Make ADAM a clean runnable source release

.gitignore

@@ -1,11 +1,27 @@
+# Python and test caches
 __pycache__/
 *.py[cod]
 .pytest_cache/
 .venv/
 venv/
-logs/*.log
-data/projects/*
-!data/projects/.gitkeep
+
+# Local application state and user-connected tools
+config/settings.json
 config/settings.local.json
-*.tmp
+config/external_tools.json
+data/*
+!data/.gitkeep
+logs/
 
+# Generated datasets, models, and media
+ADAM_Datasets/
+artifacts/
+build/
+dist/
+*.safetensors
+*.ckpt
+*.pt
+*.pth
+*.bin
+*.onnx
+*.tmp

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 SyntheticMDProductions
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,346 +1,53 @@
-# ADAM — AI Development and Automation Manager
-
-ADAM is a local, safety-first desktop hub for orchestrating AI project tools.
-It includes registered dataset, DDPM, and SDXL LoRA workflows with background
-planning, approval gates, progress reporting, and persistent asset history.
-
-Dataset preparation, captioning, and preview placeholders remain clearly marked
-as demo tools. The connected Dataset Collector, DDPM trainer, and Local SDXL
-LoRA Trainer use real adapters and never fall back to simulated training.
-
-Existing program folders can be connected from **Settings → Tool folders**.
-ADAM stores only the path and scans for likely entry points; it does not copy or
-modify the external project. Folder assignments can also be pasted into chat:
-
-```text
-DDPM Trainer: D:\AI\DDPM
-Flow Matching Trainer: D:\AI\FlowMatchImageGenerator
-```
-
-Detection does not automatically authorize training. A real training adapter
-remains gated until its dataset, model name, run settings, and output location
-are explicit.
-
-## Training agents
-
-ADAM's training lifecycle is divided into four explainable responsibilities:
-
-- **EVE** reviews dataset membership and leaves uncertain images for the user.
-- **ORION** reviews planned epochs, batch size, resolution, image exposures, and
-  estimated optimizer steps. He can require approval but never silently changes
-  the requested settings. In the Model Creation Assistant, **ORION: apply a
-  starting recipe** fills a conservative, editable draft from the image count
-  and selected resolution before a plan is built.
-- **ATLAS** watches active training for non-finite loss, sustained critical GPU
-  temperature, critically low disk space, stalls, and large runtime overruns.
-  Critical conditions pause the trainer process tree so the user can inspect it.
-- **NOVA** examines available post-training previews and samples for unreadable
-  files and exact-looking duplicate collapse. Her report explicitly separates
-  technical sample health from subjective or subject-quality review.
-
-ORION, ATLAS, and NOVA reports are stored with each durable job record and are
-shown in Current Plan, Active Job, and Jobs / History respectively. ATLAS's
-default thresholds can be overridden in `config/settings.json` with the
-`atlas_*` settings defined in `adam/config.py`.
-
-## Real image collection
-
-When a valid Dataset Collector folder is connected, the `dataset_collector`
-registry entry uses ADAM's real visible-browser adapter. After plan approval it:
-
-- opens Bing Images in a normal visible Chrome window;
-- waits when consent/CAPTCHA/human-verification text is detected;
-- resumes automatically after the user resolves the page;
-- downloads valid images at least 256×256;
-- removes exact duplicate downloads;
-- writes a matching `.txt` caption beside every image; and
-- records URLs, captions, sources, and dimensions in `metadata.csv`.
-
-No CAPTCHA or website restriction is bypassed. Closing Chrome or stopping the
-job ends collection safely. A new timestamped dataset folder is used rather
-than overwriting an existing collection.
-
-ADAM keeps an incomplete DDPM request in conversation memory. A follow-up such
-as `dataset folder Mario, model name Mario V2, epoch count 100, output D:\Runs`
-fills the pending fields and validates named datasets against the connected
-collector. It will not start if the dataset cannot be found.
-
-## Showcase videos
-
-The **Showcase Video** workspace creates a finished MP4 directly from completed
-DDPM and Flow Matching models. Select and reorder the models, choose 12–24
-images per model, a 3-, 4-, or 5-second image duration, shared steps and aspect
-ratio, provider-compatible samplers, seed, and 720p or 1080p output. ADAM runs
-the image batches sequentially and then renders a request-list interface that
-tracks the active model, image number, trainer, steps, sampler, and aspect ratio.
-LoRA models are intentionally excluded from this streamlined workflow.
+---
+license: mit
+tags:
+- desktop-application
+- ai-tools
+- dataset-management
+- lora-training
+- windows
+---
 
