Maintainer: ShadoWxShinigamI

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Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The vray-render model is a Textual Inversion Embedding created by ShadoWxShinigamI for the Stable Diffusion 2.0 (768) model. It is designed to generate images with a V-Ray Render style, resulting in slightly soft outputs. The model was trained on 44 images at 768x768 resolution with a batch size of 4, gradient accumulation of 11, and 500 training steps.

Similar models created by ShadoWxShinigamI include the SD2-768-Papercut and Midjourney-v4-PaintArt embeddings, which focus on Papercut and Midjourney-inspired art styles respectively.

Model inputs and outputs


  • Text prompts that describe the desired output image


  • Images generated based on the input text prompt, with a V-Ray Render style


The vray-render model can generate a variety of photorealistic images with a distinctive V-Ray Render aesthetic, including scenes like cabins, cars, lions, ships, and human portraits.

What can I use it for?

The vray-render model can be used to create visually striking and photorealistic images for a range of applications, such as digital art, product visualizations, or architectural renderings. Its unique style can also be useful for creative projects that require a specific look and feel.

Things to try

Experimenting with different prompts and prompt engineering techniques can help unlock the full potential of the vray-render model. Trying out various subjects, scene compositions, and combinations of keywords may result in unexpected and compelling outputs.

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




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The SD2-768-Papercut model is a Textual Inversion Embedding for Stable Diffusion 2.0 created by ShadoWxShinigamI. It is based on ShadoWxShinigamI's previous MJv4-Paper Cut Model, and is designed to be fully customizable with prompts. The model was trained using 106 manually captioned images at 768x768 resolution over 150 steps, with 8 Textual Inversion vectors. Model inputs and outputs Inputs Textual prompts that can be customized for a variety of subjects, including ships, dogs, lions, people, landscapes, and buildings. Outputs High-quality, photorealistic images generated in the style of the training data, which includes a variety of scenes and subject matter. Capabilities The SD2-768-Papercut model is capable of generating detailed, visually striking images across a range of subject matter. The examples provided show its versatility in depicting ships, animals, people, and landscapes with a distinctive photographic quality. The model's ability to handle a variety of prompt inputs without extensive engineering suggests it could be a useful tool for content creators or designers looking to quickly generate concept art or visual references. What can I use it for? The SD2-768-Papercut model could be used for a variety of projects that require high-quality, customizable images. Some potential use cases include: Generating visual concepts or references for game, film, or product design Creating unique images for blog posts, social media, or marketing materials Exploring different artistic styles or photographic techniques through prompt experimentation Things to try One interesting aspect of the SD2-768-Papercut model is its ability to generate visually cohesive images from diverse prompt inputs. For example, the model can depict a wide range of subjects, from natural landscapes to man-made objects, while maintaining a consistent level of detail and photorealism. This suggests the model has learned robust representations of visual elements that can be flexibly combined to create novel compositions. Prompt engineers or artists could experiment with the model by trying to push the boundaries of its capabilities, such as by combining multiple subjects in a single image, or by introducing more abstract or fantastical elements into the prompts. The model's performance on these types of prompts could reveal interesting insights about its inner workings and potential areas for further development.

