Encoder/Decoders/Transformers and Stable Diffusion

Table 1: The Big Picture

Term	Purpose	Input	Output	Example
Encoder	Compress / understand data	Raw data	Latent representation (embedding)	BERT, Sentence-BERT, CLIP Text Encoder
Decoder	Generate or reconstruct data	Latent representation	Output data	GPT, VAE Decoder
Autoencoder	Learn compressed representations	Input data	Reconstructed input	Image Autoencoder
Autodecoder	Learn latent vectors directly	Learned latent code	Output data	DeepSDF, Neural Shape Models

Table 2: Encoder vs Decoder

Aspect	Encoder	Decoder
Main Goal	Understanding	Generation / Reconstruction
Direction	Input → Latent	Latent → Output
Typical Output	Embedding	Text, Image, Audio, etc.
Used For	Search, Retrieval, Classification	Generation, Reconstruction
Example	BERT	GPT

Table 3: Common Encoder Examples

Model	Architecture	Input	Output	Purpose
BERT	Transformer Encoder	Text	Embedding	Understanding
Sentence-BERT	Transformer Encoder	Text	Sentence Embedding	Semantic Search
E5	Transformer Encoder	Text	Embedding	RAG Retrieval
BGE	Transformer Encoder	Text	Embedding	Vector Search
CLIP Text Encoder	Transformer Encoder	Text	Text Embedding	Text-to-Image
ResNet	CNN Encoder	Image	Feature Vector	Vision Tasks
ViT	Transformer Encoder	Image	Image Embedding	Vision Tasks

Table 4: Is an Embedding Model an Encoder?

Model Type	Encoder?	Example
Embedding Model	Yes (usually)	BERT, E5, BGE
RAG Embedding Model	Yes	E5, BGE
GPT	No (Decoder-only)	GPT-4, Llama
CLIP Text Encoder	Yes	Stable Diffusion

Rule of Thumb:

Embedding Model ≈ Encoder

Table 5: Decoder Types

Decoder Type	Input	Output	Example
VAE Decoder	Latent Vector	Image	Stable Diffusion VAE
CNN Decoder	Feature Maps	Segmentation Mask / Image	U-Net
RNN Decoder	Context Vector	Sequence	Old Translation Models
Transformer Decoder	Previous Tokens	Next Token	GPT, Llama
Diffusion Decoder*	Noise	Image	Stable Diffusion

Note: Diffusion Decoder is not a formal category; it is commonly used informally.

Table 6: Transformer Decoder vs Generic Decoder

Feature	Decoder	Transformer Decoder
Meaning	General Concept	Specific Architecture
Purpose	Latent → Output	Sequence Generation
Attention Mechanism	Optional	Yes
Token-by-Token Generation	Not Required	Yes
Example	VAE Decoder	GPT

Relationship Hierarchy

Decoder
├── VAE Decoder
├── CNN Decoder
├── RNN Decoder
└── Transformer Decoder
      ├── GPT
      ├── Llama
      ├── Gemini
      └── Claude

Table 7: Can Transformer Decoders Work Only on Text?

Data Type	Can Use Transformer Decoder?	Example
Text	Yes	GPT
Images (tokenized)	Yes	ImageGPT
Audio	Yes	AudioLM
Music	Yes	MusicLM
Protein Sequences	Yes	ProGen
Video (tokenized)	Yes	Various Video Transformers

Better Definition

Transformer Decoder = Sequence Generator

NOT

Transformer Decoder = Text Generator

Table 8: How GPT (Decoder-Only) is Trained

Training Sentence:

I love Kubernetes

Input	Target
I	love
I love	Kubernetes
I love Kubernetes	<EOS>

Loss Function

CrossEntropy(PredictedToken, ActualToken)

Thus a decoder does have a target: the next token.

Table 9: Autoencoder vs Autodecoder

Feature	Autoencoder	Autodecoder
Encoder Present?	Yes	No
Decoder Present?	Yes	Yes
Latent Vector Source	Produced by Encoder	Directly Learned
Typical Use	Compression, Denoising	3D Shapes, Neural Fields
Example	Variational Autoencoder	DeepSDF

Autoencoder Flow

Input
 ↓
Encoder
 ↓
Latent
 ↓
Decoder
 ↓
Reconstructed Input

Autodecoder Flow

Learned Latent Code
        ↓
      Decoder
        ↓
      Output

Table 10: Stable Diffusion Components

Component	Type	Purpose
Text Encoder	Transformer Encoder	Understand Prompt
U-Net	Diffusion Network	Denoising
VAE Encoder	Encoder	Compress Images
VAE Decoder	Decoder	Reconstruct Images
Scheduler	Control Logic	Manage Denoising Steps

Table 11: When is the VAE Encoder Used in Stable Diffusion?

Operation	VAE Encoder Used?
Model Training	Yes
Text → Image	No
Image → Image	Yes
Inpainting	Yes
Outpainting	Yes

Table 12: Stable Diffusion (Training)

Image
  ↓
VAE Encoder
  ↓
Image Latent
  ↓
Add Noise
  ↓
U-Net
  ↓
Predict Noise

During training, Stable Diffusion learns how to remove noise from latent image representations.

Table 13: Stable Diffusion (Text-to-Image Inference)

Prompt
   ↓
Text Encoder
   ↓
Text Embeddings
                +
Random Latent Noise
                ↓
             U-Net
                ↓
         Clean Latent
                ↓
           VAE Decoder
                ↓
              Image

Important Observation

VAE Encoder is NOT used during standard Text-to-Image generation.

Table 11: When is the VAE Encoder Used in Stable Diffusion?

Operation	VAE Encoder Used?
Model Training	Yes
Text → Image	No
Image → Image	Yes
Inpainting	Yes
Outpainting	Yes

Table 12: Stable Diffusion (Training)

Image
  ↓
VAE Encoder
  ↓
Image Latent
  ↓
Add Noise
  ↓
U-Net
  ↓
Predict Noise

Table 13: Stable Diffusion (Text-to-Image Inference)

Prompt
    ↓
Text Encoder
    ↓
Text Embeddings
                +
Random Latent Noise
                ↓
            U-Net
            ↓
        Clean Latent
        ↓
      VAE Decoder
      ↓
     Image

Notice:

VAE Encoder is NOT used here.

Table 14: BERT vs GPT vs Stable Diffusion

Feature	BERT	GPT	Stable Diffusion
Architecture	Encoder-only	Decoder-only	Diffusion + VAE
Input	Text	Text	Text Prompt
Output	Embedding	Text	Image
Primary Goal	Understanding	Generation	Image Generation
Uses Embeddings?	Yes	Internally	Yes
Uses VAE?	No	No	Yes
Uses Transformer Decoder?	No	Yes	No
Uses Transformer Encoder?	Yes	No	Text Encoder Only