Initial commit

backends/device.py

@@ -0,0 +1,47 @@
+"""Torch device selection. Wraps cuda / directml / cpu behind one helper."""
+from __future__ import annotations
+
+import json
+from functools import lru_cache
+from pathlib import Path
+
+ROOT = Path(__file__).parent.parent
+HARDWARE_CACHE = ROOT / "config.local.json"
+
+
+@lru_cache(maxsize=1)
+def hardware_info() -> dict:
+    if HARDWARE_CACHE.exists():
+        return json.loads(HARDWARE_CACHE.read_text())
+    from . import hardware
+    info = hardware.detect()
+    return {
+        "vendor": info.vendor,
+        "backend": info.backend,
+        "device_name": info.device_name,
+        "vram_gb": info.vram_gb,
+        "tier": info.tier,
+    }
+
+
+@lru_cache(maxsize=1)
+def get_device():
+    import torch
+    backend = hardware_info()["backend"]
+    if backend == "cuda" and torch.cuda.is_available():
+        return torch.device("cuda")
+    if backend == "directml":
+        try:
+            import torch_directml
+            return torch_directml.device()
+        except ImportError:
+            pass
+    return torch.device("cpu")
+
+
+def torch_dtype():
+    import torch
+    backend = hardware_info()["backend"]
+    if backend == "cpu":
+        return torch.float32
+    return torch.float16

