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Recurrent Interface Network Pytorch

A public Python repository implementing Recurrent Interface Network (RIN) with PyTorch, including model and Gaussian diffusion classes and a trainer for image generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Recurrent Interface Network Pytorch — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a code-level starting point for experimenting with RIN-based diffusion training or image sampling in PyTorch.

Who it is for

  • Researchers and developers prototyping diffusion-based image-generation workflows.
  • PyTorch users exploring latent self-conditioning, attention mechanisms, or noise schedules.

Documented uses

  • Configure a RIN model and Gaussian diffusion process for image-generation experiments.
  • Train on an image directory and save periodic results.
  • Apply the diffusion class to image tensors, calculate training loss, and sample images after training.
Before you choose

Requirements & limits

The public repository is MIT-licensed. Its README documents installation with pip install rin-pytorch.

  • The README examples use CUDA and show 128-by-128 RGB image tensors in the direct sampling workflow.
  • Trainer settings include an image-directory path, batch size, learning rate, training-step count, and periodic saving.
  • The README describes video generation as a project aim, but its usage examples demonstrate image training and sampling rather than a video workflow.
  • For higher-resolution training, the GaussianDiffusion scale setting is documented as a way to increase noising.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train an RIN diffusion model on an image directory

Configure and train the repository’s RIN and GaussianDiffusion classes with Trainer.

Before you start

  • Install the package with pip install rin-pytorch.
  • Provide a directory of images at the path passed to Trainer.
  • The example uses CUDA via .cuda().

Steps

  1. Import GaussianDiffusion, RIN, and Trainer from rin_pytorch.

  2. Create an RIN model, for example with dim=256, image_size=128, patch_size=8, depth=6, num_latents=128, dim_latent=512, and latent_self_attn_depth=4, then call .cuda().

  3. Wrap the model in GaussianDiffusion with settings such as timesteps=400, train_prob_self_cond=0.9, and scale=1.0, then call .cuda().

  4. Create Trainer with the diffusion object, your image-directory path, and training settings; call trainer.train(). The README example uses train_batch_size=4, train_lr=1e-4, and train_num_steps=700000.

Expected outcome

Training results are saved periodically to the `./results` folder.

Guide limitations

  • The README does not specify supported image-directory formats or preprocessing requirements for this Trainer workflow.
  • For higher-resolution images such as 512 or 1024, the README says scale should be set below 1 for more noising and improved convergence; it does not prescribe a specific value.
  • The repository author says generation fidelity needs verification on their own machine; the example is not evidence of independently verified results.
Documentation 1 ↗
Source-based practical guide

Compute diffusion loss and sample images with RIN classes

Use RIN and GaussianDiffusion directly, outside Trainer, to compute loss and request image samples.

Before you start

  • Install the package with pip install rin-pytorch.
  • The README example imports PyTorch and uses CUDA tensors and .cuda().
  • The example input tensor has shape (8, 3, 128, 128) and is described as normalized from 0 to 1.

Steps

  1. Import torch, RIN, and GaussianDiffusion.

  2. Create an RIN model, for example with dim=256, image_size=128, patch_size=8, depth=6, num_latents=128, and latent_self_attn_depth=4, then call .cuda().

  3. Create GaussianDiffusion around the model, using settings such as timesteps=1000, train_prob_self_cond=0.9, and scale=1.0.

  4. Create the example input with torch.randn(8, 3, 128, 128).cuda(), call the diffusion object to obtain loss, then call loss.backward(). After training, request samples with diffusion.sample(batch_size=4).

Expected outcome

The example computes a loss and requests a batch of four sampled images, with the documented expected shape `(4, 3, 128, 128)`.

Guide limitations

  • The README comments that sampling is after a lot of training; it does not provide a trained checkpoint or a quick-training procedure for this direct-class example.
  • The example input is random data, not a procedure for loading a real image dataset.
  • The author notes that generation fidelity needs verification on their own machine.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
8 October 2026
Original website title
lucidrains/recurrent-interface-network-pytorch · Official homepage title · retained in its source language
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