Stability-ai
Models by this creator
stable-diffusion
107.8K
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.
Updated 5/2/2024
sdxl
49.5K
sdxl is a text-to-image generative AI model created by Stability AI, the same company behind the popular Stable Diffusion model. Like Stable Diffusion, sdxl can generate beautiful, photorealistic images from text prompts. However, sdxl has been designed to create even higher-quality images with additional capabilities such as inpainting and image refinement. Model inputs and outputs sdxl takes a variety of inputs to generate and refine images, including text prompts, existing images, and masks. The model can output multiple images per input, allowing users to explore different variations. The specific inputs and outputs are: Inputs Prompt**: A text description of the desired image Negative Prompt**: Text that specifies elements to exclude from the image Image**: An existing image to use as a starting point for img2img or inpainting Mask**: A black and white image indicating which parts of the input image should be preserved or inpainted Seed**: A random number to control the image generation process Refine**: The type of refinement to apply to the generated image Scheduler**: The algorithm used to generate the image Guidance Scale**: The strength of the text guidance during image generation Num Inference Steps**: The number of denoising steps to perform during generation Lora Scale**: The additive scale for any LoRA (Low-Rank Adaptation) weights used Refine Steps**: The number of refinement steps to perform (for certain refinement methods) High Noise Frac**: The fraction of noise to use (for certain refinement methods) Apply Watermark**: Whether to apply a watermark to the generated image Outputs One or more generated images, returned as image URLs Capabilities sdxl can generate a wide range of high-quality images from text prompts, including scenes, objects, and creative visualizations. The model also supports inpainting, where you can provide an existing image and a mask, and sdxl will fill in the masked areas with new content. Additionally, sdxl offers several refinement options to further improve the generated images. What can I use it for? sdxl is a versatile model that can be used for a variety of creative and commercial applications. For example, you could use it to: Generate concept art or illustrations for games, books, or other media Create custom product images or visualizations for e-commerce or marketing Produce unique, personalized art and design assets Experiment with different artistic styles and visual ideas Things to try One interesting aspect of sdxl is its ability to refine and enhance generated images. You can try using different refinement methods, such as the base_image_refiner or expert_ensemble_refiner, to see how they affect the output quality and style. Additionally, you can play with the Lora Scale parameter to adjust the influence of any LoRA weights used by the model.
Updated 5/2/2024
stable-diffusion-inpainting
16.5K
stable-diffusion-inpainting is a model created by Stability AI that can fill in masked parts of images using the Stable Diffusion text-to-image model. It is built on top of the Diffusers Stable Diffusion v2 model and can be used to edit and manipulate images in a variety of ways. This model is similar to other inpainting models like GFPGAN, which focuses on face restoration, and Real-ESRGAN, which can enhance the resolution of images. Model inputs and outputs The stable-diffusion-inpainting model takes in an initial image, a mask indicating which parts of the image to inpaint, and a prompt describing the desired output. It then generates a new image with the masked areas filled in based on the given prompt. The model can produce multiple output images based on a single input. Inputs Prompt**: A text description of the desired output image. Image**: The initial image to be inpainted. Mask**: A black and white image used to indicate which parts of the input image should be inpainted. Seed**: An optional random seed to control the generated output. Scheduler**: The scheduling algorithm to use during the diffusion process. Guidance Scale**: A value controlling the trade-off between following the prompt and staying close to the original image. Negative Prompt**: A text description of things to avoid in the generated image. Num Inference Steps**: The number of denoising steps to perform during the diffusion process. Disable Safety Checker**: An option to disable the safety checker, which can be useful for certain applications. Outputs Image(s)**: One or more new images with the masked areas filled in based on the provided prompt. Capabilities The stable-diffusion-inpainting model can be used to edit and manipulate images in a variety of ways. For example, you could use it to remove unwanted objects or people from a photograph, or to fill in missing parts of an image. The model can also be used to generate entirely new images based on a text prompt, similar to other text-to-image models like Kandinsky 2.2. What can I use it for? The stable-diffusion-inpainting model can be useful for a variety of applications, such as: Photo editing**: Removing unwanted elements, fixing blemishes, or enhancing photos. Creative projects**: Generating new images based on text prompts or combining elements from different images. Content generation**: Producing visuals for articles, social media posts, or other digital content. Prototype creation**: Quickly mocking up designs or visualizing concepts. Companies could potentially monetize this model by offering image editing and manipulation services, or by incorporating it into creative tools or content generation platforms. Things to try One interesting thing to try with the stable-diffusion-inpainting model is to use it to remove or replace specific elements in an image, such as a person or object. You could then generate a new image that fills in the masked area based on the prompt, creating a seamless edit. Another idea is to use the model to combine elements from different images, such as placing a castle in a forest scene or adding a dragon to a cityscape.
