### Install RIN Pytorch

Source: https://github.com/lucidrains/recurrent-interface-network-pytorch/blob/main/README.md

Install the package via pip.

```bash
$ pip install rin-pytorch
```

--------------------------------

### Initialize and Train RIN Model

Source: https://context7.com/lucidrains/recurrent-interface-network-pytorch/llms.txt

Initializes a RIN model and Gaussian Diffusion process, then configures and starts the training pipeline using the Trainer class. Resuming training from a checkpoint is also demonstrated.

```python
from rin_pytorch import GaussianDiffusion, RIN, Trainer

# Initialize model and diffusion
model = RIN(
    dim=256,
    image_size=128,
    patch_size=8,
    depth=6,
    num_latents=128,
    dim_latent=512,
    latent_self_attn_depth=4,
).cuda()

diffusion = GaussianDiffusion(
    model,
    timesteps=400,
    train_prob_self_cond=0.9,
    scale=1.0,                    # Use scale < 1.0 for 512x512 or larger
).cuda()

# Create trainer with all training configurations
trainer = Trainer(
    diffusion,
    folder='/path/to/your/images',     # Directory containing training images
    train_batch_size=4,                 # Batch size per GPU
    gradient_accumulate_every=4,        # Effective batch = 4 * 4 = 16
    train_lr=1e-4,                      # Learning rate
    train_num_steps=700000,             # Total training steps
    ema_decay=0.995,                    # EMA decay for model averaging
    ema_update_every=10,                # Update EMA every N steps
    save_and_sample_every=1000,         # Checkpoint and sample frequency
    num_samples=16,                     # Number of samples to generate
    results_folder='./results',         # Output directory for checkpoints
    amp=False,                          # Enable automatic mixed precision
    mixed_precision_type='fp16',        # 'fp16' or 'bf16' when amp=True
    augment_horizontal_flip=True,       # Data augmentation
    max_grad_norm=1.0,                  # Gradient clipping
)

# Start training - results saved to ./results/
trainer.train()

# Resume training from checkpoint
trainer.load(milestone=50)  # Load model-50.pt
trainer.train()             # Continue training
```

--------------------------------

### RIN PyTorch Attention with Flash Attention

Source: https://context7.com/lucidrains/recurrent-interface-network-pytorch/llms.txt

Demonstrates initializing and using the Attend class for attention mechanisms, with support for standard PyTorch and optimized Flash Attention. Includes examples with and without attention masks.

```python
import torch
from rin_pytorch.attend import Attend

# Standard attention (compatible with all PyTorch versions)
attend_standard = Attend(dropout=0.1, flash=False)

# Flash attention (requires PyTorch 2.0+, auto-detects GPU capabilities)
attend_flash = Attend(dropout=0.1, flash=True)

# Prepare query, key, value tensors
batch_size, heads, seq_len, dim_head = 4, 8, 256, 64
q = torch.randn(batch_size, heads, seq_len, dim_head).cuda()
k = torch.randn(batch_size, heads, seq_len, dim_head).cuda()
v = torch.randn(batch_size, heads, seq_len, dim_head).cuda()

# Forward pass - returns attended values
out = attend_flash(q, k, v)
print(f"Attention output shape: {out.shape}")  # torch.Size([4, 8, 256, 64])

# With attention mask (for padding or causal attention)
mask = torch.ones(batch_size, seq_len, dtype=torch.bool).cuda()
mask[:, 200:] = False  # Mask out last 56 positions

out_masked = attend_flash(q, k, v, mask=mask)
print(f"Masked attention output shape: {out_masked.shape}")
```

--------------------------------

### Train RIN with Trainer

Source: https://github.com/lucidrains/recurrent-interface-network-pytorch/blob/main/README.md

Initialize the RIN model, GaussianDiffusion, and Trainer to begin the training process.

```python
from rin_pytorch import GaussianDiffusion, RIN, Trainer

model = RIN(
    dim = 256,                  # model dimensions
    image_size = 128,           # image size
    patch_size = 8,             # patch size
    depth = 6,                  # depth
    num_latents = 128,          # number of latents. they used 256 in the paper
    dim_latent = 512,           # can be greater than the image dimension (dim) for greater capacity
    latent_self_attn_depth = 4, # number of latent self attention blocks per recurrent step, K in the paper
).cuda()

diffusion = GaussianDiffusion(
    model,
    timesteps = 400,
    train_prob_self_cond = 0.9,  # how often to self condition on latents
    scale = 1.                   # this will be set to < 1. for more noising and leads to better convergence when training on higher resolution images (512, 1024) - input noised images will be auto variance normalized
).cuda()

trainer = Trainer(
    diffusion,
    '/path/to/your/images',
    num_samples = 16,
    train_batch_size = 4,
    gradient_accumulate_every = 4,
    train_lr = 1e-4,
    save_and_sample_every = 1000,
    train_num_steps = 700000,         # total training steps
    ema_decay = 0.995,                # exponential moving average decay
)

trainer.train()
```

