Lumina-T2X is a unified framework for Text to Any Modality Generation
-
Updated
May 20, 2024 - Python
Lumina-T2X is a unified framework for Text to Any Modality Generation
DiffModeler: a diffusion model based protein complex structure modeling tool.
collection of diffusion model papers categorized by their subareas
Simple and readable code for training and sampling from diffusion models
[ICLR 2024] Official Implementation of "Diffusion-TS: Interpretable Diffusion for General Time Series Generation"
Tools for Stochastic Simulation using diffusion models (R).
A controllable image composition model which could be used for image blending, image harmonization, view synthesis.
A curated list of papers, code, and resources pertaining to generative image composition or object insertion.
A simple baseline for image composition using text-guided inpainting model
[TMI 2024] "High-Frequency Space Diffusion Model for Accelerated MRI"
3D Shape Completion - adaptation and improvement of DiffComplete model.
This repo is the official implementation of "MineDreamer: Learning to Follow Instructions via Chain-of-Imagination for Simulated-World Control "
Unconditional Image Generation using a [modifiable] pretrained VQVAE based Latent Diffusion Model, adapted from huggingface diffusers.
[ICASSP 2024] 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
[CVPR 2024] DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis
Official implementation of "Graph Signal Diffusion Model for Collaborative Filtering" (SIGIR 2024)
Official implement code of LAMP: Learn a Motion Pattern by Few-Shot Tuning a Text-to-Image Diffusion Model (Few-shot-based text-to-video diffusion)
[CVPR 2024 Highlight] Official PyTorch implementation of "MindBridge: A Cross-Subject Brain Decoding Framework"
Implementation of a diffusion model in pytorch.
[ICLR 2024] Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach Link: https://arxiv.org/abs/2401.15652
Add a description, image, and links to the diffusion-model topic page so that developers can more easily learn about it.
To associate your repository with the diffusion-model topic, visit your repo's landing page and select "manage topics."