ControlNet Guidance
Accepts a source image URL to provide structural or compositional control over the generated output, enabling guided image generation from a reference.
Qwen
Z Image Turbo Controlnet
Z Image Turbo Controlnet is an image generation model developed by Alibaba's Tongyi-MAI lab, built on a single-stream diffusion transformer architecture with 6 billion parameters. It uses a few-step distillation approach (the Turbo variant) to accelerate inference while preserving output quality, and incorporates ControlNet to allow structural guidance from a source image. The model was trained with a multi-level captioning system and a data infrastructure that includes a Cross-modal Vector Engine and World Knowledge Topological Graph to improve semantic alignment between prompts and outputs. This model is well-suited for workflows that require both speed and structural control over generated images, such as guided creative generation, image editing pipelines, and rapid prototyping. It accepts image URLs as source inputs alongside configurable parameters including seed values for reproducibility. An RLHF alignment pipeline using DPO and GRPO stages was applied to bring outputs closer to human aesthetic preferences, and a built-in prompt enhancer with reasoning chain helps produce better results from short or underspecified prompts.
Unknown
10,000 context
N/A output
ControlNet Guidance
Turbo Inference
Text-to-Image Generation
Seed-Based Reproducibility
RLHF Alignment
Configurable Generation Parameters
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Qwen
Input Context Window
The number of tokens supported by the input context window.
10,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
N/A
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Unknown
Knowledge Cut-off Date
When the model's knowledge was last updated.
Unknown
API Providers
The providers that offer this model. This is not an exhaustive list.
Qwen
Modalities
Types of data this model can process.
Image
What is Z Image Turbo Controlnet
A fuller summary of positioning, capabilities, and source-specific details for Z Image Turbo Controlnet.
Z Image Turbo Controlnet is an image generation model developed by Alibaba's Tongyi-MAI lab, built on a single-stream diffusion transformer architecture with 6 billion parameters. It uses a few-step distillation approach (the Turbo variant) to accelerate inference while preserving output quality, and incorporates ControlNet to allow structural guidance from a source image. The model was trained with a multi-level captioning system and a data infrastructure that includes a Cross-modal Vector Engine and World Knowledge Topological Graph to improve semantic alignment between prompts and outputs.
This model is well-suited for workflows that require both speed and structural control over generated images, such as guided creative generation, image editing pipelines, and rapid prototyping. It accepts image URLs as source inputs alongside configurable parameters including seed values for reproducibility. An RLHF alignment pipeline using DPO and GRPO stages was applied to bring outputs closer to human aesthetic preferences, and a built-in prompt enhancer with reasoning chain helps produce better results from short or underspecified prompts.
Capabilities
What Z Image Turbo Controlnet supports
Turbo Inference
Uses few-step distillation to reduce the number of diffusion steps required at inference time, producing results faster without significant quality degradation.
Text-to-Image Generation
Generates images from text prompts using a 6-billion-parameter single-stream diffusion transformer, with a built-in prompt enhancer that applies a reasoning chain to improve results from short inputs.
Seed-Based Reproducibility
Accepts a numeric seed input so that generation results can be reproduced exactly across multiple runs with the same parameters.
RLHF Alignment
Trained with a reinforcement learning from human feedback pipeline using DPO and GRPO stages to align generated images with human aesthetic preferences.
Configurable Generation Parameters
Exposes multiple select-type inputs allowing users to configure generation options such as style or quality mode directly within the request.
Pricing for Z Image Turbo Controlnet
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
N/A
Per million tokens
Output tokens
N/A
Per million tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Qwen
Configuration & Parameters
The configurable options currently documented for this model.
Reference Image
Image URLReference image URL for ControlNet to extract structural guidance from.
ControlNet Mode
SelectControlNet mode: 'depth' for depth map guidance, 'canny' for edge detection, 'pose' for human pose estimation, 'none' for no control.
Size
SelectOutput image size in pixels (width*height).
Strength
NumberControls how strongly the ControlNet guidance affects the output. Higher values follow the control signal more strictly.
Seed
SeedRandom seed for reproducible generation. Use -1 for random seed.
Supported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
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Community discussion
What people think about Z Image Turbo Controlnet
Z Image Turbo Controlnet discussions are most active in r/comfyui, r/StableDiffusion, r/CinematicAnimationAI. The strongest match in this snapshot has 1865 upvotes and 248 comments.
[https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union](https://huggingface.co/alibaba-pai/Z-Image-Turbo-Fun-Controlnet-Union)
Well, I’d tried Z Image Turbo before, but last night I made my first character LoRA and it turned out pretty good. I’m a bit confused about ControlNet with this model, because some people say it works well and others say it works poorly if you use a LoRA… could you share an effective workflow?
Well, I’d tried Z Image Turbo before, but last night I made my first character LoRA and it turned out pretty good. I’m a bit confused about ControlNet with this model, because some people say it works well and others say it works poorly if you use a LoRA… could you share an effective workflow?
FAQ
Common questions about Z Image Turbo Controlnet
What is the context window for this model?
The model has a context window of 10,000 tokens, as specified in its metadata.
Who developed Z Image Turbo Controlnet?
It was developed by Alibaba's Tongyi-MAI lab and is published under the Qwen publisher on MindStudio.
What inputs does this model accept?
The model accepts an image URL (for ControlNet source guidance), two select-type configuration inputs, a numeric parameter, and a seed value for reproducibility.
What is the training cutoff date for this model?
According to the metadata, the model's training date is November 2024.
How does the Turbo variant differ from the base Z-Image model?
The Turbo variant applies few-step distillation to the base 6-billion-parameter Z-Image model, reducing the number of diffusion steps needed at inference time for faster generation while aiming to preserve output quality.
Do I need to provide an API key to use this model on MindStudio?
No API key is required. You can use Z Image Turbo Controlnet directly through MindStudio without managing separate API credentials.
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