mirror of
https://github.com/SHOGGOTH-SECTOR/sica-fondt.git
synced 2026-08-01 16:40:24 +00:00
24f816b6a3
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):
- brain/ LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
dispatch, A51 channels, and the OSINT cluster
- knowledge/ LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
MITRE ATT&CK data
- reference/ defensive threat-reference (C3, shhbruh doc) + AdaYaml parser
License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.
Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.
https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
50 lines
1.4 KiB
JSON
50 lines
1.4 KiB
JSON
{
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"_comment": "SDXL text-to-image at 1024x1024. Required model: sd_xl_base_1.0.safetensors (or any SDXL checkpoint).",
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"3": {
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"class_type": "KSampler",
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"_meta": {"title": "KSampler"},
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"inputs": {
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"seed": 42,
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"steps": 30,
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"cfg": 7.5,
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"sampler_name": "dpmpp_2m",
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"scheduler": "karras",
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"denoise": 1.0,
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"model": ["4", 0],
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"positive": ["6", 0],
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"negative": ["7", 0],
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"latent_image": ["5", 0]
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}
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},
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"4": {
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"class_type": "CheckpointLoaderSimple",
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"_meta": {"title": "Load SDXL Base"},
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"inputs": {"ckpt_name": "sd_xl_base_1.0.safetensors"}
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},
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"5": {
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"class_type": "EmptyLatentImage",
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"_meta": {"title": "Empty Latent"},
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"inputs": {"width": 1024, "height": 1024, "batch_size": 1}
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},
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"6": {
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"class_type": "CLIPTextEncode",
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"_meta": {"title": "Positive Prompt"},
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"inputs": {"text": "cinematic photograph, dramatic lighting, intricate detail", "clip": ["4", 1]}
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},
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"7": {
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"class_type": "CLIPTextEncode",
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"_meta": {"title": "Negative Prompt"},
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"inputs": {"text": "ugly, blurry, low quality, deformed, watermark", "clip": ["4", 1]}
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},
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"8": {
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"class_type": "VAEDecode",
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"_meta": {"title": "VAE Decode"},
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"inputs": {"samples": ["3", 0], "vae": ["4", 2]}
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},
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"9": {
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"class_type": "SaveImage",
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"_meta": {"title": "Save Image"},
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"inputs": {"filename_prefix": "sdxl", "images": ["8", 0]}
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}
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}
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