Diffusion-Based Generation Approach
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A Diffusion-Based Generation Approach is a generative modeling approach that uses noise diffusion processes to create outputs.
- Context:
- It can (typically) perform Forward Diffusion through gaussian noise addition.
- It can (typically) execute Reverse Diffusion through learned denoising process.
- It can often achieve Training Stability through gradual noise transition.
- It can often provide Sample Diversity through stochastic sampling.
- It can often prevent Mode Collapse through controlled noise schedule.
- ...
- It can range from being a Simple Unconditional Generator to being a Complex Conditional System, depending on its architecture complexity.
- It can range from being a Raw Data Space Model to being a Latent Space Model, depending on its operational domain.
- ...
- It can maintain Data Distribution Quality through markov chain process.
- It can support Conditional Generation through text encoder integration.
- It can enable High Quality Generation through u-net architecture.
- It can integrate with Text Encoder for text-guided generation.
- It can connect to Variational Autoencoder for dimension reduction.
- It can support U-Net Architecture for denoising process.
- ...
- Examples:
- Image Generation Models, such as:
- Text-to-Image Systems, such as:
- Image Editing Systems, such as:
- Audio Generation Models, such as:
- Music Generation Systems, such as:
- MusicLM for musical composition.
- AudioGen for sound synthesis.
- Music Generation Systems, such as:
- 3D Content Models, such as:
- 3D Asset Generators, such as:
- Point-E for 3d object creation.
- GET3D for 3d mesh generation.
- 3D Asset Generators, such as:
- ...
- Image Generation Models, such as:
- Counter-Examples:
- Generative Adversarial Network, which uses adversarial training instead of noise diffusion.
- Variational Autoencoder, which relies on direct latent encoding rather than gradual denoising.
- Autoregressive Model, which generates sequential output without parallel processing.
- See: Noise Scheduling, Denoising Process, Markov Chain, U-Net Architecture, Latent Diffusion, Conditional Generation.
2024-12-29
- Perplexity.ai
- Question: Explain how diffusion models work in AI, including their key components, advantages over other generative approaches, and main applications. Please cover both the forward and reverse processes, and discuss why they have become increasingly popular compared to GANs and VAEs.
- Answer: Diffusion models are a revolutionary type of generative AI that simulates how particles spread over time, transforming random noise into high-quality, structured data.
- Core Components:
- Forward Diffusion Process:
- The forward process gradually adds Gaussian noise to data through a Markov chain.
- Uses mathematical formula with noise level and Gaussian random noise parameters.
- Transforms input data through successive noise additions.
- Reverse Diffusion Process:
- Neural network learns to reverse noise addition step by step.
- Transforms random noise back into structured data.
- Implements gradual denoising until clear output emerges.
- Forward Diffusion Process:
- Model Advantages:
- Compared to GANs:
- More stable training without mode collapse issues.
- Better output diversity.
- More controlled generation process.
- Compared to VAEs:
- Higher quality outputs without blurriness.
- Better preservation of fine details.
- More flexible latent representations.
- Compared to GANs:
- Applications:
- Core Components:
- Citations:
[1] https://kanerika.com/blogs/diffusion-models/ [2] https://generativeailab.org/l/generative-ai/a-tale-of-three-generative-models-comparing-diffusion-gan-and-vae-techniques/569/ [3] https://www.ultralytics.com/blog/what-are-diffusion-models-a-quick-and-comprehensive-guide [4] https://www.superannotate.com/blog/diffusion-models [5] https://www.geeksforgeeks.org/what-are-diffusion-models/ [6] https://arxiv.org/html/2408.08751v1 [7] https://www.calibraint.com/blog/beginners-guide-to-diffusion-models [8] https://blog.marvik.ai/2023/11/28/an-introduction-to-diffusion-models-and-stable-diffusion/ [9] https://bigblue.academy/en/diffusion-models [10] https://pub.towardsai.net/diffusion-models-vs-gans-vs-vaes-comparison-of-deep-generative-models-67ab93e0d9ae?gi=a91cd25d29b5
2024
- (LCM team et al., 2024) ⇒ LCM team, Loïc Barrault, Paul-Ambroise Duquenne, Maha Elbayad, Artyom Kozhevnikov, Belen Alastruey, Pierre Andrews, Mariano Coria, Guillaume Couairon, Marta R. Costa-jussà, David Dale, Hady Elsahar, Kevin Heffernan, João Maria Janeiro, Tuan Tran, Christophe Ropers, Eduardo Sánchez, Robin San Roman, Alexandre Mourachko, Safiyyah Saleem, and Holger Schwenk. (2024). “Large Concept Models: Language Modeling in a Sentence Representation Space.” doi:10.48550/arXiv.2412.08821
- NOTE: The paper explores continuous generative modeling for sentences through diffusion processes, showing how noisy embeddings can be iteratively denoised into coherent sentence representations.
- NOTE: The authors investigate a discrete alternative to diffusion by quantizing SONAR embeddings with Residual Vector Quantization, enabling discrete sampling at the sentence level, albeit with potential trade-offs in quality.