Writing study: Error Propagation Mechanisms and Compensation Strategies for Quantized Diffusion Models
← ICML 2026 oral writing corpus
Songwei Liu, Chao Zeng, Chenqian Yan, Xurui Peng, Xing Wang, Fangmin Chen, Xing Mei · Official ICML 2026 oral record
Figure, table and section ledger
Editorial contribution type: Training, objectives & optimization (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 18 physical pages
- Before references / appendix
- ≈ 8.1 page-equivalents
- Main figures / tables
- 2 / 3
- Attached appendix figures / tables
- 2 / 7
- Attached appendix span
- ≈ 7.91 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 1 | ≈ 1.25 pp. | 6.94% |
| 2. Related Work | 2 | ≈ 1.24 pp. | 6.89% |
| 3. Method | 3 | ≈ 2.76 pp. | 15.33% |
| 4. Experiments | 6 | ≈ 2.24 pp. | 12.44% |
| 5. Conclusion | 8 | ≈ 0.21 pp. | 1.17% |
| Impact Statement | 9 | ≈ 0.05 pp. | 0.28% |
| References | 9 | ≈ 1.99 pp. | 11.06% |
| A. Explanation of Approximation 1 | 11 | ≈ 0.6 pp. | 3.33% |
| B. Derivation of Error Accumulation Steps | 11 | ≈ 1.67 pp. | 9.28% |
| C. Empirical Study of Error Accumulation Steps | 13 | ≈ 0.46 pp. | 2.56% |
| D. Compatibility with Different Solvers. | 13 | ≈ 0.96 pp. | 5.33% |
| E. Timestep-Aware Quantization Error | 14 | ≈ 0.98 pp. | 5.44% |
| F. Ablation experiment of regularization term λ1 | 15 | ≈ 0.45 pp. | 2.5% |
| G. The impact of inference steps on TCEC performance | 16 | ≈ 0.42 pp. | 2.33% |
| H. Inference Overhead of TCEC | 16 | ≈ 0.15 pp. | 0.83% |
| I. Calibration Dataset Ablation | 16 | ≈ 0.7 pp. | 3.89% |
| J. Additional comparison with QNCD | 17 | ≈ 0.61 pp. | 3.39% |
| K. Visual Quality Results. | 18 | ≈ 0.51 pp. | 2.83% |
| L. Human Evaluation | 18 | ≈ 0.4 pp. | 2.22% |
All detected figure and table locations
Argument and organization
- 1 Introduction; 2 Related Work
Moves from diffusion deployment cost to the distinction between per-step quantization error and its accumulation.
- 3 Method
Derives propagation, introduces computational approximations and then constructs timestep-aware compensation.
- 4 Experiments; 5 Conclusion; Appendix A-L
Separates implementation and image/video comparisons from overhead, with derivations, sensitivity and human evaluation navigated in the appendix.
Problem, claim and contribution
The abstract connects an error-propagation explanation to a correction overlay and states both a fidelity gain and its additional latency.
Abstract and Section 1, p.1 · Main paper
Argument and section order
The method roadmap names two obstacles to using the derivation directly, and later subsections answer computational complexity and estimating per-step error in that order.
Section 3 opening, p.3; Sections 3.2-3.3, p.5 · Main paper
Figures, captions and visual explanation
The overview aligns full-precision, quantized and corrected denoising paths with a separate trajectory schematic; arrows and labels redundantly explain the gray/red/green encoding.
Figure 1, p.3 · Main paper
Tables, comparisons and evidence
The main image table separates quality from similarity and groups model, bit width and compensation overlay, while the following overhead table compares the same engine with and without correction.
Table 1, p.7; Table 3 and Hardware Resource Savings, p.8 · Main paper
Appendix and reproducibility
Appendix G gives the lower-step regime where gains weaken; H explains overhead and I investigates calibration size. These qualify the broad main conclusion rather than merely add examples.
Appendices G-I, p.16 · Main paper
Clarity and writing conventions
Approximations are explicitly labelled before the simplified correction, but the conclusion uses broad superiority language that must still be read with the model/metric conditions in the tables.
Section 3.2, p.5; Section 5, p.8 · Main paper
What to reuse—and what not to infer
Transferable lesson: Present a theoretical derivation, the approximations needed to implement it, and the measured cost of the resulting correction as distinct stages.
Boundary: No independent proof validation was performed; favorable fidelity does not mean every quality metric or low-step setting improves.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-10-07
- Reading status
- Writing-focused manuscript scan + selected visuals inspected
- Main text
- Writing-oriented scan of the main argument through physical p.8, with focused physical-page text inspection on 1, 3, 5, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
- Appendix / supplement
- Appendix organization and main-text pointers were checked; focused text inspection of physical pages 12, 16. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
- Visually inspected PDF pages
- 3, 7
This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.