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A classic **OpenPose ControlNet LoRA** for **Krea2 Turbo**. Feed an **OpenPose / DWPose skeleton map** as the control image — the model follows the **body pose and structure** while the **text prompt** defines appearance, style, clothing, and scene. This is a straightforward **pose ControlNet-style LoRA** — not a reference + pose fusion model. It was primarily trained on **humans**, but also works with **stylized characters**. The LoRA teaches the model to treat the input image as an **OpenPose control map** and to align the generated subject with that skeleton. Appearance, outfit, lighting, and environment come from the **prompt** (and optional style cues), not from a second reference image.

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A LoRA for **FLUX.2 Klein 9B Base** that fuses a **reference image** with a **depth map**. It preserves **identity and style** from the reference while following the **pose and structure** from the depth map. This LoRA was primarily trained on **humans**, but it also works with **objects** and stylized characters. Its main purpose is to **preserve identity** — facial features, clothing, style, or object characteristics — from the reference image, while adapting the subject to the **pose and composition** defined by the depth map. Part of the **RefControl** family: reference + control fusion for consistent, controllable generation on **FLUX.2 Klein 9B Base**.

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A LoRA for **FLUX.2 Klein 9B Base** that fuses a **reference image** (identity) with a **pose control map** (skeleton / keypoints). It preserves **identity and style** from the reference while following the **body pose and structure** from the pose map. Each preview is a **single combined image** from ComfyUI: **Pose → Reference → Result** (left to right). This LoRA was primarily trained on **humans**, but it also works with **stylized characters** and some objects. Its main purpose is to **preserve identity** — facial features, hairstyle, clothing, or object details — from the reference image, while adapting the subject to the **pose and skeleton structure** defined by the control map.

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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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