-When Ollama is reachable, messages that are not workflow commands receive a
-short conversational answer. Ollama may explain or plan, but it still cannot
-bypass the registry or confirmation gates.
-
-## Web search in Chat Mode
-
-Chat Mode can give local Ollama current web context without an API key. Enable
-it in **Settings → Planning model**, then ask naturally, for example:
-
-```text
-Search the web for Dandy's World character ideas.
-What are the latest Ollama release notes?
-Look up a reference for a cyberpunk city character.
-```
-
-ADAM sends only that search query to Bing's public results feed, reads the
-result titles and snippets,
-and passes up to five titles, snippets, and links to Ollama. It does not open
-the result pages, download anything, or let web content run tools. Results are
-untrusted reference material, so ADAM is instructed to cite the links and flag
-uncertainty. Disable the setting to keep Chat Mode fully local.
-
-When you explicitly ask ADAM to **read**, **open**, or **research** result links,
-it can read up to three public HTML/text pages and give Ollama short extracts.
-For example: `Search the web for Undertale character ideas and read the most
-relevant links.` Direct links can be read with `Read https://example.com/ and
-summarize it.` Private/local addresses, non-web protocols, oversized pages,
-downloads, and more than three pages are blocked. This control can be disabled
-in Settings.
-
-Planning runs away from the interface thread, and conversational Ollama output
-is streamed into the chat. ADAM validates training commands against a strict
-schema and each registered trainer's declared capabilities before offering a
-job.
-
-In **Settings → Planning model**, **Chat response length** sets the maximum
-number of generated tokens for a Chat Mode reply. Higher values allow longer
-research summaries but use more time and GPU memory. The default is 1,024,
-which gives Qwen3 enough room to reason and still produce a visible response.
-
-ADAM stores friendly dataset/model names, paths, trainer types, epochs, and
-resume checkpoints in `data/assets.json`. Requests such as:
+# ADAM — AI Development and Automation Manager
 
-```text
-From the Mario dataset, train it on a DDPM for 300 epochs.
-With the Mario dataset, train it on a LoRA for 100 epochs.
-Continue the Mario model from the DDPM for 50 epochs.
-```
+ADAM is a local Windows desktop hub for organizing AI image-development workflows. It helps you prepare and review datasets, connect your own training tools, plan runs with explicit approval, monitor jobs, and keep a history of your own assets.
 
-are resolved to real paths before approval. Continuation is offered only when a
-compatible checkpoint exists. New DDPM runs retain the latest resume checkpoint.
+## What is included
 
-## Run
+- ADAM source code and the built-in tool registry.
+- A clean, empty external-tool registry.
+- No model weights, LoRAs, checkpoints, datasets, generated images, job history, personal paths, API keys, or local settings.
 
-```powershell
-python main.py
-```
+## Requirements
 
-On Windows, you can also double-click `Launch ADAM.bat`.
+- Windows 10/11
+- Python 3.10 or later
+- Optional: NVIDIA GPU for compatible training workflows
+- Optional: Ollama for local chat assistance
 
-The app requires Python 3.10+ and PySide6. Optional integrations use `psutil`
-for system information and `pynvml` for NVIDIA GPU information.
+## Install and run
 
 ```powershell
+git clone https://huggingface.co/SyntheticMDProductions/AI_Development_Automation_Manager
+cd AI_Development_Automation_Manager
 python -m pip install -r requirements.txt
+python main.py
 ```
 
