Rethinking Classifier-Free Guidance for TI2I Flow Matching

Andreas Floros; Elena Koritska; Teofilo de Campos; Christoph Vogel; Chu-Tak Li; Anuj Sharma; Alexandros Neophytou

cs.CVOct 15, 2026

Rethinking Classifier-Free Guidance for TI2I Flow Matching

Authors: Andreas Floros, Elena Koritska, Teofilo de Campos, Christoph Vogel, Chu-Tak Li, Anuj Sharma, Alexandros Neophytou

Organizations: Imperial College London · Microsoft

Abstract

We study Text-and-Image-to-Image (TI2I) flow matching methods for controllable image editing. In a broader generative modeling context, Classifier-Free Guidance (CFG) is established as a key component responsible for high quality outputs. However, we argue that its role in TI2I modeling is poorly understood. Specifically, we make the surprising observation that the CFG mechanism is implicitly harmful for editing in the noisy phase of flow modeling, hindering instruction following and controllability. We propose a simple correction in the early flow steps, enabling a richer alignment-preservation tradeoff at no extra cost. The effectiveness of our approach is demonstrated on both pixel-space and latent TI2I flows.

Figures & tables

Figure 1: We propose a simple modification in TI2I flows that substantially improves source preservation while maintaining text alignment. In the examples above, source images are on the left, baseline edits in the middle (we evaluate Qwen, FLUX.2 and PixelGen models) and editing with our correction on the right.

Figure 2: Visualization of two representative TI2I modeling scenarios with our PixelGen finetune. The first rows show the iterates $\boldsymbol{x}_{t}$ . The second and third rows show the unconditional and conditional predictions of the network for the output edit image given $\boldsymbol{x}_{t}$ , which can be derived given knowledge of the corresponding velocities. The last rows show the evolution of the differential velocity, equivalent to the difference of the second and third rows. Specifically, we visualize its magnitude as a heatmap that is normalized for each $t$ . In both cases, the results of the last row concentrate on the edit regions near $t=0$ and become less localized and noisy as $t\to 1$ . Moreover, the failure in the right example is evident immediately at $t=0$ and we can intuitively attribute it to the unconditional prediction of the second row, where the field has been repainted.

Figure 3: Average localization gain (

\pm

the standard error) for our PixelGen finetune. We use SAM 3 ( Carion et al., 2026 ) for the oracle masks. Results labeled as

\psi_{t}(\cdot;\cdot)

correspond to using the analytical flow model, previously discussed in Section 2.1 .

Figure 6: We ablate the proposed correction on our PixelGen finetune with simple edit tasks, setting $\tau=0.05$ . Of tested configurations, only the one corresponding to Equation 6 is successful.

		Preservation			Alignment
	Method	PSNR $\uparrow$	LPIPS $\downarrow$	DINOv2 $\uparrow$	CLIP $\uparrow$	PickScore $\uparrow$	HPSv3 $\uparrow$
$1024{\times}1024$	Qwen-Image-Edit	13.06	0.4948	0.7082	0.2853	22.15	7.65
	+ Classifier-free guidance ( $\omega=4.00$ )	11.25	0.5616	0.5750	0.2905	22.03	6.88
	+ Early correction ( $\tau=0.10$ )	14.05	0.4159	0.6606	0.2946	22.28	7.97
	FLUX.2 [klein] 4B Base	27.65	0.0516	0.9926	0.2389	21.63	7.92
	+ Classifier-free guidance ( $\omega=4.00$ )	20.22	0.1973	0.9163	0.2605	21.88	7.91

Table 1: Editing with Qwen, FLUX.2 and PixelGen. Without CFG we tend to observe strong preservation but insufficient alignment. That is, CFG is often essential for image editing. However, recommended weights,

\omega

, tend to improve alignment while harming preservation. Early correction mitigates this tradeoff by largely improving preservation while maintaining strong alignment for Qwen-Image-Edit and PixelGen. For FLUX.2, found relatively conservative at

1024\times 1024

resolution, it recovers part of the distortion introduced by CFG while retaining its alignment gains. Moreover, our intervention is a key ingredient for unlocking robust masked guidance, i.e., removing it in the last row of the PixelGen experiments introduces severe distortion.

Figure 7: Alignment-preservation tradeoffs via PSNR and CLIP. We evaluate our PixelGen finetune, FLUX.2 and Qwen models. Curves form by varying

\omega

, with faint colors given to small values. Our method redefines the Pareto front, enabling robust editing.

Figure 8: Qualitative analysis showing the effect of $\omega$ and $\tau$ at $1024\times 1024$ resolution. On the left we show edits with Qwen-Image-Edit and on the right with FLUX.2 [klein] 4B Base. The first columns use $\tau=0$ , i.e., no early correction. In both cases, $\tau=0$ tends to give generic results at large $\omega$ that may disregard the source. That is, on the left the raccoon is changed to a cat and on the right, eventually, no elements from the source survive. The proposed correction, activated when $\tau>0$ , produces edits that are more faithful to the source without compromising instruction-following ability. See Figure 11 in Appendix B for more examples.

Figure 9: We show DINOv2-HPSv3, Aesthetics tradeoffs for the PixelGen, FLUX.2 and Qwen models with different

\omega

and

\tau

. Curves represent fixed

\omega

and form by varying

\tau

, with faint colors given to small values. A positive

\tau

often improves all metrics.

Panel of Figure 10 — Figure 10: Compositions of our two editing benchmarks, with approximately 200 entries each. The panels pair two donuts, showing the distribution of source images on the left and of edit prompts on the right. The benchmark on the left panel targets single objects on plain backdrops with localized recolor and material edits, i.e., confined within a specific region of the image. The benchmark on the right targets complex, typically crowded scenes and was specifically designed as a stress-test of TI2I capabilities. Together, they cover the spectrum from precise local edits to scene-wide changes that must still preserve surrounding context.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Figure 11: Editing with FLUX.2, showing the effect of $\omega$ and $\tau$ at $512\times 512$ resolution. Ours, with $\tau>0$ , maintains the lemon shape (left), perspective (middle) and booth shape (right) while adhering to the text.

Classifier-Free Guidance

Classifier-free guidance (CFG) steers conditional generation by extrapolating between conditional and unconditional model predictions during sampling.

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Classifier-free guidance (CFG) improves conditional generation in Flow Matching, but strong guidance can distort the generated distribution and reduce diversity. We provide a geometric account of this behavior by viewing Flow Matching as a time-varying gradient flow and characterizing how CFG reshapes its underlying potential. This view explains how stronger alignment can be accompanied by mean displacement and trajectory concentration, and motivates controlling guidance through the model-implied terminal posterior mean. We therefore propose Posterior-Mean-Capped CFG (PMC-CFG), a training-free, per-sample method that adaptively retains the strongest feasible guidance without additional network evaluations. Experiments on synthetic and large-scale image-generation benchmarks show that PMC-CFG limits guidance-induced distortion and concentration while improving the alignment--diversity trade-off, with particularly strong benefits when nominal guidance is large.

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