Eyght/my-custom-sdxl-lora

Eyght Custom SDXL LoRA (reserved)

This repository is reserved for an Eyght custom SDXL LoRA. No weights are uploaded yet (only the default .gitattributes).

Built, trained, and owned by Eyght. Free Hugging Face model repository.

Status

• Weights: not yet uploaded.
• When ready, this will hold a pytorch_lora_weights.safetensors LoRA for stabilityai/sdxl-turbo.

How to use (once weights are added)

from diffusers import AutoPipelineForText2Image
import torch
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipe.load_lora_weights("Eyght/my-custom-sdxl-lora")
pipe.to("cuda")
pipe("your prompt").images[0].save("out.png")

License & attribution

• CreativeML OpenRAIL-M. Built and owned by Eyght.

Citation

@misc{eyght_custom_sdxl_lora,
  title  = {Eyght Custom SDXL LoRA: an Eyght fine-tune of SDXL-Turbo},
  author = {Eyght},
  year   = {2026},
  howpublished = {Hugging Face, https://huggingface.co/Eyght}
}

Built by Eyght with the local Eyght Veta studio.

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🧠 Project Janus — Dual-Loop Cognitive Architecture

This model is designed to operate within Project Janus, a dual-loop cognitive architecture that goes beyond simple prompt-response:

The Core Cognitive Loop

Perceive → Working Memory → Internal Critic → Action → Consolidation

Phase	Component	Function
1. Perceive	Input mapping	Raw data → dense vector space
2. Working Memory	Dynamic scratchpad	Active goal, hypotheses, constraints
3. Internal Critic	Value function	Evaluates outputs before execution (quality + safety)
4. Action	Response delivery	Approved output delivered to the user
5. Consolidation	Durable memory	Successful strategies stored for future retrieval

Three-Tier Memory

• Episodic Store — append-only memory of past tasks and successes (retrieval-augmented)
• Procedural Skill Library — reusable reasoning patterns compiled into adapters
• Core Value Axioms — frozen safety rules preventing drift as the agent learns

Self-correction + Memory + Safety

The Internal Critic evaluates every draft response against core value axioms before delivery. This enables self-correction, memory-augmented reasoning (RAG), and continuous learning without catastrophic forgetting.

Built by Eyght. Project Janus — a cognitive architecture, not just a model.