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Linköping University
- Norrköping, Sweden
Stars
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
This is the official PyTorch implementation of ShadowRefiner. Our method is winner of Perceptual Track and achieves the second-best performance for Fidelity Track in NTIRE 2024 Shadow Removal Chall…
Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion
Collection of recent shadow removal works, including papers, codes, datasets, and metrics.
CVPR 2024: Learned representation-guided diffusion models for large-image generation
ShadowDiffusion (CVPR2023), Pytorch implementation
High-fidelity performance metrics for generative models in PyTorch
Unofficial implementation of the paper "The Chosen One: Consistent Characters in Text-to-Image Diffusion Models"
Official code for "Style Aligned Image Generation via Shared Attention"
Taming Transformers for High-Resolution Image Synthesis
A collection of resources on controllable generation with text-to-image diffusion models.
Diffusion Reading Group at EleutherAI
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
Implementation of a framework for Genie2 in Pytorch
PyTorch implementation of RCG https://arxiv.org/abs/2312.03701
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
The repository of Expanding Small-Scale Datasets with Guided Imagination (NeurIPS 2023).
source code for NeurIPS'23 paper "Dream the Impossible: Outlier Imagination with Diffusion Models"
A jekyll template for easy creation of course websites. Checkout the template here:
CMU Lecture: Machine Learning In Production / AI Engineering / Software Engineering for AI-Enabled Systems (SE4AI)
Some helpers and examples for creating an LLM fine-tuning dataset
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
Official pytorch implementation of the paper: "An Edit Friendly DDPM Noise Space: Inversion and Manipulations". CVPR 2024.
Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"
Materials for the course Deep Learning for Natural Language Processing