Shefa

turbo-enigma

shefa

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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.

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