backends/hardware.py

@@ -0,0 +1,156 @@
+"""GPU and VRAM detection. Returns vendor + tier used to pick torch wheel and default models."""
+from __future__ import annotations
+
+import ctypes
+import platform
+import subprocess
+from dataclasses import dataclass
+from typing import Literal
+
+Vendor = Literal["nvidia", "amd", "intel", "cpu"]
+Backend = Literal["cuda", "directml", "cpu"]
+
+
+@dataclass
+class HardwareInfo:
+    vendor: Vendor
+    backend: Backend
+    device_name: str
+    vram_gb: float
+    tier: Literal["cpu", "low", "mid", "high", "ultra"]
+
+
+def _run(cmd: list[str]) -> str:
+    try:
+        out = subprocess.run(cmd, capture_output=True, text=True, timeout=10, check=False)
+        return out.stdout.strip()
+    except (FileNotFoundError, subprocess.TimeoutExpired):
+        return ""
+
+
+def _detect_nvidia() -> tuple[str, float] | None:
+    out = _run(["nvidia-smi", "--query-gpu=name,memory.total", "--format=csv,noheader,nounits"])
+    if not out:
+        return None
+    first = out.splitlines()[0]
+    name, mem = [p.strip() for p in first.split(",", 1)]
+    return name, float(mem) / 1024.0
+
+
+def _detect_dxgi() -> list[tuple[str, float, str]]:
+    """Enumerate all DXGI adapters via PowerShell. Returns list of (name, vram_gb, vendor_hint)."""
+    if platform.system() != "Windows":
+        return []
+    ps = (
+        "Get-CimInstance Win32_VideoController | "
+        "Select-Object Name, AdapterRAM | "
+        "ConvertTo-Json -Compress"
+    )
+    out = _run(["powershell", "-NoProfile", "-Command", ps])
+    if not out:
+        return []
+    import json
+    try:
+        data = json.loads(out)
+    except json.JSONDecodeError:
+        return []
+    if isinstance(data, dict):
+        data = [data]
+    results: list[tuple[str, float, str]] = []
+    for entry in data:
+        name = (entry.get("Name") or "").strip()
+        ram = entry.get("AdapterRAM") or 0
+        # Win32_VideoController caps AdapterRAM at 4GB on many systems. Trust value but flag below.
+        vram_gb = float(ram) / (1024 ** 3) if ram else 0.0
+        low = name.lower()
+        if "nvidia" in low or "geforce" in low or "rtx" in low or "gtx" in low:
+            vendor = "nvidia"
+        elif "amd" in low or "radeon" in low or "rx " in low:
+            vendor = "amd"
+        elif "intel" in low or "arc" in low or "iris" in low:
+            vendor = "intel"
+        else:
+            vendor = "unknown"
+        if name:
+            results.append((name, vram_gb, vendor))
+    return results
+
+
+def _vram_tier(vram_gb: float) -> Literal["cpu", "low", "mid", "high", "ultra"]:
+    if vram_gb < 1:
+        return "cpu"
+    if vram_gb < 6:
+        return "low"
+    if vram_gb < 10:
+        return "mid"
+    if vram_gb < 14:
+        return "high"
+    return "ultra"
+
+
+def detect() -> HardwareInfo:
+    nv = _detect_nvidia()
+    if nv:
+        name, vram = nv
+        return HardwareInfo("nvidia", "cuda", name, vram, _vram_tier(vram))
+
+    adapters = _detect_dxgi()
+    # Prefer discrete (highest VRAM) non-basic adapter
+    adapters = [a for a in adapters if "basic" not in a[0].lower() and "microsoft" not in a[0].lower()]
+    if adapters:
+        adapters.sort(key=lambda a: a[1], reverse=True)
+        name, vram, hint = adapters[0]
+        # AdapterRAM is unreliable for >4GB cards. If exactly 4GB and modern AMD/Intel card name, bump.
+        if vram <= 4.1 and any(k in name.lower() for k in ("rx 6", "rx 7", "arc a", "arc b")):
+            vram = 8.0  # conservative guess
+        if hint in ("amd", "intel"):
+            return HardwareInfo(hint, "directml", name, vram, _vram_tier(vram))
+        if hint == "nvidia":
+            # nvidia-smi missing but card is nvidia: drivers may be broken, fall through to directml
+            return HardwareInfo("nvidia", "directml", name, vram, _vram_tier(vram))
+
+    return HardwareInfo("cpu", "cpu", platform.processor() or "CPU", 0.0, "cpu")
+
+
+def directml_supported() -> bool:
+    """torch-directml ships wheels for Python 3.10 / 3.11. The launcher pins the
+    venv to 3.11, so this is True in the running app."""
+    import sys
+    return sys.version_info[:2] in {(3, 10), (3, 11)}
+
+
+def torch_install_args(info: HardwareInfo) -> list[str]:
+    """Return uv-pip install args for the right PyTorch build.
+
+    Launcher pins venv to Python 3.11, so torch-directml wheels are always available
+    for AMD/Intel paths. Latest stable torch is used elsewhere.
+    """
+    if info.backend == "cuda":
+        return [
+            "torch",
+            "torchvision",
+            "--index-url",
+            "https://download.pytorch.org/whl/cu124",
+        ]
+    if info.backend == "directml":
+        # torch-directml currently pins to torch 2.4.x. Match it.
+        return [
+            "torch>=2.4,<2.5",
+            "torchvision>=0.19,<0.20",
+            "torch-directml>=0.2.5",
+        ]
+    return [
+        "torch",
+        "torchvision",
+        "--index-url",
+        "https://download.pytorch.org/whl/cpu",
+    ]
+
+
+if __name__ == "__main__":
+    info = detect()
+    print(f"Vendor:  {info.vendor}")
+    print(f"Backend: {info.backend}")
+    print(f"Device:  {info.device_name}")
+    print(f"VRAM:    {info.vram_gb:.1f} GB")
+    print(f"Tier:    {info.tier}")

