u/Fun-Heron-7092

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"I’ve been running some tests on high-density infographics using SenseNova-U1 and some custom nodes I wrote.

To be honest, the image quality hits about 80% of what Nano Banana 2 can do—which is actually pretty impressive for an open-source model.

What sets SenseNova apart from other text-to-image models is its follow-up capability. It acts more like a general-purpose Agent; if your prompt is a bit vague, it won't just guess. It’ll keep asking questions until it has enough info to actually start the generation."

Pretty good stuff

Example Prompt:

Input Variable: Semaglutide

Language: English

System Instruction:

Create an image of premium liquid glass Bento grid product infographic with 8 modules (card 2 to 8 show text titles only).

  1. Product Analysis:

→ Identify product's dominant natural color → "hero color"

→ Identify category: MEDICINE

  1. Color Palette (derived from hero):

→ Product + accents: full saturation hero color

→ Icons, borders: muted hero (30-40% saturation, never black)

  1. Visual Style:

→ Hero product: real photography (authentic, premium), 3D Glass version [choose one]

→ Cards: Apple liquid glass (85-90% transparent) with Whisper-thin borders and Subtle drop shadow for floating depth and reflecting the background color

→ Background stays behind cards and high blur where cards are [choose one]:

- Ethereal: product essence, light caustics, abstract glow

- Macro: product texture close-up, heavily blurred

- Pattern: product repeated softly at 10-15% opacity

- Context: relevant environment, blurred + desaturated

→ Add subtle motion effect

→ Asymmetric Bento grid, 16:9 landscape

→ Hero card: 28-30% | Info modules: 70-72%

  1. Module Content (8 Cards):

M1 — Hero: Product displayed as real photo / 3D glass / stylized interpretation (choose one)in beautiful form + product name label

M2 — Core Benefits: 4 unique benefits + hero-color icons

M3 — How to Use: 4 usage methods + icons

M4 — Key Metrics: 5 EXACT data points

Format: [icon] [Label] [Bold Value] [Unit]

FOOD: Calories: [X] kcal/100g, Carbs: [X]g (fiber [X]g, sugar [X]g), Protein: [X]g, [Key Vitamin]: [X]mg ([X]% DV), [Key Mineral]: [X]mg ([X]% DV)

MEDICINE:Active: [name], Strength: [X] mg, Onset: [X] min, Duration: [X] hrs, Half-life: [X] hrs

TECH:Chip: [model], Battery: [X] hrs, Weight: [X]g,[Key spec]: [value], Connectivity: [protocols]

M5 — Who It's For: 4 recommended groups with green checkmark icons | 3 caution groups with amber warning icons

M6 — Important Notes: 4 precautions + warning icons

M7 — Quick Reference:

→ FOOD: Glycemic Index + dietary tags with icons

→ MEDICINE: Side effects + severity with icons

→ TECH: Compatibility + certifications with icons

M8 — Did You Know: 3 facts (origin, science, global stat) + icons

Output: 1 image, 16:9 landscape, ultra-premium liquid glass infographic.

Repo: https://github.com/OpenSenseNova/SenseNova-U1

u/Fun-Heron-7092 — 14 days ago

"I’ve been running some tests on high-density infographics using SenseNova-U1 and some custom nodes I wrote.

To be honest, the image quality hits about 80% of what Nano Banana 2 can do—which is actually pretty impressive for an open-source model.

What sets SenseNova apart from other text-to-image models is its follow-up capability. It acts more like a general-purpose Agent; if your prompt is a bit vague, it won't just guess. It’ll keep asking questions until it has enough info to actually start the generation."

Pretty good stuff

Repo: https://github.com/OpenSenseNova/SenseNova-U1

u/Fun-Heron-7092 — 14 days ago