adam/tools/ddpm_generator.py

"""Image-generation bridge for the connected DDPM project."""

from __future__ import annotations

import importlib.util
import json
import random
import re
import sys
from datetime import datetime, timezone
from pathlib import Path
from types import ModuleType

from adam.config import ConfigManager
from adam.executor import ToolContext, ToolExecutionError
from adam.generations import generation_metadata_path, generation_output_folder
from adam.generation_previews import accepts_preview_callback, publish_generation_preview
from adam.image_preferences import GenerationPreferenceEvaluator, PreferenceProfile


_backend_module: ModuleType | None = None
_backend_script: Path | None = None


def _load_backend(script: Path) -> ModuleType:
    global _backend_module, _backend_script
    if _backend_module is not None and _backend_script == script:
        return _backend_module
    module_name = "_adam_connected_ddpm_generator"
    spec = importlib.util.spec_from_file_location(module_name, script)
    if spec is None or spec.loader is None:
        raise ToolExecutionError("The connected DDPM generator could not be loaded.")
    module = importlib.util.module_from_spec(spec)
    sys.modules[module_name] = module
    try:
        spec.loader.exec_module(module)
    except Exception as exc:
        sys.modules.pop(module_name, None)
        raise ToolExecutionError(f"Could not load the DDPM generator: {exc}") from exc
    if not callable(getattr(module, "generate_images", None)):
        raise ToolExecutionError(
            "The connected DDPM app does not expose its generate_images function."
        )
    _backend_module = module
    _backend_script = script
    return module


def _safe_label(value: str) -> str:
    label = value.strip()[:96] or "DDPM"
    label = re.sub(r"[<>:\"/\\|?*\x00-\x1f]+", " ", label)
    label = re.sub(r"\s+", " ", label).strip(" .")
    return label or "DDPM"


def generate_ddpm_images(
    context: ToolContext,
    model_name: str,
    model_path: str,
    prompt: str,
    image_count: int,
    steps: int,
    seed: int,
    sampler: str,
    aspect_ratio: str,
    reference_image: str = "",
    reference_strength: int = 65,
    width: int = 0,
    height: int = 0,
    preview_interval: int = 0,
    smart_generation: bool = False,
    smart_wanted_results: int = 0,
    smart_max_candidates: int = 0,
    smart_min_score: float = 0.70,
    smart_mode: str = "threshold",
    smart_keep_rejected: bool = True,
) -> dict[str, object]:
    config = ConfigManager(context.root)
    trainer_root = Path(
        str(config.get("tool_folders", {}).get("ddpm_trainer", ""))
    ).expanduser().resolve()
    script = trainer_root / "appStableDiffusion.py"
    if not script.is_file():
        raise ToolExecutionError(
            "DDPM appStableDiffusion.py was not found. Re-scan the DDPM folder in Settings."
        )

    model = Path(model_path).expanduser().resolve()
    allowed_root = (trainer_root / "output").resolve()
    try:
        model.relative_to(allowed_root)
    except ValueError as exc:
        raise ToolExecutionError(
            "The DDPM model must be inside the connected DDPM output folder."
        ) from exc
    if not model.is_dir() or not (model / "model_index.json").is_file():
        raise ToolExecutionError(
            "Choose a completed DDPM pipeline. Resume checkpoint folders cannot generate images."
        )
    missing_packages = [
        package
        for package in ("torch", "diffusers", "numpy", "PIL")
        if importlib.util.find_spec(package) is None
    ]
    if missing_packages:
        raise ToolExecutionError(
            "ADAM's Python environment is missing DDPM generation packages: "
            + ", ".join(missing_packages)
            + ". Close ADAM and open Launch ADAM.bat to install the connected DDPM requirements."
        )

