turbo-enigma

Maintainer: shefa

Total Score

1.8K

Last updated 5/21/2024
AI model preview image
PropertyValue
Model LinkView on Replicate
API SpecView on Replicate
Github LinkNo Github link provided
Paper LinkView on Arxiv

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Model overview

turbo-enigma is a text-to-image model developed by shefa that applies Distribution Matching Distillation to a SDXL base. It supports zero-shot identity generation, producing high-quality images in 2-5 seconds. This model can be compared to similar fast text-to-image models like sdxl-lightning-4step and uform-gen.

Model inputs and outputs

turbo-enigma takes in a text prompt and various optional parameters to control the generation process. The output is a generated image.

Inputs

  • Prompt: The text prompt to generate the image from
  • Seed: A random seed value to control the image generation (leave blank to randomize)
  • Image: An input image to guide the generation
  • Width: The desired width of the output image
  • Height: The desired height of the output image
  • Guidance Scale: The scale for classifier-free guidance
  • Num Refine Steps: The number of refinement steps to apply
  • Num Inference Steps: The number of denoising steps to apply
  • Faceswap Fast: Whether to use ONNXRUNTIME-GPU for fast faceswapping
  • Faceswap Slow: Whether to use CPU-only ONNXRUNTIME and GFPGAN for slower but higher-quality faceswapping
  • Save Embeddings: Whether to save the optimization experiment embeddings
  • Disable Safety Checker: Whether to disable the safety checker for the generated images

Outputs

  • Generated Image: The output image generated based on the provided inputs

Capabilities

turbo-enigma is capable of producing high-quality, zero-shot identity-preserving images in just 2-5 seconds using its SDXL-based architecture and Distribution Matching Distillation techniques. This makes it a fast and powerful text-to-image generation tool.

What can I use it for?

turbo-enigma can be used for a variety of applications, such as rapid prototyping, content creation, and visual ideation. Its speed and quality make it well-suited for tasks like quickly generating concept art, illustrating stories, or creating visuals for presentations and marketing materials. As with any text-to-image model, the model's capabilities and limitations should be considered when selecting it for a specific use case.

Things to try

Try experimenting with different prompts and parameter settings to see the range of outputs turbo-enigma can produce. You can also try combining it with other models like gfpgan for enhanced face restoration or clip-interrogator-turbo for more accurate image-to-text analysis.



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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