モデル情報

Qwen-Image-2.1-Fun-Controlnet-Union

alibaba-pai · Qwen-Image-2.1-Fun-Controlnet-Union

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.

翻訳待ちのため原文を表示しています。

画像生成license:other
コンテキスト長非公開
入力 → 出力テキスト → 画像
モデルウェイト未確認
情報更新日時2026/11/2 19:17:17
OVERVIEW

モデル紹介

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.

主な特徴

特点摘要待依据来源补齐,不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

出典・チャネル入力出力課金単位収集日時
確認可能な料金がありません。出典を確認してください。

月額の基本トークン費用を試算

—

試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

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。

収録済みの全ランキング →

よくある質問

オープンウェイトなら無料で商用利用できますか?

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか?

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

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。