Model overview
Qwen-Image-2.1-Fun-Controlnet-Union is a **ControlNet-Union branch for Qwen-Image 2.1** (the flow-matching text-to-image DiT). A single checkpoint drives **8 structural control conditions** (Canny, Depth, Grayscale, HED, Lineart, MLSD, Pose, Scribble) *and* image inpainting, without per-condition weights. The checkpoint holds **only the control branch** (`control_img_in` plus 16 `control_blocks`, about 7.0 GB) and is loaded on top of the base Qwen-Image 2.1 transformer. - **Union control over 8 conditions**: one checkpoint handles Canny, Depth, Grayscale, HED, Lineart, MLSD, Pose and Scribble control images for text-to-image generation — no per-condition checkpoint switching. - **Dense control injection**: the control branch attaches a skip to every 2nd of the 32 transformer blocks (`control_layers = [0, 2, 4, …, 30]`, 16 injection points). Each control skip is added back to the main branch through zero-gated `before_proj` / `after_proj` projections, giving tight structural adherence while the base model stays frozen. - **Control and inpainting share one branch**: the control input is widened to `control_in_dim = 129` — `control latents (64) | mask (1) | masked-image latents (64)`. For pure control the mask / masked-image channels are zero-padded; for inpainting the same branch re-draws the masked region from the prompt. The two can also be **combined** — a control image and a mask are fed together, so the re-drawn region follows both the prompt and the given structure. - **CFG-distilled fast sampling**: the standalone example scripts run with `guidance_scale = 1.0` (single forward pass per step, no classifier-free guidance needed). - `control_context_scale` scales every control skip before it is added to the main branch: `1.0` is the strongest control (used for all results below), lower values weaken the guidance, `0.0` switches the control branch off. - **Prompt-friendly**: write a prompt that describes the **whole target image**; the masked region is conveyed by the mask channel, not by the text. Detailed prompts give better stability. - Qwen-Image 2.1 encodes the prompt (and any condition image) with a **Qwen3-VL** text encoder + processor, and its VAE decodes to **RGBA**, so every preview is saved as PNG. Any ordinary RGB control image at the target canvas works; the model tolerates different line thickness, thresholds and crops.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。