Unverified50% confidenceFactExact time
DiffusionGemma依赖双向注意力，让前后位置互相照应，这是其生成质量不输自回归的核心机制
1
Sources
50%
Confidence
Long-term
Relevance
8/27/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedThe self-attention mechanism of the Transformer allows the model to directly compute dependencies between any two positions in a sequence, breaking through the parallelization bottleneck caused by the sequential computation of RNNs/LSTMs.65% similarUnverifiedDiffusionGemma采用整片并行去噪的生成机制，先铺开256个位置的噪声，然后逐轮迭代，将置信度最高的位置先固定，其余位置重新注入噪声65% similarUnverified主流图像/视频生成模型如Stable Diffusion、Midjourney、Sora缺乏持久化记忆机制，每次生成依赖当前提示词和随机噪声种子，不内置跨次调用的状态保持能力64% similarUnverifiedIn open-source models such as Stable Diffusion, the negative prompt is a separate input field used to tell the model to actively avoid certain visual features.63% similarUnverifiedLLM的自回归生成机制使初始的微小浮点差异通过注意力机制的非线性变换逐步累积，产生雪球效应63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/808089API
curl https://kongchang.com/api/v1/knowledge/claims/808089MCP
get_claim(id=808089)