Unverified50% confidenceFactExact time
Diffusion models and flow models generate samples through iterative steps, and each step requires a full forward pass through a large network, a process that dominates the generation cost.
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10/16/2026
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Related Claims
UnverifiedAutoregressive model inference must be executed strictly in serial; generating each token requires one complete forward pass, which is an important structural reason for the high cost of large model inference.78% similarUnverifiedA single forward pass means the model only needs to compute the network weights in full once rather than looping autoregressively token by token, giving it a structural advantage in latency and computational resource consumption.77% similarUnverified输出生成是严格串行的自回归过程，每生成一个token都需要一次完整的模型前向传播76% similarVerifiedThe denoising process of diffusion models typically requires tens to hundreds of iterations (called sampling steps), each of which requires a complete forward pass through the neural network.76% similarUnverifiedDiffusion model inference requires a fixed number of forward passes with each pass acting on the entire sequence, making the total cost a function of the number of steps multiplied by the sequence length.76% similar
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