Unverified50% confidenceFactExact time
When diffusion models generate large-scale crowds, individual faces occupy extremely low resolution, making it difficult for the model to maintain clear and natural facial features within small regions.
1
Sources
50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedDiffusion models struggle to impose precise constraints on specific regions and have long performed poorly on fine details such as pixel-level small text, precise mathematical formulas, and nested UI interfaces.73% similarUnverifiedThe Hunyuan model tends to oversimplify and smooth, with noticeable jagged artifacts, indicating that resolution is still insufficient.70% similarUnverified扩散模型生成图像的宽高设置若偏离训练分辨率太远，可能导致重复构图、人物比例失调等问题69% similarUnverifiedSmaller models are more prone to embedding collapse than larger models, due to reasons including limited parameter capacity, a tendency toward optimization shortcuts, and amplified anisotropy issues.68% similarUnverifiedLong-tail distribution refers to a data distribution characteristic where a small number of categories account for a large number of samples while a large number of categories have very few samples, causing the model to overfit head categories and achieve very low prediction accuracy on tail categories.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/916435API
curl https://kongchang.com/api/v1/knowledge/claims/916435MCP
get_claim(id=916435)