## Paper 5: Flatten-DiT: Mitigating Activation Outliers for Accurate Post-Training Quantization of Diffusion Transformers
**Authors:** Zhou, Hou, Huang, Zhou, Xiao, Wang, Chenwei
**Year:** 2025
**DOI:** [10.2139/ssrn.5413755](https://doi.org/10.2139/ssrn.5413755)

### Research Question
Can the "activation outlier" techniques developed for LLMs be applied to Diffusion Transformers (DiTs) for image generation?

### Methodology
- **Design:** Empirical.
- **Approach:** Analyzes activation distributions in Diffusion Transformers.
- **Technique:** "Flattening" outliers—likely a smoothing or clamping technique similar to SmoothQuant but adapted for the diffusion process.

### Key Findings
1. **Domain Transfer:** Confirms that Activation Outliers are not unique to LLMs; they also plague Diffusion Transformers (DiTs).
2. **Quantization Accuracy:** Mitigating these outliers allows for accurate PTQ of diffusion models, which are notoriously sensitive to noise.

### Implications
Expands the scope of quantization research. It suggests that "outlier management" is a universal requirement for Transformer-based architectures, regardless of modality (Text vs. Image).

### Relevance to Your Research
**Score:** ⭐⭐⭐⭐ (4/5)
**Why:** Demonstrates the cross-domain applicability of the outlier theory. If you are researching general Transformer efficiency, this proves the robustness of the outlier hypothesis.

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