feat: add full creative studio + DevOps tools (Pixel, Lyra, Reel personas)

Adds 3 new personas (Pixel, Lyra, Reel) and 5 new tool modules:

- Git/DevOps tools (GitPython): clone, status, diff, log, blame, branch,
  add, commit, push, pull, stash — wired to Forge and Helm personas
- Image generation (FLUX via diffusers): text-to-image, storyboards,
  variations — Pixel persona
- Music generation (ACE-Step 1.5): full songs with vocals+instrumentals,
  instrumental tracks, vocal-only tracks — Lyra persona
- Video generation (Wan 2.1 via diffusers): text-to-video, image-to-video
  clips — Reel persona
- Creative Director pipeline: multi-step orchestration that chains
  storyboard → music → video → assembly into 3+ minute final videos
- Video assembler (MoviePy + FFmpeg): stitch clips, overlay audio,
  title cards, subtitles, final export

Also includes:
- Spark Intelligence tool-level + creative pipeline event capture
- Creative Studio dashboard page (/creative/ui) with 4 tabs
- Config settings for all new models and output directories
- pyproject.toml creative optional extra for GPU dependencies
- 107 new tests covering all modules (624 total, all passing)

https://claude.ai/code/session_01KJm6jQkNi3aA3yoQJn636c

src/tools/image_tools.py

@@ -0,0 +1,171 @@
+"""Image generation tools — Pixel persona.
+
+Uses FLUX.2 Klein 4B (or configurable model) via HuggingFace diffusers
+for text-to-image generation, storyboard frames, and variations.
+
+All heavy imports are lazy so the module loads instantly even without
+a GPU or the ``creative`` extra installed.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import uuid
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+# Lazy-loaded pipeline singleton
+_pipeline = None
+
+
+def _get_pipeline():
+    """Lazy-load the FLUX diffusers pipeline."""
+    global _pipeline
+    if _pipeline is not None:
+        return _pipeline
+
+    try:
+        import torch
+        from diffusers import FluxPipeline
+    except ImportError:
+        raise ImportError(
+            "Creative dependencies not installed. "
+            "Run: pip install 'timmy-time[creative]'"
+        )
+
+    from config import settings
+
+    model_id = getattr(settings, "flux_model_id", "black-forest-labs/FLUX.1-schnell")
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dtype = torch.float16 if device == "cuda" else torch.float32
+
+    logger.info("Loading image model %s on %s …", model_id, device)
+    _pipeline = FluxPipeline.from_pretrained(
+        model_id, torch_dtype=dtype,
+    ).to(device)
+    logger.info("Image model loaded.")
+    return _pipeline
+
+
+def _output_dir() -> Path:
+    from config import settings
+    d = Path(getattr(settings, "image_output_dir", "data/images"))
+    d.mkdir(parents=True, exist_ok=True)
+    return d
+
+
+def _save_metadata(image_path: Path, meta: dict) -> Path:
+    meta_path = image_path.with_suffix(".json")
+    meta_path.write_text(json.dumps(meta, indent=2))
+    return meta_path
+
+
+# ── Public tools ──────────────────────────────────────────────────────────────
+
+def generate_image(
+    prompt: str,
+    negative_prompt: str = "",
+    width: int = 1024,
+    height: int = 1024,
+    steps: int = 4,
+    seed: Optional[int] = None,
+) -> dict:
+    """Generate an image from a text prompt.
+
+    Returns dict with ``path``, ``width``, ``height``, and ``prompt``.
+    """
+    pipe = _get_pipeline()
+    import torch
+
+    generator = torch.Generator(device=pipe.device)
+    if seed is not None:
+        generator.manual_seed(seed)
+
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt or None,
+        width=width,
+        height=height,
+        num_inference_steps=steps,
+        generator=generator,
+    ).images[0]
+
+    uid = uuid.uuid4().hex[:12]
+    out_path = _output_dir() / f"{uid}.png"
+    image.save(out_path)
+
+    meta = {
+        "id": uid, "prompt": prompt, "negative_prompt": negative_prompt,
+        "width": width, "height": height, "steps": steps, "seed": seed,
+    }
+    _save_metadata(out_path, meta)
+
+    return {"success": True, "path": str(out_path), **meta}
+
+
+def generate_storyboard(
+    scenes: list[str],
+    width: int = 1024,
+    height: int = 576,
+    steps: int = 4,
+) -> dict:
+    """Generate a storyboard: one keyframe image per scene description.
+
+    Args:
+        scenes: List of scene description strings.
+
+    Returns dict with list of generated frame paths.
+    """
+    frames = []
+    for i, scene in enumerate(scenes):
+        result = generate_image(
+            prompt=scene, width=width, height=height, steps=steps,
+        )
+        result["scene_index"] = i
+        result["scene_description"] = scene
+        frames.append(result)
+    return {"success": True, "frame_count": len(frames), "frames": frames}
+
+
+def image_variations(
+    prompt: str,
+    count: int = 4,
+    width: int = 1024,
+    height: int = 1024,
+    steps: int = 4,
+) -> dict:
+    """Generate multiple variations of the same prompt with different seeds."""
+    import random
+    variations = []
+    for _ in range(count):
+        seed = random.randint(0, 2**32 - 1)
+        result = generate_image(
+            prompt=prompt, width=width, height=height,
+            steps=steps, seed=seed,
+        )
+        variations.append(result)
+    return {"success": True, "count": len(variations), "variations": variations}
+
+
+# ── Tool catalogue ────────────────────────────────────────────────────────────
+
+IMAGE_TOOL_CATALOG: dict[str, dict] = {
+    "generate_image": {
+        "name": "Generate Image",
+        "description": "Generate an image from a text prompt using FLUX",
+        "fn": generate_image,
+    },
+    "generate_storyboard": {
+        "name": "Generate Storyboard",
+        "description": "Generate keyframe images for a sequence of scenes",
+        "fn": generate_storyboard,
+    },
+    "image_variations": {
+        "name": "Image Variations",
+        "description": "Generate multiple variations of the same prompt",
+        "fn": image_variations,
+    },
+}