Nitrosocke

Models by this creator

🔍

mo-di-diffusion

nitrosocke

Total Score

939

The mo-di-diffusion model is a fine-tuned Stable Diffusion 1.5 model, trained by maintainer nitrosocke on screenshots from a popular animation studio. Using the tokens modern disney style in your prompts will produce images with that distinctive visual effect. This model can be compared to other Stable Diffusion variants like Stable Diffusion v2 and the original Stable Diffusion model. Model inputs and outputs The mo-di-diffusion model takes text prompts as input and generates corresponding images as output. The model is based on the Stable Diffusion architecture, which utilizes a diffusion process to transform latent representations into photo-realistic images. Inputs Text prompt**: A text description that describes the desired image Outputs Image**: A generated image that matches the provided text prompt Capabilities The mo-di-diffusion model excels at producing images with a distinctive "modern Disney" visual style, incorporating elements from popular animated films. Example outputs showcase the model's ability to render detailed videogame characters, animals, cars, and landscapes in this artistic aesthetic. What can I use it for? The mo-di-diffusion model can be used for a variety of creative and artistic projects that require images in a modern Disney-inspired style. This could include concept art, character design, illustration, and more. The model's capabilities make it well-suited for creative industries, game development, and entertainment applications where this visual style is desirable. Things to try One interesting aspect of the mo-di-diffusion model is its ability to capture the nuances of the "modern Disney" style through the use of specific tokens in the text prompt. Experimenting with different prompt variations, such as adding descriptors like "detailed", "colorful", or "whimsical", can result in unique and expressive image outputs that further showcase the model's strengths.

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Updated 5/28/2024

🔍

Arcane-Diffusion

nitrosocke

Total Score

749

Arcane-Diffusion is a fine-tuned version of the Stable Diffusion model, trained on images from the TV show Arcane. This model can produce images in the distinctive "Arcane style" by using the tokens arcane style in your prompts. The maintainer nitrosocke has also created other fine-tuned Stable Diffusion models, such as mo-di-diffusion which is trained on images in a "modern Disney style". Model inputs and outputs Arcane-Diffusion is a text-to-image model that takes a text prompt as input and generates a corresponding image as output. The model can be used just like the original Stable Diffusion model, with the addition of the arcane style token to produce images in the Arcane aesthetic. Inputs Text prompt: A text description of the desired image, including the **arcane style token. Outputs Generated image**: An image that corresponds to the input text prompt, rendered in the Arcane art style. Capabilities Arcane-Diffusion can generate a wide variety of Arcane-themed images, from fantastical characters and creatures to elaborate environments and scenes. The model is able to capture the distinct visual style of the Arcane universe, including its unique color palette, lighting, and artistic flourishes. What can I use it for? Arcane-Diffusion can be used to create original artwork and illustrations inspired by the Arcane universe. This could include character designs, background environments, promotional materials, and more. The model can also be used to generate images for creative projects, such as fanart, game assets, or digital art commissions. Things to try One interesting aspect of Arcane-Diffusion is its ability to blend the Arcane art style with other elements. Try combining the arcane style token with prompts that introduce other themes, such as "a magical princess with golden hair, arcane style" or "a cyberpunk city at night, arcane style". This can lead to unique and unexpected results that push the boundaries of the model's capabilities.

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Updated 5/28/2024

Ghibli-Diffusion

nitrosocke

Total Score

607

The Ghibli-Diffusion model is a fine-tuned Stable Diffusion model trained on images from modern anime feature films from Studio Ghibli. This model allows users to generate images in the distinct Ghibli art style by including the ghibli style token in their prompts. The model is maintained by nitrosocke, who has also created similar fine-tuned models like Mo Di Diffusion and Arcane Diffusion. Model inputs and outputs The Ghibli-Diffusion model takes text prompts as input and generates high-quality, Ghibli-style images as output. The model can be used to create a variety of content, including character portraits, scenes, and landscapes. Inputs Text Prompts**: The model accepts text prompts that can include the ghibli style token to indicate the desired art style. Outputs Images**: The model generates images in the Ghibli art style, with a focus on high detail and vibrant colors. Capabilities The Ghibli-Diffusion model is particularly adept at generating character portraits, cars, animals, and landscapes in the distinctive Ghibli visual style. The provided examples showcase the model's ability to capture the whimsical, hand-drawn aesthetic of Ghibli films. What can I use it for? The Ghibli-Diffusion model can be used to create a wide range of Ghibli-inspired content, from character designs and fan art to concept art for animation projects. The model's capabilities make it well-suited for creative applications in the animation, gaming, and digital art industries. Users can also experiment with combining the Ghibli style with other elements, such as modern settings or fantastical elements, to generate unique and imaginative images. Things to try One interesting aspect of the Ghibli-Diffusion model is its ability to generate images with a balance of realism and stylization. Users can try experimenting with different prompts and negative prompts to see how the model handles a variety of subjects and compositions. Additionally, users may want to explore how the model performs when combining the ghibli style token with other artistic styles or genre-specific keywords.

