Creates pose dummy mannequins from reference images.
The intended use case is to use the resulting images as pose references for image generation with e.g. Nano Banana Pro or for LoRA training.
Version 2.0 notes
Version 2.0 for Qwen-Image-2.1 was trained on a slightly different dataset using the same control images as Version 1.0 but with target images generated by Version 1.0. These new target images are more stylistically consistent and have better spatial alignment with the control images. While I consider it an improvement overall, the Version 2.0 LoRA may behave a little differently than Version 1.0.
I trained the version 2.0 LoRA for Qwen-Image-2.1 for 100 epochs (1600 steps), but the final epoch had poor prompt adherence (the wireframe overlay was nearly impossible to disable, even with negative prompting) which is why I chose to release epoch 60 instead.
Training prompt:
Generate a picture of abstract CGI mannequins in the exact same pose as the people in the image. The mannequins should have fingers, toes, and a detailed facial expression. The mannequins do not have any hair. They have perfectly clean gray skin with a black wireframe overlaid on top without any discolorations. The background of the image is a plain featureless middle gray void. Do not include any objects the people in the image may or may not be interacting with.Version 1.0 notes
Training prompt:
Generate a picture of abstract CGI mannequins in the exact same pose as the people in the image. The mannequins should have fingers, toes, and an abstract representation of facial expression. The mannequins do not have any hair. They have perfectly clean gray skin with a black wireframe overlaid on top without any discolorations. The background of the image is a plain featureless middle gray void. Do not include any objects the people in the image may or may not be interacting with.You can get somewhat similar results out of base Qwen-Image-Edit with appropriate prompting, but it is a lot less consistent in my experience.
It seems to work okay with cfg = 1.0 and steps = 20. Higher cfg values can produce better results, but may affect how faithful the result is.
The 8-step Lightning LoRA can also be very viable and often produces cleaner results.