### Install lightweight-gan

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Install the library using pip. This is the first step before using any of the command-line tools.

```bash
pip install lightweight-gan
```

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

### Basic Training Command

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Start training a GAN model with specified data and image size. Models and results are saved automatically.

```bash
lightweight_gan --data ./path/to/images --image-size 512
```

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

### Install Aim for Training Insights

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Install the Aim experiment tracker using pip. This is required for visualizing training runs and metrics.

```bash
pip install aim
```

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

### Start Aim UI

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Launch the Aim UI to visualize training runs, metrics, and hyperparameters. Ensure you specify the correct repository path.

```bash
aim up --repo ./path/to/logs/
```

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

### Train with Aim Tracking

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Installs the Aim library and trains the GAN with Aim tracking enabled. Launch the Aim UI to visualize the experiment results.

```bash
# Install aim
pip install aim

# Train with Aim tracking
lightweight_gan \
    --data ./path/to/images \
    --image-size 512 \
    --use-aim \
    --aim_repo ./experiments/

# Launch Aim UI to visualize results
aim up --repo ./experiments/
```

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

### Full Augmentation Setup

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Configure all available augmentation types: color, cutout, offset, and translation. This provides the most comprehensive augmentation strategy.

```bash
--aug-types [color,cutout,offset,translation]
```

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

### Train GANs via Command Line Interface

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Use the CLI to initiate training, configure hyperparameters, enable mixed precision, or utilize multi-GPU setups.

```bash
# Basic training on a folder of images at 512x512 resolution
lightweight_gan --data ./path/to/images --image-size 512

# Advanced training with custom settings
lightweight_gan \
    --data ./path/to/images \
    --name my_experiment \
    --image-size 512 \
    --batch-size 16 \
    --gradient-accumulate-every 4 \
    --num-train-steps 200000 \
    --learning-rate 2e-4 \
    --save-every 1000 \
    --evaluate-every 1000

# Training with augmentation (essential for low data)
lightweight_gan \
    --data ./path/to/images \
    --image-size 512 \
    --aug-prob 0.25 \
    --aug-types [translation,cutout,color]

# Mixed precision training for faster performance
lightweight_gan --data ./path/to/images --image-size 512 --amp

# Multi-GPU distributed training
lightweight_gan --data ./path/to/images --image-size 512 --multi-gpus
```

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

### Run Lightweight GAN with Aim Logging

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Execute the Lightweight GAN training script, specifying the Aim repository for logging. Logs will be stored in the specified directory.

```bash
lightweight_gan --data ./path/to/images --image-size 512 --use-aim --aim_repo ./path/to/logs/
```

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

### Visualize Training Progress

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Initializes the Trainer and generates progress images from all saved checkpoints. The output is saved in the specified results directory. Use ffmpeg to convert images to video.

```python
from lightweight_gan import Trainer

# Initialize and configure trainer
trainer = Trainer(
    name='my_experiment',
    image_size=512,
    results_dir='./results',
    models_dir='./models'
)

# Generate progress images from all checkpoints
trainer.show_progress(
    num_images=4,  # Images per checkpoint
    types=['default', 'ema']
)
# Output: ./results/my_experiment-progress/ with numbered images

# Convert to video with ffmpeg:
# ffmpeg -framerate 10 -pattern_type glob -i '*-ema.jpg' progress.mp4
```

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

### Show Training Progress

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Generate a sequence of images to visualize the training progress. The output can be converted to a video using ffmpeg.

```bash
lightweight_gan \
  --name {name of run} \
  --show-progress \
  --generate-types {types of result, default: [default,ema]} \
  --num-image-tiles {count of image result}
```

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

### Train with Transparent Images

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Enable training with transparent images by using the `--transparent` flag.

```bash
lightweight_gan --data ./path/to/images --transparent
```

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

### Enable Multi-GPU Training

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Utilize multiple GPUs for training by including the `--multi-gpus` flag in your command.

```bash
--multi-gpus
```

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

### Advanced Training Settings

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Configure training parameters such as run name, batch size, gradient accumulation, and total training steps for more control over the training process.

```bash
lightweight_gan \
    --data ./path/to/images \
    --name {name of run} \
    --batch-size 16 \
    --gradient-accumulate-every 4 \
    --num-train-steps 200000
```

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

### Initialize and Use LightweightGAN

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Initializes the complete LightweightGAN module, which combines Generator, Discriminator, EMA generator, and optimizers. Access individual components and use the EMA generator for sample generation.

```python
import torch
from lightweight_gan import LightweightGAN

# Initialize full GAN
gan = LightweightGAN(
    latent_dim=256,
    image_size=512,
    optimizer='adam',  # or 'adabelief'
    fmap_max=512,
    transparent=False,
    greyscale=False,
    disc_output_size=1,
    attn_res_layers=[32, 64],
    freq_chan_attn=False,
    lr=2e-4,
    ttur_mult=1.0  # Two time-scale update rule multiplier for discriminator
)

# Access individual components
generator = gan.G  # Main generator
discriminator = gan.D  # Discriminator
ema_generator = gan.GE  # EMA generator (for smoother outputs)
d_augmented = gan.D_aug  # Discriminator with augmentation wrapper

# Optimizers
g_optimizer = gan.G_opt
d_optimizer = gan.D_opt

# Update EMA weights (call periodically during training)
gan.EMA()

# Generate samples
latents = torch.randn(4, 256).cuda()
with torch.no_grad():
    samples = gan.GE(latents)  # Use EMA generator for best quality
```