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Updated 5/28/2024

⛏️

Future-Diffusion

nitrosocke

Total Score

402

Future-Diffusion is a fine-tuned version of the Stable Diffusion 2.0 base model, trained by nitrosocke on high-quality 3D images with a futuristic sci-fi theme. This model allows users to generate images with a distinct "future style" by incorporating the future style token into their prompts. Compared to similar models like redshift-diffusion-768, Future-Diffusion has a 512x512 resolution, while the redshift model has a higher 768x768 resolution. The Ghibli-Diffusion and Arcane-Diffusion models, on the other hand, are fine-tuned on anime and Arcane-themed images respectively, producing outputs with those distinct visual styles. Model inputs and outputs Future-Diffusion is a text-to-image model, taking text prompts as input and generating corresponding images as output. The model was trained using the diffusers-based dreambooth training approach with prior-preservation loss and the train-text-encoder flag. Inputs Text prompts**: Users provide text descriptions to guide the image generation, such as future style [subject] Negative Prompt: duplicate heads bad anatomy for character generation or future style city market street level at night Negative Prompt: blurry fog soft for landscapes. Outputs Images**: The model generates 512x512 or 1024x576 pixel images based on the provided text prompts, with a futuristic sci-fi style. Capabilities Future-Diffusion can generate a wide range of images with a distinct futuristic aesthetic, including human characters, animals, vehicles, and landscapes. The model's ability to capture this specific style sets it apart from more generic text-to-image models. What can I use it for? The Future-Diffusion model can be useful for various creative and commercial applications, such as: Generating concept art for science fiction stories, games, or films Designing futuristic product visuals or packaging Creating promotional materials or marketing assets with a futuristic flair Exploring and experimenting with novel visual styles and aesthetics Things to try One interesting aspect of Future-Diffusion is the ability to combine the "future style" token with other style tokens, such as those from the Ghibli-Diffusion or Arcane-Diffusion models. This can result in unique and unexpected hybrid styles, allowing users to expand their creative possibilities.

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Updated 5/28/2024

🔄

Nitro-Diffusion

nitrosocke

Total Score

378

Nitro-Diffusion is a fine-tuned Stable Diffusion model trained by nitrosocke on three distinct art styles simultaneously, allowing for high control of mixing, weighting, and single-style use. The model was trained on images from the Arcane, modern disney, and an archer style. Users can incorporate these styles into their prompts using the tokens archer style, arcane style or modern disney style. Model inputs and outputs The Nitro-Diffusion model takes text prompts as input and generates corresponding images as output. The model was trained using the Stable Diffusion architecture, which utilizes a diffusion process to transform noise into photorealistic images based on the provided text prompt. Inputs Text prompt**: A natural language description of the desired image, which can incorporate the style tokens to guide the generated output. Outputs Image**: A high-quality, photorealistic image that matches the provided text prompt, with the specified artistic style(s) applied. Capabilities The Nitro-Diffusion model can generate a wide variety of scenes and characters in multiple art styles, from realistic to stylized. Examples include fantasy characters, futuristic landscapes, and whimsical scenes. By using the style tokens, users can create images that blend different artistic influences or focus on a single style. What can I use it for? The Nitro-Diffusion model can be used for a variety of creative and artistic applications, such as concept art, character design, and illustration. The ability to mix and match styles makes it a versatile tool for designers, artists, and hobbyists alike. Additionally, the model's high-quality output makes it suitable for use in commercial projects, such as game development, movie production, and marketing materials. Things to try Experiment with different combinations of the style tokens to see how they influence the generated images. Try blending the styles in various ways, or focus on a single style to create a more cohesive look. Additionally, explore the model's capabilities by providing detailed, complex prompts to see the level of detail and realism it can achieve.

