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Best resources to learnGenerative Image & Video Models

Study sheet

Diffusion and related models generate images and video from text. You will learn how they work, how to run and prompt them, control techniques like ControlNet and LoRA, and the creative and legal issues around them.

8 resources · · How we curate

🎓Courses(1)

course

Practical Deep Learning for Coders, Part 2: From Deep Learning Foundations to Stable Diffusion

Over thirty hours of free video lessons rebuilding Stable Diffusion from scratch, starting at matrix multiplication and autograd and working up through DDPM, DDIM, conditional sampling, textual inversion and DreamBooth, with paper-reading practice throughout. The only free course taking a reader from tensor primitives to a working diffusion implementation without hand-waving, and the depth means the understanding survives model churn. Long and demanding.

advanced🇬🇧

📚Books(1)

bookPaid

Hands-On Generative AI with Transformers and Diffusion Models

A practitioner's book from the Hugging Face Diffusers team covering how diffusion models work, Stable Diffusion internals, sampling and guidance, fine-tuning with LoRA and DreamBooth, ControlNet and inpainting workflows, and chapters on audio and video generation.

intermediate🇬🇧

🌐Websites(2)

website

Hugging Face Diffusers Documentation

Official documentation for the Diffusers library: pipelines for image, video and audio generation, scheduler choices, LoRA and adapter loading, ControlNet usage, DreamBooth and textual-inversion training scripts, quantization and memory-offloading guides, and conceptual explanations of each component. The practical half of the topic — running models, choosing schedulers, applying LoRA and ControlNet, fine-tuning — has no better free source, and the maintainers' own docs avoid the huge volume of blog tutorials written against long-dead API versions.

intermediate🇬🇧
website

What are Diffusion Models?

A continuously revised technical survey covering forward and reverse diffusion processes, the DDPM training objective, score-based formulations, classifier and classifier-free guidance, latent diffusion, ControlNet, diffusion transformers, and acceleration methods including DDIM and consistency models. The map of the whole field in one page, with consistent notation across papers that each use their own. Kept updated since 2021 rather than abandoned, so it now covers guidance, latent diffusion, ControlNet and DiT in the same framework. The reference you return to after the Nakkiran tutorial teaches the core derivation.

advanced🇬🇧

📄Papers(4)

paper

Extracting Training Data from Diffusion Models

A USENIX Security 2023 study that recovers over a thousand memorized training images from Stable Diffusion, Imagen and DALL-E 2, including photographs of identifiable people and trademarked logos, and measures which design choices worsen memorization. It quantifies how often models reproduce training images and reports the copyright status of what it extracted.

advanced🇬🇧
paper

Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet)

The paper introducing ControlNet, which adds spatial conditioning such as edges, depth, segmentation and human pose to a frozen text-to-image diffusion model using trainable copies of its encoder joined by zero-initialized convolutions.

advanced🇬🇧
paper

High-Resolution Image Synthesis with Latent Diffusion Models

The CVPR 2022 paper behind Stable Diffusion. It runs the diffusion process in the latent space of a pretrained autoencoder and adds cross-attention conditioning, cutting compute enough for consumer hardware while handling text, layout, inpainting and super-resolution. Every practical tool in this topic — SD 1.5, SDXL, and their descendants — is an implementation of this paper.

advanced🇬🇧
paper

Step-by-Step Diffusion: An Elementary Tutorial

A 35-page tutorial deriving diffusion models and flow matching from first principles, deliberately avoiding SDEs, ELBOs and score functions. Assumes probability, calculus and linear algebra, and gives pseudocode for the sampling algorithms it derives. The single best free entry point to the mathematics. Needs no stochastic calculus or variational-bound machinery, so a reader with undergraduate probability can follow a complete derivation of DDPM and DDIM sampling and then implement it.

advanced🇬🇧
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Frequently Asked Questions

What are the best free resources to learn Generative Image & Video Models?

Dantes has curated 8 resources for Generative Image & Video Models, including 1 books, 1 courses, 2 websites, 4 papers. All resources are hand-picked for quality — no algorithmic filler. Browse the full list above to find the format that works best for you.

Is Generative Image & Video Models hard to learn?

Generative Image & Video Models is approachable at the beginner level — there are resources here specifically for those starting from scratch. As you progress, intermediate and advanced material is also available to take your skills further.

What types of Generative Image & Video Models learning resources are available on Dantes?

For Generative Image & Video Models, Dantes has curated 1 books, 1 courses, 2 websites, 4 papers. Each resource type serves a different learning style: videos and YouTube for visual learners, books for depth, courses for structured progression, and websites for quick reference.

How does Dantes select Generative Image & Video Models resources?

Dantes is an algorithm-free learning directory. Resources are hand-curated based on quality, accuracy, and usefulness — not engagement metrics or paid placements. The goal is to surface the best learning material for Generative Image & Video Models, whether it's a free YouTube series, a classic textbook, or an open courseware from a top university.

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