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arXiv lens breakdown of this paper 👉 https://arxivlens.com/PaperView/Details/guiding-a-diffusion-transformer-with-the-internal-dynamics-of-itself-3782-46c86a94

• Executive Summary
• Detailed Breakdown
• Practical Applications

Main Results of Internal Guidance for Diffusion Transformers

Core Innovation: Internal Guidance (IG)

The paper introduces Internal Guidance (IG), a simple yet highly effective strategy that guides diffusion transformers using their own internal dynamics. The key insight is to leverage the difference between intermediate and deep layer outputs within the same model to improve generation quality.

Framework Overview

Figure 1: The IG framework introduces auxiliary supervision during training and uses intermediate layer outputs during sampling to guide final generation results. With IG scale=1.4, CFG scale=1.45, and guidance interval, they achieved state-of-the-art FID=1.19.

Key Technical Contributions

1. Training with Intermediate Supervision

• Adds auxiliary loss on intermediate layer outputs during training:
  • L_intermediate = ||D_i(x_t, t) - x_0||²
  • L_final = ||D_f(x_t, t) - x_0||²
  • Total loss: L = L_final + λ × L_intermediate

2. Sampling with Internal Guidance

During sampling, extrapolates between intermediate and deep outputs:

D_w(x; c) = D_i(x; c) + w(D_f(x; c) - D_i(x; c))

This creates a self-guidance effect without requiring additional sampling steps or degraded models.

Quantitative Results

State-of-the-Art Performance on ImageNet 256×256

Key Achievement: LightningDiT-XL/1+IG achieves the current state-of-the-art FID of 1.19 when combined with CFG.

Visual Quality Comparison

Figure 3: Analysis on a 2D toy dataset shows:

• (a) Standard sampling produces outliers
• (b) CFG reduces outliers but loses diversity
• (c) IG maintains diversity like Autoguidance
• (d) IG+CFG achieves superior results by combining their strengths

Figure 7: IG alone produces better results than baseline, and combining IG with CFG further improves generation quality by reducing inaccurate content while maintaining visual fidelity.

Scalability Analysis

Figure 6: The relative improvement of IG over vanilla models becomes increasingly significant as model size grows, demonstrating excellent scalability properties.

Key Advantages

1. Plug-and-Play Design

• No extra training required for guidance
• Compatible with existing diffusion transformers
• Minimal computational overhead (<0.5% increase in parameters)

2. Diversity Preservation

Unlike CFG which can lead to over-simplified results, IG maintains sample diversity while improving quality.

3. Training Acceleration

The intermediate supervision alone alleviates gradient vanishing and achieves convergence comparable to complex self-supervised regularization methods.

4. Complementary with Existing Methods

• Combines effectively with CFG
• Compatible with guidance intervals
• Works across different model architectures (DiT, SiT, LightningDiT)

Computational Efficiency

IG achieves substantial FID improvement with negligible computational overhead.

Conclusion

Internal Guidance represents a breakthrough in diffusion model guidance strategies by:

1. Eliminating the need for complex degradation strategies or additional training
2. Achieving state-of-the-art results with minimal overhead
3. Providing a scalable solution that works better with larger models
4. Enabling practical deployment in large-scale image generation systems

The method's simplicity combined with its effectiveness makes it a compelling addition to the diffusion model toolkit, particularly for large-scale applications where efficiency and quality are both critical.