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

### Convert Progress Images to Video

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Use ffmpeg to convert the generated progress images into a video file. Adjust the framerate as needed.

```bash
ffmpeg -framerate 10 -pattern_type glob -i '*-ema.jpg' out.mp4
```

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

### Generate Samples After Training

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Generate sample images after training is complete. You can specify the checkpoint number to load from; otherwise, the latest checkpoint is used.

```bash
lightweight_gan \
  --name {name of run} \
  --load-from {checkpoint num} \
  --generate \
  --generate-types {types of result, default: [default,ema]} \
  --num-image-tiles {count of image result}
```

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

### Initialize Trainer with Aim Integration

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Initialize the Trainer class with parameters for experiment tracking using Aim. Set `use_aim=True` and specify the `aim_repo` path. `aim_run_hash` can be auto-generated or set to resume a previous run. Hyperparameters are passed via the `hparams` dictionary.

```python
trainer = Trainer(
    name='tracked_experiment',
    image_size=512,
    use_aim=True,
    aim_repo='./experiments/',
    aim_run_hash=None,  # Auto-generated, or specify to resume
    hparams={
        'image_size': 512,
        'batch_size': 16,
        'learning_rate': 2e-4
    }
)
```

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

### Train with Greyscale Images

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Configure the model to train with greyscale images using the `--greyscale` flag.

```bash
lightweight_gan --data ./path/to/images --greyscale
```

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

### Load Models and Generate Images

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Load existing checkpoints to generate static images or interpolation animations.

```python
from lightweight_gan import Trainer

# Initialize trainer and load from checkpoint
trainer = Trainer(
    name='flowers_512',
    image_size=512,
    results_dir='./results',
    models_dir='./models'
)
trainer.load(checkpoint_num=100)  # Load specific checkpoint, or -1 for latest

# Generate images (saves to results directory)
output_dir = trainer.generate(
    num=0,
    num_image_tiles=16,  # Generate 16 images
    checkpoint=trainer.checkpoint_num,
    types=['default', 'ema']  # Generate both regular and EMA versions
)
print(f"Generated images saved to: {output_dir}")

# Generate interpolation animation (GIF)
trainer.generate_interpolation(
    num=0,
    num_image_tiles=4,  # 4x4 grid
    num_steps=100,  # Frames in animation
    save_frames=True  # Also save individual frames
)
```

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

### Enable Mixed Precision Training

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Activate automatic mixed precision training with the `--amp` flag. This can significantly speed up training and reduce memory usage.

```bash
--amp
```

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

### Augmentation with Color

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Enable augmentation with translation, cutout, and color effects by setting the augmentation probability and types. This is crucial for low-data settings.

```bash
lightweight_gan --data ./path/to/images --aug-prob 0.25 --aug-types [translation,cutout,color]
```

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

### Initialize and Use Generator Network

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Directly instantiate the Generator to synthesize images from latent vectors.

```python
import torch
from lightweight_gan import Generator

# Initialize generator for 512x512 images
generator = Generator(
    image_size=512,
    latent_dim=256,
    fmap_max=512,
    transparent=False,
    greyscale=False,
    attn_res_layers=[32, 64],  # Add attention at 32x32 and 64x64 resolutions
    freq_chan_attn=False  # Use GlobalContext instead of FCANet
)
generator.cuda()
generator.eval()

# Generate images from random latent vectors
batch_size = 4
latents = torch.randn(batch_size, 256).cuda()

with torch.no_grad():
    generated_images = generator(latents)
    # Output shape: (4, 3, 512, 512), values in range [0, 1]

print(f"Generated {generated_images.shape[0]} images of size {generated_images.shape[2]}x{generated_images.shape[3]}")
```

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

### Test Augmentation Basic Usage

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Test how augmentations are applied to a single image. Specify the image path using `--data` and enable testing with `--aug-test`. An augmented image file will be created.

```bash
lightweight_gan \
    --aug-test \
    --data ./path/to/lena.jpg
```

