### Install Muse MaskGit PyTorch

Source: https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/README.md

Install the package via pip.

```bash
$ pip install muse-maskgit-pytorch
```

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

### Get T5 Model and Tokenizer

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Retrieve and cache the T5 model and its corresponding tokenizer for direct use. This is efficient for repeated access.

```python
# Get model and tokenizer directly (cached for reuse)
model, tokenizer = get_model_and_tokenizer('t5-small')
```

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

### Get T5 Encoded Dimension

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Retrieve the embedding dimension for various T5 model sizes without loading the model. Supports 't5-small', 't5-base', 't5-large', and a default T5 name.

```python
import torch
from muse_maskgit_pytorch.t5 import (
    t5_encode_text,
    get_encoded_dim,
    get_model_and_tokenizer,
    DEFAULT_T5_NAME
)

# Get the embedding dimension for a T5 model (without loading the model)
dim_small = get_encoded_dim('t5-small')      # 512
dim_base = get_encoded_dim('t5-base')        # 768
dim_large = get_encoded_dim('t5-large')      # 1024
dim_default = get_encoded_dim(DEFAULT_T5_NAME)  # google/t5-v1_1-base = 768
```

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

### Initialize MaskGit Model

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Set up the complete MaskGit text-to-image generation model by combining a VAE and a MaskGitTransformer. Configure parameters like image size, conditioning dropout, and self-conditioning probability.

```python
import torch
from muse_maskgit_pytorch import VQGanVAE, MaskGit, MaskGitTransformer

# Initialize VAE and load pretrained weights
vae = VQGanVAE(dim=256, codebook_size=65536).cuda()
vae.load('./pretrained_vae.pt')

# Create transformer for base generation
transformer = MaskGitTransformer(
    num_tokens=65536,
    seq_len=256,            # 16x16 feature map
    dim=512,
    depth=8,
    dim_head=64,
    heads=8,
    ff_mult=4,
    t5_name='t5-small'
)

# Create MaskGit model
maskgit = MaskGit(
    vae=vae,
    transformer=transformer,
    image_size=256,              # Output image size
    cond_drop_prob=0.25,         # Classifier-free guidance dropout
    self_cond_prob=0.9,          # Self-conditioning probability during training
    no_mask_token_prob=0.0,      # Probability of keeping tokens unmasked
    critic_loss_weight=1.0       # Weight for token critic loss (if using)
).cuda()
```

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

### Initialize and Run VQGanVAETrainer

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Initializes the VQGanVAETrainer for managing the training loop of the VQGanVAE. This trainer integrates with Accelerate for distributed training and handles checkpointing and validation.

```python
import torch
from muse_maskgit_pytorch import VQGanVAE, VQGanVAETrainer

# Create the VAE model
vae = VQGanVAE(
    dim=256,
    codebook_size=65536,
    use_vgg_and_gan=True
)

# Initialize the trainer with training configuration
trainer = VQGanVAETrainer(
    vae=vae,
    folder='/path/to/image/dataset',    # Folder containing training images
    image_size=128,                      # Training image size (can curriculum learn to larger sizes)
    batch_size=4,                        # Batch size per GPU
    grad_accum_every=8,                  # Gradient accumulation steps
    num_train_steps=50000,               # Total training steps
    lr=3e-4,                             # Learning rate for both VAE and discriminator
    valid_frac=0.05,                     # Fraction for validation split
    save_results_every=100,              # Save reconstruction samples every N steps
    save_model_every=1000,               # Save model checkpoint every N steps
    results_folder='./vae_results',      # Output directory
    use_ema=True,                        # Use exponential moving average
    ema_beta=0.995,                      # EMA decay rate
    max_grad_norm=1.0,                   # Gradient clipping for VAE
    discr_max_grad_norm=1.0,             # Gradient clipping for discriminator
    apply_grad_penalty_every=4,          # Apply gradient penalty every N steps
    accelerate_kwargs=dict()             # Additional Accelerate configuration
).cuda()

# Run training - automatically handles distributed training if launched with accelerate
trainer.train()

# Training loop with custom logging
def custom_log(logs):
    print(f"VAE Loss: {logs.get('loss', 'N/A')}, Discr Loss: {logs.get('discr_loss', 'N/A')}")

trainer.train(log_fn=custom_log)
```