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Updated 5/28/2024

🛠️

spider-verse-diffusion

nitrosocke

Total Score

345

spider-verse-diffusion is a fine-tuned Stable Diffusion model trained on movie stills from Sony's Into the Spider-Verse. This model can be used to generate images in the distinctive visual style of the Spider-Verse animated film using the spiderverse style prompt token. Similar fine-tuned models from the same maintainer, nitrosocke, include Arcane-Diffusion, Ghibli-Diffusion, elden-ring-diffusion, and mo-di-diffusion, each trained on a different animation or video game art style. Model inputs and outputs The spider-verse-diffusion model takes text prompts as input and generates corresponding images in the Spider-Verse visual style. Sample prompts might include "a magical princess with golden hair, spiderverse style" or "a futuristic city, spiderverse style". The model outputs high-quality, detailed images that capture the unique aesthetic of the Spider-Verse film. Inputs Text prompts describing the desired image content and style Outputs Images generated from the input prompts, in the Spider-Verse art style Capabilities The spider-verse-diffusion model excels at generating compelling character portraits, landscapes, and scenes that evoke the vibrant, dynamic visuals of the Into the Spider-Verse movie. The model is able to capture the distinct animated, comic book-inspired look and feel, with stylized character designs, bold colors, and dynamic camera angles. What can I use it for? This model could be useful for creating fan art, illustrations, and other creative content inspired by the Spider-Verse universe. The distinctive visual style could also be incorporated into graphic design, concept art, or multimedia projects. Given the model's open-source license, it could potentially be used in commercial applications as well, though certain usage restrictions apply as specified in the CreativeML OpenRAIL-M license. Things to try Experiment with different prompts to see how the model captures various Spider-Verse elements, from characters and creatures to environments and cityscapes. Try combining the spiderverse style token with other descriptors to see how the model blends styles. You could also try using the model to generate promotional materials, book covers, or other commercial content inspired by the Spider-Verse franchise.

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Updated 5/28/2024

elden-ring-diffusion

nitrosocke

Total Score

321

The elden-ring-diffusion model is a fine-tuned Stable Diffusion model trained on game art from the popular video game Elden Ring. This allows the model to generate images in the distinct style of the game's visuals. Similar models created by the same maintainer, nitrosocke, include Arcane Diffusion, Ghibli Diffusion, and Nitro Diffusion, each trained on different artistic styles. Model inputs and outputs The elden-ring-diffusion model takes text prompts as input and generates corresponding images in the style of Elden Ring. Users can influence the output by including the token elden ring style in their prompts. Inputs Text prompts**: Descriptive text that the model uses to generate images, e.g. "a magical princess with golden hair, elden ring style" Outputs Images**: The generated images based on the provided text prompts, in the distinct visual style of Elden Ring. Capabilities The elden-ring-diffusion model can generate a wide variety of images, including portraits, landscapes, and fantastical scenes, all with the signature look and feel of the Elden Ring game world. The model is particularly adept at capturing the atmospheric, somber, and ominous tone that permeates the Elden Ring aesthetic. What can I use it for? The elden-ring-diffusion model can be a powerful tool for artists, designers, and content creators who want to incorporate the Elden Ring visual style into their projects. This could include creating concept art, promotional materials, fan art, and more. The model's ability to generate images quickly and with high fidelity makes it a valuable asset for those working in the fantasy and gaming spaces. Things to try One interesting aspect of the elden-ring-diffusion model is its ability to blend the Elden Ring style with other artistic influences. By combining the elden ring style token with other keywords, users can experiment with mixing the game's visuals with other aesthetic elements, such as different character archetypes or environmental settings. This can lead to the creation of unique and unexpected imagery that captures the essence of Elden Ring while introducing new creative twists.

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Updated 5/28/2024

🤷

redshift-diffusion-768

nitrosocke

Total Score

141

The redshift-diffusion-768 model is a fine-tuned version of the Stable Diffusion 2.0 model, trained on high-quality 3D images with a 768x768 pixel resolution. It was developed by the Hugging Face creator nitrosocke. This model can produce images in a unique "redshift style" by using the prompt tokens redshift style. Similar models include the Ghibli-Diffusion, elden-ring-diffusion, mo-di-diffusion, Arcane-Diffusion, and Nitro-Diffusion, all of which are fine-tuned on different art styles and datasets. Model inputs and outputs The redshift-diffusion-768 model takes text prompts as input and generates corresponding images as output. The text prompts can describe a wide variety of subjects, including characters, scenes, and objects, and the model will attempt to render them in the unique "redshift style". Inputs Text prompt**: A description of the desired image, using the redshift style tokens for the specific effect. Outputs Image**: A generated image that matches the provided text prompt, rendered in the "redshift style". Capabilities The redshift-diffusion-768 model can generate highly detailed and visually striking images in a wide range of subjects, from characters and portraits to landscapes and scenes. The "redshift style" gives the images a distinct look, with vibrant colors, strong lighting, and a futuristic or science-fiction aesthetic. What can I use it for? The redshift-diffusion-768 model can be used for a variety of creative and artistic applications, such as concept art, character design, and world-building for science-fiction or fantasy projects. The unique visual style of the model's outputs could also be leveraged for commercial applications, such as product design, advertising, or visual effects. Things to try One interesting aspect of the redshift-diffusion-768 model is its ability to generate highly detailed and visually striking images with a wide range of subjects. Try experimenting with different types of prompts, from detailed character descriptions to abstract or surreal scenes, to see the versatility of the model's capabilities. Additionally, you can try mixing the "redshift style" with other art styles, such as those from the Ghibli-Diffusion or Elden Ring Diffusion models, to create unique and unexpected visual combinations.

