Research map / Image generation / Fast diffusion sampling
Towards a Golden Classifier-Free Guidance Path via Foresight Fixed Point Iterations
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Reframes classifier-free guidance as fixed-point iterations toward a "golden path" and proposes Foresight Guidance (FSG), which uses longer-interval subproblems with more iterations in early diffusion steps to improve text-to-image quality and efficiency.
Problem
Existing CFG variants stem from divergent theoretical interpretations, obscuring design choices; CFG-style guidance also trades off image quality and diversity. The paper asks how to systematically explore the design space of conditional guidance from a unified view.
Approach
Unify CFG, CFG++, Z-sampling and Resampling as single-step short-interval fixed-point iterations that calibrate latents so unconditional and conditional denoising agree; then allocate more iterations to fewer, longer-interval subproblems in early timesteps (FSG), which the paper argues is theoretically more efficient.
Key results
- On SDXL with DrawBench at NFE=50, FSG reaches IR 82.81 and HPSv2 29.42 versus 59.02 and 28.73 for CFG.
- On Geneval (SDXL), FSG achieves overall 57.95% versus 48.39% for CFG, with counting improving by 23.75 points over CFG.
- Gains are reported on SD-2.1 (IR +8.19 over other methods) and Hunyuan-DiT (IR +4.16), with synergy when combined with NPNet and SPO.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FSG instead of the strongest listed baseline | SDXL · DrawBench, NFE 50 | IR: 72.75 -> 82.81 | Helped |
| Use FSG instead of the strongest listed baseline | SDXL · DrawBench, NFE 50 | HPSv2: 29.08 -> 29.42 | Helped |
| Use FSG instead of CFG++ | SDXL · DrawBench, NFE 50 | AES: 6.08 -> 6.01 | Hurt |
| Use FSG instead of the strongest listed baseline | SDXL · DrawBench, NFE 50 | CLIP: 32.60 -> 32.65 | Helped |
| Use FSG instead of the strongest listed baseline | SDXL · Pick-a-Pic, NFE 50 | IR: 96.77 -> 98.59 | Helped |
| Use FSG instead of the strongest listed baseline | SDXL · Pick-a-Pic, NFE 50 | HPSv2: 28.72 -> 28.89 | Helped |
| Use FSG instead of CFG | SDXL · Pick-a-Pic, NFE 50 | AES: 6.73 -> 6.60 | Hurt |
| Use FSG instead of the strongest listed baseline | SDXL · Pick-a-Pic, NFE 50 | CLIP: 33.97 -> 34.32 | Helped |
| Increase CFG from NFE 50 to CFG ×3 | SDXL · Geneval | Overall: 48.39 % -> 55.94 % | Helped |
| Use FSG instead of the strongest listed baseline | SDXL · Geneval | Overall: 56.70 % -> 57.95 % | Helped |
| Use FSG instead of Resampling | SD-2.1 with DDIM · Pick-a-Pic | IR: 8.07 -> 16.26 | Helped |
| Use FSG instead of CFG ×3 | SD-2.1 with DDIM · Pick-a-Pic | HPSv2: 27.25 -> 27.60 | Helped |
Surprising findings
- Resampling's empirical contraction rate exceeds 1 at some timesteps, while longer-interval backward-forward operators show lower contraction rates, motivating longer intervals.
- Some CFG and Resampling settings show negative IR at NFE 50 on SD-2.1 (CFG IR -62.83), indicating unstable baselines.
Limitations
- The theoretical benefit relies on mild assumptions (boundedness, smoothness, contraction) that are not verified for real diffusion networks.
- FSG adds hyperparameters (interval, iteration schedule across stages) and its total NFE is comparable rather than lower than baselines at matched settings.
Experimental setup
Datasets: DrawBench Pick-a-Pic Geneval PartiPrompts ImageNet
Benchmarks: DrawBench Pick-a-Pic Geneval PartiPrompts ImageNet
Metrics: ImageReward (IR) HPSv2 AES CLIPScore Geneval accuracy FID Vendi score
Baselines: CFG CFG++ Z-Sampling Resampling NPNet SPO
Builds on: classifier-free guidance CFG++ Z-Sampling Resampling DDIM
Closest papers by meaning
- Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models (AAAI 2025 · similarity 0.92)
- Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance (CVPR 2024 · similarity 0.91)
- CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models (ICLR 2025 · similarity 0.91)
- Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models (ICLR 2025 · similarity 0.91)
- REG: Rectified Gradient Guidance for Conditional Diffusion Models (ICML 2025 · similarity 0.91)
- Elucidating the design space of classifier-guided diffusion generation (ICLR 2024 · similarity 0.90)
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