realistic-vision-v3

Maintainer: mixinmax1990

Total Score

97

Last updated 5/21/2024
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Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The realistic-vision-v3 model is a powerful text-to-image generation tool created by the AI researcher mixinmax1990. This model builds upon the previous Realistic Vision models, including realisitic-vision-v3-inpainting, realistic-vision-v5 by lucataco, and realistic-vision-v6.0-b1 by asiryan. The model is capable of generating high-quality, photorealistic images from textual descriptions.

Model inputs and outputs

The realistic-vision-v3 model takes a textual prompt as input and generates a corresponding image. The input prompt can include details about the desired subject, style, and other visual attributes. The output is a URI pointing to the generated image.

Inputs

  • Prompt: The textual description of the desired image, such as "RAW photo, a portrait photo of Katie Read in casual clothes, natural skin, 8k uhd, high quality, film grain, Fujifilm XT3".
  • Negative Prompt: A textual description of attributes to avoid in the generated image, such as "deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4, text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck".
  • Steps: The number of inference steps to perform, ranging from 0 to 100.
  • Width: The width of the output image, up to 1920 pixels.
  • Height: The height of the output image, up to 1920 pixels.

Outputs

  • URI: A URI pointing to the generated image.

Capabilities

The realistic-vision-v3 model is capable of generating highly realistic and detailed images from textual descriptions. It can capture a wide range of subjects, styles, and visual attributes, including portraits, landscapes, and still-life scenes. The model is particularly adept at rendering natural textures, such as skin, fabric, and natural environments, with a high degree of realism.

What can I use it for?

The realistic-vision-v3 model can be used for a variety of applications, such as creating stock photography, concept art, and product visualizations. It can also be used for personal creative projects, such as generating custom illustrations or fantasy scenes. Additionally, the model can be integrated into various applications and workflows, such as design tools, e-commerce platforms, and content creation platforms.

Things to try

To get the most out of the realistic-vision-v3 model, you can experiment with different prompts and negative prompts to refine the generated images. You can also try adjusting the model's parameters, such as the number of inference steps, to find the optimal balance between image quality and generation time. Additionally, you can explore the similar models created by the same maintainer, mixinmax1990, to see how they compare and complement the realistic-vision-v3 model.



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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