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

### Train VQGanVAE

Source: https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/README.md

Instantiate and train a VQGanVAE on a folder of images.

```python
import torch
from muse_maskgit_pytorch import VQGanVAE, VQGanVAETrainer

vae = VQGanVAE(
    dim = 256,
    codebook_size = 65536
)

# train on folder of images, as many images as possible

trainer = VQGanVAETrainer(
    vae = vae,
    image_size = 128,             # you may want to start with small images, and then curriculum learn to larger ones, but because the vae is all convolution, it should generalize to 512 (as in paper) without training on it
    folder = '/path/to/images',
    batch_size = 4,
    grad_accum_every = 8,
    num_train_steps = 50000
).cuda()

trainer.train()
```

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

### Initialize and Use VQGanVAE

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Initializes the VQGanVAE model for learning discrete image representations. Use this for encoding images to tokens, decoding tokens back to images, and computing reconstruction and GAN losses.

```python
import torch
from muse_maskgit_pytorch import VQGanVAE

# Initialize the VQGanVAE with standard configuration
vae = VQGanVAE(
    dim=256,                    # Base model dimension
    channels=3,                 # RGB images
    layers=4,                   # Number of encoder/decoder layers
    codebook_size=65536,        # Size of the discrete codebook
    lookup_free_quantization=True,  # Use LFQ for improved codebook usage
    use_vgg_and_gan=True,       # Enable perceptual and GAN losses
    l2_recon_loss=False,        # Use L1 reconstruction loss
    discr_layers=4              # Discriminator depth
).cuda()

# Encode images to discrete tokens
images = torch.randn(4, 3, 256, 256).cuda()
encoded_fmap, token_ids, vq_loss = vae.encode(images)
# token_ids shape: (4, 16, 16) - discrete token indices

# Decode from token IDs back to images
reconstructed = vae.decode_from_ids(token_ids)
# reconstructed shape: (4, 3, 256, 256)

# Forward pass with loss computation for training
loss = vae(images, return_loss=True)
loss.backward()

# Get discriminator loss separately
discr_loss = vae(images, return_discr_loss=True, add_gradient_penalty=True)
discr_loss.backward()

# Save and load model weights
vae.save('./vae_checkpoint.pt')
vae.load('./vae_checkpoint.pt')

# Get feature map size for a given image size
fmap_size = vae.get_encoded_fmap_size(256)  # Returns 16 for 256x256 images with 4 layers
```

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

### Initialize and Use Unified Muse Model

Source: https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/README.md

Load trained base and super-resolution MaskGit models. Initialize the unified Muse model by passing these trained models. Generate images by providing a list of text prompts.

```python
from muse_maskgit_pytorch import Muse

base_maskgit.load('./path/to/base.pt')

superres_maskgit.load('./path/to/superres.pt')

# pass in the trained base_maskgit and superres_maskgit from above

muse = Muse(
    base = base_maskgit,
    superres = superres_maskgit
)

images = muse([
    'a whale breaching from afar',
    'young girl blowing out candles on her birthday cake',
    'fireworks with blue and green sparkles',
    'waking up to a psychedelic landscape'
])

images # List[PIL.Image.Image]
```

