stable-diffusion

Maintainer: cjwbw

Total Score

65

Last updated 5/17/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkNo paper link provided

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

Model overview

Stable Diffusion is an AI model developed by Replicate and maintained by cjwbw. It is built on top of the Diffusers Stable Diffusion v2.1 model, adding support for negative prompts and additional schedulers. This allows users to generate high-quality images from text prompts, with the ability to further refine the results by specifying negative prompts or using different scheduling algorithms. The model is available as a Cog package, making it easy to deploy and use in a variety of applications.

Model inputs and outputs

The Stable Diffusion model takes a variety of inputs to control the image generation process, including the text prompt, image dimensions, seed value, and scheduling algorithm. The model outputs one or more generated images as URI links.

Inputs

  • Prompt: The text prompt that describes the desired image.
  • Scheduler: The scheduling algorithm used to generate the image, such as PNDM or other options.
  • Init Image: An initial image that can be used as a starting point for the generation process.
  • Mask: A black and white image that can be used to control which parts of the init image are inpainted.
  • Seed: A random seed value that can be used to reproduce the same results.
  • Width and Height: The dimensions of the output image.
  • Guidance Scale: The scale used for classifier-free guidance, which controls the trade-off between the input prompt and the generated image.
  • Prompt Strength: The strength of the prompt when using an init image, ranging from 0 (no influence) to 1 (full destruction of the init image).
  • Num Inference Steps: The number of denoising steps used in the generation process.

Outputs

  • One or more generated images: The model outputs one or more images as URI links.

Capabilities

Stable Diffusion is capable of generating a wide variety of high-quality images from text prompts, with the ability to further refine the results using negative prompts or different scheduling algorithms. The model can be used to create everything from realistic scenes to abstract, surreal imagery, and its flexible input options allow for a high degree of control over the final output.

What can I use it for?

The Stable Diffusion model can be used for a variety of applications, such as creating content for marketing, design, or entertainment purposes. The ability to generate unique, custom images from text prompts can be especially useful for applications like social media, web design, and virtual worlds. Additionally, the model's support for inpainting and negative prompts opens up possibilities for tasks like image editing and content creation.

Things to try

Some interesting things to try with the Stable Diffusion model include experimenting with different prompts and negative prompts to see how they affect the generated images, using the inpainting feature to modify existing images, and exploring the various scheduling algorithms to find the one that works best for your specific use case.



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

stable-diffusion

stability-ai

Total Score

107.9K

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

stable-diffusion-v1-5

cjwbw

Total Score

34

stable-diffusion-v1-5 is a text-to-image AI model created by cjwbw. It is a variant of the popular Stable Diffusion model, which is capable of generating photo-realistic images from text prompts. This version, v1-5, includes updates and improvements over the original Stable Diffusion model. Similar models created by cjwbw include stable-diffusion-v2, stable-diffusion-2-1-unclip, and stable-diffusion-v2-inpainting. Model inputs and outputs stable-diffusion-v1-5 takes in a variety of inputs, including a text prompt, an optional initial image, a seed value, and other parameters to control the image generation process. The model then outputs one or more images based on the provided inputs. Inputs Prompt**: The text prompt that describes the desired image. Mask**: A black and white image to use as a mask for inpainting over an initial image. Seed**: A random seed value to control the image generation process. Width and Height**: The desired size of the output image. Scheduler**: The algorithm used to generate the image. Init Image**: An initial image to generate variations of. Num Outputs**: The number of images to generate. Guidance Scale**: The scale for classifier-free guidance. Prompt Strength**: The strength of the prompt when using an initial image. Num Inference Steps**: The number of denoising steps to take. Outputs The generated image(s) in the form of a URI(s). Capabilities stable-diffusion-v1-5 is capable of generating a wide range of photo-realistic images from text prompts, including scenes, objects, and even abstract concepts. The model can also be used for tasks like image inpainting, where it can fill in missing parts of an image based on a provided mask. What can I use it for? stable-diffusion-v1-5 can be used for a variety of creative and practical applications, such as: Generating unique and custom artwork for personal or commercial projects Creating illustrations, concept art, and other visual assets for games, films, and other media Experimenting with different text prompts to explore the model's capabilities and generate novel ideas Incorporating the model into existing workflows or applications to automate and enhance image creation tasks Things to try One interesting aspect of stable-diffusion-v1-5 is its ability to incorporate an initial image and use that as a starting point for generating new variations. This can be a powerful tool for creative exploration, as you can use existing artwork or photographs as a jumping-off point and see how the model interprets and transforms them.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-v2

