facebookresearch/DiTArchived
DiT
DiT is a latent diffusion model and transformer-based generative AI framework implemented in PyTorch. It functions as a class-conditional image generator that replaces traditional convolutional backbones with a transformer architecture to synthesize high-fidelity images.
The project utilizes patch-based latent processing and latent space compression to operate on low-dimensional image representations. It incorporates class-conditional guidance and adjustable guidance scales to control the visual content of generated images during the sampling process.
The framework covers distributed model training, iterative noise sampling, and the creation of synthetic image datasets. It also includes tools for model quality evaluation to calculate accuracy and quality scores against standard benchmarks.
Features
- Diffusion Transformers - Combines transformer-based attention with diffusion-based denoising to synthesize high-fidelity images.
- Diffusion Model Training - Provides distributed training workflows for transformer-based latent diffusion models to improve scalability and speed.
- Patch-Based Processing - Splits latent image representations into small patches to be processed as tokens by the transformer architecture.
- Latent Space Compression - Utilizes low-dimensional representations to reduce computational overhead while maintaining high visual fidelity.
- Latent Diffusion Models - Performs iterative denoising within compressed latent spaces to produce high-fidelity synthetic images.
- Conditional Image Generation - Implements guidance mechanisms that use category labels to control the visual content of generated images.
- Transformer-Based Generative Backbones - Replaces traditional convolutional backbones with transformer blocks to improve scaling in image generation.
- Latent Noise Prediction - Generates images by iteratively predicting and removing noise within a compressed latent representation.
- Synthetic Content Generators - Produces synthetic visual content by adjusting sampling steps and guidance scales from pre-trained weights.
- Diffusion Model Evaluators - Provides tools to calculate accuracy and quality scores for diffusion model outputs against standard benchmarks.
- Conditional Training - Provides scripts to train models that are conditioned on specific class labels via embedding concatenation.
- Image Set Sampling - Produces batches of images by adjusting class-conditional weights and guidance scales during the sampling process.
- Large Scale Training - Provides distributed training capabilities to scale model training across multiple GPUs for large-scale image datasets.
- Synthetic Dataset Generation - Generates large batches of high-quality synthetic images to create datasets for visual benchmarking.
- Diffusion Model Research - Scalable diffusion models using transformer backbones.
- Vision Transformers - Scalable diffusion models built upon transformer architectures.
- Generation - Listed in the “Generation” section of the Awesome Diffusion Models awesome list.
Star history
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