llama.cpp/examples/diffusion/README.md

# Diffusion Text Generation

This directory contains implementations for Diffusion LLMs (DLLMs)

More Info:
- https://github.com/ggml-org/llama.cpp/pull/14644
- https://github.com/ggml-org/llama.cpp/pull/14771

## Parameters
The diffusion CLI supports various parameters to control the generation process:

### Core Diffusion Parameters
- `--diffusion-steps`: Number of diffusion steps (default: 256)
- `--diffusion-algorithm`: Algorithm for token selection
  - `0`: DIFFUSION_ALGORITHM_ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
  - `1`: DIFFUSION_ALGORITHM_ENTROPY_BASED - Entropy-based selection
  - `2`: DIFFUSION_ALGORITHM_MARGIN_BASED - Margin-based selection
  - `3`: DIFFUSION_ALGORITHM_RANDOM - Random selection
  - `4`: DIFFUSION_ALGORITHM_CONFIDENCE_BASED - Confidence-based selection (default)
  - More documentation here https://github.com/DreamLM/Dream
- `--diffusion-visual`: Enable live visualization during generation

### Scheduling Parameters
Choose one of the following scheduling methods:

**Timestep-based scheduling:**
- `--diffusion-eps`: Epsilon value for timestep scheduling (e.g., 0.001)

**Block-based scheduling:**
- `--diffusion-block-length`: Block size for block-based scheduling (e.g., 32)

### Sampling Parameters
- `--temp`: Temperature for sampling (0.0 = greedy/deterministic, higher = more random)
- `--top-k`: Top-k filtering for sampling
- `--top-p`: Top-p (nucleus) filtering for sampling
- `--seed`: Random seed for reproducibility

### Model Parameters
- `-m`: Path to the GGUF model file
- `-p`: Input prompt text
- `-ub`: Maximum sequence length (ubatch size)
- `-c`: Context size
- `-b`: Batch size

### Examples
#### Dream architecture:
```
llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual
```

#### LLaDA architecture:
```
llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual
```

#### RND1 architecture:
```
llama-diffusion-cli -m RND1-Base-0910.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-algorithm 1 --diffusion-steps 256 --diffusion-visual --temp 0.5 --diffusion-eps 0.001
```