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Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques
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Identifies "content shift" in diffusion features and proposes GATE, which uses off-the-shelf generation techniques (fine-grained prompts, ControlNet, LoRA) plus regularized feature amalgamation to suppress it, improving semantic correspondence and segmentation.
Problem
Diffusion features (activations of pre-trained diffusion UNets used for discriminative tasks) drift in content from the input image due to the noise-and-reconstruction process, degrading their quality for downstream discrimination.
Approach
Attribute content shift to the reconstruction drift inside the diffusion UNet and suppress it by reusing off-the-shelf generation control techniques (prompts, ControlNet with the input image as control, LoRA), selected via an Img2Img-based guideline (GATE), then amalgamating features from different technique combinations with regularized weight assignment.
Key results
- Semantic correspondence PCK@0.1 (img) rises from 73.96 (conv baseline) to 76.60 with GATE on SPair-71k.
- Label-scarce segmentation: GATE reaches 53.1 mIoU on Bedroom-28 and 67.2 on Horse-21, versus 49.4 and 65.0 for DDPM.
- Standard segmentation: GATE achieves 40.51 mIoU on ADE20K (VPD 37.63) and 64.20 on CityScapes (VPD 55.06).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add GATE generation techniques to diffusion features | Stable Diffusion v1.5 features · SPair-71k | PCK_img: 73.96 -> 76.60 | Helped |
| add GATE generation techniques to diffusion features | Stable Diffusion v1.5 features · SPair-71k | PCK_bbox: 65.74 -> 69.10 | Helped |
| add GATE generation techniques to diffusion features | Stable Diffusion v1.5 features · Bedroom-28 | mIoU: 49.4 ± 1.9 -> 53.1 ± 2.7 | Helped |
| add GATE generation techniques to diffusion features | Stable Diffusion v1.5 features · Horse-21 | mIoU: 65.0 ± 0.8 -> 67.2 ± 1.1 | Helped |
| add GATE generation techniques | Stable Diffusion v1.5 segmentation features · ADE20K | mIoU: 37.63 -> 40.51 | Helped |
| add GATE generation techniques | Stable Diffusion v1.5 segmentation features · CityScapes | mIoU: 55.06 -> 64.20 | Helped |
| add a prompt and ControlNet enhancements | SDXL diffusion features · SDXL semantic segmentation | mIoU: 53.40 -> 53.96 | Helped |
| add feature-amalgamation components, including an additional timestep feature (ablation) | Horse-21 basic diffusion feature · Horse-21 | mIoU: 58.13 -> 59.44 | Helped |
| add fine-grained prompts (ablation) | Horse-21 basic diffusion feature · Horse-21 | mIoU: 58.13 -> 60.12 | Helped |
| add ControlNet (ablation) | Horse-21 basic diffusion feature · Horse-21 | mIoU: 58.13 -> 60.02 | Helped |
| add LoRA (ablation) | Horse-21 basic diffusion feature · Horse-21 | mIoU: 58.13 -> 60.27 | Helped |
| combine fine-grained prompts, ControlNet, and LoRA features (ablation) | Horse-21 basic diffusion feature · Horse-21 | mIoU: 58.13 -> 60.74 | Helped |
Surprising findings
- Low-quality prompts surprisingly improve CIFAR10 classification from diffusion features, which the authors attribute to input image quality.
- Classifier-free guidance and IP-Adapter give little or limited suppression of content shift, so not every generation technique helps.
Limitations
- Effectiveness depends on which generation techniques are selected; only three were integrated.
- Evaluated on only three tasks (and five datasets), so broader potential is unexplored.
- Multi-step attention extraction and multiple weight assigners increase cost.
Experimental setup
Datasets: SPair-71k Bedroom-28 Horse-21 ADE20K CityScapes CIFAR10
Benchmarks: SPair-71k Bedroom-28 Horse-21 ADE20K CityScapes
Metrics: PCK@0.1 mIoU aAcc mAcc
Baselines: DINO DHPF DIFT DHF DDPM DatasetGAN MAE SwAV MaskCLIP ODISE VPD
Builds on: ControlNet LoRA DIFT DDPM
Closest papers by meaning
- Diffusion PID: Interpreting Diffusion via Partial Information Decomposition (NeurIPS 2024 · similarity 0.88)
- Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models (ICLR 2025 · similarity 0.88)
- Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models (ICLR 2025 · similarity 0.87)
- Get What You Want, Not What You Don't: Image Content Suppression for Text-to-Image Diffusion Models (ICLR 2024 · similarity 0.87)
- Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception (ICLR 2025 · similarity 0.87)
- Why DiT Models Underperform as Representation Learners without Long Skip Connections (NeurIPS 2026 · similarity 0.87)
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