    count = int(image_count)
    step_count = int(steps)
    smart_enabled = bool(smart_generation)
    wanted_results = int(smart_wanted_results or count)
    max_candidates = int(smart_max_candidates or count)
    if smart_enabled:
        if not 1 <= wanted_results <= 48:
            raise ToolExecutionError("Wanted Smart Generation results must be between 1 and 48.")
        if not wanted_results <= max_candidates <= 256:
            raise ToolExecutionError("Maximum Smart Generation candidates must be between wanted results and 256.")
        if not 0.0 <= float(smart_min_score) <= 1.0:
            raise ToolExecutionError("Minimum Smart Generation score must be between 0.00 and 1.00.")
        if str(smart_mode).casefold() not in {"threshold", "top_n"}:
            raise ToolExecutionError("Smart Generation mode must be threshold or top_n.")
        count = wanted_results
    if not 1 <= count <= 48:
        raise ToolExecutionError("Image count must be between 1 and 48.")
    if not 5 <= step_count <= 500:
        raise ToolExecutionError("Generation steps must be between 5 and 500.")
    sampler = sampler.upper().strip()
    if sampler not in {"DDPM", "DDIM"}:
        raise ToolExecutionError("DDPM generation supports the DDPM and DDIM samplers.")
    allowed_aspects = {
        "1:1 (Square)", "16:9 (Widescreen)", "9:16 (Portrait)",
        "4:3 (Classic)", "3:4 (Portrait Classic)", "3:2 (Photo)",
        "2:3 (Portrait Photo)",
    }
    if aspect_ratio not in allowed_aspects:
        raise ToolExecutionError("Choose one of the supported DDPM aspect ratios.")
    if len(prompt) > 500:
        raise ToolExecutionError("The generation label must be 500 characters or shorter.")
    reference_path = Path(reference_image).expanduser() if reference_image.strip() else None
    if reference_path is not None:
        reference_path = reference_path.resolve()
        if not reference_path.is_file():
            raise ToolExecutionError("Choose an existing DDPM reference image.")
        if reference_path.suffix.casefold() not in {".png", ".jpg", ".jpeg", ".webp", ".bmp"}:
            raise ToolExecutionError("DDPM reference images must be PNG, JPG, WEBP, or BMP files.")
    try:
        strength = int(reference_strength)
    except (TypeError, ValueError) as exc:
        raise ToolExecutionError("DDPM reference strength must be a whole number from 0 to 100.") from exc
    if not 0 <= strength <= 100:
        raise ToolExecutionError("DDPM reference strength must be between 0 and 100.")
    try:
        custom_width, custom_height = int(width or 0), int(height or 0)
    except (TypeError, ValueError) as exc:
        raise ToolExecutionError("Custom DDPM dimensions must be whole numbers.") from exc
    if bool(custom_width) != bool(custom_height):
        raise ToolExecutionError("Set both custom DDPM width and height, or leave both blank.")
    if custom_width and not (64 <= custom_width <= 2048 and 64 <= custom_height <= 2048):
        raise ToolExecutionError("Custom DDPM dimensions must be between 64 and 2048 pixels.")
    if not 0 <= int(preview_interval) <= step_count:
        raise ToolExecutionError("Preview interval must be between 0 and the total number of steps.")

    base_seed = int(seed)
    if base_seed <= 0:
        base_seed = random.randint(1, 2_147_483_647 - count)
    seed_count = max_candidates if smart_enabled else count
    if base_seed + seed_count - 1 > 2_147_483_647:
        raise ToolExecutionError("The seed is too large for this image count.")

    timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
    output = generation_output_folder(context.root, context.tool.id, _safe_label(model_name))
    backend = _load_backend(script)
    preview_enabled = int(preview_interval) > 0
    preview_supported = accepts_preview_callback(backend.generate_images)
    if reference_path:
        preview_supported = preview_supported and accepts_preview_callback(backend.generate_reference_images)
    if reference_path and not callable(getattr(backend, "generate_reference_images", None)):
        raise ToolExecutionError(
            "The connected DDPM app does not support reference-image generation. Update the DDPM app and restart ADAM."
        )

