Unverified50% confidenceConceptExact time
The 'noise stage' or 'early flow steps' of flow matching refers to the first few steps of integration where t is close to 0 and the sample is still close to pure noise; at this stage the image structure information has not yet taken shape, and this is exactly the time window in which CFG behavior becomes anomalous.
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Long-term
Relevance
10/16/2026
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UnverifiedThe study proposes correcting CFG in the early flow steps: reducing or reweighting the CFG influence during the noise stage, then gradually restoring standard CFG; this method requires no retraining of the model and adds no inference cost, being completed entirely within the sampling stage.71% similarUnverifiedImage graininess or noise is a common issue with many lightweight diffusion models, typically stemming from trade-offs in sampling steps, the degree of model fine-tuning, or the denoising process.69% similarUnverifiedIn the Prefill phase, performance differences among the three model types are within the noise range; in the Decode phase, performance is actually about 1.4% faster after applying the patch.66% similarUnverifiedDithering textures and dotted textures in AI image generation typically stem from insufficient sampling steps during the diffusion model denoising process, improper CFG guidance strength, or training quality issues.64% similarUnverifiedModel collapse occurs in two progressive stages: in the early collapse stage the output mean drifts, and in the late collapse stage the output variance approaches zero and generated content converges into a homogeneous, singular pattern.63% similar
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