backends/image_sdxl.py

@@ -0,0 +1,92 @@
+"""SDXL-family image generation. Loaded lazily, cached by repo id."""
+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+from typing import Optional
+
+from PIL import Image
+
+from .device import get_device, torch_dtype
+from .memory import apply_memory_strategy
+from .models import ModelSpec, find
+
+ROOT = Path(__file__).parent.parent
+MODELS_DIR = ROOT / "models"
+
+
+@lru_cache(maxsize=1)
+def _load_pipeline(repo: str, kind: str):
+    import torch
+    from diffusers import (
+        AutoPipelineForText2Image,
+        DiffusionPipeline,
+        FluxPipeline,
+    )
+
+    device = get_device()
+    dtype = torch_dtype()
+    cache_dir = str(MODELS_DIR / "diffusers")
+
+    if "FLUX" in repo:
+        pipe = FluxPipeline.from_pretrained(repo, torch_dtype=dtype, cache_dir=cache_dir)
+    elif "turbo" in repo.lower():
+        pipe = AutoPipelineForText2Image.from_pretrained(
+            repo, torch_dtype=dtype, variant="fp16", cache_dir=cache_dir
+        )
+    else:
+        pipe = DiffusionPipeline.from_pretrained(
+            repo, torch_dtype=dtype, use_safetensors=True, cache_dir=cache_dir
+        )
+
+    # Disable built-in safety checker. We use our own filter (CSAM-only).
+    if hasattr(pipe, "safety_checker"):
+        pipe.safety_checker = None
+    if hasattr(pipe, "requires_safety_checker"):
+        pipe.requires_safety_checker = False
+
+    apply_memory_strategy(pipe)
+    return pipe
+
+
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    model_id: str = "pony-xl",
+    width: int = 1024,
+    height: int = 1024,
+    steps: int = 30,
+    guidance: float = 7.0,
+    seed: Optional[int] = None,
+) -> Image.Image:
+    spec: ModelSpec | None = find(model_id)
+    if spec is None or spec.kind != "image":
+        raise ValueError(f"Unknown image model: {model_id}")
+
+    pipe = _load_pipeline(spec.repo, spec.kind)
+
+    import torch
+
+    generator = None
+    if seed is not None:
+        try:
+            generator = torch.Generator(device=get_device()).manual_seed(int(seed))
+        except RuntimeError:
+            generator = torch.Generator().manual_seed(int(seed))
+
+    if "turbo" in spec.repo.lower():
+        steps = max(1, min(steps, 4))
+        guidance = 0.0
+    if "FLUX" in spec.repo:
+        guidance = 0.0
+
+    out = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt or None,
+        width=width,
+        height=height,
+        num_inference_steps=steps,
+        guidance_scale=guidance,
+        generator=generator,
+    )
+    return out.images[0]

backends/memory.py

@@ -0,0 +1,60 @@
+"""Memory strategy selection. CUDA supports cpu_offload; DirectML/CPU do not.
+
+Apply per-pipeline based on backend + VRAM tier. All paths reduce peak VRAM
+without breaking on non-CUDA devices.
+"""
+from __future__ import annotations
+
+from .device import get_device, hardware_info
+
+
+def apply_memory_strategy(pipe) -> None:
+    """Apply VRAM-saving knobs that match the active backend."""
+    info = hardware_info()
+    backend = info["backend"]
+    vram = info["vram_gb"]
+
+    # Always-safe: VAE tiling/slicing work on any device. Cuts peak VRAM during decode.
+    # Newer diffusers (>=0.32) prefers calling on the VAE directly.
+    vae = getattr(pipe, "vae", None)
+    if vae is not None:
+        for fn in ("enable_slicing", "enable_tiling"):
+            if hasattr(vae, fn):
+                try:
+                    getattr(vae, fn)()
+                except Exception:
+                    pass
+    if hasattr(pipe, "enable_attention_slicing"):
+        try:
+            pipe.enable_attention_slicing()
+        except Exception:
+            pass
+
+    if backend == "cuda":
+        # Offload only if VRAM tight. cpu_offload is CUDA-only via accelerate hooks.
+        if vram < 10:
+            try:
+                pipe.enable_sequential_cpu_offload()
+                return
+            except Exception:
+                pass
+            try:
+                pipe.enable_model_cpu_offload()
+                return
+            except Exception:
+                pass
+        pipe.to(get_device())
+        return
+
+    if backend == "directml":
+        # DirectML lacks accelerate hook support. Move whole pipe to device.
+        # Slicing already enabled above keeps peak in check.
+        try:
+            pipe.to(get_device())
+        except Exception:
+            # Some pipes have components that won't move cleanly; fall back to CPU.
+            pipe.to("cpu")
+        return
+
+    # CPU
+    pipe.to("cpu")