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

### Initialize and Train Super-Resolution MaskGit

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Configures a MaskGit model for super-resolution tasks and demonstrates training with conditioning images.

```python
# Create super-resolution MaskGit
superres_maskgit = MaskGit(
    vae=vae,
    transformer=superres_transformer,
    image_size=512,              # Output image size (larger than base)
    cond_image_size=256,         # Conditioning image size (from base model)
    cond_drop_prob=0.25
).cuda()

# Training with conditioning images
high_res_images = torch.randn(4, 3, 512, 512).cuda()
texts = ['a detailed landscape painting'] * 4

loss = superres_maskgit(high_res_images, texts=texts)
loss.backward()

# Generate super-resolution images from low-res conditioning
low_res_images = torch.randn(4, 3, 256, 256).cuda()  # From base model
cond_images = F.interpolate(low_res_images, 256, mode='bilinear')

superres_images = superres_maskgit.generate(
    texts=[
        'a whale breaching from afar',
        'young girl blowing out candles',
        'fireworks with sparkles',
        'psychedelic landscape'
    ],
    cond_images=cond_images,    # Low-res conditioning images
    cond_scale=3.0,
    timesteps=18
)
# superres_images shape: (4, 3, 512, 512)
```

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

### Train Base MaskGit Model

Source: https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/README.md

Prepare training data with text descriptions and corresponding images. Feed them into the MaskGit instance with return_loss set to True for training. Backpropagate the loss and repeat for extended training.

```python
texts = [
    'a child screaming at finding a worm within a half-eaten apple',
    'lizard running across the desert on two feet',
    'waking up to a psychedelic landscape',
    'seashells sparkling in the shallow waters'
]

images = torch.randn(4, 3, 512, 512).cuda()

# feed it into your maskgit instance, with return_loss set to True

loss = superres_maskgit(
    images,
    texts = texts
)

loss.backward()
```

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

### Initialize MaskGitTransformer

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Instantiate the MaskGitTransformer for masked image token prediction. Ensure num_tokens matches the VAE codebook size and seq_len corresponds to the feature map size squared. Supports T5 text conditioning and self-conditioning.

```python
import torch
from muse_maskgit_pytorch import MaskGitTransformer

# Create the MaskGit transformer
transformer = MaskGitTransformer(
    num_tokens=65536,           # Must match VAE codebook size
    seq_len=256,                # Sequence length = fmap_size^2 (16x16 = 256)
    dim=512,                    # Model dimension
    depth=8,                    # Number of transformer layers
    dim_head=64,                # Attention head dimension
    heads=8,                    # Number of attention heads
    ff_mult=4,                  # Feedforward expansion factor
    t5_name='t5-small',         # T5 model for text encoding ('t5-small', 't5-base', 't5-large')
    self_cond=True              # Enable self-conditioning for improved quality
).cuda()
```

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

### Implement End-to-End Muse Pipeline

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Combines base and super-resolution models into a single Muse pipeline for high-resolution text-to-image generation.

```python
import torch
from muse_maskgit_pytorch import VQGanVAE, MaskGit, MaskGitTransformer, Muse

# Setup base model
vae = VQGanVAE(dim=256, codebook_size=65536).cuda()
vae.load('./pretrained_vae.pt')

base_transformer = MaskGitTransformer(
    num_tokens=65536, seq_len=256, dim=512,
    depth=8, dim_head=64, heads=8, t5_name='t5-small'
)
base_maskgit = MaskGit(
    vae=vae, transformer=base_transformer,
    image_size=256, cond_drop_prob=0.25
).cuda()
base_maskgit.load('./base_maskgit.pt')

# Setup super-resolution model
superres_transformer = MaskGitTransformer(
    num_tokens=65536, seq_len=1024, dim=512,
    depth=8, dim_head=64, heads=8, t5_name='t5-small'
)
superres_maskgit = MaskGit(
    vae=vae, transformer=superres_transformer,
    image_size=512, cond_image_size=256, cond_drop_prob=0.25
).cuda()
superres_maskgit.load('./superres_maskgit.pt')

# Create end-to-end Muse model
muse = Muse(
    base=base_maskgit,
    superres=superres_maskgit
)

# Generate high-resolution images from text
images = muse(
    texts=[
        'a whale breaching from afar',
        'young girl blowing out candles on her birthday cake',
        'fireworks with blue and green sparkles',
        'waking up to a psychedelic landscape'
    ],
    cond_scale=3.0,             # Classifier-free guidance scale
    temperature=1.0,            # Sampling temperature
    timesteps=18,               # Base model timesteps
    superres_timesteps=18,      # Super-resolution timesteps
    return_pil_images=True,     # Return PIL Images instead of tensors
    return_lowres=False         # Only return final high-res images
)
# images: List[PIL.Image.Image] of 512x512 images

# Get both low-res and high-res outputs
superres_images, lowres_images = muse(
    texts=['beautiful mountain landscape'],
    return_lowres=True,
    return_pil_images=True
)
# superres_images: List[PIL.Image.Image] at 512x512
# lowres_images: List[PIL.Image.Image] at 256x256
```

