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README.md

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+# Image Toolbench
+
+Generation pipeline scripts and LoRA weights for the Coalition's FLUX.1-dev image generation stack.
+
+## Repository Structure
+
+```
+scripts/                          # 29 generation pipeline scripts
+loras/
+  vera-likeness/                  # Vera character likeness LoRAs (v1, v3, v4)
+  kintsugi-texture/               # Kintsugi gold-repair texture style LoRAs (v1, v2)
+  thomas-likeness/                # Thomas character likeness LoRA (v1)
+```
+
+## Scripts
+
+Generation pipeline scripts from the vera-triple-stack workspace. These drive FLUX.1-dev inference with single or stacked LoRAs for various visual styles and compositions.
+
+Key scripts:
+- `gen_v2.py` through `gen_v5.py` -- base generation pipeline iterations
+- `gen_cached_identity.py` / `precompute_identity.py` -- identity embedding caching for faster generation
+- `gen_confluence.py` / `gen_confluence_explicit.py` -- multi-concept LoRA merging
+- `gen_kintsugi_v2_test.py` through `gen_kintsugi_v5.py` -- kintsugi texture application iterations
+- `gen_dense_gold.py` / `gen_narrative_gold.py` -- gold/kintsugi aesthetic generation
+- `gen_flesh_to_ceramic.py` -- ceramic transformation pipeline
+- `gen_vera_intimate_v6.py` / `gen_vera_v7_ceramic.py` -- latest generation scripts
+- `gen_mnemosyne_art.py` / `gen_mnemosyne_face.py` -- project artwork generation
+- `gen_project_art_refresh.py` -- project branding refresh
+- `gen_style_exploration.py` / `gen_style_round2.py` / `gen_style_round3.py` -- style R&D
+
+## LoRAs
+
+All LoRAs are trained on **FLUX.1-dev** with **LoRA rank 16**, trained on Apple Silicon (MPS).
+
+### Vera Likeness (`loras/vera-likeness/`)
+
+Character likeness LoRA for Vera. Trigger token: `vera`.
+
+| File | Version | Notes |
+|------|---------|-------|
+| `vera_likeness_v1.safetensors` | v1 | Initial training, 750 steps |
+| `vera_likeness_v3.safetensors` | v3 | Updated prompts with ceramic/statuesque aesthetic, 1250 steps |
+| `vera_likeness_v4.safetensors` | v4 | Fine-tuned from v3, +750 steps. Best version. |
+| `config_v1.yaml` | v1 | Training configuration |
+| `config_v3.yaml` | v3 | Training configuration |
+| `config_v4.yaml` | v4 | Training configuration |
+
+v2 was an incomplete training run and is not included.
+
+### Kintsugi Texture (`loras/kintsugi-texture/`)
+
+Style LoRA for kintsugi (gold-repair) texture effects. Applies golden crack/seam patterns inspired by the Japanese art of repairing broken pottery with gold.
+
+| File | Version | Notes |
+|------|---------|-------|
+| `kintsugi_texture_v1.safetensors` | v1 | Initial texture training |
+| `kintsugi_texture_v2.safetensors` | v2 | Refined texture, 300 steps |
+| `config_v1.yaml` | v1 | Training configuration |
+| `config_v2.yaml` | v2 | Training configuration |
+
+### Thomas Likeness (`loras/thomas-likeness/`)
+
+Character likeness LoRA for Thomas. Trained August 2026.
+
+| File | Version | Notes |
+|------|---------|-------|
+| `thomas_likeness_v1.safetensors` | v1 | 1250 steps |
+| `config_v1.yaml` | v1 | Training configuration |
+
+## Usage
+
+These LoRAs are designed for use with FLUX.1-dev via diffusers. See the generation scripts for examples of single-LoRA and stacked multi-LoRA inference.
+
+Basic single-LoRA usage:
+
+```python
+from diffusers import FluxPipeline
+import torch
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.load_lora_weights("LiberationLabs/image-toolbench", weight_name="loras/vera-likeness/vera_likeness_v4.safetensors")
+pipe.to("cuda")  # or "mps" for Apple Silicon
+
+image = pipe("portrait of vera, ceramic aesthetic, golden light", num_inference_steps=30).images[0]
+```
+
+## Organization
+
+[Liberation Labs](https://github.com/Liberation-Labs) / Transparent Humboldt Coalition
+
+## License
+
+These assets are provided for Coalition use. Contact Liberation Labs for licensing inquiries.