Diffusion Model
Diffusion Model
What is a diffusion model?
- Generates images by removing noise
- Noise → Image
- The mainstream of modern image generation
What diffusion models have really changed is that images have shifted from assets you find and use to assets you create on demand. You used to dig through stock libraries or book a studio; now the cost structure of the draft stage has changed completely. As the cost of one shot converges on compute time, working teams no longer pick the one best image. They generate dozens and curate.
In practice, you need to distinguish pixel-space methods from latent-space methods. Stable Diffusion-family models remove noise in a compressed latent space, so they reach the same quality with far less compute. Early models took 1,000 steps, but today’s samplers produce an image in 20–50 steps, and distillation techniques bring that down to 1–4. The line with GANs is clear too. A GAN generates in one pass, which is fast, but its training is unstable and its outputs tend to cluster. Diffusion takes more steps but leads in diversity and controllability. Seed values, conditioning inputs such as ControlNet, and small-scale additional training such as LoRA are the means of control.
The most common misstep in the field is the belief that good prompts are all you need. The same sentence produces a different picture with a different seed or model version, so if you don’t record your settings, you can’t recreate a follow-up shot in the same tone three months later. Replacing product photos with generated images is also risky. If a shot with a smudged logo or the wrong number of handles goes onto a detail page, it comes back as advertising-labeling disputes and returns. It is safer to draw the line so that generated images serve as drafts, backgrounds, and supporting shots, not as a replacement for photography, and to assign a final reviewer.
John KimInstructor John Kim, author of "B2B Marketing Design" and CEO of Nanum Consulting
Seungtae ParkAX instructor who teaches by building AI business development and marketing automation himselfProposed in 2015 in research by Sohl-Dickstein et al. and made practical by the 2020 DDPM paper, diffusion models became the foundation of image-generation AI such as DALL·E and Midjourney.
Consider a hypothetical case. Company A sells home appliances online, with 140 employees and annual revenue of about KRW 90 billion (≈ US$67,335,000). Each month it builds detail pages for 30-plus new products, and outsourcing a single background shot cost KRW 600,000 (≈ US$450) and took two weeks. When the first-half new-product launch slipped by three weeks, the head of marketing ordered the team to look for options other than photo shoots.
- A digital marketing team lead counted 1,280 product detail-page shots from the past year and found that half were background and mood shots showing atmosphere without the product.
- They decided to generate only that half, fine-tuned a lightweight LoRA model on 300 of the company’s own photos to match its visual tone, and ran a pilot through June.
- In the first review, a product planning team manager pointed out that “the refrigerator handle looks like two,” and the team set a firm rule: shots that include the product use real photography only.
- Two designers adopted a reproducible-records rule: keep the model version, prompt, and seed for six months, tracked in a spreadsheet.
- Three months later, lead time for a background shot had dropped from two weeks to one day, the monthly outsourcing bill had fallen by KRW 7 million (≈ US$5,240) (from KRW 18 million (≈ US$13,500)), and new-product launch delays had narrowed from three weeks to two days.