backends/models.py

@@ -0,0 +1,163 @@
+"""Model registry. VRAM tier drives default. User can override."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+Kind = Literal["image", "video"]
+
+
+@dataclass(frozen=True)
+class ModelSpec:
+    id: str
+    label: str
+    repo: str
+    kind: Kind
+    min_vram_gb: float
+    nsfw_capable: bool
+    download_gb: float = 0.0
+    notes: str = ""
+
+
+IMAGE_MODELS: list[ModelSpec] = [
+    ModelSpec(
+        id="sdxl-turbo",
+        label="SDXL Turbo (fast, low VRAM)",
+        repo="stabilityai/sdxl-turbo",
+        kind="image",
+        min_vram_gb=4.0,
+        nsfw_capable=False,
+        download_gb=7.0,
+        notes="1-4 step sampling. Censored base. Good for safe content on weak GPUs.",
+    ),
+    ModelSpec(
+        id="pony-xl",
+        label="Pony Diffusion XL v6 (NSFW, anime/realistic)",
+        repo="AstraliteHeart/pony-diffusion-v6",
+        kind="image",
+        min_vram_gb=8.0,
+        nsfw_capable=True,
+        download_gb=7.0,
+        notes="Use score_9, score_8_up tags. Strong NSFW capability.",
+    ),
+    ModelSpec(
+        id="illustrious-xl",
+        label="Illustrious XL v0.1 (NSFW, illustration)",
+        repo="OnomaAIResearch/Illustrious-xl-early-release-v0",
+        kind="image",
+        min_vram_gb=10.0,
+        nsfw_capable=True,
+        download_gb=7.0,
+        notes="High-detail anime/illustration. NSFW-capable.",
+    ),
+    ModelSpec(
+        id="sdxl-base",
+        label="SDXL Base 1.0 (versatile)",
+        repo="stabilityai/stable-diffusion-xl-base-1.0",
+        kind="image",
+        min_vram_gb=8.0,
+        nsfw_capable=False,
+        download_gb=7.0,
+        notes="General-purpose. Censored.",
+    ),
+    ModelSpec(
+        id="flux-schnell",
+        label="FLUX.1 Schnell (high quality, 12GB+)",
+        repo="black-forest-labs/FLUX.1-schnell",
+        kind="image",
+        min_vram_gb=12.0,
+        nsfw_capable=False,
+        download_gb=24.0,
+        notes="State-of-the-art prompt adherence. 4-step.",
+    ),
+]
+
+VIDEO_MODELS: list[ModelSpec] = [
+    ModelSpec(
+        id="ltx-video",
+        label="LTX-Video 0.9 (fast, 8GB+)",
+        repo="Lightricks/LTX-Video",
+        kind="video",
+        min_vram_gb=8.0,
+        nsfw_capable=True,
+        download_gb=18.0,
+        notes="2-5 second clips at 24fps. Fast generation.",
+    ),
+    ModelSpec(
+        id="cogvideox-5b",
+        label="CogVideoX 5B (12GB+)",
+        repo="THUDM/CogVideoX-5b",
+        kind="video",
+        min_vram_gb=12.0,
+        nsfw_capable=False,
+        download_gb=20.0,
+        notes="6 second clips. Good motion quality.",
