BlackList-2.0-Prompt-Enhancer

BlackList 2.0 – Prompt Enhancer AI

BlackList 2.0 is a production-ready text-to-text generative AI designed to transform ultra-simple visual concepts (1–4 words) into technically rich, masterpiece-grade prompts for modern text-to-image engines such as Stable Diffusion, Flux, and similar systems.

This version introduces a significantly expanded neural architecture (93M parameters), deeper aesthetic reasoning, and improved structural stability over V1.0.

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Model Details

Model Description

BlackList 2.0 is a task-specific decoder-only Transformer trained exclusively for prompt enhancement.

It ingests short conceptual inputs such as: SIMPLE] cyberpunk assassin Salin kode

And outputs fully structured, high-aesthetic prompts such as: [ENHANCED] cyberpunk assassin, full body shot, neon city background, cinematic lighting, ultra detailed face, dynamic pose, 4k resolution, highly detailed, digital painting, concept art The model specializes in:

• Aesthetic enrichment
• Technical prompt structuring
• Lighting & render injection
• Composition balancing
• Controlled artistic style blending

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Core Identity

• Official Name: BlackList 2.0 – Prompt Enhancer AI
• Creator: Bl4ckSpaces
• Creative Direction Influence: Nano Banana visual philosophy
• Model Type: Text-to-Text Generative (Task-Specific Auto-Enhancer)
• Language: English (visual art optimized)
• License: Apache 2.0
• Training Base: Custom GPT-2 architecture (trained from scratch)

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Architecture – The Brain

BlackList 2.0 is built on a custom GPT-2 inspired decoder-only Transformer with significant scaling improvements over V1.0.

• Total Parameters: 93,129,216 (~93.13M)
• Embedding Dimension (n_embd): 768
• Transformer Layers: 12
• Attention Heads: 12
• Maximum Context Length: 512 tokens

This architecture provides:

• Stronger contextual memory
• Reduced repetition artifacts
• Stable keyword expansion
• Improved semantic blending

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Tokenizer & Language System – The Vocabulary

• Tokenizer: Custom Byte-Pair Encoding (BPE)
• Vocabulary Size: 10,000 curated visual-art tokens
• Domain Isolation: Trained exclusively on visual art terminology

Includes technical vocabulary covering:

• Lighting systems
• Camera angles
• Rendering styles
• Artist influences
• Resolution descriptors
• Composition terminology

Control Tokens

• [SIMPLE] → Locks user input
• [ENHANCED] → Triggers structured aesthetic output

This ensures deterministic transformation behavior.

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Training Details – The Kitchen

Dataset

• 73,678 high-quality prompt pairs
• Clean, structured, no aggressive truncation
• Focused purely on aesthetic + structural enrichment

Training Configuration

• Hardware: 1x NVIDIA T4 Tensor Core GPU
• Precision: FP16 mixed precision
• VRAM Usage: ~8GB optimized
• Batch Size: 64
• Epochs: 3
• Optimization Steps: 3,456
• Training Duration: ~35 minutes
• Final Loss: 2.61

Loss 2.61 represents a calibrated sweet spot:

• Creative enough for expressive expansion
• Disciplined enough to maintain structured keyword format

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V2.0 Improvements Over V1.0

1. Elimination of Small-Model Stuttering

The 93M parameter architecture eliminates common small-model repetition issues such as:

• Keyword looping
• Redundant adjective stacking
• Structural breakdown

V2.0 maintains coherent keyword flow.

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2. Advanced Artistic Anatomy Understanding

The model now understands proportional composition of prompts:

• Subject hierarchy
• Clothing & visual detail layering
• Shot type (portrait, full body, close-up)
• Facial detailing
• Lighting realism
• Resolution scaling

It no longer randomly injects artist names — stylistic blending is now context-aware.

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3. Production-Ready Deployment

• Low-latency inference
• Stable output formatting
• Optimized for API integration
• Suitable for web-based image generation backends

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Intended Use

Direct Use

• Prompt enhancement for Stable Diffusion
• Prompt preprocessing layer for text-to-image engines
• Creative AI image generation systems
• Backend service for image apps

Downstream Use

• Web API deployment
• Integration into creative AI SaaS
• Automated aesthetic enrichment pipelines

Out-of-Scope Use

• Conversational AI
• Factual Q&A
• Long-form content writing
• Sensitive or high-stakes decision systems

This is a domain-specialized aesthetic enhancer.

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Evaluation

Evaluation performed via qualitative stress testing on:

• 1–4 word minimal prompts
• Style blending stability
• Repetition resistance
• Structural consistency

The model demonstrates:

• Strong format discipline
• Stable enhancement structure
• High aesthetic density

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Bias, Risks & Limitations

• Model inherits stylistic bias from curated visual dataset
• May prefer high-detail cinematic styles
• Not intended for general language understanding
• Does not guarantee optimal performance across all diffusion configurations

Users should calibrate outputs according to target engine sampling settings.

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Environmental Impact

• Hardware: NVIDIA T4 GPU
• Training Duration: ~35 minutes
• Precision: FP16 (energy efficient)
• Estimated Carbon Impact: Minimal due to short training window

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How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Bl4ckSpaces/BlackList-2.0"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

input_text = "[SIMPLE] fantasy warrior"

inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_length=120,
    temperature=0.8,
    top_p=0.95
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation
If you use BlackList 2.0 in your project, please credit:
Bl4ckSpaces – BlackList 2.0 Prompt Enhancer AI
Model Card Contact
Creator: Bl4ckSpaces
Hugging Face: https://huggingface.co/Bl4ckSpaces