Implementation of Classifier Free Guidance in Pytorch, with emphasis on text conditioning, and flexibility to include multiple text embedding models

Creating a diffusion model from scratch in PyTorch to learn exactly how they work.

Training-Free (Inversion-Free) methods meet WAN2.1-T2V🤗

Remaining Useful Life estimation and sensor data generation by VAE and diffusion model on C-MAPSS dataset.

Zero-shot voice cloning text-to-speech (TTS) with explicit emotion class conditioning built on F5-TTS

ReNeg: Learning Negative Embedding with Reward Guidance

A latent flow-based diffusion model trained on the 2012 ImageNet dataset from scratch.

The implementation for "3D Scene Diffusion Guidance using Scene Graphs" paper. A Diffusion Model for Conditional 3D Scene Generation with Classifier-Free Guidance on Scene Graphs

Exploring classifier-free guidance in a DDPM language model for text generation towards emotion targets.

NeurIPS25 Spotlight | Classifier-free guidance (CFG) can be viewed as fixed-point iteration and thus be upgraded.

Diffusion Tutorials (Enhanced Chinese Edition) — A Chinese-translated and theory-enhanced version of tsmatz/diffusion-tutorials, bridging diffusion model theory (DDPM/SMLD/SDE) with practical PyTorch implementations.

TeEFusion: Blending Text Embeddings to Distill Classifier-Free Guidance (ICCV 2025)

Quantitative framework for measuring how conditioning effectiveness varies with noise level in diffusion model inference (SD 1.5 & SDXL)

DDPM (Denoising Diffusion Probabilistic Models) and DDIM (Denoising Diffusion Implicit Models) for conditional image generation

A Simplified notebook for Smoothed Energy Guidance utilised for Stable Diffusion 2.1 base

extension for old Forge webui; methods to modify the CFG during diffusion; can bypass uncond calculations for free performance gain

Medical Image Synthesis project (MedSyn). In-depth evaluation of the efffects of different synthesis models (i.e., CFG ccDDPM) for medical image synthesis for class balancing on image datasets (i.e., PathMNIST).

Text-to-Image Generation with Mamba State Space Models and Kolmogorov-Arnold Networks — a linear-complexity alternative to Stable Diffusion

Implementing DDPM, U-Net, Classifier-Free Guidance, and CLIP-style Cosine Similarity from scratch on FashionMNIST — based on NVIDIA DLI Generative AI with Diffusion Models curriculum. / NVIDIA DLI 'Generative AI with Diffusion Models' 커리큘럼 기반으로 DDPM, U-Net, CFG, CLIP 코사인 유사도를 처음부터 직접 구현한 생성형 AI 포트폴리오.

PyTorch implementation of 'CFG' (Ho et al., 2022).