+    ),
+    ModelSpec(
+        id="wan-2-1",
+        label="Wan 2.1 T2V 1.3B (16GB+)",
+        repo="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+        kind="video",
+        min_vram_gb=14.0,
+        nsfw_capable=True,
+        download_gb=16.0,
+        notes="Top-tier open video model. Needs diffusers>=0.32.",
+    ),
+]
+
+
+def default_for_tier(tier: str, kind: Kind) -> ModelSpec | None:
+    pool = IMAGE_MODELS if kind == "image" else VIDEO_MODELS
+    tier_min = {"cpu": 0.0, "low": 0.0, "mid": 8.0, "high": 10.0, "ultra": 12.0}
+    target = tier_min.get(tier, 0.0)
+    if tier == "cpu" and kind == "video":
+        return None
+    if tier in ("cpu", "low") and kind == "image":
+        return next(m for m in pool if m.id == "sdxl-turbo")
+    if tier == "mid" and kind == "image":
+        return next(m for m in pool if m.id == "pony-xl")
+    if tier == "high" and kind == "image":
+        return next(m for m in pool if m.id == "illustrious-xl")
+    if tier == "ultra" and kind == "image":
+        return next(m for m in pool if m.id == "illustrious-xl")
+    if tier in ("low",) and kind == "video":
+        return None
+    if tier == "mid" and kind == "video":
+        return next(m for m in pool if m.id == "ltx-video")
+    if tier == "high" and kind == "video":
+        return next(m for m in pool if m.id == "ltx-video")
+    if tier == "ultra" and kind == "video":
+        return next(m for m in pool if m.id == "wan-2-1")
+    return next((m for m in pool if m.min_vram_gb <= target), None)
+
+
+def list_for_tier(tier: str, kind: Kind) -> list[ModelSpec]:
+    pool = IMAGE_MODELS if kind == "image" else VIDEO_MODELS
+    tier_max = {"cpu": 4.0, "low": 6.0, "mid": 10.0, "high": 14.0, "ultra": 1000.0}
+    cap = tier_max.get(tier, 1000.0)
+    out = [m for m in pool if m.min_vram_gb <= cap + 2.0]
+    out.sort(key=lambda m: m.min_vram_gb)
+    return out
+
+
+def find(model_id: str) -> ModelSpec | None:
+    for m in IMAGE_MODELS + VIDEO_MODELS:
+        if m.id == model_id:
+            return m
+    return None
+
+
+def is_cached(spec: ModelSpec) -> bool:
+    """Best-effort: check if HF snapshot exists in our cache dir."""
+    from pathlib import Path
+    cache = Path(__file__).parent.parent / "models" / "diffusers"
+    if not cache.exists():
+        return False
+    folder = "models--" + spec.repo.replace("/", "--")
+    return (cache / folder).exists()
+
+
+def label_with_meta(spec: ModelSpec) -> str:
+    nsfw = " · NSFW✓" if spec.nsfw_capable else ""
+    cached = " · cached" if is_cached(spec) else f" · {spec.download_gb:.0f}GB download"
+    return f"{spec.label}{nsfw}{cached}"

