Custom implementation of iterative image diffusion process
docker build -t sdiffusion .- to build docker- To run a container, use:
docker run -d -it --init \
--gpus=all \
--ipc=host \
--volume="$PWD:/app" \
--volume="/home/:/hhome" \
--volume="/usr/local/cuda:/usr/local/cuda" \
--publish="5555:5555" \
--publish="5556:5556" \
sdiffusion bash
jupyter lab --no-browser --ip 0.0.0.0 --port 5555 --allow-root
- Yannic Kilcher | DDPM - Diffusion Models Beat GANs on Image Synthesis (Machine Learning Research Paper Explained)
- Jeremy Howard | Lesson 9: Deep Learning Foundations to Stable Diffusion, 2022)
- https://github.com/fastai/diffusion-nbs - Tools and Notebooks from fast.ai
- From Autoencoder to Beta-VAE
- What are Diffusion Models?
- Tools and Resources for AI Art
- Private Local Waifu Diffusion colab
- Stable Diffusion Tutorial Part 1: Run Dreambooth in Gradient Notebooks
- Stable Diffusion Tutorial Part 2: Using Textual Inversion Embeddings to gain substantial control over your generated images
- Generating images with Stable Diffusion
- Stable Diffusion Models
- DreamFusion: Text-to-3D using 2D Diffusion
- High-Resolution Image Synthesis with Latent Diffusion Models | GitHub
- Denoising Diffusion Probabilistic Models
- Improved Denoising Diffusion Probabilistic Models
- DiffRF: Rendering-guided 3D Radiance Field Diffusion
- 3D Neural Field Generation using Triplane Diffusion
- Hugging Face Diffusion Models Course
- Diffusion samplers easy to use implementations
- DDPM(Denoising Diffusion Probabilistic Models) was trained on MNIST and Anime Face Dataset