Double-Exposure-Diffusion
Maintainer: joachimsallstrom
167
💬
| 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 Double-Exposure-Diffusion model is a version 2 of the Double Exposure Diffusion model, trained specifically on images of people and a few animals. It can be used to generate double exposure style images by using the token
Similar models include the Double-Exposure-Embedding model, which provides a pre-trained embedding for Stable Diffusion 2.x to achieve the double exposure effect. The Stable Diffusion v2 model is another relevant AI model, which can be used for a wide range of text-to-image generation tasks.
Model inputs and outputs
Inputs
- Prompt: The text prompt provided to the model, which can include the tokens
dublex style ordublex to trigger the double exposure effect. - Sampler, CFG scale, Seed, Size: The various parameters used to control the image generation process.
Outputs
- Images: The model generates unique double exposure style images based on the provided prompt and parameters.
Capabilities
The Double-Exposure-Diffusion model can create visually striking double exposure style images by blending multiple elements into a single image. Examples include a man with a galaxy background, an image of Emma Stone with a galaxy effect, and a portrait of young Elijah Wood as Frodo with a dark nature background.
What can I use it for?
The Double-Exposure-Diffusion model can be used to create unique and eye-catching images for a variety of applications, such as:
- Graphic design and photo editing
- Social media content creation
- Artistic and creative projects
- Advertising and marketing materials
Since the model is open-access and available under a CreativeML OpenRAIL-M license, you can use it commercially and/or as a service, as long as you follow the terms of the license.
Things to try
One interesting aspect of the Double-Exposure-Diffusion model is its ability to blend various elements, such as portraits, landscapes, and abstract patterns, into a single cohesive image. You can experiment with different prompts and parameter settings to see how the model combines these elements in unique and creative ways.
Additionally, you can try using the model to generate double exposure style images of different subjects, such as animals, buildings, or scenes from nature, to explore its versatility.
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
🏅
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
🐍
epic-diffusion
127
epic-diffusion is a general-purpose text-to-image model based on Stable Diffusion 1.x, intended to replace the official SD releases as a default model. It is focused on providing high-quality output in a wide range of styles, with support for NSFW content. The model is a heavily calibrated merge of several SD 1.x models, including Stable Diffusion 1.4, Stable Diffusion 1.5, Analog Diffusion, Wavy Diffusion, Openjourney Diffusion, Samdoesarts Ultramerge, postapocalypse, Elldreth's Dream, Inkpunk Diffusion, Arcane Diffusion, and Van Gogh Diffusion. The maintainer, johnslegers, has blended and reblended these models multiple times to achieve the desired quality and consistency. Similar models include loliDiffusion, a model specialized for generating loli characters, EimisAnimeDiffusion_1.0v, a model trained on high-quality anime images, and mo-di-diffusion, a fine-tuned Stable Diffusion 1.5 model trained on screenshots from a popular animation studio. Model inputs and outputs Inputs Text prompt**: A natural language description of the desired image, such as "scarlett johansson, in the style of Wes Anderson, highly detailed, unreal engine, octane render, 8k". Outputs Image**: A generated image that matches the text prompt, such as a highly detailed portrait of Scarlett Johansson in the style of Wes Anderson. Capabilities epic-diffusion can generate a wide variety of high-quality images based on text prompts. The model's diverse training data and extensive fine-tuning allows it to produce outputs in many artistic styles, from realism to surrealism, and across a range of subject matter, from portraits to landscapes. The model's support for NSFW content also makes it suitable for more mature or adult-oriented use cases. What can I use it for? epic-diffusion can be used for a variety of creative and commercial applications, such as: Generating concept art, illustrations, or digital paintings for use in games, films, or other media Producing personalized artwork or creative content for clients or customers Experimenting with different artistic styles and techniques through text-to-image generation Supplementing or enhancing human-created artwork and design work The model's open access and commercial usage allowance under the CreativeML OpenRAIL-M license make it a versatile tool for both individual creators and businesses. Things to try One interesting aspect of epic-diffusion is its ability to blend and incorporate various existing Stable Diffusion models, resulting in a unique and flexible model that can adapt to a wide range of prompts and use cases. Experimenting with different prompt styles, from highly detailed and technical to more abstract or conceptual, can help users discover the model's full potential and uncover new creative possibilities. Additionally, leveraging the model's support for NSFW content could open up opportunities for more mature or adult-oriented applications, while still adhering to the usage guidelines specified in the CreativeML OpenRAIL-M license.
