{"id":27036970,"url":"https://github.com/louis-alexandre-laguet/diffusion_model","last_synced_at":"2025-04-05T01:15:25.776Z","repository":{"id":283815216,"uuid":"952972884","full_name":"louis-alexandre-laguet/Diffusion_Model","owner":"louis-alexandre-laguet","description":"This repository contains multiple implementations of Diffusion Models, starting from the Denoising Diffusion Probabilistic Model (DDPM) and progressing to its conditional version (CDM). 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These exercises allow exploring image generation using a diffusion and denoising process.\n\n## Repository Content\n\n### 1. DDPM Implementation and Sampling Process\n**Objective:**\n- Understand how probabilistic diffusion models (DDPM) work.\n- Learn how images are generated by reversing a noise diffusion process.\n- Implement the sampling process to progressively denoise an image from pure noise.\n\n### 2. DDPM Implementation and Training Process\n**Objective:**\n- Train a DDPM model on image data.\n- Implement the loss function and train the denoising network.\n- Observe the improvement in image quality as the model progresses.\n\n### 3. Training a Conditional Diffusion Model (CDM)\n**Objective:**\n- Extend the DDPM approach to allow for conditional image generation.\n- Condition the model on additional information (e.g., class labels, textual descriptions, structured attributes).\n- Experiment with a Conditional Denoising Diffusion Model (CDDM) for more precise control over generated images.\n\nTrained models are saved in the `models` directory.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flouis-alexandre-laguet%2Fdiffusion_model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flouis-alexandre-laguet%2Fdiffusion_model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flouis-alexandre-laguet%2Fdiffusion_model/lists"}