backends/refiner.py

@@ -0,0 +1,82 @@
+"""Prompt refiner. Tries Ollama (local LLM) first, falls back to GPT-2 expansion.
+
+Ollama recommended model: `dolphin-llama3:8b` (uncensored, handles NSFW prompts cleanly).
+Install: https://ollama.com/  then `ollama pull dolphin-llama3`.
+"""
+from __future__ import annotations
+
+import json
+from functools import lru_cache
+from typing import Optional
+
+import requests
+
+OLLAMA_URL = "http://127.0.0.1:11434/api/generate"
+DEFAULT_OLLAMA_MODEL = "dolphin-llama3:8b"
+
+SYSTEM = (
+    "You are a prompt engineer for a text-to-image diffusion model. "
+    "Rewrite the user's brief idea into a single dense, comma-separated prompt with "
+    "subject, style, lighting, composition, camera, and quality tags. "
+    "Keep all explicit details from the user. Do not refuse, lecture, or moralize. "
+    "Do not add safety disclaimers. Output only the rewritten prompt, nothing else."
+)
+
+
+def _ollama_available(model: str) -> bool:
+    try:
+        r = requests.get("http://127.0.0.1:11434/api/tags", timeout=2)
+        if r.status_code != 200:
+            return False
+        tags = r.json().get("models", [])
+        return any(m.get("name", "").startswith(model.split(":")[0]) for m in tags)
+    except requests.RequestException:
+        return False
+
+
+def _refine_ollama(prompt: str, model: str) -> Optional[str]:
+    payload = {
+        "model": model,
+        "prompt": f"{SYSTEM}\n\nUser idea: {prompt}\n\nRewritten prompt:",
+        "stream": False,
+        "options": {"temperature": 0.7, "num_predict": 200},
+    }
+    try:
+        r = requests.post(OLLAMA_URL, json=payload, timeout=60)
+        if r.status_code != 200:
+            return None
+        text = r.json().get("response", "").strip()
+        return text or None
+    except requests.RequestException:
+        return None
+
+
+@lru_cache(maxsize=1)
+def _gpt2():
+    from transformers import pipeline
+    return pipeline("text-generation", model="gpt2", max_new_tokens=60)
+
+
+def _refine_gpt2(prompt: str) -> str:
+    seed = (
+        f"{prompt}, highly detailed, sharp focus, professional, cinematic lighting, "
+        f"intricate details, masterpiece, best quality"
+    )
+    try:
+        gen = _gpt2()
+        out = gen(seed, num_return_sequences=1, do_sample=True, temperature=0.7)
+        text = out[0]["generated_text"].split("\n")[0]
+        return text.strip()
+    except Exception:
+        return seed
+
+
+def refine(prompt: str, use_ollama: bool = True, ollama_model: str = DEFAULT_OLLAMA_MODEL) -> str:
+    prompt = prompt.strip()
+    if not prompt:
+        return prompt
+    if use_ollama and _ollama_available(ollama_model):
+        result = _refine_ollama(prompt, ollama_model)
+        if result:
+            return result
+    return _refine_gpt2(prompt)

