Tiny-Diffusion

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tiny-diffusion is an open-source coding & development tool that provides a character-level language diffusion model for text generation. It includes a tiny GPT implementation and is trained on Tiny Shakespeare.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is tiny-diffusion?

tiny-diffusion offers a character-level language diffusion model for text generation, implemented in just 365 lines of Python code. This compact model, with 10.7 million parameters, is trained on Tiny Shakespeare, making it suitable for local experimentation and learning. The repository also features a tiny GPT implementation in 313 lines, with significant code overlap between the two models. It supports parallel decoding for diffusion and autoregressive generation for GPT. Users can train both models from scratch, visualize the generation process, and compare the diffusion and GPT models side-by-side. The diffusion model introduces key modifications like a mask token, bidirectional attention, confidence-based parallel decoding, and a training objective focused on unmasking.

Best used for

Ideal for developers and AI researchers who need to understand and experiment with compact language models for text generation. Especially valuable for those looking to compare character-level diffusion models with traditional GPT architectures and visualize their operational differences.

Common actions

generate text

train language models

experiment with diffusion models

compare text generation models

"AI Agents"face swappingautomated workflowdeepfakecollaborationopen-sourceworkflowslow-code/no-codegithub copilot

Capabilities

Key features

Character-level diffusion model
Tiny GPT implementation
Parallel text decoding
Bidirectional attention
Confidence-based parallel decoding
Training scripts included
Generation visualization

Target Audience

ai/ml researchersdeep learning studentsdevelopers interested in generative aieducators in ai/ml

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary difference between the diffusion and GPT models in tiny-diffusion?

The GPT model generates text sequentially using causal attention and predicts the next token. In contrast, the diffusion model generates text in parallel, uses bidirectional attention, and iteratively fills in masked tokens based on confidence, predicting original tokens from partially masked sequences.

How long does it take to train the models from scratch?

The GPT model trains for 5,000 iterations, taking approximately 10 minutes on an A100 GPU. The diffusion model trains for 10,000 iterations, requiring about 20 minutes on the same hardware, as fewer tokens contribute to the loss during its training.

Can I visualize the text generation process?

Yes, tiny-diffusion includes a visualization script. You can visualize the diffusion model's generation process step-by-step, or compare the diffusion and GPT models side-by-side to observe their different generation mechanisms.

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