Unverified50% confidenceOpinionExact time
Fine-tuning was chosen on a causal base model (Qwen-Image-Edit-Causal) rather than a bidirectional diffusion model because multi-turn editing inherently has sequential dependencies, and the causal structure better matches the reasoning form of "generating the next turn based on historical context."
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedDiffusion models exhibit quality degradation during multi-round iterative editing, and the fundamental solution typically involves explicit tracking of editing history or switching to a non-destructive editing strategy.73% similarUnverified传统自回归推理中,模型每一步只能基于已有上下文预测下一个token,是串行依赖关系71% similarUnverifiedEditing effects are mutually dependent: the impact of modifying one token depends on which other tokens are edited simultaneously, so editing sets must be considered rather than evaluating individual positions in isolation.69% similarUnverifiedThe difference matting method relies on the foreground subject remaining highly consistent across two generations, which places demands on the controllability of the image-generation model.68% similarUnverifiedDiscrete diffusion models construct complete sequences by iteratively and in parallel resolving multiple tokens across multiple steps, unlike the left-to-right word-by-word generation of autoregressive models.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1065772API
curl https://kongchang.com/api/v1/knowledge/claims/1065772MCP
get_claim(id=1065772)