stable-diffusion-2-inpainting

Maintainer: stabilityai

Total Score

412

Last updated 5/27/2024

๐ŸŒ

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The stable-diffusion-2-inpainting model is a text-to-image diffusion model that can be used to generate and modify images. It is a continuation of the stable-diffusion-2-base model, trained for an additional 200k steps. The model follows the mask-generation strategy presented in LAMA, which, in combination with the latent VAE representations of the masked image, are used as additional conditioning. This allows the model to generate images that are consistent with the provided input, while also allowing for creative modifications.

Similar models include the stable-diffusion-2 and stable-diffusion-2-1-base models, which also build upon the base Stable Diffusion model with various improvements and training strategies.

Model inputs and outputs

Inputs

  • Text prompt: A text description of the desired image, which the model uses to generate the output image.
  • Mask image: An optional input image, with a mask indicating the regions that should be modified or inpainted.

Outputs

  • Generated image: The output image, generated based on the provided text prompt and (optionally) the mask image.

Capabilities

The stable-diffusion-2-inpainting model can be used to generate and modify images based on text prompts. It is particularly well-suited for tasks that involve inpainting or image editing, where the user can provide a partially masked image and the model will generate the missing regions based on the text prompt. This can be useful for a variety of applications, such as object removal, image restoration, and creative visual effects.

What can I use it for?

The stable-diffusion-2-inpainting model can be used for a variety of research and creative applications. Some potential use cases include:

  • Creative image generation: Use the model to generate unique and visually striking images based on text prompts, for use in art, design, or other creative projects.
  • Image editing and restoration: Leverage the model's inpainting capabilities to remove or modify elements of existing images, or to restore damaged or incomplete images.
  • Educational and research purposes: Explore the model's capabilities, limitations, and biases to gain insights into the field of generative AI and text-to-image modeling.

Things to try

One interesting aspect of the stable-diffusion-2-inpainting model is its ability to blend and integrate new visual elements into an existing image based on the provided text prompt. For example, you could try providing a partially masked image of a landscape and a prompt like "a majestic unicorn standing in the field", and the model would generate the missing regions in a way that seamlessly incorporates the unicorn into the scene.

Another interesting experiment would be to compare the outputs of the stable-diffusion-2-inpainting model to those of the related stable-diffusion-2 and stable-diffusion-2-1-base models, to see how the additional inpainting training affects the model's performance and the types of images it generates.



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

โš™๏ธ

stable-diffusion-2-1

stabilityai

Total Score

3.7K

The stable-diffusion-2-1 model is a text-to-image generation model developed by Stability AI. It is a fine-tuned version of the stable-diffusion-2 model, with an additional 55k steps on the same dataset and then a further 155k steps with adjusted "unsafety" settings. Similar models include the stable-diffusion-2-1-base which fine-tunes the stable-diffusion-2-base model. Model inputs and outputs The stable-diffusion-2-1 model is a diffusion-based text-to-image generation model that takes text prompts as input and generates corresponding images as output. The text prompts are encoded using a fixed, pre-trained text encoder, and the generated images are 768x768 pixels in size. Inputs Text prompt**: A natural language description of the desired image. Outputs Image**: A 768x768 pixel image generated based on the input text prompt. Capabilities The stable-diffusion-2-1 model can generate a wide variety of images based on text prompts, from realistic scenes to fantastical creations. It demonstrates impressive capabilities in areas like generating detailed and complex images, rendering different styles and artistic mediums, and combining diverse visual elements. However, the model still has limitations in terms of generating fully photorealistic images, rendering legible text, and handling more complex compositional tasks. What can I use it for? The stable-diffusion-2-1 model is intended for research purposes only. Possible use cases include generating artworks and designs, creating educational or creative tools, and probing the limitations and biases of generative models. The model should not be used to intentionally create or disseminate images that could be harmful, offensive, or propagate stereotypes. Things to try One interesting aspect of the stable-diffusion-2-1 model is its ability to generate images with different styles and artistic mediums based on the text prompt. For example, you could try prompts that combine realistic elements with more fantastical or stylized components, or experiment with prompts that evoke specific artistic movements or genres. The model's performance may also vary depending on the language and cultural context of the prompt, so exploring prompts in different languages could yield interesting results.

