Description:
This advanced workflow leverages the Qwen2.5-VL (Vision-Language Model) to perform semantic pose analysis and control for image generation. Unlike traditional ControlNets that strictly follow pixel maps, this workflow uses the "PowerVision" (or similar Qwen-VL integration) to visually understand the pose, composition, and details of a reference image, creating a text-based or conditioning-based guide for the generative model. It is ideal for complex scene replication where understanding the context of the pose is as important as the geometry.
Key Features:
- Vision-Language Intelligence: Uses Qwen2.5-VL-7B-Instruct to "see" and interpret the reference image, providing higher-level understanding than standard preprocessors.
- Pose & Composition Control: Accurately transfers pose and structural elements from a reference image to the generation.
- Automated Prompting: Can generate detailed captions or "system prompts" based on the visual input to guide the diffusion model (likely Flux or SDXL).
- Notification System: Includes PlaySound nodes to alert you when the generation is complete or if an error occurs.
Links to my patreon and youtube:
https://www.youtube.com/@ASF_One-Click-PRO