    context.log(f"Loading completed DDPM model: {model_name}")
    if reference_path:
        context.log(f"Reimagining reference image at {strength}% influence.")
    context.log("DDPM models generate learned visual samples; the label is saved as metadata, not used as a text prompt.")
    if preview_enabled and not preview_supported:
        context.log("This connected DDPM generator does not yet expose denoising previews; generation will continue normally.")
    image_paths: list[str] = []
    image_evaluations: dict[str, dict[str, object]] = {}
    selected_paths: list[str] = []
    generated_total = max_candidates if smart_enabled else count
    profile = PreferenceProfile(context.root, context.tool.id, _safe_label(model_name), str(model)) if smart_enabled else None
    evaluator = GenerationPreferenceEvaluator(context.root) if smart_enabled else None
    threshold = float(smart_min_score)
    top_n_mode = str(smart_mode).casefold() == "top_n"
    for index in range(generated_total):
        context.checkpoint()
        current_seed = base_seed + index
        context.progress(
            max(1, round(index * 100 / generated_total)),
            f"Generating image {index + 1} of {generated_total}" if smart_enabled else f"Generating image {index + 1} of {count}",
        )
        try:
            settings = {
                "seed": current_seed,
                "num_inference_steps": step_count,
                "batch_size": 1,
                "sampler": sampler,
                "aspect_ratio": aspect_ratio,
            }
            if preview_enabled and preview_supported:
                settings["preview_callback"] = lambda payload, step=0, total_steps=step_count, current=index: publish_generation_preview(
                    context, output, payload, image_index=current, image_count=count,
                    step=step, total_steps=total_steps,
                )
                settings["preview_interval"] = int(preview_interval)
            if custom_width:
                settings.update({"width": custom_width, "height": custom_height})
            if reference_path:
                images = backend.generate_reference_images(
                    str(model), str(reference_path), reference_strength=strength, **settings
                )
            else:
                images = backend.generate_images(str(model), **settings)
            image = images[0]
            destination = output / f"{timestamp}_{context.job_id}_{sampler}_seed_{current_seed}.png"
            image.save(destination, format="PNG")
        except Exception as exc:
            raise ToolExecutionError(f"DDPM generation failed: {exc}") from exc
        image_paths.append(str(destination))
        if smart_enabled and profile is not None and evaluator is not None:
            score = evaluator.score(profile, [destination], keep_threshold=threshold, reject_threshold=profile.reject_threshold)[0]
            image_evaluations[str(destination.resolve())] = {
                "score": score.score,
                "confidence": score.confidence,
                "category": score.category,
                "reason": score.reason,
            }
            if not top_n_mode and score.score is not None and score.score >= threshold:
                selected_paths.append(str(destination))
                if len(selected_paths) >= wanted_results:
                    break

    created_at = datetime.now(timezone.utc).isoformat()
    if smart_enabled and top_n_mode:
        ranked = sorted(
            image_paths,
            key=lambda path: float(image_evaluations.get(str(Path(path).resolve()), {}).get("score") or -1.0),
            reverse=True,
        )
        selected_paths = ranked[:wanted_results]
    if smart_enabled:
        ordered_images = [*selected_paths, *[path for path in image_paths if path not in set(selected_paths)]]
    else:
        ordered_images = image_paths
    metadata = {
        "version": 1,
        "provider_id": context.tool.id,
        "provider_name": context.tool.name,
        "model_name": _safe_label(model_name),
        "model_path": str(model),
        "prompt": prompt.strip(),
        "prompt_behavior": "label_only",
        "seed": base_seed,
        "image_seeds": [base_seed + index for index in range(count)],
        "image_count": len(ordered_images) if smart_enabled and smart_keep_rejected else count,
        "steps": step_count,
        "sampler": sampler,
        "aspect_ratio": aspect_ratio,
        "width": custom_width or None,
        "height": custom_height or None,
        "reference_image": str(reference_path) if reference_path else "",
        "reference_strength": strength if reference_path else None,
        "preview_interval": int(preview_interval),
        "preview_supported": preview_supported,
        "images": ordered_images if smart_keep_rejected or not smart_enabled else selected_paths,
        "image_evaluations": image_evaluations,
        "smart_generation": {
            "enabled": smart_enabled,
            "mode": str(smart_mode),
            "wanted_results": wanted_results if smart_enabled else count,
            "maximum_candidates": max_candidates if smart_enabled else count,
            "minimum_score": threshold,
            "selected_count": len(selected_paths) if smart_enabled else count,
            "candidate_count": len(image_paths),
            "profile_id": profile.id if profile else "",
            "keep_rejected_candidates": bool(smart_keep_rejected),
        },
        "created_at": created_at,
    }
    generation_metadata_path(output, timestamp, context.job_id).write_text(
        json.dumps(metadata, indent=2), encoding="utf-8"
    )
    if evaluator is not None:
        evaluator.vision.unload()
    if smart_enabled:
        context.progress(100, f"Smart Generation selected {len(selected_paths)} of {wanted_results} requested image(s) from {len(image_paths)} candidate(s)")
    else:
        context.progress(100, f"Generated {count} image(s)")
    return {
        "output_folder": str(output),
        "assets": [
            {
                "kind": "generation",
                "name": f"{_safe_label(model_name)} · {timestamp}",
                "path": str(output),
                "trainer": "ddpm",
            }
        ],
    }