I need a fully automated face-and-body swap pipeline that runs inside my Runninghub cloud workspace. The workflow must take a standard-resolution photo of any subject—male, female, or child, including both average and obese body types—and convincingly transplant them into a target image so they retain • the identical clothing style and fit • the original pose and background of the target shot • an exact match of the target person’s facial expression Reliability is key: every result should look like the same outfit was tailored to the new physique without distortion or mismatched wrinkles. I am open to your preferred deep-learning stack (e.g., Stable Diffusion, ControlNet, LoRA fine-tuning, face-landmark-driven warping, etc.) as long as it installs smoothly in a Python environment and consumes the standard GPU instance already available on Runninghub. Deliverables • Source code or notebook with clear, reproducible steps • Requirements file / Docker snippet so the setup works out-of-the-box on Runninghub • Simple CLI or REST entry point for batch processing • A small test set demonstrating success on both average and obese examples, with before-and-after images for verification I will consider the project complete when the workflow consistently produces swaps that are indistinguishable from an original photo in terms of clothing continuity, pose alignment, and facial-expression accuracy.