Universal Techniques — Weighting, Negative Prompts & img2img

imgai1-4–imgai1-6 each covered one tool's own particular syntax. This chapter zooms back out to three techniques that recur — in some form — across most of them, despite genuinely different syntax: weighting individual terms, excluding content, and starting from an existing image rather than pure noise. Each section cross-references back to the tool-specific version already covered.

Term Weighting

A whole-prompt CFG scale (imgai1-5) controls how strongly the entire prompt's embedding pulls generation. Term weighting is the more granular version of the same idea — controlling how strongly one specific word or phrase contributes to that overall pull, relative to the rest of the prompt.

Negative Prompts, Recapped Across Tools

imgai1-4 and imgai1-5 already covered this in tool-specific depth — this section is a deliberately short recap, not new material. The goal is the same everywhere (exclude unwanted content), but the depth of mechanism genuinely differs:

img2img — Starting From an Image, Not Pure Noise

imgai1-2 described generation as starting from pure random noise and denoising down to a coherent image. img2img changes the starting point: instead of pure noise, the process starts from a real, existing image with only a partial amount of noise added, then runs the same trained denoising steps from there. Because the starting point already resembles a real image rather than static, the result tends to preserve the original's overall composition and structure while the denoising process fills in whatever the new text prompt specifies.

The amount of noise added to the starting image — often exposed directly as a denoising strength parameter — controls the trade-off precisely: a low value keeps the result very close to the original image (only lightly re-touched by the prompt), while a high value approaches full, pure-noise generation, where the original image's influence becomes minimal.

Inpainting & Outpainting — A Genuinely Distinct Capability

img2img re-generates an entire image, just anchored to a starting point. Inpainting is more surgical: a mask marks a specific region of an existing image (a bad hand, an unwanted object), and only that masked region is regenerated — the rest of the image is held fixed at every denoising step, rather than merely encouraged to stay similar. Outpainting runs the same masking idea in reverse, extending an image past its original borders and generating new content in the newly added space that plausibly continues what's already there.

Both are available, with different interfaces, across all three tools — Stable Diffusion exposes inpainting/outpainting directly as a masking tool in most community interfaces; Midjourney and DALL-E both offer a comparable region-select-and-regenerate workflow through their own editing interfaces, without exposing the underlying masking mechanism directly.

Hands-On Exercises