Fine-Tuning a Stable Diffusion Model for Generating Artist-Style Soccer Player Graphics

We are seeking an experienced deep learning researcher or engineer with expertise in fine-tuning generative models, specifically Stable Diffusion models, for an image generation project. The goal of the project is to generate artist-style soccer player graphics using an existing dataset of artist-style player images. Project requirements: 1. Utilize the provided dataset of artist-style player graphics for fine-tuning the Stable Diffusion model. Ensure the dataset is large enough to capture a diverse range of styles and players. 2. Preprocess the data by resizing and normalizing the images to prepare them for fine-tuning. The images should be compatible with the input requirements of the Stable Diffusion model. 3. Fine-tune a pre-trained Stable Diffusion model on the provided dataset of artist-style soccer player graphics. The fine-tuning process should adapt the model to generate high-quality artist-style soccer player images. 4. Evaluate the model's performance using qualitative and quantitative metrics to ensure the generated graphics meet the desired quality and style. Provide examples of generated images and any relevant evaluation metrics. Skills required: A. Strong knowledge of deep learning and generative models, specifically Stable Diffusion models B. Experience in fine-tuning pre-trained models for image generation tasks C. Proficiency in Python and deep learning frameworks like TensorFlow or PyTorch D. Ability to evaluate model performance using quantitative and qualitative metrics E. Excellent communication and documentation skills To be considered for this project, please provide examples of your previous work with generative models, specifically Stable Diffusion models, and demonstrate your experience in fine-tuning models for image generation tasks. Additionally, briefly outline your approach to fine-tuning the Stable Diffusion model for generating artist-style soccer player graphics using the provided dataset.