Curiosity · After 12th
How to learn Diffusion Models after 12th in Delhi
Diffusion models here are generative neural networks. They are unrelated to physical diffusion — the transport process governed by Fick's laws — studied in physics and chemical engineering. Diffusion models learn to reverse a gradual noising process, generating an image or audio clip by denoising pure noise step by step. They are the architecture behind most current text-to-image systems. Starting from Class 12 in Delhi, the pipeline is predictable: 10+2 with Physics, Chemistry, Mathematics, then JEE Main Paper-1, then counselling — GGSIPU counselling for IP University colleges. The real decision is choosing a college whose Diffusion Models coverage is genuine rather than a brochure keyword.
At a glance
- Topic
- Diffusion Models
- VSET programme
- B.Tech CSE (AI & ML)
- Coverage at VSET
- Elective-level coverage
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
The degree route
The degree route is a B.Tech with genuine Diffusion Models depth. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, that means elective-level coverage inside B.Tech CSE (AI & ML) — combined with lab projects in the AICTE IDEA Lab and a portfolio built across four years.
How VSET teaches Diffusion Models
Diffusion models learn to reverse a gradual noising process, generating an image or audio clip by denoising pure noise step by step. They are the architecture behind most current text-to-image systems. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & ML).
- The generative AI and deep learning material published at learn.engineering.vips.edu covers the model families diffusion belongs to.
- Diffusion sits at the advanced end of that material and is normally taken up as elective or project-level work rather than a core lab exercise.
- The computer vision content in the same curriculum provides the image-domain background it needs.
- Delivered inside the B.Tech CSE (AI & ML) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.
Where Diffusion Models skills lead
Graduates applying Diffusion Models skills typically target roles such as Generative AI Engineer, Computer Vision Engineer, Deep Learning Engineer, AI Research Associate, Applied Scientist. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.
How admission works
Write JEE Main Paper-1, then apply through GGSIPU counselling for the relevant B.Tech programme at VSET. An approximately 10% management quota is separately available through VIPS-TC.
Frequently asked questions
Can I learn Diffusion Models after 12th without coding background?
Yes — B.Tech programmes assume no prior coding; years one and two build programming and mathematics foundations before Diffusion Models-specific work begins. What matters at entry is 10+2 PCM and a JEE Main score.
Are diffusion models part of the core syllabus?
They are elective-level: the published curriculum covers generative AI, deep learning and computer vision, and diffusion is the advanced extension students usually meet in projects.
Can students run diffusion models on campus hardware?
Inference and light adaptation of open-weight models run on the AICTE IDEA Lab's GPU workstations; training a diffusion model from scratch is outside undergraduate compute budgets.
How does this relate to the LLM material?
Both are generative model families. The curriculum's generative AI content treats language and image generation as two branches of the same engineering problem.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & ML) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31