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Updated 5/28/2024

🖼️

disco-elysium

nitrosocke

Total Score

64

The disco-elysium model is a fine-tuned Stable Diffusion model trained on the character portraits from the game Disco Elysium. By incorporating the discoelysium style tokens in your prompts, you can generate images with a distinct visual style inspired by the game. This model is similar to other Stable Diffusion fine-tuned models, such as the disco-diffusion-style model, which applies the Disco Diffusion style to Stable Diffusion using Dreambooth, and the elden-ring-diffusion model, which is trained on art from the Elden Ring game. Model inputs and outputs The disco-elysium model is a text-to-image AI model, meaning it takes a text prompt as input and generates a corresponding image as output. The model can create a wide variety of images, from character portraits to landscapes, as long as the prompt is related to the Disco Elysium game world and art style. Inputs Text prompt**: A natural language description of the desired image, including the discoelysium style token to invoke the specific visual style. Outputs Generated image**: A visually striking, game-inspired image that matches the provided text prompt. Capabilities The disco-elysium model excels at generating high-quality images with a distinct visual flair inspired by the Disco Elysium game. The model can create detailed character portraits, imaginative landscapes, and other visuals that capture the unique aesthetic of the game. By using the discoelysium style token, you can ensure that the generated images maintain the characteristic look and feel of Disco Elysium. What can I use it for? The disco-elysium model can be a valuable tool for various creative projects and applications. Artists and designers can use it to quickly generate concept art, character designs, or illustrations with a Disco Elysium-inspired style. Writers and worldbuilders can leverage the model to visualize scenes and characters from their Disco Elysium-inspired stories or campaigns. The model can also be used for commercial purposes, such as generating promotional materials or artwork for Disco Elysium-themed products and merchandise. Things to try Experiment with different prompts that incorporate the discoelysium style token, and see how the model's output varies in terms of subject matter, composition, and overall aesthetic. Try combining the discoelysium style with other descriptors, such as specific character types, emotions, or narrative elements, to see how the model blends these elements. Additionally, consider using the disco-elysium model in conjunction with other Stable Diffusion fine-tuned models, such as the elden-ring-diffusion or mo-di-diffusion models, to create unique and visually striking hybrid styles.

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Updated 5/28/2024

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arcane-diffusion

nitrosocke

Total Score

36

Arcane-Diffusion is a fine-tuned version of the Stable Diffusion model, trained on images from the TV show Arcane. This model can produce images in the distinctive "Arcane style" by using the tokens arcane style in your prompts. The maintainer nitrosocke has also created other fine-tuned Stable Diffusion models, such as mo-di-diffusion which is trained on images in a "modern Disney style". Model inputs and outputs Arcane-Diffusion is a text-to-image model that takes a text prompt as input and generates a corresponding image as output. The model can be used just like the original Stable Diffusion model, with the addition of the arcane style token to produce images in the Arcane aesthetic. Inputs Text prompt: A text description of the desired image, including the **arcane style token. Outputs Generated image**: An image that corresponds to the input text prompt, rendered in the Arcane art style. Capabilities Arcane-Diffusion can generate a wide variety of Arcane-themed images, from fantastical characters and creatures to elaborate environments and scenes. The model is able to capture the distinct visual style of the Arcane universe, including its unique color palette, lighting, and artistic flourishes. What can I use it for? Arcane-Diffusion can be used to create original artwork and illustrations inspired by the Arcane universe. This could include character designs, background environments, promotional materials, and more. The model can also be used to generate images for creative projects, such as fanart, game assets, or digital art commissions. Things to try One interesting aspect of Arcane-Diffusion is its ability to blend the Arcane art style with other elements. Try combining the arcane style token with prompts that introduce other themes, such as "a magical princess with golden hair, arcane style" or "a cyberpunk city at night, arcane style". This can lead to unique and unexpected results that push the boundaries of the model's capabilities.

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Updated 5/30/2024