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

### Initialize MaskGitTransformer for Super-Resolution

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Configure the MaskGitTransformer for super-resolution tasks by setting a larger sequence length to handle higher resolution feature maps. Ensure VAE is also initialized appropriately.

```python
import torch
import torch.nn.functional as F
from muse_maskgit_pytorch import VQGanVAE, MaskGit, MaskGitTransformer

# Initialize VAE for super-resolution
vae = VQGanVAE(dim=256, codebook_size=65536).cuda()
vae.load('./pretrained_vae.pt')

# Create transformer for super-resolution (larger sequence length)
superres_transformer = MaskGitTransformer(
    num_tokens=65536,
    seq_len=1024,           # 32x32 feature map for 512px output
    dim=512,
    depth=8,
    dim_head=64,
    heads=8,
    ff_mult=4,
    t5_name='t5-small'
)
```

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

### Generate Images with Super-Resolution MaskGit

Source: https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/README.md

Generate images using the trained super-resolution MaskGit model. Conditioning images are required and must be passed in, interpolated to the appropriate size. Adjust cond_scale for generation.

```python
images = superres_maskgit.generate(
    texts = [
        'a whale breaching from afar',
        'young girl blowing out candles on her birthday cake',
        'fireworks with blue and green sparkles',
        'waking up to a psychedelic landscape'
    ],
    cond_images = F.interpolate(images, 256),  # conditioning images must be passed in for generating from superres
    cond_scale = 3.
)

images.shape # (4, 3, 512, 512)
```

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

### Train MaskGit Transformer

Source: https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/README.md

Load a trained VAE and train a MaskGit transformer for text-to-image generation.

```python
import torch
from muse_maskgit_pytorch import VQGanVAE, MaskGit, MaskGitTransformer

# first instantiate your vae

vae = VQGanVAE(
    dim = 256,
    codebook_size = 65536
).cuda()

vae.load('/path/to/vae.pt') # you will want to load the exponentially moving averaged VAE

# then you plug the vae and transformer into your MaskGit as so

# (1) create your transformer / attention network

transformer = MaskGitTransformer(
    num_tokens = 65536,       # must be same as codebook size above
    seq_len = 256,            # must be equivalent to fmap_size ** 2 in vae
    dim = 512,                # model dimension
    depth = 8,                # depth
    dim_head = 64,            # attention head dimension
    heads = 8,                # attention heads,
    ff_mult = 4,              # feedforward expansion factor
    t5_name = 't5-small',     # name of your T5
)

# (2) pass your trained VAE and the base transformer to MaskGit

base_maskgit = MaskGit(
    vae = vae,                 # vqgan vae
    transformer = transformer, # transformer
    image_size = 256,          # image size
    cond_drop_prob = 0.25,     # conditional dropout, for classifier free guidance
).cuda()

# ready your training text and images

texts = [
    'a child screaming at finding a worm within a half-eaten apple',
    'lizard running across the desert on two feet',
    'waking up to a psychedelic landscape',
    'seashells sparkling in the shallow waters'
]

images = torch.randn(4, 3, 256, 256).cuda()

# feed it into your maskgit instance, with return_loss set to True

loss = base_maskgit(
    images,
    texts = texts
)

loss.backward()

# do this for a long time on much data
# then...

images = base_maskgit.generate(texts = [
    'a whale breaching from afar',
    'young girl blowing out candles on her birthday cake',
    'fireworks with blue and green sparkles'
], cond_scale = 3.) # conditioning scale for classifier free guidance

images.shape # (3, 3, 256, 256)
```