-Try:
-
-- Click **Create a model…** in Trainer Mode for the guided Model Creation Assistant.
-- `Adam, train a LoRA of Hatsune Miku`
-- `Adam, collect a dataset of liminal spaces`
-- `Adam, generate previews`
-- `Adam, check GPU status`
-- `From the Mario dataset, train it on a DDPM for 300 epochs`
-- `With the Mario dataset, train it on a LoRA for 100 epochs`
-
-Training and large collection plans are never started until you approve the
-plan. All actions are recorded in `logs/adam.log`, while project artifacts live
-under `data/projects/`.
-
-The Model Creation Assistant can start from a built-in Character LoRA, Style
-LoRA, DDPM, or Flow Matching preset. It can create a dataset or select a
-registered one, recommends starting values, and saves personal presets. The
-result still goes through ADAM's normal validated planner and approval gate.
-Use **+ Add model** to build a multi-model training batch. Each wide model tab
-keeps its own dataset, trainer, name, and settings; the minus button removes an
-unwanted model, and tabs can be dragged to change the run order. ADAM validates
-all models, presents one combined approval plan, and runs them sequentially so
-only one training workflow uses the GPU at a time. A failed step stops the batch
-before a later model starts.
-Before approval, ADAM adds checks for connected tools, dataset contents, the
-LoRA base model, and output-drive free space. Completed dataset and training
-jobs also include a suggested next step.
-
-### Model Batch Builder
-
-Use **Create model batch…** to paste one requested subject per line. ADAM turns
-the list into editable model tabs, removes duplicate names, and lets the current
-trainer recipe be applied to any multi-selection of models. The batch is saved
-as a draft so it can be closed and resumed later.
-
-For a review-first workflow, choose **Collect missing datasets first**. This
-queues only sequential dataset collection and leaves training in the saved
-draft. After collection, reopen the draft, use **Find collected datasets**, and
-review each dataset in Training Studio. **Exclude rejected** moves rejected
-images out of the training folder into a recoverable quarantine, and **Restore
-excluded** reverses it. **Keep all images** marks the whole selected dataset as
-accepted in one action, after which individual bad images can still be rejected.
-Training remains locked until each model is explicitly
-marked as reviewed and ready. If every linked dataset is acceptable as-is,
-**Approve all datasets** marks the entire batch ready after one confirmation;
-it does not inspect individual images or apply pending rejection decisions.
-
-Completed Flow Matching models can be selected in **Fine-tune**. ADAM uses the
-saved Flow model folder as the continuation source, locks the continuation to
-the model's original resolution, and writes the fine-tuned result to a new
-output folder. This continues the saved weights while starting a fresh optimizer
-and learning-rate schedule; it does not overwrite the original model.
-
-## Training Studio
-
-The **Training Studio** turns completed work into a reviewable experiment loop:
-
-- **Datasets** provides an image gallery, keep/reject decisions, caption editing,
-  exact duplicate detection, and visually similar duplicate candidates.
-- **Experiments** compares job settings and outcomes, opens outputs, marks a
-  preferred model, and converts successful settings into reusable recipes.
-- **Checkpoint Lab** browses model checkpoints and output images, records
-  consistent prompt/seed evaluations, and sends preview requests through the
-  normal approval-aware planner.
-- **Recipes** preserves training starting points and can import or export
-  portable JSON recipe files.
-
-### EVE AI Dataset Review
-
-In Training Studio → Datasets, **EVE AI Review…** performs a local reference-
-guided visual review. Add one or more good reference images and optional bad
-references, then choose Keep and Reject confidence thresholds. EVE uses a small
-DINOv2 vision model to divide the selected dataset into **Keep**, **Reject**, and
-**Uncertain** galleries with confidence scores. The model is downloaded once on
-first use and subsequent analysis stays local.
-
-Nothing is applied automatically. Inspect both sides, double-click images for a
-full view, and move selected results between the three groups before choosing
-**Apply EVE review**. EVE's decisions remain ordinary Training Studio review
-marks: they can be manually changed, and rejected files are not moved until
-**Exclude rejected** is selected. The latest proposal is also saved under
