studio-ghibli
Maintainer: karanchawla
21
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The studio-ghibli model is a public SDXL model trained on the style of the renowned Japanese animation studio, Studio Ghibli. This model was created by Karan Chawla, who has developed several other AI models, including the StudioGhibli, gfpgan, animagine-xl-3.1, and sdxl models. The studio-ghibli model aims to capture the distinctive visual style of Studio Ghibli's films, allowing users to generate images with a similar aesthetic.
Model inputs and outputs
The studio-ghibli model accepts a variety of inputs, including a prompt, an input image (for img2img or inpaint mode), a mask (for inpaint mode), and various configuration options such as seed, width, height, guidance scale, and number of inference steps. The model then generates one or more output images that reflect the provided prompt and settings.
Inputs
- Prompt: The text prompt that describes the desired image
- 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
- Refine: The refine style to use
- Scheduler: The scheduler to use for the diffusion process
- LoRA Scale: The LoRA additive scale (only applicable on trained models)
- Num Outputs: The number of images to generate
- Refine Steps: The number of steps to refine (for base_image_refiner)
- Guidance Scale: The scale for classifier-free guidance
- Apply Watermark: Whether to apply a watermark to the generated images
- High Noise Frac: The fraction of noise to use (for expert_ensemble_refiner)
- Negative Prompt: An optional negative prompt to guide the image generation
- Prompt Strength: The prompt strength when using img2img or inpaint mode
Outputs
- Output Images: The generated images that reflect the provided prompt and settings
Capabilities
The studio-ghibli model can generate images with a distinct visual style inspired by the iconic anime films of Studio Ghibli. The model is capable of producing a wide range of subjects, including characters, landscapes, and various objects, all with a unique Ghibli-esque aesthetic. Users can experiment with different prompts and settings to explore the model's capabilities and create highly imaginative and visually striking images.
What can I use it for?
The studio-ghibli model can be useful for a variety of applications, such as generating concept art, illustrations, or background images for creative projects, games, or animations. The model's ability to capture the distinctive Ghibli style can be particularly valuable for fans of the studio's work, as it allows them to create their own Ghibli-inspired art and content. Additionally, the model could be used to explore and experiment with various visual styles and techniques, fostering creativity and artistic expression.
Things to try
One interesting aspect of the studio-ghibli model is its ability to generate images with a unique blend of realism and whimsy, capturing the essence of Studio Ghibli's signature aesthetic. Users can experiment with different prompts and settings to explore the model's versatility, such as creating fantastical landscapes, enchanting character designs, or imaginative scenes that evoke the wonder and magic of Ghibli's cinematic worlds.
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
sdxl-akira
1
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.
Updated Invalid Date
dreamlike-anime
3
The dreamlike-anime model from maintainer replicategithubwc is designed for creating "Dreamlike Anime 1.0 for Splurge Art." This model can be compared to similar offerings from the same maintainer, such as anime-pastel-dream, dreamlike-photoreal, and neurogen, all of which are focused on generating artistic, dreamlike imagery. Model inputs and outputs The dreamlike-anime model takes a text prompt as input and generates one or more corresponding images as output. The model also allows for configuring various parameters such as image size, number of outputs, guidance scale, and the number of inference steps. Inputs Prompt**: The text prompt that describes the desired image Seed**: A random seed value to control the image generation process Width**: The width of the output image in pixels Height**: The height of the output image in pixels Num Outputs**: The number of images to generate (up to 4) Guidance Scale**: The scale for classifier-free guidance, which controls the balance between the input prompt and the model's internal knowledge Num Inference Steps**: The number of denoising steps to perform during image generation Negative Prompt**: Specify things you don't want to see in the output Outputs Output Images**: The generated images, returned as a list of image URLs Capabilities The dreamlike-anime model is capable of generating highly imaginative, surreal anime-inspired artwork based on text prompts. The model can capture a wide range of styles and subjects, from fantastical landscapes to whimsical character designs. What can I use it for? The dreamlike-anime model can be used for a variety of creative projects, such as generating concept art, illustrations, and album covers. It could also be used to create unique, one-of-a-kind digital artworks for sale or personal enjoyment. Given the model's focus on dreamlike, anime-inspired imagery, it may be particularly well-suited for projects within the anime, manga, and animation industries. Things to try Experiment with different prompts to see the range of styles and subjects the dreamlike-anime model can produce. Try combining the model with other creative tools or techniques, such as post-processing the generated images or incorporating them into larger artistic compositions. You can also explore the model's capabilities by generating images with varying levels of guidance scale and inference steps to achieve different levels of detail and abstraction.
Updated Invalid Date
animeganv3
2
AnimeGANv3 is a novel double-tail generative adversarial network developed by researcher Asher Chan for fast photo animation. It builds upon previous iterations of the AnimeGAN model, which aims to transform regular photos into anime-style art. Unlike AnimeGANv2, AnimeGANv3 introduces a more efficient architecture that can generate anime-style images at a faster rate. The model has been trained on various anime art styles, including the distinctive styles of directors Hayao Miyazaki and Makoto Shinkai. Model inputs and outputs AnimeGANv3 takes a regular photo as input and outputs an anime-style version of that photo. The model supports a variety of anime art styles, which can be selected as input parameters. In addition to photo-to-anime conversion, the model can also be used to animate videos, transforming regular footage into anime-style animations. Inputs image**: The input photo or video frame to be converted to an anime style. style**: The desired anime art style, such as Hayao, Shinkai, Arcane, or Disney. Outputs Output image/video**: The input photo or video transformed into the selected anime art style. Capabilities AnimeGANv3 can produce high-quality, anime-style renderings of photos and videos with impressive speed and efficiency. The model's ability to capture the distinct visual characteristics of various anime styles, such as Hayao Miyazaki's iconic watercolor aesthetic or Makoto Shinkai's vibrant, detailed landscapes, sets it apart from previous iterations of the AnimeGAN model. What can I use it for? AnimeGANv3 can be a powerful tool for artists, animators, and content creators looking to quickly and easily transform their work into anime-inspired art. The model's versatility allows it to be applied to a wide range of projects, from personal photo edits to professional-grade animated videos. Additionally, the model's ability to convert photos and videos into different anime styles can be useful for filmmakers, game developers, and other creatives seeking to create unique, anime-influenced content. Things to try One exciting aspect of AnimeGANv3 is its ability to animate videos, transforming regular footage into stylized, anime-inspired animations. Users can experiment with different input videos and art styles to create unique, eye-catching results. Additionally, the model's wide range of supported styles, from the classic Hayao and Shinkai looks to more contemporary styles like Arcane and Disney, allows for a diverse array of creative possibilities.
Updated Invalid Date
sdxl-lightning-4step
412.2K
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.
Updated Invalid Date