openjourney

Maintainer: prompthero

Total Score

11.7K

Last updated 5/27/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkView on Arxiv

Get summaries of the top AI models delivered straight to your inbox:

Model overview

openjourney is a Stable Diffusion model fine-tuned on Midjourney v4 images by the Replicate creator prompthero. It is similar to other Stable Diffusion models like stable-diffusion, stable-diffusion-inpainting, and the midjourney-style concept, which can produce images in a Midjourney-like style.

Model inputs and outputs

openjourney takes in a text prompt, an optional image, and various parameters like the image size, number of outputs, and more. It then generates one or more images that match the provided prompt. The outputs are high-quality, photorealistic images.

Inputs

  • Prompt: The text prompt describing the desired image
  • Image: An optional image to use as guidance
  • Width/Height: The desired size of the output image
  • Seed: A random seed to control image generation
  • Scheduler: The algorithm used for image generation
  • Guidance Scale: The strength of the text guidance
  • Negative Prompt: Aspects to avoid in the output image

Outputs

  • Image(s): One or more generated images matching the input prompt

Capabilities

openjourney can generate a wide variety of photorealistic images from text prompts, with a focus on Midjourney-style aesthetics. It can handle prompts related to scenes, objects, characters, and more, and can produce highly detailed and imaginative outputs.

What can I use it for?

You can use openjourney to create unique, Midjourney-inspired artwork and illustrations for a variety of applications, such as:

  • Generating concept art or character designs for games, films, or books
  • Creating custom stock images or graphics for websites, social media, and marketing materials
  • Exploring new ideas and visual concepts through freeform experimentation with prompts

Things to try

Some interesting things to try with openjourney include:

  • Experimenting with different prompt styles and structures to see how they affect the output
  • Combining openjourney with other Stable Diffusion-based models like qrcode-stable-diffusion or stable-diffusion-x4-upscaler to create unique visual effects
  • Exploring the limits of the model's capabilities by pushing the boundaries of what can be generated with text prompts


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

AI model preview image

openjourney-v4

prompthero

Total Score

140

openjourney-v4 is a Stable Diffusion 1.5 model fine-tuned by PromptHero on over 124,000 Midjourney v4 images. It is an extension of the openjourney model, which was also trained by PromptHero on Midjourney v4 images. The openjourney-v4 model aims to produce high-quality, Midjourney-style artwork from text prompts. Model inputs and outputs The openjourney-v4 model takes in a variety of inputs, including a text prompt, an optional starting image, image dimensions, and various other parameters to control the output image. The outputs are one or more images generated based on the provided inputs. Inputs Prompt**: The text prompt describing the desired image Image**: An optional starting image from which to generate variations Width/Height**: The desired dimensions of the output image Seed**: A random seed to control the image generation Scheduler**: The denoising scheduler to use Num Outputs**: The number of images to generate Guidance Scale**: The scale for classifier-free guidance Negative Prompt**: Text to avoid in the output image Prompt Strength**: The strength of the prompt when using an init image Num Inference Steps**: The number of denoising steps Outputs Image(s)**: One or more generated images, returned as a list of image URLs Capabilities The openjourney-v4 model can generate a wide variety of Midjourney-style images from text prompts, ranging from fantastical landscapes and creatures to realistic portraits and scenes. The model is particularly skilled at producing detailed, imaginative artwork with a distinct visual style. What can I use it for? The openjourney-v4 model can be used for a variety of creative and artistic applications, such as conceptual art, game asset creation, and illustration. It could also be used to quickly generate ideas or concepts for creative projects. The model's ability to produce high-quality, visually striking images makes it a valuable tool for designers, artists, and content creators. Things to try Experiment with different types of prompts, from specific and descriptive to more open-ended and abstract. Try combining the openjourney-v4 model with other Stable Diffusion-based models, such as openjourney-lora or dreamshaper, to see how the results can be further refined or enhanced.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion

stability-ai

Total Score

108.0K

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.