-`data/eve_reviews/` for auditing. Use **Select all in current group** (or
-Ctrl/Shift selection) to move many images at once; EVE transfers only the
-chosen thumbnails so manual sorting stays responsive on large datasets.
-
-Training panels show elapsed time, a progress-based ETA, recent logs, and a
-loss sparkline when the connected trainer reports `loss`. Preflight summaries
-include clearly labelled workload, duration, VRAM, and disk estimates. These
-estimates are planning hints rather than hardware guarantees.
-
-Create a Model also supports live training previews with a configurable
-epoch interval, prompt, and reproducible seed for each model tab. While a
-training job is active, its newest 256×256 preview appears in the right sidebar
-with the source epoch and next scheduled preview. The full-size trainer output
-can be opened from the card. Built-in adapters may publish previews directly;
-registered DDPM, Flow, LoRA, APVD, MaskGit, and other trainers can also
-participate by writing conventionally named `preview`, `sample`, or `epoch`
-images beneath their declared output folder.
-
-## Generations
-
-The **Generations** workspace runs compatible registered image generators
-without opening their separate desktop interfaces. The connected DDPM and Flow
-Matching projects can generate from completed models with a reproducible seed,
-sampler or ODE method, step count, image count, and aspect ratio. Generation
-work uses the normal ADAM job queue, progress reporting, cancellation, and
-logging.
-
-Every completed batch is stored under `data/generations/` with its images and a
-`generation.json` sidecar. The history gallery can open an image or batch folder
-and restore the exact settings for another run. DDPM creative notes are stored
-with a batch for organization; they are not presented as text conditioning for
-an unconditional DDPM model.
-
-**Generation Cycle…** selects multiple compatible completed models and queues
-one generation step per model. Choose images per model, a shared prompt or
-creative note, starting seed, slideshow duration, looping, fullscreen playback,
-and an optional model/trainer label. When the cycle finishes, ADAM opens the
-results as a local slideshow while preserving every ordinary generation record
-in history.
-
-If ADAM discovers a job interrupted by an unexpected shutdown, it offers to
-open Jobs & History. The previous record remains intact and can be retried as a
-new approval-gated job. Job logs can also be exported for troubleshooting.
-
-## Connect an existing tool
-
-ADAM supports importable Python functions and command-line Python scripts.
-For a no-code setup, open **Settings → External Tools → Add external tool**.
-Choose the program folder, select its training entry script and important
-configuration files, then review ADAM's static compatibility and safety report.
-The report covers:
-
-- detected command-line options and required inputs;
-- likely dataset formats;
-- output and checkpoint behavior;
-- progress reporting;
-- resume-training support; and
-- potentially risky operations visible in the selected entry script.
-
-The 1–10 rating measures how clearly the script fits ADAM's safe command-line
-contract. It is not a guarantee that third-party code is harmless. ADAM does
-not execute a script while scanning it, external tools cannot replace built-in
-registry entries, and every external-tool run requires explicit approval.
-
-After registration, a tool can be planned with a request such as:
-
-```text
-Run APVD Model Trainer with dataset=D:\DreamData, epochs=20, output=D:\APVD\output
-```
-
-ADAM will ask for any required inputs that were omitted before it offers the
-approval plan.
-
-For manual registry configuration, edit the relevant item in
-`config/tools.json`:
-
-```json
-{
-  "backend": {
-    "type": "python",
-    "module": "my_tools.lora",
-    "function": "train"
-  },
-  "demo": false
-}
-```
-
-The function receives a `ToolContext` as its first argument and keyword
-arguments from the approved plan. This keeps training code in one place: your
-existing GUI and ADAM can both call the same backend.
-
-For scripts:
-
-```json
-{
-  "backend": {
-    "type": "script",
-    "path": "D:/AI/LoRATrainer/train.py"
-  },
-  "demo": false
-}
-```
-
-ADAM invokes scripts directly with the current Python interpreter, captures
-stdout/stderr, and never drives another GUI with mouse clicks.
+On first launch, ADAM creates your personal `config/settings.json` automatically. In **Settings → Tool folders**, connect the local projects, datasets, and models that you own and want ADAM to manage. ADAM does not bundle or download model weights for you.
 