--------------------------------

### Initialize and Run RIN Model

Source: https://context7.com/lucidrains/recurrent-interface-network-pytorch/llms.txt

Configure the RIN model and perform forward passes with or without latent self-conditioning.

```python
import torch
from rin_pytorch import RIN

# Initialize the RIN model with custom configuration
model = RIN(
    dim=256,                      # Model dimension for patch embeddings
    image_size=128,               # Input image size (must be divisible by patch_size)
    patch_size=8,                 # Size of image patches (smaller = more patches)
    channels=3,                   # Number of input channels (3 for RGB)
    depth=6,                      # Number of RIN blocks (recurrent iterations)
    num_latents=128,              # Number of latent tokens (paper used 256)
    dim_latent=512,               # Latent dimension (can exceed dim for more capacity)
    latent_self_attn_depth=4,     # Self-attention layers per recurrent step (K in paper)
    learned_sinusoidal_dim=16,    # Dimension for learned sinusoidal time embeddings
    latent_token_time_cond=False, # Use latent token vs adaptive layernorm for time
    dual_patchnorm=True,          # Apply normalization before and after patch projection
    patches_self_attn=True,       # Enable linear self-attention on patches
).cuda()

# Forward pass with image and time conditioning
batch_size = 4
images = torch.randn(batch_size, 3, 128, 128).cuda()  # Noised images
time = torch.rand(batch_size).cuda()                   # Diffusion timesteps [0, 1]

# Basic forward pass - returns denoised output
output = model(images, time)
print(f"Output shape: {output.shape}")  # torch.Size([4, 3, 128, 128])

# Forward pass with self-conditioning (latent warm-starting)
x_self_cond = torch.randn(batch_size, 3, 128, 128).cuda()      # Previous prediction
latent_self_cond = torch.randn(batch_size, 128, 512).cuda()    # Previous latents

output, latents = model(
    images,
    time,
    x_self_cond=x_self_cond,
    latent_self_cond=latent_self_cond,
    return_latents=True
)
print(f"Output: {output.shape}, Latents: {latents.shape}")
# Output: torch.Size([4, 3, 128, 128]), Latents: torch.Size([4, 128, 512])
```

--------------------------------

### Configure and Train RIN for High-Resolution Images

Source: https://context7.com/lucidrains/recurrent-interface-network-pytorch/llms.txt

Initializes a RIN model for 512x512 images, configures GaussianDiffusion with specific noise schedules, and sets up a Trainer for the full pipeline.

```python
model = RIN(
    dim=384,                      # Larger dimension for high-res
    image_size=512,
    patch_size=16,                # Larger patches to manage memory
    depth=8,
    num_latents=256,              # More latents for detail
    dim_latent=768,
    latent_self_attn_depth=4,
).cuda()

# Use scale < 1.0 for high resolution - adds more noise during training
# This helps convergence as shown in "simple diffusion" paper
diffusion = GaussianDiffusion(
    model,
    timesteps=1000,
    noise_schedule='sigmoid',     # Sigmoid schedule works best for large images
    train_prob_self_cond=0.9,
    scale=0.5,                    # Key parameter: more noise for high-res
    min_snr_loss_weight=True,
).cuda()

# Training loop with scale normalization
training_images = torch.randn(2, 3, 512, 512).cuda()
loss = diffusion(training_images)
print(f"High-res training loss: {loss.item():.4f}")

# Trainer for full pipeline
trainer = Trainer(
    diffusion,
    folder='/path/to/512x512/images',
    train_batch_size=2,           # Smaller batch for memory
    gradient_accumulate_every=8,  # Effective batch = 16
    train_lr=1e-4,
    train_num_steps=1000000,      # More steps for high-res
    save_and_sample_every=2000,
    num_samples=4,
    amp=True,                     # Use mixed precision to save memory
)

trainer.train()
```

--------------------------------

### High Resolution Image Training Configuration

Source: https://context7.com/lucidrains/recurrent-interface-network-pytorch/llms.txt

Configuration snippet for training RIN models on high-resolution images (512x512 and above). It highlights the use of the 'scale' parameter in GaussianDiffusion for improved convergence.

```python
import torch
from rin_pytorch import RIN, GaussianDiffusion, Trainer
```

--------------------------------

### RIN PyTorch Noise Schedules

Source: https://context7.com/lucidrains/recurrent-interface-network-pytorch/llms.txt

