Mastering AIGC: From Prompt Design to Custom Stable Diffusion Models

How to Build High‑Quality AI Images

Before starting, obtain proper authorization for any personal model training to avoid portrait‑rights violations.

The recommended workflow follows a three‑step human‑machine blueprint:

Blueprint Design (Human) : Define creative concepts, scenarios, or software architecture.

Mechanized Generation (Machine) : Convert the blueprint into detailed prompts (including Negative Prompt) and feed them to an AIGC model such as Stable Diffusion.

Detail Repair (Human) : Manually correct illegal, inaccurate, or low‑quality sections.

For image generation, the process can be broken down into three concrete steps:

Write a comprehensive Negative Prompt to filter out undesirable content.

Use ControlNet as a structural guide to steer the diffusion model toward the intended composition.

Train or fine‑tune a personal model (e.g., with DreamBooth) to capture domain‑specific style.

Applying these steps improves image fidelity and reduces the need for extensive post‑editing.

Negative Prompt: Strict Acceptance Criteria

Negative Prompt forces the model to generate low‑probability samples that contradict the negative cue, compelling it to focus on finer details and produce more realistic results.

Example prompt translation: smiling girl leaning out the train window Without a Negative Prompt, the model may produce odd artifacts; adding constraints guides it toward the desired output.

ControlNet: Precise Structural Guidance

ControlNet adds extra conditioning to diffusion models, enabling generation of intermediate sketches, normal maps, or depth maps that can be used for tasks such as pose control, line‑art coloring, and precise perspective reconstruction.

In portrait work, a hand‑drawn pose or a photo‑derived skeleton can serve as the ControlNet input, producing images that respect the intended structure.

DreamBooth: Lightweight Personal Models and Stylization

Fine‑tuning a diffusion model with personal data (DreamBooth) injects a unique style, making generated artwork align with the creator’s aesthetic.

Typical use‑case: training on a personal avatar to produce anime‑style portraits.

Inpaint: Local Repair for Missing Details

AI‑generated images often miss fine details such as hands or feet. Inpaint can be used to manually restore these regions, either directly in the diffusion interface or via external editors like Photoshop.

Personal AI Strategy: Blueprint + Refinement + Small Models

The author proposes four strategic pillars for effective personal AI use:

Strict Acceptance Criteria : Use detailed prompts and Negative Prompts to eliminate unwanted outputs.

Blueprint Architecture : Employ ControlNet to create a structural skeleton for the desired content.

Lightweight Domain‑Specific Models : Fine‑tune with DreamBooth or similar tools to embed personal style.

Refinement : Apply Inpaint or other post‑processing to fix defects.

Beyond image creation, the same mindset applies to text generation (e.g., using ChatGPT with well‑crafted outlines) and to building small, task‑specific models that run on modest cloud GPU resources.

Strategic Steps

Embrace change: recognize that AIGC boosts efficiency even if it cannot fully replace human intuition.

Strengthen architectural thinking: continuously learn design concepts and master advanced AIGC features like Negative Prompt and ControlNet.

Build domain‑focused small models: select appropriate datasets and lightweight architectures to solve personal problems quickly.

Explore and hone techniques: integrate AI into daily workflows, experiment with tools like GitHub Copilot, and contribute to open‑source resources such as phodal/prompt-patterns.

In summary, while current AIGC systems are still limited, a disciplined approach—combining clear prompts, structural guidance, personalized fine‑tuning, and targeted post‑processing—can dramatically increase creative productivity.