Editing Diffusion Models (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Generative Model Comparison
! Model Type !! Training Stability !! Sample Quality !! Sample Diversity !! Speed
|-
| GAN || Poor (mode collapse) || Very high (when tuned) || Low (mode collapse) || Fast (single forward pass)
|-
| VAE || Stable || Moderate (blurry) || Good || Fast
|-
| Diffusion (pixel) || Stable || State-of-the-art || Excellent || Slow (100-1000 steps)
|-
| Diffusion (latent) || Stable || State-of-the-art || Excellent || Moderate (20-50 steps in latent)
|-
| Flow Matching || Stable || State-of-the-art || Excellent || Fast (8-20 steps)
|}

'''Failure modes and limitations:'''
* '''Text rendering''' — Diffusion models notoriously struggle to generate coherent text within images. The denoising process doesn't understand character shapes.
* '''Consistent identities''' — Generating the same face or character across multiple images is difficult without techniques like DreamBooth or IP-Adapter.
* '''Prompt sensitivity''' — Small prompt changes can cause large output variation. "A cat sitting on a red chair" vs "A red chair with a cat" may produce very different images.
* '''NSFW and copyright concerns''' — Models trained on internet data have absorbed copyrighted images and potentially harmful content. Content filtering and fine-tuning on curated data are important mitigations.
* '''Computational cost''' — Running a 50-step reverse diffusion process is expensive. Inference optimization (distillation, SDXL-Turbo, LCM) reduces steps to 1–4.
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