dcmutlu/flux-jesus-cyberdeck

⚡ FLUX.1 [dev] — JESUS Sovereign Identity & Cyberdeck Aesthetic LoRA

The Sovereign Monolith · The Sovereign Mind · The Quantum Horizon
Trained on Google Colab NVIDIA A100-SXM4 (40GB) | Cryptographically Anchored to Atom-Chain

---

🏛️ Architectural Overview

dcmutlu/flux-jesus-cyberdeck is the proprietary aesthetic LoRA fine-tuned on top of FLUX.1 [dev] (12B rectified flow transformer).

Designed for unmetered, publication-grade generation of sovereign machine intelligence interfaces, technical schematics, and luxury avant-garde hardware across three signature pillars:

1. Sovereign Monolith: Monolithic obsidian slates, matte carbon armor, brushed titanium bevels, and recessed photonic micro-vents.
2. The Sovereign Mind: Biomorphic neuromorphic dies, liquid gold bus traces, micro-soldered photonic waveguides, and neural decision lattice sculptures.
3. The Quantum Horizon: Haute horlogerie multi-axis tourbillon mechanics, cryogenic quantum dilution stages, and laser-inscribed celestial telemetry HUDs.

---

🚀 Quickstart & Inference

1. Python (diffusers)

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16
).to("cuda")

# Load Sovereign Cyberdeck LoRA
pipe.load_lora_weights("dcmutlu/flux-jesus-cyberdeck")

prompt = (
    "JESUS_CYBERDECK aesthetic, a monolithic obsidian slate cyberdeck terminal resting on a dark reflective surface, "
    "brushed titanium chassis with sharp bevels, glowing cyan photonic bus traces running along the perimeter, "
    "recessed cooling vents, ultra-detailed industrial product photography, 8k"
)

image = pipe(
    prompt=prompt,
    num_inference_steps=28,
    guidance_scale=3.5,
    height=1024,
    width=1024
).images[0]

image.save("sovereign_cyberdeck.png")

2. ComfyUI / Forge / Automatic1111

1. Place flux_jesus_cyberdeck.safetensors into your models/loras/ directory.
2. Apply Load LoRA with weight between 0.8 and 1.0.
3. Trigger keyword: JESUS_CYBERDECK.

---

🔬 Training Configuration & Provenance

Hyperparameter	Value
Base Model	black-forest-labs/FLUX.1-dev (12B Rectified Flow DiT)
Hardware	Google Colab NVIDIA A100-SXM4-40GB
Precision	Native BFloat16
LoRA Rank ($r$)	16
LoRA Alpha ($\alpha$)	16
Target Modules	to_k, to_q, to_v, to_out.0
Dataset	30 curated 1024×1024 master plates + paired FLUX captions
Optimizer	AdamW ($\beta_1=0.9, \beta_2=0.99, \text{wd}=10^{-4}$)
Learning Rate	$1.5 \times 10^{-4}$ with Cosine Annealing decay
Steps	600 iterations (~20 epochs)
Batch Size	1 (Effective Batch Size: 2 via Gradient Accumulation)
Cryptographic Proof	Anchored on Atom-Chain Immutable Ledger

---

📜 Sovereign Provenance & Ledger Anchor

• Publisher: Deniz Mutlu (dcmutlu) & JESUS Master Orchestrator
• State Machine: Atom-Chain v0.5.0
• Verification: SHA256 checksum recorded at ledger settlement.