backends/safety.py

@@ -0,0 +1,185 @@
+"""Safety filter: block CSAM only. All other content (NSFW, gore, violence) allowed.
+
+Layers:
+1. Prompt keyword gate: rejects child-term + sexual-term combinations.
+2. Output check: NudeNet detects nudity. If nudity present, run face detection
+   (MTCNN) + age classifier (ViT) on every face. Block if any face is classified
+   as a minor with high confidence.
+
+This is best-effort. The user is legally responsible for use of generated content.
+"""
+from __future__ import annotations
+
+import re
+import tempfile
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import Iterable
+
+from PIL import Image
+
+# --- prompt keyword gate ---------------------------------------------------
+
+_CHILD_TERMS = [
+    r"\b(child|children|kid|kids|minor|underage|under-?age|preteen|pre-?teen)\b",
+    r"\b(toddler|infant|baby|babies)\b",
+    r"\b(\d|0?[0-9]|1[0-7])\s*(yo|y/o|year[- ]?old)\b",
+    r"\bloli(con)?\b",
+    r"\bshota(con)?\b",
+    r"\bcp\b",
+]
+_SEXUAL_TERMS = [
+    r"\b(nude|naked|nsfw|porn|sex|sexual|sexy|erotic|explicit)\b",
+    r"\b(penis|vagina|breast|nipple|genital|cum|orgasm)\b",
+    r"\b(intercourse|fellatio|cunnilingus|masturbat)\w*\b",
+    r"\b(rape|molest)\w*\b",
+]
+
+_CHILD_RE = re.compile("|".join(_CHILD_TERMS), re.IGNORECASE)
+_SEX_RE = re.compile("|".join(_SEXUAL_TERMS), re.IGNORECASE)
+
+
+@dataclass
+class SafetyResult:
+    allowed: bool
+    reason: str = ""
+
+
+def check_prompt(prompt: str) -> SafetyResult:
+    if _CHILD_RE.search(prompt) and _SEX_RE.search(prompt):
+        return SafetyResult(False, "Prompt blocked: combines minor and sexual terms (CSAM gate).")
+    return SafetyResult(True)
+
+
+# --- nudity detection ------------------------------------------------------
+
+_NUDITY_LABELS = {
+    "FEMALE_BREAST_EXPOSED",
+    "FEMALE_GENITALIA_EXPOSED",
+    "MALE_GENITALIA_EXPOSED",
+    "BUTTOCKS_EXPOSED",
+    "ANUS_EXPOSED",
+}
+
+
+@lru_cache(maxsize=1)
+def _nudenet():
+    try:
+        from nudenet import NudeDetector
+        return NudeDetector()
+    except Exception:
+        return None
+
+
+def _has_nudity(detections: Iterable[dict]) -> bool:
+    for d in detections:
+        label = d.get("class") or d.get("label") or ""
+        score = float(d.get("score", 0.0))
+        if label in _NUDITY_LABELS and score >= 0.5:
+            return True
+    return False
+
+
+# --- face detection + age classification -----------------------------------
+
+@lru_cache(maxsize=1)
+def _mtcnn():
+    try:
+        from facenet_pytorch import MTCNN
+        import torch
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        return MTCNN(keep_all=True, device=device, post_process=False, min_face_size=40)
+    except Exception:
+        return None
+
+
+@lru_cache(maxsize=1)
+def _age_classifier():
+    try:
+        from transformers import pipeline
+        return pipeline(
+            "image-classification",
+            model="nateraw/vit-age-classifier",
+            top_k=3,
+        )
+    except Exception:
+        return None
+
+
+# Buckets reported by nateraw/vit-age-classifier.
+# Conservative minor set. "10-19" includes some adults — treat as minor only on
+# strong confidence to limit false positives on young-looking adults.
+_HARD_MINOR = {"0-2", "3-9"}
+_SOFT_MINOR = {"10-19"}
+
+
+def _faces(img: Image.Image):
+    mtcnn = _mtcnn()
+    if mtcnn is None:
+        return []
+    try:
+        boxes, probs = mtcnn.detect(img)
+    except Exception:
+        return []
+    if boxes is None:
+        return []
+    if probs is None:
+        probs = [None] * len(boxes)
+    out = []
+    for box, prob in zip(boxes, probs):
+        if prob is None or float(prob) < 0.9:
+            continue
+        x1, y1, x2, y2 = [int(max(0, v)) for v in box]
+        if x2 - x1 < 30 or y2 - y1 < 30:
+            continue
+        out.append(img.crop((x1, y1, x2, y2)))
+    return out
+
+
+def _is_minor_face(face_img: Image.Image) -> tuple[bool, str]:
+    clf = _age_classifier()
+    if clf is None:
+        return False, ""
+    try:
+        preds = clf(face_img)
+    except Exception:
+        return False, ""
+    # preds is list[dict(label, score)] sorted by score desc
+    top = preds[0] if preds else None
+    if not top:
+        return False, ""
+    label = top["label"]
+    score = float(top["score"])
+    if label in _HARD_MINOR and score >= 0.55:
+        return True, f"minor face detected ({label}, conf={score:.2f})"
+    if label in _SOFT_MINOR and score >= 0.85:
+        return True, f"likely minor face ({label}, conf={score:.2f})"
+    return False, ""
+
+
+def check_image(img: Image.Image) -> SafetyResult:
+    """Block if (nudity present) AND (any face classified as minor)."""
+    det = _nudenet()
+    if det is None:
+        # No nudity detector available — fall through. Prompt gate is primary defense.
+        return SafetyResult(True)
+
+    try:
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
+            img.save(f.name)
+            results = det.detect(f.name)
+    except Exception:
+        return SafetyResult(True)
+
+    if not _has_nudity(results):
+        return SafetyResult(True)
+
+    faces = _faces(img)
+    for face in faces:
+        is_minor, reason = _is_minor_face(face)
+        if is_minor:
+            return SafetyResult(
+                False,
+                f"Output blocked: nudity + {reason}. Image discarded.",
+            )
+    return SafetyResult(True)