-## Safety model
+## Notes for users
 
-- Plans are shown before execution.
-- Long, destructive, or high-volume work requires confirmation.
-- Unregistered tools cannot be invoked.
-- External paths and arguments are validated before execution.
-- The LLM may propose a plan, but only registered tools can execute it.
-- Pause, resume, and cancel controls are available for active jobs.
-- Every tool action and state transition is logged.
+- Training and collection plans require approval before ADAM starts them.
+- You are responsible for the licenses, permissions, and rights for any datasets, models, and third-party tools you connect.
+- This repository is a downloadable desktop application. It is not a hosted Hugging Face Space or an inference model.
 
-## Tests
+## Development
 
 ```powershell
 python -m pytest -q
 ```
+
+## License
+
+ADAM is released under the [MIT License](LICENSE).

config/tools.json

@@ -0,0 +1,278 @@
+{
+  "tools": [
+    {
+      "id": "youtube_video_collector",
+      "name": "ADAM Video Dataset Collector",
+      "description": "Previews and downloads supplied YouTube videos or playlists, normalizes MP4 files, extracts traceable frames, and writes provenance and credits.",
+      "category": "Dataset",
+      "entry_function": "collect_youtube_dataset",
+      "arguments": ["dataset_name", "urls", "output_root", "max_videos", "max_duration_seconds", "max_total_duration_seconds", "max_total_size_mb", "preferred_resolution", "download_audio", "skip_beginning_seconds", "skip_ending_seconds", "mode", "frames_per_second", "max_accepted_frames", "remove_blurry_frames", "remove_black_frames", "remove_near_duplicates", "duplicate_threshold", "keep_mp4", "separate_source_folders", "mix_accepted_frames", "generate_captions", "generate_credits", "save_exact_timestamps", "dry_run", "permission_status", "retry_limit"],
+      "required_arguments": ["dataset_name", "urls"],
+      "capabilities": ["manual_urls", "playlist_limit", "metadata_preview", "mp4_normalization", "frame_provenance", "progress", "pause", "cancel"],
+      "requires_confirmation": true,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.youtube_video_collector",
+        "function": "collect_youtube_dataset"
+      }
+    },
+    {
+      "id": "dataset_collector",
+      "name": "Dataset Collector",
+      "description": "Collects image references and produces a reviewable dataset manifest.",
+      "category": "Dataset",
+      "entry_function": "collect_dataset",
+      "arguments": ["subject", "image_count", "collection_mode", "project_name", "output_dir"],
+      "required_arguments": ["subject", "image_count", "project_name"],
+      "capabilities": ["fresh_collection", "named_output"],
+      "requires_confirmation": true,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.real_dataset_collector",
+        "function": "collect_dataset"
+      }
+    },
+    {
+      "id": "dataset_preparer",
+      "name": "Dataset Preparation",
+      "description": "Validates, filters, deduplicates, and prepares collected images.",
+      "category": "Dataset",
+      "entry_function": "prepare_dataset",
+      "arguments": ["project_name"],
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": true,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.demo_backends",
+        "function": "prepare_dataset"
+      }
+    },
+    {
+      "id": "caption_generator",
+      "name": "Caption Generator",
+      "description": "Creates editable training captions from a prepared dataset.",
+      "category": "Dataset",
+      "entry_function": "generate_captions",
+      "arguments": ["subject", "project_name"],
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": true,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.demo_backends",
+        "function": "generate_captions"
+      }
+    },
+    {
+      "id": "lora_trainer",
+      "name": "LoRA Trainer",
+      "description": "Launches and monitors a registered SD/SDXL LoRA training backend.",
+      "category": "Training",
+      "entry_function": "train_lora",
+      "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "base_model", "resume_from", "preview_enabled", "preview_every", "preview_prompt", "preview_seed"],
+      "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "base_model"],
+      "capabilities": ["fresh_training", "resume_training", "progress", "pause", "cancel"],
+      "requires_confirmation": true,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.lora_adapter",
+        "function": "train_lora"
+      }
+    },
+    {
+      "id": "preview_generator",
+      "name": "Preview Generator",
+      "description": "Produces review previews from the latest registered model output.",
+      "category": "Output",
+      "entry_function": "generate_previews",
+      "arguments": [
+        "subject",
+        "project_name",
+        "preview_count",
+        "model_name",
+        "checkpoint",
+        "prompt",
+        "seed"
+      ],
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": true,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.demo_backends",
+        "function": "generate_previews"
+      }
+    },
+    {
+      "id": "ddpm_generator",
+      "name": "DDPM Generator",
+      "description": "Generates reproducible image batches from completed models in the connected DDPM project.",
+      "category": "Output",
+      "entry_function": "generate_ddpm_images",
+      "arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio", "reference_image", "reference_strength", "width", "height", "preview_interval"],
+      "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"],
+      "capabilities": ["image_generation", "seed", "sampler", "batch", "aspect_ratio", "reference_image", "live_preview", "progress", "cancel"],
+      "model_trainers": ["ddpm"],
+      "generation_options": {
+        "samplers": ["DDIM", "DDPM"],
+        "aspect_ratios": ["1:1 (Square)", "16:9 (Widescreen)", "9:16 (Portrait)", "4:3 (Classic)", "3:4 (Portrait Classic)", "3:2 (Photo)", "2:3 (Portrait Photo)"],
