knollingcase-embeddings-sd-v2-0
Maintainer: ProGamerGov
141
🌿
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The knollingcase-embeddings-sd-v2-0 is a set of text embeddings trained by ProGamerGov for use with the Stable Diffusion v2.0 model. These embeddings are designed to produce images with a "knollingcase" style, which is described as a concept inside a sleek, sometimes sci-fi, display case with transparent walls and a minimalistic background.
The embeddings were trained through several iterations, with the v4 version using 116 high-quality training images and producing the best results. Other similar models like the Double-Exposure-Embedding and Min-Illust-Background-Diffusion also aim to produce unique artistic styles for Stable Diffusion.
Model inputs and outputs
Inputs
- Text prompts using the provided "knollingcase" trigger words (e.g. "kc8", "kc16", "kc32") to activate the embedding
Outputs
- Images in the "knollingcase" style, with a concept or object displayed in a sleek, futuristic case
Capabilities
The knollingcase-embeddings-sd-v2-0 model excels at generating highly detailed, photorealistic images with a distinct sci-fi or minimalistic aesthetic. The transparent display case and clean background create a striking visual effect that sets the generated images apart.
What can I use it for?
This model could be valuable for creating product visualizations, conceptual art, or promotional imagery with a futuristic, high-tech feel. The diverse range of prompts and the ability to fine-tune the style through the various embedding versions provide a lot of creative flexibility.
Things to try
Experiment with different prompt structures that incorporate the "knollingcase" trigger words, such as:
- "A highly detailed, photorealistic [CONCEPT], encased in a transparent, minimalist display, kc32-v4-5000"
- "A [CONCEPT] inside a sleek, sci-fi case, very detailed, kc16-v4-5000"
- "A [CONCEPT] in a futuristic, transparent display, kc8-v4-5000"
Try using different samplers like DPM++ SDE Karras or DPM++ 2S a Karras, as suggested by the maintainer, to see how they affect the output.
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
↗️
knollingcase
204
The knollingcase model is a Dreambooth-trained AI model created by Aybeeceedee using TheLastBen's fast-DreamBooth notebook. This model is designed to generate images in a unique "knolling" style, where objects are arranged in a clean, minimalistic display case with transparent walls and a sleek, technical background. The model can be used to create photorealistic images of various concepts, from natural objects to futuristic designs. The knollingcase model shares some similarities with other Dreambooth-trained models, such as knollingcase-embeddings-sd-v2-0 and ANYTHING-MIDJOURNEY-V-4.1, which also explore the knolling concept in various ways. However, the knollingcase model has its own unique style and set of capabilities. Model inputs and outputs Inputs Text prompts that include the keyword "knollingcase" and describe the desired concept, such as "knollingcase, isometric render, a single cherry blossom tree, isometric display case" Outputs Photorealistic images of the specified concept, arranged in a clean, minimalistic display case with transparent walls and a sleek, technical background Capabilities The knollingcase model can generate a wide variety of photorealistic images by combining the "knollingcase" keyword with different concepts and details. The results often feature high-quality, technical-looking renderings with a focus on precise, micro-level details. The model can create images of everything from natural objects to futuristic designs, all with a consistent, visually striking "knolling" style. What can I use it for? The knollingcase model could be a useful tool for various applications, such as product design, technical illustration, and data visualization. The model's ability to create detailed, photorealistic images in a clean, minimalistic style could be valuable for creating visually appealing and informative graphics for presentations, marketing materials, or even scientific publications. Things to try One interesting aspect of the knollingcase model is its ability to generate images with a wide range of moods and atmospheres, from dramatic, high-contrast lighting to more subtle, ambient settings. Experimenting with different prompt variations, such as adding keywords like "dramatic lighting," "glow," or "reflections," can result in unique and visually striking images that showcase the model's versatility.
Updated Invalid Date
🏅
Double-Exposure-Embedding
82
Double-Exposure-Embedding is a custom image embedding trained by joachimsallstrom to apply a distinct "double exposure" visual effect when used with the Stable Diffusion 2.x text-to-image model. This embedding was trained on a dataset of layered 768px images of people with a variety of surroundings, allowing it to handle objects and animals well. It can be triggered in prompts by writing "dblx". Similar models like stable-diffusion-2 and stable-diffusion-2-base provide the underlying Stable Diffusion 2.x model which can be used with various custom embeddings and tunings. Model inputs and outputs Inputs Text prompt**: A text description of the desired image. Model embedding**: The dblx embedding file which applies the double exposure effect. Outputs Generated image**: A unique image created by the Stable Diffusion 2.x model based on the input text prompt and "dblx" embedding. Capabilities The Double-Exposure-Embedding model can generate diverse and visually striking images by applying a "double exposure" effect. This allows for creative and surreal image composition, combining elements like faces, objects, and environments in unexpected ways. The model performs well on a range of subject matter, from portraits to landscapes. What can I use it for? The Double-Exposure-Embedding model can be a powerful tool for creative projects and artistic expression. It can be used to generate unique album covers, book illustrations, concept art, and more. The distinct visual style it produces may also be useful for film, photography, and design applications that require an unconventional or dreamlike aesthetic. Things to try Experiment with different text prompts that play to the model's strengths, such as those describing people, nature, or fantastical elements. Try combining the "dblx" embedding with prompts for specific artistic styles or subjects to see how it transforms the generated images. The model's ability to blend disparate elements in a cohesive way opens up many possibilities for imaginative and evocative visual compositions.
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
👁️
SD2-768-Papercut
66
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.
Updated Invalid Date