Every essential technical term in prompt engineering, diffusion physics, and LLM sampling defined with actionable pro tips.
A numerical parameter that dictates how strictly the AI diffusion model adheres to your prompt text versus freely improvising artistic variations. Standard values range from 3.5 to 7.0.
A complete, pre-trained neural network weights file (such as SDXL 1.0 or Flux.1 Dev) capable of generating standalone imagery without external adapters.
A neural network structure that provides explicit spatial conditioning (depth maps, edge detection, pose skeletons) to guide diffusion composition with pixel precision.
The prompt engineering technique of providing 2 to 5 concrete input-output demonstration examples inside the prompt to guide formatting and tone consistency.
The process of masking a specific region of an image and regenerating ONLY that portion with a new prompt while preserving the surrounding composition.
A compressed mathematical multi-dimensional space where conceptual relationships between visual and semantic tokens are mapped and navigated by AI models.
A lightweight adapter file (typically 10MB to 200MB) trained on top of a foundational checkpoint model to teach it a specific art style, character face, or clothing item.
Instructions provided to diffusion models (via the --no flag in Midjourney) that penalize unwanted visual artifacts like extra limbs, blurriness, or watermarks.
Extending the borders of an existing image outwards into new canvas territory, allowing panoramic reveals while matching lighting and style.
Compressing model weights from 16-bit floating point down to 4-bit or 8-bit integers, enabling massive LLMs like DeepSeek-R1 to run locally on consumer VRAM.
A starting numerical integer (e.g. --seed 42819) used to initialize the pseudo-random noise field. Reusing the exact same seed and prompt produces near-identical outputs.
Hyperparameters controlling token selection randomness. Lower temperatures (0.2) force deterministic logical answers, while higher values (0.8) boost creative divergence.
Prompting an AI model to perform a task directly without providing any prior examples or demonstration pairs, relying purely on pre-trained understanding.
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