This is a simplified guide to an AI model called Turbo-Enigma maintained by Shefa. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
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...
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