cjwbw

Total Score

273

The stable-diffusion-v2 model is a test version of the popular Stable Diffusion model, developed by the AI research group Replicate and maintained by cjwbw. The model is built on the Diffusers library and is capable of generating high-quality, photorealistic images from text prompts. It shares similarities with other Stable Diffusion models like stable-diffusion, stable-diffusion-2-1-unclip, and stable-diffusion-v2-inpainting, but is a distinct test version with its own unique properties. Model inputs and outputs The stable-diffusion-v2 model takes in a variety of inputs to generate output images. These include: Inputs Prompt**: The text prompt that describes the desired image. This can be a detailed description or a simple phrase. Seed**: A random seed value that can be used to ensure reproducible results. Width and Height**: The desired dimensions of the output image. Init Image**: An initial image that can be used as a starting point for the generation process. Guidance Scale**: A value that controls the strength of the text-to-image guidance during the generation process. Negative Prompt**: A text prompt that describes what the model should not include in the generated image. Prompt Strength**: A value that controls the strength of the initial image's influence on the final output. Number of Inference Steps**: The number of denoising steps to perform during the generation process. Outputs Generated Images**: The model outputs one or more images that match the provided prompt and other input parameters. Capabilities The stable-diffusion-v2 model is capable of generating a wide variety of photorealistic images from text prompts. It can produce images of people, animals, landscapes, and even abstract concepts. The model's capabilities are constantly evolving, and it can be fine-tuned or combined with other models to achieve specific artistic or creative goals. What can I use it for? The stable-diffusion-v2 model can be used for a variety of applications, such as: Content Creation**: Generate images for articles, blog posts, social media, or other digital content. Concept Visualization**: Quickly visualize ideas or concepts by generating relevant images from text descriptions. Artistic Exploration**: Use the model as a creative tool to explore new artistic styles and genres. Product Design**: Generate product mockups or prototypes based on textual descriptions. Things to try With the stable-diffusion-v2 model, you can experiment with a wide range of prompts and input parameters to see how they affect the generated images. Try using different types of prompts, such as detailed descriptions, abstract concepts, or even poetry, to see the model's versatility. You can also play with the various input settings, such as the guidance scale and number of inference steps, to find the right balance for your desired output.

Read more

Updated Invalid Date

AI model preview image

stable-diffusion-high-resolution

cjwbw

Total Score

72

stable-diffusion-high-resolution is a Cog implementation of a text-to-image model that generates detailed, high-resolution images. It builds upon the popular Stable Diffusion model by applying the GOBIG mode from progrockdiffusion and using Real-ESRGAN for upscaling. This results in images with more intricate details and higher resolutions compared to the original Stable Diffusion output. Model inputs and outputs stable-diffusion-high-resolution takes a text prompt as input and generates a high-resolution image as output. The model first creates a standard Stable Diffusion image, then upscales it and applies further refinement to produce the final detailed result. Inputs Prompt**: The text description used to generate the image. Seed**: The seed value used for reproducible sampling. Scale**: The unconditional guidance scale, which controls the balance between the text prompt and the model's own prior. Steps**: The number of sampling steps used to generate the image. Width/Height**: The dimensions of the original Stable Diffusion output image, which will be doubled in the final high-resolution result. Outputs Image**: A high-resolution image generated from the input prompt. Capabilities stable-diffusion-high-resolution can generate detailed, photorealistic images from text prompts, with a higher level of visual complexity and fidelity compared to the standard Stable Diffusion model. The upscaling and refinement steps allow for the creation of intricate, high-quality images that can be useful for various creative and design applications. What can I use it for? With its ability to produce detailed, high-resolution images, stable-diffusion-high-resolution can be a powerful tool for a variety of use cases, such as digital art, concept design, product visualization, and more. The model can be particularly useful for projects that require highly realistic and visually striking imagery, such as illustrations, advertising, or game asset creation. Things to try Experiment with different types of prompts, such as detailed character descriptions, complex scenes, or imaginative landscapes, to see the level of detail and realism the model can achieve. You can also try adjusting the input parameters, like scale and steps, to fine-tune the output to your preferences.

Read more

Updated Invalid Date