turbo-enigma
Maintainer: shefa
2.9K
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | View 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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