Z Image Turbo
Z Image Turbo is an image generation model from Qwen that supports LoRA adapters and source image input at $0.005 per image.
Qwen
WaveSpeedFast image generation with LoRA and image input
Z Image Turbo is an image generation model published by Qwen and served through the Wavespeed provider on MindStudio. It accepts a source image as input alongside text prompts, allowing users to guide generation from an existing visual reference with a configurable strength parameter. The model also supports LoRA adapters, enabling fine-tuned style or subject conditioning on top of the base generation pipeline.
Z Image Turbo is suited for workflows that require rapid, cost-efficient image generation at $0.005 per image, particularly when custom LoRA weights or image-to-image transformations are part of the pipeline. Users can specify output dimensions via width and height inputs, apply a negative prompt to steer results away from unwanted content, and set a seed for reproducible outputs. The model was released in November 2025 and is currently available on MindStudio.
What Z Image Turbo supports
Source Image Input
Accepts an existing image URL as a conditioning reference, with a configurable strength parameter to control how closely the output follows the source.
LoRA Support
Allows custom LoRA adapter weights to be applied at inference time, enabling fine-tuned style or subject conditioning without retraining the base model.
Negative Prompting
Accepts a negative prompt as a text input to explicitly exclude unwanted visual elements or styles from the generated output.
Custom Output Dimensions
Supports user-specified width and height values so generated images can be sized to match specific layout or aspect ratio requirements.
Reproducible Generation
Accepts a seed value as input, allowing users to reproduce identical outputs across multiple runs given the same prompt and parameters.
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Get Started FreeCommon questions about Z Image Turbo
How much does Z Image Turbo cost per image?
Z Image Turbo is priced at $0.005 per image generated.
What is the context window for Z Image Turbo?
Z Image Turbo has a context window of 10,000 tokens.
Does Z Image Turbo support image-to-image generation?
Yes. The model accepts a source image URL as an input and includes an Input Image Strength parameter that controls how much the output is influenced by the provided reference image.
Can I use custom LoRA weights with Z Image Turbo?
Yes. Z Image Turbo includes a dedicated LoRAs input, allowing you to apply custom LoRA adapters to influence the style or subject of generated images.
When was Z Image Turbo released?
Z Image Turbo was released in November 2025.
What people think about Z Image Turbo
Community discussions around Z Image Turbo are active and generally positive, with users frequently praising its prompt-following accuracy and image quality relative to other locally-runnable models. Threads comparing it to models like FLUX Dev, Qwen-Image-2512, and GLM-Image have attracted hundreds of upvotes, indicating strong interest in how it performs in direct side-by-side evaluations.
A recurring use case in community threads is LoRA training and character consistency, with users sharing comparisons of character LoRAs trained on Z Image Turbo versus other models. Some discussions also focus on post-processing pipelines that combine Z Image Turbo with upscaling tools like SeedVR2 and face enhancement, suggesting users sometimes find the base output benefits from additional refinement steps.
Z-Image-Turbo vs Qwen Image 2512
🚀 ⚡ Z-Image-Turbo-Boosted 🔥 — One-Click Ultra-Clean Images (SeedVR2 + FlashVSR + Face Upscale + Qwen-VL)
Same prompt, same seed, 6 models — Chroma vs Flux Dev vs Qwen vs Klein 4B vs Z-Image Turbo vs SDXL
Comparison: Trained the same character LoRAs on Z-Image Turbo vs Qwen 2512
[Pt2] Local Comparison: GLM-Image vs Flux.2 Dev vs Z-Image Turbo vs Qwen-Image-2512 , All BF16
Parameters & options
Up to 3 LoRAs.
Controls the strength of the transformation if an input image is provided. Higher values produce outputs more different from the input image.
Description of what to exclude from the video.
A specific value that is used to guide the 'randomness' of the generation.
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