The desired dimensions of the output image
  • Num Outputs: The number of images to generate
  • Scheduler: The denoising scheduler algorithm to use
  • Guidance Scale: The scale for classifier-free guidance
  • Num Inference Steps: The number of denoising steps to perform
  • Prompt Strength: The strength of the prompt when using img2img or inpaint modes
  • Refine: The refine style to use
  • LoRA Scale: The additive scale for LoRA (if applicable)
  • High Noise Frac: The fraction of high noise to use (for expert_ensemble_refiner)
  • Apply Watermark: Whether to apply a watermark to the generated images

Outputs

  • Image URI: A URI pointing to the generated image

Capabilities

The sdxl-akira model can generate visually striking images inspired by the Akira anime. It can depict characters, environments, and scenes from the film, as well as imaginative new interpretations of the Akira aesthetic. The model is particularly adept at capturing the distinct cyberpunk, post-apocalyptic, and neo-Tokyo visual style of the source material.

What can I use it for?

The sdxl-akira model could be used to create original Akira-inspired artwork, fan art, and illustrations. It could also be used to generate concept art or visual assets for video games, films, or other media projects with a similar futuristic, dystopian aesthetic. The model's capabilities could be leveraged by artists, designers, and creative professionals to explore and expand the Akira universe through new visual interpretations.

Things to try

Experiment with different prompt variations to see how the model interprets and renders various elements of the Akira universe, such as the iconic motorcycle chase scenes, the sprawling Neo-Tokyo cityscapes, or the towering mecha. You can also try using the img2img or inpaint modes to refine or modify existing Akira-inspired images. Additionally, playing with the model's settings like guidance scale, number of inference steps, and LoRA scale can produce a wide range of unique 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!

Related Models

AI model preview image

sdxl-hiroshinagai

doriandarko

Total Score

28

The sdxl-hiroshinagai model is a SDXL (Stable Diffusion XL) model trained on the illustrations of Hiroshi Nagai, a renowned Japanese artist known for his vibrant, retro-style landscape paintings. This model shares similarities with other SDXL models created by the maintainer, doriandarko, such as sdxl-allaprima, sdxl-bladerunner2049, and sdxl-niji-se. Model inputs and outputs The sdxl-hiroshinagai model accepts a variety of inputs, including text prompts, images for img2img or inpainting, and various parameters to control the output, such as seed, width, height, and scheduler. The model can generate one or more images as output, which are returned as a list of image URLs. Inputs Prompt**: The text prompt that describes the desired image. Negative Prompt**: An optional text prompt that specifies elements to exclude from the generated image. Image**: An input image for img2img or inpainting mode. Mask**: An input mask for inpainting mode, where black areas will be preserved, and white areas will be inpainted. Width**: The desired width of the output image. Height**: The desired height of the output image. Seed**: An optional random seed value. Scheduler**: The scheduler algorithm to use for image generation. Guidance Scale**: The scale for classifier-free guidance. Num Inference Steps**: The number of denoising steps to perform. Refine**: The refine style to use. LoRA Scale**: The LoRA additive scale, applicable only on trained models. Refine Steps**: The number of steps to refine the image, defaults to num_inference_steps. High Noise Frac**: The fraction of noise to use for the expert_ensemble_refiner. Apply Watermark**: Whether to apply a watermark to the generated image. Num Outputs**: The number of images to output. Outputs Image URLs**: A list of URLs pointing to the generated images. Capabilities The sdxl-hiroshinagai model is capable of generating high-quality images based on text prompts, with a visual style inspired by Hiroshi Nagai's iconic landscape paintings. The model can also perform img2img and inpainting tasks, allowing users to modify or refine existing images. The variety of input parameters provides users with fine-grained control over the output, enabling them to experiment and create unique, visually striking images. What can I use it for? The sdxl-hiroshinagai model could be used for a variety of creative applications, such as generating illustrations for book covers, album art, or promotional materials. Its ability to perform inpainting and img2img tasks makes it a useful tool for photo editing and manipulation, allowing users to seamlessly incorporate Nagai's signature style into their own visual projects. Additionally, the model's versatility could be leveraged for developing interactive applications, such as virtual environments or games, where the user can generate custom content on demand. Things to try One interesting aspect of the sdxl-hiroshinagai model is its ability to capture the essence of Hiroshi Nagai's distinct visual style. By experimenting with different prompts and input parameters, users can create a wide range of images that evoke the artist's iconic retro-futuristic landscapes, from lush, vibrant coastal scenes to dreamlike, fantastical compositions. Additionally, the model's inpainting and img2img capabilities allow users to seamlessly integrate Nagai-inspired elements into their own artwork, opening up new avenues for creative exploration and collaboration.

