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Z Image Turbo LoRA: Fast Text-to-Image with Bilingual Precision

tongyi-mai/z-image/turbo/lora

Z Image Turbo LoRA by Tongyi Mai is a fast text-to-image model featuring multi-LoRA support for rapid, customized, and high-quality image generation.

LoRAs 1
Path to the LoRA model.
Scale of the LoRA model.
List of LoRAs to apply (maximum 3).
The width of the generated image in pixels. Each dimension should be between 256 and 1536.
The height of the generated image in pixels. Each dimension should be between 256 and 1536.
The random seed to use for the generation. -1 means a random seed will be used.

Introduction to Z Image Turbo LoRA

Z Image Turbo LoRA is a high-efficiency text-to-image model designed by Tongyi Mai that combines rapid generation speeds with advanced style customization via LoRA support. It enables creators to generate high-quality visuals in seconds while applying up to three distinct LoRA models for precise control over art style and character consistency. For developers, Z Image Turbo LoRA on RunComfy can be used both in the browser and via an HTTP API, so you don’t need to host or scale the model yourself.

Examples Created with Z Image Turbo LoRA

Z Image Turbo LoRA on X Platform

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Frequently Asked Questions

How fast is the Z Image Turbo Lora API compared to local hosting?

It is significantly faster and more scalable. Because Z Image Turbo Lora is engineered for low-latency generation, RunComfy’s auto-scaling GPUs allow it to handle high concurrency effortlessly. This eliminates the queue times and significant hardware overhead associated with running high-speed diffusion models locally on consumer-grade equipment.

What are the input limits for resolution and LoRAs?

You can generate images with dimensions ranging from 256px up to 1536px. The model accepts standard text prompts and allows you to stack up to three simultaneous LoRA models. This capability enables Z Image Turbo Lora to handle complex style blending tasks that typically require much heavier workflow configurations.

How do I deploy Z Image Turbo Lora to production?

Transitioning is seamless. Once you refine your parameters in the playground, simply switch to the "API" view to copy the JSON payload. You can then trigger the Z Image Turbo Lora model via standard HTTP requests (Python or cURL), ensuring your production app matches the exact quality and settings defined during testing.