BlackList is a lightweight decoder-only Transformer designed to transform simple visual keywords into masterpiece-level generative prompts for text-to-image engines such as Stable Diffusion, Flux, and Midjourney.

Model Details

Model Description

BlackList is a task-specific text-to-text generative AI model trained to enhance short visual keywords into rich, structured, high-aesthetic prompts optimized for modern text-to-image systems.

Instead of generating long-form text, BlackList specializes in prompt enhancement, automatically enriching minimal input with:

Lighting descriptions
Rendering quality (8K, ultra-detailed, cinematic, etc.)
Artistic medium (digital painting, concept art, illustration)
Stylistic elements
Composition cues

The model is optimized for speed, low latency, and efficient deployment.

Developed by: Bl4ckSpaces
Model type: Decoder-only Transformer (Mini GPT-2 Base inspired)
Language(s): English (optimized for visual art terminology)
License: Apache 2.0
Finetuned from model: Trained from scratch

Intended Use

Direct Use

BlackList is designed to:

Convert short keyword input into enhanced prompts
Improve text-to-image prompt quality
Serve as a backend API model for image generator applications
Be embedded into lightweight or mobile-first AI systems

Example:

Input: [SIMPLE] cyberpunk girl

Output: [ENHANCED] cyberpunk girl, neon lighting, ultra detailed, cinematic composition, 8k resolution, digital painting, concept art, highly detailed background

Downstream Use

Integration into Stable Diffusion pipelines
Prompt preprocessing layer before large T2I engines
Web-based AI image generation platforms
Mobile AI creative tools

Out-of-Scope Use

Long-form content generation
Conversational AI
Factual QA systems
Sensitive or high-risk decision making

This model is not designed for general language understanding tasks.

Architecture & Technical Specifications

Model Architecture

Architecture: Custom Decoder-only Transformer
Parameters: 23,273,472 (~23.27M)
Hidden Size: 512
Layers: 6
Attention Heads: 8
Max Context Length: 512 tokens

This architecture provides a balance between expressive capacity and low-latency inference.

Tokenizer & Language Design

Tokenizer: Custom Byte-Pair Encoding (BPE)
Vocab Size: 8,000 tokens
Special Tokens:
- [SIMPLE] → Input trigger
- [ENHANCED] → Output activation

The tokenizer was trained from scratch with a strong focus on visual art terminology, including rendering engines, lighting styles, artistic mediums, and aesthetic descriptors.

Training Details

Training Data

~30,000 curated and filtered high-quality "masterpiece" prompt pairs
Custom extracted dataset optimized for visual aesthetics

Training Procedure

Hardware: NVIDIA T4 Tensor Core GPU
Training Duration: ~7 minutes
Optimization Steps: 3,748
Epochs: 4
Final Loss: ~3.37

The final loss was intentionally stabilized to maintain keyword fluency while minimizing grammatical hallucination and overfitting.

Training Regime

Mixed precision training (FP16)

Capabilities

Zero-Shot Aesthetic Enrichment

The model can interpret 1–3 base keywords and automatically inject:

Lighting (cinematic lighting, soft shadows, neon glow)
Render quality (8k, ultra-detailed)
Artistic medium (digital painting, illustration, concept art)
Composition and detail enhancements

Embedded Artist Knowledge

The model has internalized stylistic patterns inspired by legendary artists such as:

Greg Rutkowski
Artgerm
Alphonse Mucha

This allows stylistic richness without requiring explicit artist invocation.

Lightweight & Fast

Model Size: ~93MB
Low-latency inference
Suitable for API deployment
Mobile-first friendly

Evaluation

Evaluation was performed qualitatively on:

Aesthetic richness
Prompt structure coherence
Stability of enhancement
Overfitting detection

The model demonstrates strong consistency in structured aesthetic enrichment while maintaining controlled expansion.

Bias, Risks & Limitations

The model inherits stylistic bias from the curated dataset.
It may overuse common high-quality descriptors (e.g., "8k", "ultra-detailed").
It is not suitable for non-visual text tasks.
It does not guarantee compatibility with every image model configuration.

Users should test and calibrate outputs depending on their target engine.

Environmental Impact

Hardware: NVIDIA T4 GPU
Training Time: ~7 minutes
Compute Region: Unknown
Carbon Emission: Minimal due to short training duration

How to Use

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Bl4ckSpaces/BlackList-Prompt-Enhancer"

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=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Citation
If you use BlackList in your project, please credit:
Bl4ckSpaces – BlackList Prompt Enhancer AI
Model Card Contact
Developer: Bl4ckSpaces
Hugging Face: https://huggingface.co/Bl4ckSpaces