Read more

Updated Invalid Date

AI model preview image

sdxl-bladerunner2049

doriandarko

Total Score

1

The sdxl-bladerunner2049 is a specialized SDXL model trained on Blade Runner 2049 still frames. It is maintained by doriandarko. This model is similar to other SDXL models like sdxl-deep-down, sdxl, sdxl-black-light, sdxl, and sdxl_overwatch, which are fine-tuned on various datasets to specialize in different visual styles and themes. Model inputs and outputs The sdxl-bladerunner2049 model takes in a variety of inputs including an image, mask, prompt, and various configuration options. The outputs are an array of generated images. Inputs Prompt**: The input text prompt to guide the image generation 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 value, leave blank to randomize Width/Height**: The desired width and height 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 to perform Prompt Strength**: The strength of the prompt when using img2img/inpaint Refine**: The refine style to use Scheduler**: The scheduler algorithm to use LoRA Scale**: The LoRA additive scale (only applicable on trained models) High Noise Frac**: The fraction of noise to use for the expert_ensemble_refiner Apply Watermark**: Whether to apply a watermark to the generated images Replicate Weights**: The LoRA weights to use (leave blank for default) Outputs Array of generated images**: The model outputs an array of generated image URLs Capabilities The sdxl-bladerunner2049 model is capable of generating high-quality images in the style of Blade Runner 2049. It can produce a variety of futuristic, dystopian scenes with distinct visual elements from the film. What can I use it for? You can use the sdxl-bladerunner2049 model to create Blade Runner-inspired artwork, concept art, or visual assets for projects related to science fiction, cyberpunk, or futuristic themes. The model's specialized training allows it to capture the unique aesthetic of the Blade Runner universe, making it a valuable tool for artists, designers, and filmmakers working in these genres. Things to try Some interesting things to try with the sdxl-bladerunner2049 model include experimenting with different input prompts to explore the range of visual styles it can produce, using the img2img and inpaint modes to modify existing Blade Runner imagery, and adjusting the various configuration options to fine-tune the output to your specific needs.

Read more

Updated Invalid Date

AI model preview image

sdxl-allaprima

doriandarko

Total Score

3

The sdxl-allaprima model, created by Dorian Darko, is a Stable Diffusion XL (SDXL) model trained on a blocky oil painting and still life dataset. This model shares similarities with other SDXL models like sdxl-inpainting, sdxl-bladerunner2049, and sdxl-deep-down, which have been fine-tuned on specific datasets to enhance their capabilities in areas like inpainting, sci-fi imagery, and underwater scenes. Model inputs and outputs The sdxl-allaprima model accepts a variety of inputs, including an input image, a prompt, and optional parameters like seed, width, height, and guidance scale. The output is an array of generated images that match the input prompt and image. Inputs Prompt**: The text prompt that describes the desired image. Image**: An input image that the model can use as a starting point for generation or inpainting. Mask**: A mask that specifies which areas of the input image should be preserved or inpainted. Seed**: A random seed value that can be used to generate reproducible outputs. Width/Height**: The desired dimensions of the output image. Guidance Scale**: A parameter that controls the influence of the text prompt on the generated image. Outputs Generated Images**: An array of one or more images that match the input prompt and image. Capabilities The sdxl-allaprima model is capable of generating high-quality, artistic images based on a text prompt. It can also be used for inpainting, where the model fills in missing or damaged areas of an input image. The model's training on a dataset of blocky oil paintings and still lifes gives it the ability to generate visually striking and unique images in this style. What can I use it for? The sdxl-allaprima model could be useful for a variety of applications, such as: Creating unique digital artwork and illustrations for personal or commercial use Generating concept art and visual references for creative projects Enhancing or repairing damaged or incomplete images through inpainting Experimenting with different artistic styles and techniques in a generative AI framework Things to try One interesting aspect of the sdxl-allaprima model is its ability to generate images with a distinctive blocky, oil painting-inspired style. Users could experiment with prompts that play to this strength, such as prompts that describe abstract, surreal, or impressionistic scenes. Additionally, the model's inpainting capabilities could be explored by providing it with partially complete images and seeing how it fills in the missing details.

Read more

Updated Invalid Date

AI model preview image

sdxl-lightning-4step

bytedance

Total Score

132.2K

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