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

### MaskGitTransformer Inference with Classifier-Free Guidance

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Perform inference with the MaskGitTransformer using classifier-free guidance for stronger text conditioning. Adjust cond_scale to control the guidance strength.

```python
# Forward with classifier-free guidance at inference
scaled_logits = transformer.forward_with_cond_scale(
    token_ids,
    texts=texts,
    cond_scale=3.0  # Guidance scale (higher = stronger text conditioning)
)
```

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

### Configure TokenCritic for Generation Quality

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Integrates a TokenCritic to predict token correctness, improving iterative generation quality.

```python
import torch
from muse_maskgit_pytorch import VQGanVAE, MaskGit, MaskGitTransformer, TokenCritic

# Create token critic with same architecture as main transformer
token_critic = TokenCritic(
    num_tokens=65536,
    seq_len=256,
    dim=512,
    depth=4,                # Can be shallower than main transformer
    dim_head=64,
    heads=8,
    ff_mult=4,
    t5_name='t5-small'
).cuda()

# Create MaskGit with token critic
vae = VQGanVAE(dim=256, codebook_size=65536).cuda()
transformer = MaskGitTransformer(
    num_tokens=65536, seq_len=256, dim=512,
    depth=8, dim_head=64, heads=8, t5_name='t5-small'
)

maskgit_with_critic = MaskGit(
    vae=vae,
    transformer=transformer,
    token_critic=token_critic,      # Attach token critic
    image_size=256,
    cond_drop_prob=0.25,
    critic_loss_weight=1.0          # Weight for critic loss
).cuda()

# Training includes both generation and critic losses
images = torch.randn(4, 3, 256, 256).cuda()
texts = ['sample text'] * 4

# Combined loss (generation + critic)
total_loss = maskgit_with_critic(images, texts=texts)
total_loss.backward()

# Train only the generator (freeze critic)
gen_loss = maskgit_with_critic(images, texts=texts, train_only_generator=True)

# Alternative: Use self token critic (shares weights with transformer)
maskgit_self_critic = MaskGit(
    vae=vae,
    transformer=transformer,
    self_token_critic=True,         # Use transformer embeddings for critic
    image_size=256,
    cond_drop_prob=0.25
).cuda()
```

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

### MaskGitTransformer Forward Pass with Text Conditioning

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Perform a forward pass with the MaskGitTransformer using token IDs and text descriptions for conditioning. Classifier-free guidance can be applied during training using cond_drop_prob.

```python
# Forward pass with text conditioning
token_ids = torch.randint(0, 65536, (4, 256)).cuda()  # Batch of token sequences
texts = [
    'a beautiful sunset over mountains',
    'a cat sitting on a windowsill',
    'abstract colorful painting',
    'futuristic city skyline'
]

# Get logits for next token prediction
logits = transformer(
    token_ids,
    texts=texts,
    cond_drop_prob=0.1  # Dropout probability for classifier-free guidance training
)
# logits shape: (4, 256, 65536)
```

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

### MaskGit Image Generation

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Generate images from text descriptions using the MaskGit model. Control generation quality and style with parameters like cond_scale, temperature, timesteps, and topk_filter_thres.

```python
# Generate images from text descriptions
generated_images = maskgit.generate(
    texts=[
        'a whale breaching from afar',
        'young girl blowing out candles on her birthday cake',
        'fireworks with blue and green sparkles'
    ],
    cond_scale=3.0,             # Classifier-free guidance scale
    temperature=1.0,            # Sampling temperature
    timesteps=18,               # Number of iterative denoising steps
    topk_filter_thres=0.9,      # Top-k filtering threshold
    can_remask_prev_masked=False  # Whether to remask previously masked tokens
)
# generated_images shape: (3, 3, 256, 256)
```

