The best choices are Using a carefully labeled, high-quality training and reference image set and Fine-tuning the diffusion model on brand-specific examples. These two approaches directly increase image fidelity and produce consistent brand visuals for 6K product ads and social media content.

Using a carefully labeled, high-quality training and reference image set gives the model clear examples of lighting composition color palettes and product details that you want it to reproduce. High resolution images and consistent labels reduce artifacts and guide the model toward the specific visual features needed for high quality 6K outputs.

Fine-tuning the diffusion model on brand-specific examples adapts the foundation model to the retailer's exact aesthetic and product characteristics. Fine tuning reduces off brand variations and improves reproducibility across different prompts and campaign assets which leads to stronger brand consistency.

Tracking inference latency and throughput is helpful for deployment planning and meeting SLAs but it does not by itself improve the visual quality or the brand alignment of generated images.

Optimizing prompt token usage to reduce size can save cost and sometimes speed up generation but shortening or compressing prompts does not replace the need for high quality training data and model adaptation when the goal is better imagery.

Expanding the model's context window length is a concept that mainly applies to large language models and not to image diffusion models. Increasing context window is unlikely to materially affect image fidelity or brand consistency for diffusion based generation.

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