CHAPTERS
Why consistent brand imagery needs a repeatable process (not endless prompting)
Jamey frames the core challenge: one-off great images are easy, but building a cohesive brand library requires a tight, repeatable workflow. Claire sets the goal—creating a consistent portfolio of “pink internet-coded” brand visuals without spending all day prompting.
Step 1: Build a mood board to define the visual language
The workflow starts in Pinterest (or Cosmos) by collecting a mood board that captures the intended vibe—color, contrast, subject weirdness/juxtaposition, and overall aesthetic. The mood board becomes a shared visual spec for both the human and Midjourney.
Step 2: Turn mood board images into Style References (SREFs)
Jamey shows two ways to use a mood board in Midjourney: paste images directly as a mood board, or use them as SREFs. SREFs often work better for capturing consistent style traits like coloring, contrast, and camera treatment.
Diagnose mismatch: Compare generations vs. mood board and adjust inputs
They review early outputs that look cool but don’t match the target aesthetic. Claire highlights a technique: ask a text model (ChatGPT/Claude) to describe why outputs differ, helping non-designers build the vocabulary needed to correct course.
Fix the palette and bias: Remove overpowering references and refine SREF set
Jamey demonstrates iterative pruning: a single strong color element (e.g., green eyeshadow) can dominate generations. By removing that SREF, results shift closer to the desired neutral/pink balance and composition.
Step 3: Add Personalization Codes to lock in your signature style
To deepen consistency and make outputs feel more like ‘your’ look, Jamey layers in personalization codes (trained via Midjourney’s preference voting). She explains how to build and manage these profiles and warns about unintended style bleeding.
Step 4: Use publication/style keywords as high-information shortcuts
Jamey begins light prompting with references like “Dazed editorial,” “Vogue,” or “high fashion,” which compress complex style directions into a few words. This approach avoids long prompt blocks while still steering lighting, contrast, and editorial tone.
Image References and cropping: control composition without fighting the model
To match a specific pose/composition, Jamey uses image references—then crops out the dominating detail (bubblegum) to prevent it from hijacking results. The key idea: edit the reference, not the prompt, to remove unwanted bias quickly.
Step 5: Minimal prompting for subject + setting + style (with “camera cheat codes”)
Jamey shows when she does add prompt details: defining setting cues (NYC skyline, luxury apartment, time of day) and swapping materials (matte black leather couch). She also uses camera mentions as quick styling levers to shift realism and era.
Scaling the asset set: variations, Explore page inspiration, and prompt “stealing”
To expand from a few wins into a full brand library, Jamey keeps the same SREF stack and generates across many subjects (tech, culture, motifs). She uses Midjourney’s subtle/strong variations to fix issues (like hands) and mines the Explore page for reusable prompt patterns.
Reinforce and remix: create a new mood board from your best outputs
Once a style is working, Jamey selects ~30 strong images from her gallery and turns them into a new mood board to “lock in” the look. She also demonstrates combining multiple mood boards (e.g., ‘Real Skin’)—then notes SREFs still tend to be more reliable for consistency.
Delivering to clients: share the recipe (profiles, refs, settings) in Figma
Jamey explains how she packages the work so clients can self-serve: final prompt setup, profiles, SREF images, and key settings—often pasted into Figma since Midjourney lacks robust sharing. Claire highlights how this shifts creative services from retainer dependence to upfront system-building.
Post-production workflow: fix hands/logos and upscale with Nano Banana (via Flora/Higgsfield)
For final polish, Jamey takes Midjourney images into Flora/Higgsfield and uses Nano Banana as a “talk-to-Photoshop” tool. She demonstrates upscaling and swapping an unrealistic laptop for a specific ‘2026 midnight black MacBook Pro’ while preserving composition and style.
Inspiration and troubleshooting: build taste libraries, take breaks, and edit inputs not prompts
In the lightning round, Jamey shares her inspiration system: X/Twitter lists, Pinterest/Cosmos saving habits, and a daily taste practice. Her troubleshooting philosophy is to step away, identify the real failure mode (too busy, too many refs, wrong color), and simplify or rebuild references.
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