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

### Save and Load MaskGit Model

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Save the trained MaskGit model to a file for later use and load it back into memory. This is essential for resuming training or deploying the model.

```python
# Save and load model
maskgit.save('./maskgit_base.pt')
maskgit.load('./maskgit_base.pt')
```

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

### MaskGit Training Forward Pass

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Perform a training forward pass with the MaskGit model, providing images and corresponding text descriptions. The model returns a loss value that can be backpropagated.

```python
# Training forward pass
images = torch.randn(4, 3, 256, 256).cuda()
texts = [
    'a child screaming at finding a worm within a half-eaten apple',
    'lizard running across the desert on two feet',
    'waking up to a psychedelic landscape',
    'seashells sparkling in the shallow waters'
]

loss = maskgit(images, texts=texts)
loss.backward()
```

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

### Train Super-Resolution MaskGit

Source: https://github.com/lucidrains/muse-maskgit-pytorch/blob/main/README.md

Configure MaskGit for super-resolution by specifying cond_image_size.

```python
import torch
import torch.nn.functional as F
from muse_maskgit_pytorch import VQGanVAE, MaskGit, MaskGitTransformer

# first instantiate your ViT VQGan VAE
# a VQGan VAE made of transformers

vae = VQGanVAE(
    dim = 256,
    codebook_size = 65536
).cuda()

vae.load('./path/to/vae.pt') # you will want to load the exponentially moving averaged VAE

# then you plug the VqGan VAE into your MaskGit as so

# (1) create your transformer / attention network

transformer = MaskGitTransformer(
    num_tokens = 65536,       # must be same as codebook size above
    seq_len = 1024,           # must be equivalent to fmap_size ** 2 in vae
    dim = 512,                # model dimension
    depth = 2,                # depth
    dim_head = 64,            # attention head dimension
    heads = 8,                # attention heads,
    ff_mult = 4,              # feedforward expansion factor
    t5_name = 't5-small',     # name of your T5
)

# (2) pass your trained VAE and the base transformer to MaskGit

superres_maskgit = MaskGit(
    vae = vae,
    transformer = transformer,
    cond_drop_prob = 0.25,
    image_size = 512,                     # larger image size
    cond_image_size = 256,                # conditioning image size <- this must be set
).cuda()
```

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

### MaskGitTransformer with Pre-computed Text Embeddings

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Optimize MaskGitTransformer forward pass by pre-computing text embeddings using the `encode_text` method, then passing these embeddings to the transformer.

```python
# Use pre-computed text embeddings for efficiency
text_embeds = transformer.encode_text(texts)  # Pre-encode texts
logits = transformer(token_ids, text_embeds=text_embeds)
```

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

### Manual T5 Tokenization

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Perform manual tokenization of text using the T5 tokenizer. This allows for custom processing of input IDs and attention masks, with options for padding, truncation, and tensor format.

```python
# Manual tokenization for custom processing
encoded = tokenizer.batch_encode_plus(
    texts,
    return_tensors='pt',
    padding='longest',
    max_length=256,
    truncation=True
)
input_ids = encoded.input_ids
attention_mask = encoded.attention_mask
```

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

### Encode Batch of Texts with T5

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Encode a list of texts into their T5 embeddings. Allows specifying the output device for the embeddings.

```python
# Encode batch of texts
texts = [
    'a majestic lion in the savanna',
    'underwater coral reef with colorful fish',
    'futuristic cityscape at night',
    'cozy cabin in snowy mountains'
]
text_embeddings = t5_encode_text(
    texts,
    name='t5-base',
    output_device='cuda'  # Move output to specific device
)
# text_embeddings shape: (4, seq_len, 768)
```

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

### Encode Single Text with T5

Source: https://context7.com/lucidrains/muse-maskgit-pytorch/llms.txt

Encode a single piece of text into its T5 embedding representation using a specified T5 model size.

```python
# Encode single text
text_embedding = t5_encode_text(
    'a beautiful sunset over the ocean',
    name='t5-small'
)
# text_embedding shape: (1, seq_len, 512)
```

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