Demonstrates the usage of different noise schedule functions (linear, cosine, sigmoid) for diffusion models. The sigmoid schedule is recommended for larger image resolutions.

```python
import torch
from rin_pytorch.rin_pytorch import (
    simple_linear_schedule,
    cosine_schedule,
    sigmoid_schedule,
    gamma_to_alpha_sigma,
    gamma_to_log_snr
)

# Create timesteps from 0 to 1
t = torch.linspace(0, 1, 100)

# Linear schedule - simplest, uniform noise addition
gamma_linear = simple_linear_schedule(t, clip_min=1e-9)
print(f"Linear gamma range: [{gamma_linear.min():.4f}, {gamma_linear.max():.4f}]")

# Cosine schedule - smoother transitions, good for small images
gamma_cosine = cosine_schedule(t, start=0, end=1, tau=1, clip_min=1e-9)
print(f"Cosine gamma range: [{gamma_cosine.min():.4f}, {gamma_cosine.max():.4f}]")

# Sigmoid schedule - recommended for larger images (512+)
gamma_sigmoid = sigmoid_schedule(t, start=-3, end=3, tau=1, clamp_min=1e-9)
print(f"Sigmoid gamma range: [{gamma_sigmoid.min():.4f}, {gamma_sigmoid.max():.4f}]")

# Convert gamma to alpha (signal) and sigma (noise) coefficients
# For noised_img = alpha * img + sigma * noise
alpha, sigma = gamma_to_alpha_sigma(gamma_sigmoid, scale=1.0)
print(f"Alpha range: [{alpha.min():.4f}, {alpha.max():.4f}]")
print(f"Sigma range: [{sigma.min():.4f}, {sigma.max():.4f}]")

# Compute log signal-to-noise ratio for loss weighting
log_snr = gamma_to_log_snr(gamma_sigmoid, scale=1.0)
print(f"Log SNR range: [{log_snr.min():.4f}, {log_snr.max():.4f}]")
```

--------------------------------

### Train and Sample with GaussianDiffusion

Source: https://context7.com/lucidrains/recurrent-interface-network-pytorch/llms.txt

Wrap the RIN model with GaussianDiffusion to handle training loss calculation and image sampling.

```python
import torch
from rin_pytorch import RIN, GaussianDiffusion

# Create the base RIN model
model = RIN(
    dim=256,
    image_size=128,
    patch_size=8,
    depth=6,
    num_latents=128,
    latent_self_attn_depth=4,
).cuda()

# Wrap with GaussianDiffusion for training and sampling
diffusion = GaussianDiffusion(
    model,
    timesteps=1000,                # Number of diffusion timesteps
    use_ddim=True,                 # Use DDIM sampling (faster) vs DDPM
    noise_schedule='sigmoid',      # 'sigmoid', 'cosine', or 'linear'
    objective='v',                 # 'v' (velocity), 'x0', or 'eps' prediction
    train_prob_self_cond=0.9,      # Probability of self-conditioning during training
    min_snr_loss_weight=True,      # Use Min-SNR weighting for stable training
    min_snr_gamma=5,               # Gamma value for Min-SNR weighting
    scale=1.0,                     # Set < 1.0 for higher resolution (512, 1024)
    time_difference=0.,            # Time offset for sampling (helps with few timesteps)
).cuda()

# Training: forward pass returns loss
training_images = torch.randn(8, 3, 128, 128).cuda()  # Normalized images [0, 1]
loss = diffusion(training_images)
loss.backward()
print(f"Training loss: {loss.item():.4f}")

# Sampling: generate new images from noise
with torch.no_grad():
    sampled_images = diffusion.sample(batch_size=4)
    print(f"Sampled images shape: {sampled_images.shape}")  # torch.Size([4, 3, 128, 128])
    # Images are in range [0, 1], ready for visualization
```

--------------------------------

### Experiment with RIN and GaussianDiffusion

Source: https://github.com/lucidrains/recurrent-interface-network-pytorch/blob/main/README.md

Use the RIN and GaussianDiffusion classes directly for custom training loops or inference.

```python
import torch
from rin_pytorch import RIN, GaussianDiffusion

model = RIN(
    dim = 256,                  # model dimensions
    image_size = 128,           # image size
    patch_size = 8,             # patch size
    depth = 6,                  # depth
    num_latents = 128,          # number of latents. they used 256 in the paper
    latent_self_attn_depth = 4, # number of latent self attention blocks per recurrent step, K in the paper
).cuda()

diffusion = GaussianDiffusion(
    model,
    timesteps = 1000,
    train_prob_self_cond = 0.9,
    scale = 1.
)

training_images = torch.randn(8, 3, 128, 128).cuda() # images are normalized from 0 to 1
loss = diffusion(training_images)
loss.backward()
# after a lot of training

sampled_images = diffusion.sample(batch_size = 4)
sampled_images.shape # (4, 3, 128, 128)
```

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