Compvis
Models by this creator
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stable-diffusion-v1-4
6.3K
stable-diffusion-v1-4 is a latent text-to-image diffusion model developed by CompVis that is capable of generating photo-realistic images given any text input. It was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. Model inputs and outputs stable-diffusion-v1-4 is a text-to-image generation model. It takes text prompts as input and outputs corresponding images. Inputs Text prompts**: The model generates images based on the provided text descriptions. Outputs Images**: The model outputs photo-realistic images that match the provided text prompt. Capabilities stable-diffusion-v1-4 can generate a wide variety of images from text inputs, including scenes, objects, and even abstract concepts. The model excels at producing visually striking and detailed images that capture the essence of the textual prompt. What can I use it for? The stable-diffusion-v1-4 model can be used for a range of creative and artistic applications, such as generating illustrations, conceptual art, and product visualizations. Its text-to-image capabilities make it a powerful tool for designers, artists, and content creators looking to bring their ideas to life. However, it's important to use the model responsibly and avoid generating content that could be harmful or offensive. Things to try One interesting thing to try with stable-diffusion-v1-4 is experimenting with different text prompts to see the variety of images the model can produce. You could also try combining the model with other techniques, such as image editing or style transfer, to create unique and compelling visual content.
Updated 5/28/2024
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stable-diffusion-v-1-4-original
2.7K
stable-diffusion-v-1-4-original is a latent text-to-image diffusion model developed by CompVis that can generate photo-realistic images from text prompts. It is an improved version of the Stable-Diffusion-v1-2 model, with additional fine-tuning on the "laion-aesthetics v2 5+" dataset and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. This model is capable of generating a wide variety of images based on text descriptions, though it may struggle with more complex tasks involving compositionality or generating realistic human faces. Model inputs and outputs Inputs Text prompt**: A natural language description of the desired image to generate. Outputs Generated image**: A photo-realistic image that matches the provided text prompt. Capabilities The stable-diffusion-v-1-4-original model is capable of generating a wide range of photo-realistic images from text prompts, including scenes, objects, and even some abstract concepts. For example, it can generate images of "a photo of an astronaut riding a horse on mars", "a vibrant oil painting of a hummingbird in a garden", or "a surreal landscape with floating islands and glowing mushrooms". However, the model may struggle with more complex tasks that require fine-grained control over the composition, such as rendering a "red cube on top of a blue sphere". What can I use it for? The stable-diffusion-v-1-4-original model is intended for research purposes only, and may have applications in areas such as safe deployment of AI systems, understanding model limitations and biases, generating artwork and design, and educational or creative tools. However, the model should not be used to intentionally create or disseminate images that are harmful, offensive, or propagate stereotypes. Things to try One interesting aspect of the stable-diffusion-v-1-4-original model is its ability to generate images with a wide range of artistic styles, from photorealistic to more abstract and surreal. You could try experimenting with different prompts to see the range of styles the model can produce, or explore how the model performs on tasks that require more complex compositional reasoning.
Updated 5/28/2024
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stable-diffusion
934
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images from text prompts. This model was developed by CompVis, and improves upon previous text-to-image models through a series of training iterations. The model is available in several versions, with higher versions usually producing better image quality. stable-diffusion-v1-4 is the latest version, having been trained for 225,000 steps at 512x512 resolution on a filtered subset of the LAION-5B dataset with improved aesthetics. This version also uses 10% text conditioning dropout to improve classifier-free guidance sampling. Model inputs and outputs Stable Diffusion takes a text prompt as input and generates a corresponding photo-realistic image as output. The model encodes the text prompt using a pretrained text encoder, and then generates the image in a latent space before decoding it back to the pixel domain. Inputs Text prompt**: A natural language description of the desired image content. Outputs Image**: A photo-realistic image corresponding to the input text prompt. Capabilities Stable Diffusion is capable of generating a wide variety of photorealistic images from textual descriptions. It can create scenes, objects, characters, and more with a high level of detail and quality. The model has been found to excel at tasks like generating landscapes, portraits, and imaginative scenes. What can I use it for? Stable Diffusion can be used for a variety of creative and research applications. Artists and designers can use it to rapidly generate visual concepts and explore new ideas. Educators can incorporate it into lesson plans to spark creativity and visual thinking. Researchers can study the model's biases and limitations to better understand the capabilities and challenges of text-to-image generation. While the model has impressive capabilities, it should not be used to generate harmful or deceptive content. The Stable Diffusion v2 Model Card outlines several excluded use cases, such as generating demeaning or discriminatory content, impersonating individuals without consent, and creating misinformation. Things to try One interesting aspect of Stable Diffusion is its ability to combine disparate concepts in novel ways. Try prompting the model with unusual juxtapositions, such as "a dragon riding a bicycle" or "a penguin in a spacesuit". Explore how the model integrates these elements and the types of images it generates. Another area to experiment with is the model's treatment of scale and perspective. See how it handles requests for scenes with both small and large elements, or try varying the level of detail and realism in the prompt. The model's performance on these types of compositional challenges can provide insight into its underlying capabilities and limitations.
