Which parameters most directly influence the trade-off between image resolution and computational cost?

The parameters that most directly influence the trade-off between image resolution and computational cost in ChatGPT ImageGen are the image dimensions (width and height) and the number of sampling steps. Image dimensions directly determine the number of pixels that need to be generated. Doubling the width and height of an image quadruples the total number of pixels, leading to a significant increase in computational requirements. Generating a 1024x1024 image, for example, requires considerably more computation than generating a 512x512 image. The number of sampling steps, as discussed before, affects the quality of the image. Increasing the number of steps reduces noise and improves detail but increases the amount of computation required. Each step involves passing the image through the neural network, so more steps mean more passes and longer processing times. The model architecture itself also plays a role, but the image dimensions and sampling steps are the parameters that are most commonly adjusted by the user to manage this trade-off. Other parameters that can have a secondary influence include the batch size (generating multiple images simultaneously can improve throughput but increases memory requirements) and the choice of the diffusion model variant (some variants are more efficient than others but may sacrifice some quality).