Read more

Updated Invalid Date

๐Ÿ‘จโ€๐Ÿซ

stable-diffusion-2

stabilityai

Total Score

1.8K

The stable-diffusion-2 model is a diffusion-based text-to-image generation model developed by Stability AI. It is an improved version of the original Stable Diffusion model, trained for 150k steps using a v-objective on the same dataset as the base model. The model is capable of generating high-resolution images (768x768) from text prompts, and can be used with the stablediffusion repository or the diffusers library. Similar models include the SDXL-Turbo and Stable Cascade models, which are also developed by Stability AI. The SDXL-Turbo model is a distilled version of the SDXL 1.0 model, optimized for real-time synthesis, while the Stable Cascade model uses a novel multi-stage architecture to achieve high-quality image generation with a smaller latent space. Model inputs and outputs Inputs Text prompt**: A text description of the desired image, which the model uses to generate the corresponding image. Outputs Image**: The generated image based on the input text prompt, with a resolution of 768x768 pixels. Capabilities The stable-diffusion-2 model can be used to generate a wide variety of images from text prompts, including photorealistic scenes, imaginative concepts, and abstract compositions. The model has been trained on a large and diverse dataset, allowing it to handle a broad range of subject matter and styles. Some example use cases for the model include: Creating original artwork and illustrations Generating concept art for games, films, or other media Experimenting with different visual styles and aesthetics Assisting with visual brainstorming and ideation What can I use it for? The stable-diffusion-2 model is intended for both non-commercial and commercial usage. For non-commercial or research purposes, you can use the model under the CreativeML Open RAIL++-M License. Possible research areas and tasks include: Research on generative models Research on the impact of real-time generative models Probing and understanding the limitations and biases of generative models Generation of artworks and use in design and other artistic processes Applications in educational or creative tools For commercial use, please refer to https://stability.ai/membership. Things to try One interesting aspect of the stable-diffusion-2 model is its ability to generate highly detailed and photorealistic images, even for complex scenes and concepts. Try experimenting with detailed prompts that describe intricate settings, characters, or objects, and see the model's ability to bring those visions to life. Additionally, you can explore the model's versatility by generating images in a variety of styles, from realism to surrealism, impressionism to expressionism. Experiment with different artistic styles and see how the model interprets and renders them.

Read more

Updated Invalid Date

โ†—๏ธ

stable-diffusion-2-base

stabilityai

Total Score

329

The stable-diffusion-2-base model is a diffusion-based text-to-image generation model developed by Stability AI. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H). The model was trained from scratch on a subset of LAION-5B filtered for explicit pornographic material, using the LAION-NSFW classifier. This base model can be used to generate and modify images based on text prompts. Similar models include the stable-diffusion-2-1-base and the stable-diffusion-2 models, which build upon this base model with additional training and modifications. Model inputs and outputs Inputs Text prompt**: A natural language description of the desired image. Outputs Image**: The generated image based on the provided text prompt. Capabilities The stable-diffusion-2-base model can generate a wide range of photorealistic images from text prompts. For example, it can create images of landscapes, animals, people, and fantastical scenes. However, the model does have some limitations, such as difficulty rendering legible text and accurately depicting complex compositions. What can I use it for? The stable-diffusion-2-base model is intended for research purposes only. Potential use cases include the generation of artworks and designs, the creation of educational or creative tools, and the study of the limitations and biases of generative models. The model should not be used to intentionally create or disseminate images that are harmful or offensive. Things to try One interesting aspect of the stable-diffusion-2-base model is its ability to generate high-resolution images up to 512x512 pixels. Experimenting with different text prompts and exploring the model's capabilities at this resolution can yield some fascinating results. Additionally, comparing the outputs of this model to those of similar models, such as stable-diffusion-2-1-base and stable-diffusion-2, can provide insights into the unique strengths and limitations of each model.

Read more

Updated Invalid Date

๐Ÿงช

stable-diffusion-2-1-base

stabilityai

Total Score

583

The stable-diffusion-2-1-base model is a diffusion-based text-to-image generation model developed by Stability AI. It is a fine-tuned version of the stable-diffusion-2-base model, taking an additional 220k training steps with a punsafe=0.98 on the same dataset. This model can be used to generate and modify images based on text prompts, leveraging a fixed, pretrained text encoder (OpenCLIP-ViT/H). Model inputs and outputs The stable-diffusion-2-1-base model takes text prompts as input and generates corresponding images as output. The model can be used with the stablediffusion repository or the diffusers library. Inputs Text prompt**: A natural language description of the desired image. Outputs Generated image**: An image corresponding to the input text prompt, generated by the model. Capabilities The stable-diffusion-2-1-base model is capable of generating a wide variety of photorealistic images based on text prompts. It can create images of people, animals, landscapes, and more. The model has been fine-tuned to improve the quality and safety of the generated images compared to the original stable-diffusion-2-base model. What can I use it for? The stable-diffusion-2-1-base model is intended for research purposes, such as: Generating artworks and using them in design or other creative processes Developing educational or creative tools that leverage text-to-image generation Researching the capabilities and limitations of generative models Probing and understanding the biases of the model The model should not be used to intentionally create or disseminate images that could be harmful or offensive to people. Things to try One interesting aspect of the stable-diffusion-2-1-base model is its ability to generate diverse and detailed images from a wide range of text prompts. Try experimenting with different types of prompts, such as describing specific scenes, objects, or characters, and see the variety of outputs the model can produce. You can also try using the model in combination with other tools or techniques, like image-to-image generation, to explore its versatility and potential applications.

Read more

Updated Invalid Date