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README.md

@@ -33,18 +33,16 @@ This model was trained on a synthetic dataset generated using **Gemini 2.5-lates
 **Fun Fact:** This entire dataset took only **45 minutes** to generate. How? Thanks to **Tier 3 Gemini API access**—a status I achieved involuntarily after all the times Gemini broke while vibe coding, looped infinitely, and racked up $$$ charges. My wallet's pain is your prompt engineering gain. 💸
 
 #### Why Synthetic Data?
-Z-Image Turbo is "needy." It requires very specific, dense descriptions to look good. Most human-written prompts are too short or use "tag salad" (comma-separated lists), which the Qwen-3 encoder hates. We used Gemini to expand simple concepts into 120-180 word rich paragraphs, teaching the model to hallucinate the missing details (lighting, texture, camera specs) that Z-Image Turbo needs to trigger its "Shift 7.0" magic.
+Z-Image Turbo is "needy." It requires very specific, dense descriptions to look good. Most human-written prompts are too short or use "tag salad" (comma-separated lists), which the Qwen-3 encoder hates. We used Gemini to expand simple concepts into 120-180 word rich paragraphs, teaching the model to hallucinate the missing details (lighting, texture, camera specs) that Z-Image Turbo needs to trigger its magic.
 
 #### The "Seed Strategy" (Engineering Diversity)
-To ensure the model didn't just learn to output generic "portrait of a woman" prompts, we built a procedural generation engine for the seed prompts.
+To ensure the model didn't just learn to output generic "portrait of a woman" prompts, we built a procedural generation engine for the seed prompts that functions as a combinatorial explosion.
+
 -   **8 Major Style Pillars:** We explicitly balanced the dataset across Photorealism, Anime, Fantasy, Sci-Fi, Horror, Artistic, Documentary, and Fine Art.
--   **Procedural Complexity:** We didn't just feed Gemini "A cat." We constructed seeds by randomly mixing:
-    -   **Concepts:** (e.g., "cybernetic surgeon", "macro dew drop")
-    -   **Shot Types:** (e.g., "worm's-eye view", "dutch tilt")
-    -   **Lighting Rigs:** (e.g., "volumetric fog", "neon rim light")
-    -   **Color Grades:** (e.g., "Cinestill 800T", "Kodak Portra")
-    -   **Spatial Cues:** (e.g., "foreground hero with blurred crowd")
-This combinatorial approach ensured that the 20,000+ samples covered a massive surface area of aesthetic possibilities, preventing the model from collapsing into a single "style."
+-   **Infinite Variety:** We didn't just feed Gemini "A cat." We constructed seeds by randomly mixing ~170 base concepts with 26 styles, 10 shot types, 10 lighting setups, 11 moods, 8 texture notes, and 10 camera kits.
+-   **The Math:** This procedural engine is capable of generating over **217 Billion unique seed prompts**. From this vast latent space, we carefully sampled the 20,000 most coherent and high-impact intersections to train the model.
+
+This ensures that the model understands that "Cinestill 800T" isn't just a random word, but a specific color grading instruction that can apply to *any* concept, from a cybernetic surgeon to a medieval marketplace.
 
 #### The Training Data Prompt
 Here is the exact system prompt we used to generate the training data. You can see how we forced Gemini to focus on "Positive Constraints" and "Texture Density":