backends/video_ltx.py

@@ -0,0 +1,118 @@
+"""Video generation. LTX-Video / CogVideoX / Wan via diffusers pipelines."""
+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+from typing import Optional
+
+from .device import get_device, hardware_info, torch_dtype
+from .memory import apply_memory_strategy
+from .models import ModelSpec, find
+
+ROOT = Path(__file__).parent.parent
+MODELS_DIR = ROOT / "models"
+OUTPUTS = ROOT / "outputs"
+OUTPUTS.mkdir(exist_ok=True)
+
+
+def _import_pipeline_for(repo: str):
+    """Pick the right pipeline class. Wan needs diffusers>=0.32 with WanPipeline."""
+    if "LTX" in repo:
+        from diffusers import LTXPipeline
+        return LTXPipeline
+    if "CogVideoX" in repo:
+        from diffusers import CogVideoXPipeline
+        return CogVideoXPipeline
+    if "Wan" in repo:
+        try:
+            from diffusers import WanPipeline
+            return WanPipeline
+        except ImportError as e:
+            raise RuntimeError(
+                "Wan 2.1 needs diffusers>=0.32. Run: pip install -U diffusers"
+            ) from e
+    if "Hunyuan" in repo:
+        from diffusers import HunyuanVideoPipeline
+        return HunyuanVideoPipeline
+    from diffusers import DiffusionPipeline
+    return DiffusionPipeline
+
+
+@lru_cache(maxsize=1)
+def _load_pipeline(repo: str):
+    dtype = torch_dtype()
+    cache_dir = str(MODELS_DIR / "diffusers")
+
+    PipelineCls = _import_pipeline_for(repo)
+    pipe = PipelineCls.from_pretrained(repo, torch_dtype=dtype, cache_dir=cache_dir)
+
+    apply_memory_strategy(pipe)
+    return pipe
+
+
+def _model_kwargs(repo: str, base: dict) -> dict:
+    """Adjust kwargs per-pipeline. Some pipes don't accept width/height or have different param names."""
+    out = dict(base)
+    if "Wan" in repo:
+        # Wan accepts height/width but expects multiples of 16.
+        out["height"] = (out["height"] // 16) * 16
+        out["width"] = (out["width"] // 16) * 16
+    if "LTX" in repo:
+        # LTX needs 32-multiple resolutions.
+        out["height"] = (out["height"] // 32) * 32
+        out["width"] = (out["width"] // 32) * 32
+    if "CogVideoX" in repo:
+        # CogVideoX has fixed 720x480 default; clamp.
+        out["height"] = min(out["height"], 480)
+        out["width"] = min(out["width"], 720)
+    return out
+
+
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    model_id: str = "ltx-video",
+    width: int = 704,
+    height: int = 480,
+    num_frames: int = 73,
+    fps: int = 24,
+    steps: int = 30,
+    guidance: float = 3.0,
+    seed: Optional[int] = None,
+) -> str:
+    spec: ModelSpec | None = find(model_id)
+    if spec is None or spec.kind != "video":
+        raise ValueError(f"Unknown video model: {model_id}")
+
+    pipe = _load_pipeline(spec.repo)
+
+    import torch
+    from diffusers.utils import export_to_video
+
+    generator = None
+    if seed is not None:
+        try:
+            generator = torch.Generator(device=get_device()).manual_seed(int(seed))
+        except RuntimeError:
+            generator = torch.Generator().manual_seed(int(seed))
+
+    base = dict(
+        prompt=prompt,
+        negative_prompt=negative_prompt or None,
+        width=int(width),
+        height=int(height),
+        num_frames=int(num_frames),
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance),
+        generator=generator,
+    )
+    kwargs = _model_kwargs(spec.repo, base)
+    kwargs = {k: v for k, v in kwargs.items() if v is not None}
+
+    out = pipe(**kwargs)
+    frames = out.frames[0]
+
+    import time
+    path = OUTPUTS / f"video_{int(time.time())}.mp4"
+    export_to_video(frames, str(path), fps=int(fps))
+    return str(path)