Updated 5/2/2024
stable-diffusion-img2img
924
The stable-diffusion-img2img model, developed by Stability AI, is an AI model that can generate new images by using an existing input image as a starting point. This model builds upon the capabilities of the Stable Diffusion model, which is a powerful text-to-image generation system. The stable-diffusion-img2img model introduces the ability to use an existing image as a starting point, allowing for the creation of image variations and transformations. Model inputs and outputs The stable-diffusion-img2img model takes several inputs, including a prompting text, an initial image, and various settings that control the output generation process. The model then generates one or more new images that reflect the input prompt and build upon the provided image. Inputs Prompt**: A text description that guides the image generation process. Image**: An initial image that the model will use as a starting point. Seed**: A random seed value that can be used to control the randomness of the output. Scheduler**: The algorithm used to control the image generation process. Guidance Scale**: A value that controls the influence of the input prompt on the output image. Negative Prompt**: A text description that specifies what the model should avoid generating. Prompt Strength**: A value that controls the balance between the input image and the input prompt. Number of Inference Steps**: The number of steps the model takes to generate the output image. Outputs Generated Images**: One or more new images that reflect the input prompt and build upon the provided image. Capabilities The stable-diffusion-img2img model can be used to generate a wide variety of image variations and transformations. By starting with an existing image, the model can create new versions of the image that incorporate different elements, styles, or visual themes. This can be useful for tasks like image editing, photo manipulation, and creative exploration. What can I use it for? The stable-diffusion-img2img model can be useful for a variety of creative and practical applications. For example, you could use it to generate variations of product images for e-commerce, create unique artwork for your personal or professional projects, or explore new visual ideas and concepts. The model's ability to work with existing images also makes it a useful tool for tasks like image inpainting, where you can fill in missing or damaged parts of an image. Things to try One interesting aspect of the stable-diffusion-img2img model is its ability to preserve the overall structure and depth information of the input image while generating new variations. This can be particularly useful for applications that require maintaining the spatial relationships and 3D characteristics of the original image, such as product visualization or architectural design. You could experiment with using different input images and prompts to see how the model handles various types of visual information and produces new, compelling results.
Updated 5/2/2024
stablelm-tuned-alpha-7b
110
StableLM-Tuned-Alpha is a suite of 3B and 7B parameter decoder-only language models developed by Stability AI. These models are built on top of the StableLM-Base-Alpha models and further fine-tuned on various chat and instruction-following datasets. The models are capable of generating coherent and context-aware text, making them useful for a variety of language-based applications. Similar models developed by Stability AI include stable-diffusion, a latent text-to-image diffusion model, and japanese-stable-diffusion-xl, a version of Stable Diffusion fine-tuned on Japanese data. Another related model is japanese-stablelm-base-alpha-7b, a 7B-parameter decoder-only language model pre-trained on a diverse collection of Japanese and English datasets. Model inputs and outputs StableLM-Tuned-Alpha is a generative language model that can be used to produce human-like text based on a given prompt. The model takes in a text prompt as input and generates a continuation of the text, with the length of the output controlled by the max_tokens parameter. Inputs Prompt**: The initial text that the model will use to generate a continuation. Max Tokens**: The maximum number of tokens (roughly equivalent to words) to generate. Temperature**: A parameter that controls the randomness of the generated text, with higher values resulting in more diverse and unpredictable output. Top P**: A parameter that controls the diversity of the generated text by limiting the model to sampling from the top P% most likely tokens. Repetition Penalty**: A parameter that discourages the model from repeating the same words or phrases in the generated text. Outputs Generated Text**: The continuation of the input prompt, generated by the model. Capabilities StableLM-Tuned-Alpha can be used for a variety of language-based tasks, such as chatbots, creative writing, and question answering. The model's fine-tuning on datasets like Alpaca, GPT4All, and ShareGPT Vicuna gives it the ability to engage in helpful and contextual conversations, as well as follow instructions and generate creative content. What can I use it for? StableLM-Tuned-Alpha can be used to build chatbot applications, where the model can engage in natural conversations with users and provide helpful information or responses. The model's versatility also allows it to be used for creative writing tasks, such as generating short stories, poems, or even comedy sketches. Additionally, the model's ability to follow instructions and answer questions makes it potentially useful for educational applications, where it could be used to help students with research, analysis, or even homework assignments. Things to try One interesting aspect of StableLM-Tuned-Alpha is its ability to write poetry and make jokes, as mentioned in the model's description. Users could experiment with prompts that encourage the model to generate creative content, such as "Write a haiku about the changing seasons" or "Tell me your best joke." Another interesting direction to explore would be the model's potential for task-following and instruction-following. Users could try giving the model more complex prompts that involve multiple steps or specific instructions, and see how well it can understand and execute those tasks.
Updated 5/2/2024