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

### Orchestrate Training with Trainer Class

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

The Trainer class manages the full training lifecycle, including data loading, loop execution, and checkpointing.

```python
from lightweight_gan import Trainer

# Initialize trainer with configuration
trainer = Trainer(
    name='flowers_512',
    results_dir='./results',
    models_dir='./models',
    image_size=512,
    batch_size=16,
    gradient_accumulate_every=4,
    learning_rate=2e-4,
    num_image_tiles=8,
    save_every=1000,
    evaluate_every=1000,
    aug_prob=0.25,
    aug_types=['translation', 'cutout'],
    attn_res_layers=[32, 64],  # Add attention at these resolutions
    amp=True  # Enable mixed precision
)

# Set data source
trainer.set_data_src('./path/to/flower/images')

# Training loop
num_train_steps = 150000
while trainer.steps < num_train_steps:
    trainer.train()
    if trainer.steps % 50 == 0:
        trainer.print_log()  # Output: G: 0.45 | D: 1.23 | GP: 0.12 | SS: 0.08

# Save final checkpoint
trainer.save(trainer.checkpoint_num)
```

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

### Test Augmentation with Options

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Customize augmentation testing by adjusting image size, augmentation types, batch size, and the number of image tiles. This allows for detailed inspection of augmentation effects.

```bash
lightweight_gan \
    --aug-test \
    --data ./path/to/lena.jpg \
    --batch-size 16 \
    --num-image-tiles 4 \
    --aug-types [color,translation]
```

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

### Generate Interpolations

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Create interpolations between generated samples. This helps visualize the latent space transitions.

```bash
lightweight_gan --name {name of run} --generate-interpolation
```

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

### Initialize and Use Discriminator

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Initializes the Discriminator network and performs a forward pass to calculate outputs and auxiliary reconstruction loss. Ensure the input tensor is on the CUDA device.

```python
import torch
from lightweight_gan import Discriminator

# Initialize discriminator
discriminator = Discriminator(
    image_size=512,
    fmap_max=512,
    transparent=False,
    greyscale=False,
    disc_output_size=1,  # Use 1x1 for faces, 5x5 for art
    attn_res_layers=[32, 64]
)
discriminator.cuda()

# Forward pass with auxiliary loss calculation
batch_size = 4
real_images = torch.randn(batch_size, 3, 512, 512).cuda()

# Get discriminator outputs and self-supervised reconstruction loss
logits, logits_32x32, aux_loss = discriminator(real_images, calc_aux_loss=True)

print(f"Logits shape: {logits.shape}")  # (4, 1) or (4, 25) depending on disc_output_size
print(f"32x32 logits shape: {logits_32x32.shape}")  # (4, 1)
print(f"Auxiliary reconstruction loss: {aux_loss.item():.4f}")
```

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

### Enable Dual Contrastive Loss

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Utilize a novel contrastive loss between real and fake logits to potentially improve generation quality over the default hinge loss.

```bash
lightweight_gan --data ./path/to/images --dual-contrast-loss
```

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

### Set Discriminator Output Size

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Configure the discriminator's output size. A 5x5 size may perform better for art datasets compared to the default 1x1, which is better for faces.

```bash
lightweight_gan --data ./path/to/art --image-size 512 --disc-output-size 5
```

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

### Add Attention to Resolution Layers

Source: https://github.com/lucidrains/lightweight-gan/blob/main/README.md

Incorporate linear and axial attention into specific resolution layers of the model. Ensure no spaces within the bracketed list of resolutions.

```bash
lightweight_gan --data ./path/to/images --image-size 512 --attn-res-layers [32,64] --aug-prob 0.25
```

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

### Calculate FID Score

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Configures the Trainer to calculate the Frechet Inception Distance (FID) score. Set the data source, load the latest checkpoint, and then manually calculate the FID score.

```python
from lightweight_gan import Trainer

trainer = Trainer(
    name='flowers_512',
    image_size=512,
    calculate_fid_every=5000,  # Calculate FID every 5000 steps
    calculate_fid_num_images=12800,  # Number of images for FID calculation
    clear_fid_cache=False  # Reuse cached real image features
)

trainer.set_data_src('./path/to/images')
trainer.load(-1)  # Load latest checkpoint

# Manual FID calculation
num_batches = 128  # 12800 / batch_size
fid_score = trainer.calculate_fid(num_batches)
print(f"FID Score: {fid_score:.2f}")  # Lower is better, <50 is good
```

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

### Apply DiffAugment

Source: https://context7.com/lucidrains/lightweight-gan/llms.txt

Applies differentiable data augmentations to input images. Supported types include 'color', 'translation', 'cutout', and 'offset'. The output shape remains the same as the input.

```python
import torch
from lightweight_gan.diff_augment import DiffAugment

# Create sample images (batch_size, channels, height, width)
images = torch.randn(8, 3, 256, 256).cuda()

# Apply augmentations
augmented = DiffAugment(
    images,
    types=['color', 'translation', 'cutout']
)
# color: random brightness, saturation, contrast
# translation: random shifts with black padding
# cutout: random rectangular masks
# offset: random circular shifts (wrapping)

print(f"Input shape: {images.shape}")
print(f"Output shape: {augmented.shape}")  # Same shape, augmented content

# Test augmentation visualization (CLI)
# lightweight_gan --aug-test --data ./path/to/image.jpg --aug-types [color,cutout,translation]
```

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