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The Midjourney-v4-PaintArt model, created by ShadoWxShinigamI, is a text-to-image AI model that generates illustrations in a unique "painterly" art style. This model builds upon the capabilities of the MidJourney-PaperCut and SD2-768-Papercut models, also developed by ShadoWxShinigamI, which specialize in digital paper-cut and collage-inspired artworks. The Midjourney-v4-PaintArt model takes this concept further, producing vibrant, expressive paintings with visible brush strokes and a distinctive artistic flair. Model inputs and outputs The Midjourney-v4-PaintArt model accepts text prompts as input and generates corresponding 512x512 pixel images as output. The prompts should begin with the token "mdjrny-pntrt" to trigger the model's unique painting style. The model was trained on a dataset of 2080 images over 26 training steps, utilizing a v1-5 base. Inputs Text prompts starting with the "mdjrny-pntrt" token Outputs 512x512 pixel images in a distinctive painterly art style Capabilities The Midjourney-v4-PaintArt model is capable of generating a wide range of imaginative, expressive illustrations. The examples provided show the model's ability to create detailed, atmospheric scenes, vibrant character portraits, and intricate fantasy landscapes. The painterly style adds a unique and visually striking quality to the generated images. What can I use it for? The Midjourney-v4-PaintArt model can be a valuable tool for creative projects, such as concept art, book covers, album art, or any application where a unique, hand-painted aesthetic is desired. The model's capabilities could also be leveraged for commercial purposes, such as generating custom artwork for clients or products. Additionally, the model's similarities to the MidJourney-PaperCut and SD2-768-Papercut models suggest potential for combining or fine-tuning the models to explore different artistic styles and applications. Things to try Experimenting with the specificity and complexity of the prompts can yield a wide range of unique and unexpected results with the Midjourney-v4-PaintArt model. Combining the "mdjrny-pntrt" token with descriptive details about the desired subject matter, setting, or artistic elements can lead to fascinating and visually captivating artworks. Additionally, exploring the model's capabilities in conjunction with other text-to-image or image editing tools could unlock new creative possibilities.

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Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. Developed by Stability AI, it is an impressive AI model that can create stunning visuals from simple text prompts. The model has several versions, with each newer version being trained for longer and producing higher-quality images than the previous ones. The main advantage of Stable Diffusion is its ability to generate highly detailed and realistic images from a wide range of textual descriptions. This makes it a powerful tool for creative applications, allowing users to visualize their ideas and concepts in a photorealistic way. The model has been trained on a large and diverse dataset, enabling it to handle a broad spectrum of subjects and styles. Model inputs and outputs Inputs Prompt**: The text prompt that describes the desired image. This can be a simple description or a more detailed, creative prompt. Seed**: An optional random seed value to control the randomness of the image generation process. Width and Height**: The desired dimensions of the generated image, which must be multiples of 64. Scheduler**: The algorithm used to generate the image, with options like DPMSolverMultistep. Num Outputs**: The number of images to generate (up to 4). Guidance Scale**: The scale for classifier-free guidance, which controls the trade-off between image quality and faithfulness to the input prompt. Negative Prompt**: Text that specifies things the model should avoid including in the generated image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Array of image URLs**: The generated images are returned as an array of URLs pointing to the created images. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from text prompts. It can create images of people, animals, landscapes, architecture, and more, with a high level of detail and accuracy. The model is particularly skilled at rendering complex scenes and capturing the essence of the input prompt. One of the key strengths of Stable Diffusion is its ability to handle diverse prompts, from simple descriptions to more creative and imaginative ideas. The model can generate images of fantastical creatures, surreal landscapes, and even abstract concepts with impressive results. What can I use it for? Stable Diffusion can be used for a variety of creative applications, such as: Visualizing ideas and concepts for art, design, or storytelling Generating images for use in marketing, advertising, or social media Aiding in the development of games, movies, or other visual media Exploring and experimenting with new ideas and artistic styles The model's versatility and high-quality output make it a valuable tool for anyone looking to bring their ideas to life through visual art. By combining the power of AI with human creativity, Stable Diffusion opens up new possibilities for visual expression and innovation. Things to try One interesting aspect of Stable Diffusion is its ability to generate images with a high level of detail and realism. Users can experiment with prompts that combine specific elements, such as "a steam-powered robot exploring a lush, alien jungle," to see how the model handles complex and imaginative scenes. Additionally, the model's support for different image sizes and resolutions allows users to explore the limits of its capabilities. By generating images at various scales, users can see how the model handles the level of detail and complexity required for different use cases, such as high-resolution artwork or smaller social media graphics. Overall, Stable Diffusion is a powerful and versatile AI model that offers endless possibilities for creative expression and exploration. By experimenting with different prompts, settings, and output formats, users can unlock the full potential of this cutting-edge text-to-image technology.

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