Eyght/eyght-image-v1

Eyght Image V1 — "White Background Advertise" LoRA

A PEFT LoRA adapter for runwayml/stable-diffusion-v1-5, fine-tuned on a product photography dataset. It generates clean product shots on white backgrounds — ideal for advertisement/mockup imagery.

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

Trigger phrase: white background advertise

Model details

Base model	runwayml/stable-diffusion-v1-5
Type	PEFT LoRA adapter (adapter_config.json + adapter_model.safetensors)
Task	Text-to-image (product / ad photography)
Trigger	white background advertise
License	CreativeML OpenRAIL-M

How to use

from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, safety_checker=None)
pipe.load_lora_weights("Eyght/eyght-image-v1")
pipe.to("cuda")
img = pipe("A high-end mechanical keyboard, white background advertise", num_inference_steps=25, guidance_scale=7.5, cross_attention_kwargs={"scale": 0.7}).images[0]
img.save("product.png")

A live demo Space is available: Eyght/eyght-image-v1-space.

Training

Fine-tuned with LoRA on a curated product-photography dataset to produce clean white-background ad imagery.

License & attribution

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

Citation

@misc{eyght_image_v1,
  title  = {Eyght Image V1: an Eyght fine-tune of Stable-Diffusion v1.5},
  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.