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You're developing a system to generate realistic 3D models from text descriptions. You're using a diffusion model-based approach and find that the generated models often lack fine details and exhibit artifacts. Which of the following techniques would likely lead to the MOST significant improvement in the quality of the generated 3D models?
Correct Answer: E
Each of the above options address the lack of fine details and exhibit artifacts. Increasing diffusion steps lets the model refine the results. Using a larger U-Net architecture increases capacity of details. Classifier-free guidance allows for generating high fidelity details by better correlating text descriptions. Training on a larger dataset enables richer context. Overall improvements allow for finer details with fewer artifacts