Updated 5/28/2024
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stable-diffusion-safety-checker
103
The stable-diffusion-safety-checker model is an image identification model developed by CompVis. It is a derivative of the CLIP model, which was introduced in the CLIP Paper. The primary intended use of this model is for identifying NSFW (not safe for work) images. It can be used in conjunction with other Stable Diffusion models, such as stable-diffusion-v1-4, to help ensure the safety and appropriateness of generated images. Model inputs and outputs Inputs Image data Outputs A classification of whether the input image contains NSFW content Capabilities The stable-diffusion-safety-checker model can be used to identify NSFW images. This can be useful for researchers studying the robustness, generalization, and other capabilities, biases, and constraints of computer vision models. What can I use it for? The primary intended use of the stable-diffusion-safety-checker model is for researchers to better understand the safety and limitations of image generation models like Stable Diffusion. It can be used to help ensure that generated images do not contain harmful or inappropriate content. Things to try Researchers can experiment with the stable-diffusion-safety-checker model by integrating it into their Stable Diffusion workflows. This can involve testing the model's performance on a variety of image types, examining its biases and limitations, and exploring ways to improve its safety and reliability.
Updated 5/28/2024
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ldm-super-resolution-4x-openimages
96
The ldm-super-resolution-4x-openimages model is a Latent Diffusion Model (LDM) for super-resolution, developed by CompVis. LDMs achieve state-of-the-art synthesis results by decomposing the image formation process into a sequential application of denoising autoencoders. Unlike previous diffusion models that operate directly in pixel space, this model applies the diffusion process in the latent space of a powerful pre-trained autoencoder. This allows for a significant reduction in computational requirements while retaining the quality and flexibility of diffusion models. The key innovation of this model is the introduction of cross-attention layers, which enable the diffusion model to generate high-resolution images conditioned on various inputs such as text or bounding boxes. This flexible conditioning mechanism sets the LDM approach apart from previous diffusion models and allows for state-of-the-art performance on tasks like image inpainting and super-resolution. Similar models include the Stable Diffusion v1-4 model, which is also a latent diffusion model capable of text-to-image generation, and the LCM_Dreamshaper_v7 model, which applies latent consistency techniques to achieve fast high-quality image synthesis. Model inputs and outputs Inputs Prompt**: A text description of the desired image, which the model uses to generate a high-resolution image. Low-resolution image**: An input low-resolution image that the model will upscale to a high-resolution version. Outputs High-resolution image**: The model outputs a high-resolution image (4x the resolution of the input) that matches the provided text prompt. Capabilities The ldm-super-resolution-4x-openimages model is capable of generating high-quality, high-resolution images (4x upscaling) based on a text prompt and a low-resolution input image. This makes it a powerful tool for tasks like image enhancement, artistic creation, and content generation. The model's ability to leverage both text and image inputs sets it apart from traditional super-resolution approaches and allows for more fine-grained control over the output. What can I use it for? The ldm-super-resolution-4x-openimages model can be used for a variety of applications that require high-quality image generation and enhancement. Some potential use cases include: Content creation**: Generate high-resolution images for use in art, design, and multimedia projects. Image editing and enhancement**: Upscale low-resolution images to higher quality while maintaining fidelity to the original content. Prototyping and visualization**: Quickly generate high-resolution images from text descriptions for use in design, product development, and other creative workflows. Educational and research purposes**: Investigate the capabilities and limitations of diffusion models for image synthesis and super-resolution tasks. Things to try One interesting aspect of the ldm-super-resolution-4x-openimages model is its ability to leverage both text and image inputs. Try experimenting with different combinations of prompts and low-resolution images to see how the model responds. For example, you could provide a low-resolution image of a landscape and a prompt describing a specific change or addition, such as "a beautiful sunset over the mountains". The model should then generate a high-resolution version of the landscape with the requested modification. Another area to explore is the model's performance on more complex or abstract prompts. While the model excels at generating realistic, photographic images, it will be interesting to see how it handles more conceptual or imaginative prompts, such as "a futuristic cyberpunk city made of neon lights" or "a surreal, dreamlike forest filled with glowing mushrooms".
Updated 5/28/2024
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stable-diffusion-v1-1
59
stable-diffusion-v1-1 is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. It was trained on 237,000 steps at resolution 256x256 on laion2B-en, followed by 194,000 steps at resolution 512x512 on laion-high-resolution. The model is intended to be used with the Diffusers library. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (CLIP ViT-L/14) as suggested in the Imagen paper. Similar models like stable-diffusion-v1-4 have been trained for longer and are usually better in terms of image generation quality. The stable-diffusion model provides an overview of the various Stable Diffusion model checkpoints. Model inputs and outputs Inputs Text prompt**: A text description of the desired image to generate. Outputs Generated image**: A photo-realistic image matching the input text prompt. Capabilities stable-diffusion-v1-1 can generate a wide variety of images from text prompts, including realistic scenes, abstract art, and imaginative creations. For example, it can create images of "a photo of an astronaut riding a horse on mars", "a painting of a unicorn in a fantasy landscape", or "a surreal portrait of a robot musician". What can I use it for? The stable-diffusion-v1-1 model is intended for research purposes only. Possible use cases include: Safe deployment of models that can generate potentially harmful content Probing and understanding the limitations and biases of generative models Generation of artworks and use in design and other creative processes Applications in educational or creative tools Research on generative models The model should not be used to intentionally create or disseminate images that are disturbing, offensive, or propagate harmful stereotypes. Things to try Some interesting things to try with stable-diffusion-v1-1 include: Experimenting with different text prompts to see the range of images the model can generate Trying out different noise schedulers to see how they affect the output Exploring the model's capabilities and limitations, such as its ability to render text or handle complex compositions Investigating ways to mitigate potential biases and harmful outputs from the model
Updated 5/27/2024