Virtual Model Ai
We use Stable Diffusion XL (SDXL) RealvisxlV50 to generate a series of close-up images of model faces.
The goal of the workflow is to create realistic and consistent AI models using a structured process and advanced tools. This approach is designed to deliver technically precise and visually engaging results, ready to be used in creative projects, advertising, or technical demonstrations.
Objective
Workflow Steps
Step 1: Face Generation
Step 3: Refining Facial Details
After selecting the most suitable face, we refine it using IPAdapter Plus Face Portrait, ensuring seamless integration and a well-defined visual identity. We then generate a complete image of the model without a specific pose to evaluate the overall appearance of the chosen face in a generic context.
Step 2: Creating the Image Without a Pose
We apply InsightFace to intelligently identify the face within the image and enlarge it for more detailed processing. We then use IPAdapter again to refine skin texture, facial expressions, and specific details.
Step 4: Skin Details
As the final step, we enhance the skin texture to make it uniform and natural. This ensures that the final image is suitable for close-ups and meets the highest visual standards.
Working with Multiple Models
To add a pose, we use ControlNet with depth and edge detection (canny) preprocessing. This ensures precision in the body’s structure and movements. We then combine the pose with the face using IPAdapter, maintaining visual consistency across all elements.
Step 5: Selecting the Pose with ControlNet
We select multiple faces and apply the same principles to maintain consistency in pose and details across different models. This approach allows for exploring variations and directly comparing different results.
Applying Clothes with LoRA
If we want to apply clothing to the created models, we can use LoRA.
We create a dedicated model for specific garments, such as pants, using training on SDXL.
This allows us to apply realistic clothing to the models, making it ideal for showcasing products in various contexts.
