Bl4ckSpaces/BlackList-Prompt-Enhancer

BlackList – Prompt Enhancer AI

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

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