+        "step_min": 5,
+        "step_max": 500,
+        "step_default": 50
+      },
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.ddpm_generator",
+        "function": "generate_ddpm_images"
+      }
+    },
+    {
+      "id": "flow_generator",
+      "name": "Flow Matching Generator",
+      "description": "Generates reproducible image batches from completed models in the connected Flow Matching project.",
+      "category": "Output",
+      "entry_function": "generate_flow_images",
+      "arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio", "preview_interval"],
+      "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"],
+      "capabilities": ["image_generation", "seed", "ode_method", "batch", "aspect_ratio", "live_preview", "progress", "cancel"],
+      "model_trainers": ["flow"],
+      "generation_options": {
+        "samplers": ["Heun", "Euler"],
+        "aspect_ratios": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"],
+        "step_min": 1,
+        "step_max": 200,
+        "step_default": 20
+      },
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.flow_generator",
+        "function": "generate_flow_images"
+      }
+    },
+    {
+      "id": "lora_generator",
+      "name": "LoRA Generator",
+      "description": "Generates prompted SDXL image batches with completed LoRAs from the connected LoRA Trainer project.",
+      "category": "Output",
+      "entry_function": "generate_lora_images",
+      "arguments": ["model_name", "model_path", "prompt", "negative_prompt", "base_model_path", "image_count", "steps", "seed", "sampler", "aspect_ratio", "width", "height", "cfg_scale", "lora_strength", "reference_image", "denoise_strength", "prompt_weighting", "preview_interval"],
+      "required_arguments": ["model_name", "model_path", "prompt", "image_count", "steps", "seed", "sampler", "aspect_ratio"],
+      "capabilities": ["image_generation", "text_prompt", "seed", "sampler", "batch", "aspect_ratio", "reference_image", "live_preview", "progress", "cancel"],
+      "model_trainers": ["lora"],
+      "generation_options": {
+        "samplers": ["DPM++ 2M", "DPM++ SDE", "Euler", "Euler a", "DDIM"],
+        "aspect_ratios": ["1:1 (Square)", "4:3 (Landscape)", "3:4 (Portrait)", "3:2 (Landscape)", "2:3 (Portrait)", "16:9 (Widescreen)", "9:16 (Vertical)"],
+        "step_min": 1,
+        "step_max": 150,
+        "step_default": 30
+      },
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.lora_generator",
+        "function": "generate_lora_images"
+      }
+    },
+    {
+      "id": "showcase_video_renderer",
+      "name": "Showcase Video Renderer",
+      "description": "Composes images generated by the current job into ADAM's finished showcase MP4 interface.",
+      "category": "Output",
+      "entry_function": "render_showcase_video",
+      "arguments": ["title", "display_seconds", "resolution", "models"],
+      "required_arguments": ["title", "display_seconds", "resolution", "models"],
+      "capabilities": ["video_generation", "progress", "cancel"],
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.showcase",
+        "function": "render_showcase_video"
+      }
+    },
+    {
+      "id": "completion_notifier",
+      "name": "Completion Notification",
+      "description": "Records pipeline completion and makes the output easy to open.",
+      "category": "System",
+      "entry_function": "notify_complete",
+      "arguments": ["project_name"],
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": true,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.demo_backends",
+        "function": "notify_complete"
+      }
+    },
+    {
+      "id": "system_monitor",
+      "name": "System Monitor",
+      "description": "Reports CPU, RAM, GPU, VRAM, temperature, and active processes.",
+      "category": "System",
+      "entry_function": "inspect_system",
+      "arguments": ["project_name"],
+      "requires_confirmation": false,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.demo_backends",
+        "function": "inspect_system"
+      }
+    },
+    {
+      "id": "ddpm_trainer",
+      "name": "DDPM Trainer",
+      "description": "Launches and monitors the connected DDPM trainer with safe, explicit run settings.",
+      "category": "Training",
+      "entry_function": "train_ddpm",
+      "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from", "resolution", "batch_size", "learning_rate", "gradient_accumulation_steps", "dataloader_num_workers", "mixed_precision", "save_every", "preview_steps", "training_intensity", "preview_enabled", "preview_every", "preview_prompt", "preview_seed"],
+      "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir"],
+      "capabilities": ["fresh_training", "resume_training", "progress", "pause", "cancel"],
+      "requires_confirmation": true,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.ddpm_adapter",
+        "function": "train_ddpm"
+      }
+    },
+    {
+      "id": "flow_trainer",
+      "name": "Flow Matching Trainer",
+      "description": "Launches and monitors the connected Rectified Flow image trainer.",
+      "category": "Training",
+      "entry_function": "train_flow",
+      "arguments": ["dataset_dir", "model_name", "epochs", "output_dir", "resume_from", "resolution", "batch_size", "learning_rate", "gradient_accumulation", "workers", "mixed_precision", "save_every", "preview_every", "preview_steps", "gradient_checkpointing", "preview_enabled", "preview_prompt", "preview_seed"],
+      "required_arguments": ["dataset_dir", "model_name", "epochs", "output_dir"],
+      "capabilities": ["fresh_training", "resume_training", "progress", "cancel"],
+      "requires_confirmation": true,
+      "enabled": true,
+      "demo": false,
+      "backend": {
+        "type": "python",
+        "module": "adam.tools.flow_adapter",
+        "function": "train_flow"
+      }
+    }
+  ]
+}