🧠 NeonLLama ComfyUI Extension

NeonLLama is a custom ComfyUI node that transforms one or more idea lines into vivid, richly detailed prompts using a local LLM — either through Ollama or LM Studio. It supports avoid content to guide generation and returns that list as the negative prompt.

---

🚀 Features

• 🧠 Converts simple ideas into rich, structured positive prompts.
• ✂️ Supports multi-line input — each idea line generates its own prompt.
• ⛔ Optional "avoid" list used to steer generation and returned as the negative prompt.
• 🔁 Retries with smart temperature adaptation to meet token targets.
• 🧮 Accurate token estimation using model-specific tokenizers.
• ⚙️ Fully configurable: model choice, token bounds, retry limits, and regeneration control.
• 🖨️ Logs all prompts and supports forced regeneration.

---

🧩 How It Works

1. Input one or more idea lines (line breaks separate them).

2. Optionally include avoid terms to bias generation away from undesired concepts.

3. The node:

   • Attempts generation using your configured local model.
   • Retries if the output is too short or too long.
   • Logs inputs and outputs.

4. Outputs include the final positive prompt, your avoid list, and original idea.

---

📤 Outputs

Output	Type	Description
prompt	STRING	Combined positive prompt(s), separated with BREAK.
negative	STRING	Direct copy of avoid input, used as negative prompt.
idea	STRING	Original idea input, for UI traceability or debugging.

---

📥 Inputs / Configuration Fields

Name	Type	Description
model	Dropdown	Select the model name to use (fetched from the running backend).
idea	Text	One idea per line. Each generates a separate prompt.
negative	Text	Words to avoid — passed to model + used as negative prompt.
max_tokens	Int	Upper bound for output tokens (default: 75, max: 231).
min_tokens	Int	Lower bound for meaningful output (default: 50).
max_attempts	Int	How many times to retry if generation doesn’t meet criteria.
regen_on_each_use	Bool	Forces regeneration on every run, even with unchanged input.

---

🧪 Example

Inputs:

idea:
    haunted subway station with broken lights

avoid:
    blood, gore, screaming

Outputs:

prompt:
    dark abandoned subway, flickering fluorescent lights, cracked tiled walls, shadowy corners, old train cars, graffiti-covered pillars, dim green glow, debris scattered floor

negative:
    blood, gore, screaming

---

🔁 Token-Aware Generation

• Token count estimated using model-specific tokenizer.
• Retries generation using adaptive temperature — starts hot and cools with each failure.
• Adjusts prompt by trimming or elaborating until token limits are satisfied.
• Prompts too short or too long are discarded unless they fall within the required range.

---

🔒 Robust Logging

• Logs prompts and failures to disk for traceability.
• Logs include selected model, prompt source, token counts, and generation time.

---

🧠 Smart Prompt Structuring

Prompts follow rules enforced via system instructions to the model:

• Use short, visually descriptive phrases, not sentences.
• Avoid storytelling, abstract descriptions, or emotion-driven language.
• Keep outputs grounded and aligned with original idea’s visual core.
• Connect elements with simple language: “with”, “under”, “surrounded by”, etc.
• Avoid repetition, overgeneralization, or verbose commentary.

---

🛠️ Requirements

• At least one of:

  • Ollama running at http://localhost:11434
  • LM Studio running with JIT server enabled

• ComfyUI installed and functional

• Python packages:

  • requests
  • tokenizers
  • lmstudio (only required for LM Studio support)

---

📄 License

MIT License

---

❤️ Credits

Built by Neon Lightning ⚡ Powered by open-source local LLMs, punk energy, and rat affection 🐀