Read more

Updated Invalid Date

AI model preview image

poolsuite-diffusion

prompthero

Total Score

6

The poolsuite-diffusion model is a fine-tuned Dreambooth model that aims to reproduce the "Poolsuite" aesthetic. Dreambooth is a technique for training custom Stable Diffusion models on a small set of images, similar to dreambooth and analog-diffusion. The model was created by prompthero. Model inputs and outputs The poolsuite-diffusion model takes a text prompt as input and generates one or more images that match the provided prompt. The key inputs are: Inputs Prompt**: The text prompt describing the desired image Width/Height**: The desired dimensions of the output image Seed**: A random seed to control image generation (leave blank to randomize) Num Outputs**: The number of images to generate Guidance Scale**: The degree of influence the text prompt has on the generated image Num Inference Steps**: The number of denoising steps to take during generation Outputs Output Images**: One or more images generated based on the provided inputs Capabilities The poolsuite-diffusion model can generate images with a distinct "Poolsuite" visual style, which is characterized by vibrant colors, retro aesthetics, and a relaxed, summery vibe. The model is especially adept at producing images of vintage cars, landscapes, and poolside scenes that capture this specific aesthetic. What can I use it for? You can use the poolsuite-diffusion model to generate images for a variety of creative projects, such as album covers, social media content, or marketing materials with a distinctive retro-inspired look and feel. The model's ability to capture the "Poolsuite" aesthetic makes it well-suited for projects that aim to evoke a sense of nostalgia or relaxation. Things to try Try experimenting with different prompts that incorporate keywords or concepts related to vintage cars, California landscapes, or poolside settings. You can also play with the various input parameters, such as the guidance scale and number of inference steps, to see how they affect the final output and the degree of "Poolsuite" fidelity.

Read more

Updated Invalid Date

AI model preview image

openjourney-img2img

mbentley124

Total Score

81

The openjourney-img2img model is an AI model developed by mbentley124 that can be used for image-to-image generation tasks. It is built on top of the Stable Diffusion model, which is a powerful text-to-image diffusion model capable of generating high-quality, photo-realistic images from text prompts. The openjourney-img2img model adds the ability to use an existing image as a starting point for the generation process, allowing for more fine-grained control and creative exploration. Similar models include the openjourney-v4, openjourney, and lora_openjourney_v4 models, all of which are based on the Stable Diffusion architecture and trained on the Midjourney dataset. The stable-diffusion model itself is also a relevant and powerful text-to-image model, while the controlnet_2-1 model adds additional control and conditioning capabilities. Model inputs and outputs The openjourney-img2img model takes two main inputs: an image that will be used as the starting point for the generation process, and a text prompt that will guide the image generation. The model also allows for adjusting the strength of the image transformation, the guidance scale, and the number of inference steps and output images. Inputs Image**: The image that will be used as the starting point for the generation process. Prompt**: The text prompt that will guide the image generation. Strength**: Conceptually, indicates how much to transform the reference image. The image will be used as a starting point, adding more noise to it the larger the strength. A value of 1 essentially ignores the image. Guidance Scale**: Higher guidance scale encourages the generation of images that are closely linked to the text prompt, usually at the expense of lower image quality. Negative Prompt**: The prompt not to guide the image generation. Num Inference Steps**: The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. Num Images Per Prompt**: The number of images to generate. Outputs Array of Image URLs**: The generated image(s) in the form of an array of image URLs. Capabilities The openjourney-img2img model can be used to generate highly detailed and visually striking images by combining an existing image with a text prompt. This allows for a wide range of creative applications, from enhancing and manipulating existing artworks to generating entirely new images based on a specific concept or aesthetic. The model's ability to preserve the structure and content of the input image while incorporating the guidance of the text prompt makes it a powerful tool for artists, designers, and anyone looking to explore the boundaries of AI-generated imagery. What can I use it for? The openjourney-img2img model can be used for a variety of creative and commercial applications. Artists and designers can use it to enhance existing artworks, explore new visual directions, and generate unique images for various projects. Businesses can leverage the model to create visually striking marketing materials, product renderings, and other visual assets. Hobbyists and enthusiasts can experiment with the model to generate custom illustrations, character designs, and other imaginative content. Things to try One interesting capability of the openjourney-img2img model is its ability to generate highly detailed and visually striking images by combining an existing image with a text prompt. For example, you could start with a simple landscape photograph and use the model to transform it into a fantastical, otherworldly scene by guiding the generation with a prompt like "a magical forest with glowing mushrooms and mystical creatures". The model's ability to preserve the structure and content of the input image while incorporating the guidance of the text prompt makes it a powerful tool for creative exploration and experimentation.

Read more

Updated Invalid Date