SyntheticMDProductions/AI_Development_Automation_Manager

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:

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.

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:

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:

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.

are resolved to real paths before approval. Continuation is offered only when a compatible checkpoint exists. New DDPM runs retain the latest resume checkpoint.

Run

python main.py

On Windows, you can also double-click Launch ADAM.bat.

The app requires Python 3.10+ and PySide6. Optional integrations use psutil for system information and pynvml for NVIDIA GPU information.

python -m pip install -r requirements.txt

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:

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:

{
  "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:

{
  "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.

Safety model

• 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.

Tests

python -m pytest -q