Unverified50% confidenceFactExact time
主体漂移本质上是模型在时序一致性建模上的不足，源于扩散模型每帧去噪引入随机性叠加时序注意力对长程依赖捕获能力有限
1
Sources
50%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Gemini Omni Hands-On: A Deep Dive into Google's Omni-Modal Video Generation Capabilities
redditr/GoogleGeminiAI7/10/2026
Related Claims
Unverified帧间不一致（Temporal Inconsistency）的根源在于早期扩散模型以帧为单位独立生成，缺乏对时间维度连续性的显式建模74% similarUnverifiedThe iterative denoising process of diffusion models is sensitive to numerical errors; small errors at each step accumulate and affect the final image, and the more sampling steps there are, the greater the impact of quantization errors.74% similarVerifiedThe results of AI image expansion differ each time, and this randomness stems from the diffusion model starting from different random noise each time74% similarUnverifiedInference in diffusion models is an iterative denoising process that typically requires dozens or even hundreds of timesteps and is particularly sensitive to numerical errors.73% similarVerifiedDiffusion models restore target images or video frames from random noise through stepwise denoising, typically requiring dozens of denoising iterations, with extremely high computational overhead.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/492278API
curl https://kongchang.com/api/v1/knowledge/claims/492278MCP
get_claim(id=492278)