Updated Invalid Date
➖
Van-Gogh-diffusion
277
The Van-Gogh-diffusion model is a fine-tuned Stable Diffusion model trained on screenshots from the film Loving Vincent. This allows the model to generate images in a distinct artistic style reminiscent of Van Gogh's iconic paintings. Similar models like the Vintedois (22h) Diffusion and Inkpunk Diffusion also leverage fine-tuning to capture unique visual styles, though with different influences. Model inputs and outputs The Van-Gogh-diffusion model takes text prompts as input and generates corresponding images in the Van Gogh style. The maintainer, dallinmackay, has found that using the token lvngvncnt at the beginning of prompts works best to capture the desired artistic look. Inputs Text prompts describing the desired image, with the lvngvncnt token at the start Outputs Images generated in the Van Gogh painting style based on the input prompt Capabilities The Van-Gogh-diffusion model is capable of generating a wide range of image types, from portraits and characters to landscapes and scenes, all with the distinct visual flair of Van Gogh's brush strokes and color palette. The model can produce highly detailed and realistic-looking outputs while maintaining the impressionistic quality of the source material. What can I use it for? This model could be useful for any creative projects or applications where you want to incorporate the iconic Van Gogh aesthetic, such as: Generating artwork and illustrations for books, games, or other media Creating unique social media content or digital art pieces Experimenting with AI-generated art in various styles and mediums The open-source nature of the model also makes it suitable for both personal and commercial use, within the guidelines of the CreativeML OpenRAIL-M license. Things to try One interesting aspect of the Van-Gogh-diffusion model is its ability to handle a wide range of prompts and subject matter while maintaining the distinctive Van Gogh style. Try experimenting with different types of scenes, characters, and settings to see the diverse range of outputs the model can produce. You can also explore the impact of adjusting the sampling parameters, such as the number of steps and the CFG scale, to further refine the generated images.
Updated Invalid Date
↗️
epic-diffusion-v1.1
47
epic-diffusion-v1.1 is a general purpose text-to-image AI model that aims to provide high-quality outputs in a wide range of different styles. It is a heavily calibrated merge of various Stable Diffusion models, including SD 1.4, SD 1.5, Analog Diffusion, Wavy Diffusion, Redshift Diffusion, and many others. According to the maintainer johnslegers, the goal was to create a model that can serve as a default replacement for the official Stable Diffusion releases, offering improved quality and consistency. Similar models include epic-diffusion, which is an earlier version of this model, and epiCRealism, which also aims to provide high-quality, realistic outputs. Model inputs and outputs Inputs Text prompts that describe the desired image Outputs High-quality, photorealistic images generated based on the provided text prompts Capabilities epic-diffusion-v1.1 is capable of generating a wide variety of detailed, realistic images across many different styles and subject matter. The examples provided show its ability to create portraits, landscapes, fantasy scenes, and more, with a high level of visual fidelity. It appears to handle a diverse set of prompts well, from detailed character descriptions to abstract concepts. What can I use it for? With its broad capabilities, epic-diffusion-v1.1 could be useful for a variety of applications, such as: Conceptual art and design: Generate visuals for illustrations, album covers, book covers, and other creative projects. Visualization and prototyping: Quickly create visual representations of ideas, products, or scenes to aid in the design process. Educational and research purposes: Use the model to generate images for presentations, publications, or to explore the potential of AI-generated visuals. As the maintainer notes, the model is open access and available for commercial use, with the only restriction being that you cannot use it to deliberately produce illegal or harmful content. Things to try One interesting aspect of epic-diffusion-v1.1 is its ability to handle a wide range of visual styles, from photorealistic to more stylized or abstract. Try experimenting with prompts that blend different artistic influences, such as combining classic painting techniques with modern digital art, or blending fantasy and realism. The model's versatility allows for a lot of creative exploration. Another intriguing possibility is to fine-tune the model using DreamBooth to create personalized avatars or characters. The maintainer's mention of using some dreambooth models suggests this could be a fruitful avenue to explore.
Updated Invalid Date