retrieval-augmented-diffusion

Maintainer: afiaka87

Total Score

38

Last updated 6/12/2024
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Paper LinkView on Arxiv

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Model overview

The retrieval-augmented-diffusion model, created by Replicate user afiaka87, is a text-to-image generation model that can produce 768px images from text prompts. This model builds upon the CompVis "latent diffusion" approach, which uses a diffusion model to generate images in a learned latent space. By incorporating a retrieval component, the retrieval-augmented-diffusion model can leverage visual examples from databases like OpenImages and ArtBench to guide the generation process and produce more targeted results.

Similar models include stable-diffusion, a powerful text-to-image diffusion model, and sd-aesthetic-guidance, which uses aesthetic CLIP embeddings to make stable diffusion outputs more visually pleasing. The latent-diffusion-text2img and glid-3-xl models also leverage latent diffusion for text-to-image and inpainting tasks, respectively.

Model inputs and outputs

The retrieval-augmented-diffusion model takes a text prompt as input and generates a 768x768 pixel image as output. The model can be conditioned on the text prompt alone, or it can additionally leverage visual examples retrieved from a database to guide the generation process.

Inputs

  • Prompts: A text prompt or set of prompts separated by | that describe the desired image.
  • Image Prompt: An optional image URL that can be used to generate variations of an existing image.
  • Database Name: The name of the database to use for visual retrieval, such as "openimages" or various subsets of the ArtBench dataset.
  • Num Database Results: The number of visually similar examples to retrieve from the database (up to 20).

Outputs

  • Generated Images: The model outputs one or more 768x768 pixel images based on the provided text prompt and any retrieved visual examples.

Capabilities

The retrieval-augmented-diffusion model is capable of generating a wide variety of photorealistic and artistic images from text prompts. The retrieval component allows the model to leverage relevant visual examples to produce more targeted and coherent results compared to a standard text-to-image diffusion model.

For example, a prompt like "a happy pineapple" can produce whimsical, surreal images of anthropomorphized pineapples when using the ArtBench databases, or more realistic depictions of pineapples when using the OpenImages database.

What can I use it for?

The retrieval-augmented-diffusion model can be used for a variety of creative and generative tasks, such as:

  • Generating unique, high-quality images to illustrate articles, blog posts, or social media content
  • Designing concept art, product mockups, or other visualizations based on textual descriptions
  • Producing custom artwork or marketing materials for clients or personal projects
  • Experimenting with different artistic styles and visual interpretations of text prompts

By leveraging the retrieval component, users can tailor the generated images to their specific needs and aesthetic preferences.

Things to try

One interesting aspect of the retrieval-augmented-diffusion model is its ability to generate images at resolutions higher than the 768x768 that it was trained on. While this can produce some interesting results, it's important to note that the model's controllability and coherence may be reduced at these higher resolutions.

Another interesting technique to explore is the use of the PLMS sampling method, which can provide a speedup in generation time while maintaining good image quality. Adjusting the ddim_eta parameter can also be used to fine-tune the balance between sample quality and diversity.

Overall, the retrieval-augmented-diffusion model offers a powerful and versatile tool for generating high-quality, visually-grounded images from text prompts. By experimenting with the various input parameters and leveraging the retrieval capabilities, users can unlock a wide range of creative possibilities.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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