chore: auto-commit (95 archivos)

- cmd/fn/doctor.go
- cmd/fn/main.go
- cpp/apps/primitives_gallery/playground/tables/CMakeLists.txt
- cpp/apps/primitives_gallery/playground/tables/data_table.cpp
- cpp/apps/primitives_gallery/playground/tables/data_table_logic.cpp
- cpp/apps/primitives_gallery/playground/tables/data_table_logic.h
- cpp/apps/primitives_gallery/playground/tables/self_test.cpp
- cpp/apps/primitives_gallery/playground/tables/tql.cpp
- cpp/apps/primitives_gallery/playground/tables/viz.cpp
- cpp/apps/primitives_gallery/playground/tables/viz.h
- ...

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

python/functions/ml/diffusers_generate.md

@@ -0,0 +1,71 @@
+---
+name: diffusers_generate
+kind: function
+lang: py
+domain: ml
+version: "1.0.0"
+purity: impure
+signature: "def diffusers_generate(pipe: Any, cfg: GenerationConfig) -> ImageGenResult"
+description: "Ejecuta inferencia con un pipeline diffusers usando GenerationConfig. Mide duracion y pico de VRAM. Retorna ImageGenResult con imagen PIL, meta y metricas."
+tags: [diffusers, ml, image-generation, inference, vram, metrics]
+uses_functions: [genconfig_to_diffusers_kwargs_py_ml]
+uses_types: [generation_config_py_ml, image_gen_result_py_ml]
+returns: [image_gen_result_py_ml]
+returns_optional: false
+error_type: "error_go_core"
+imports: [torch, diffusers]
+params:
+  - name: pipe
+    desc: "Pipeline diffusers cargado y listo para inferencia (resultado de diffusers_load_pipeline, opcionalmente con scheduler y LoRA configurados)."
+  - name: cfg
+    desc: "Parametros de generacion. cfg.seed >= 0 para semilla fija; -1 usa time-based. cfg.sampler se incluye en meta pero no se aplica aqui (usar diffusers_set_scheduler antes)."
+output: "ImageGenResult con image=PIL.Image.Image, meta={backend, model, sampler, actual_steps, seed, width, height, cfg_scale}, duration_ms en entero milisegundos, vram_peak_mb (None si no hay CUDA)."
+tested: true
+tests:
+  - "genera imagen retorna ImageGenResult"
+test_file_path: "python/functions/ml/tests/test_diffusers_backend.py"
+file_path: "python/functions/ml/diffusers_generate.py"
+---
+
+## Ejemplo
+
+```python
+from diffusers_load_pipeline import diffusers_load_pipeline
+from diffusers_generate import diffusers_generate
+from generation_config import GenerationConfig
+from model_ref import ModelRef
+
+model = ModelRef(
+    name="sd-turbo",
+    model_type="sd15",
+    path="/home/lucas/vaults/imagegen_models/diffusers/sd-turbo",
+)
+cfg = GenerationConfig(
+    prompt="a photo of a cat",
+    seed=42,
+    steps=1,
+    cfg_scale=0.0,
+    sampler="euler",
+    width=512,
+    height=512,
+    model=model,
+)
+pipe = diffusers_load_pipeline(model, device="cuda", dtype="fp16")
+result = diffusers_generate(pipe, cfg)
+# result.image -> PIL.Image.Image 512x512
+# result.duration_ms -> int > 0
+# result.meta["backend"] -> "diffusers"
+```
+
+## Notas
+
+`cfg.seed = -1` genera seed aleatorio basado en `time.time()` (reproducible si
+se guarda en `result.meta["seed"]`).
+
+VRAM: `torch.cuda.reset_peak_memory_stats()` antes de inferencia,
+`torch.cuda.max_memory_allocated() // 1024 // 1024` despues.
+
+`genconfig_to_diffusers_kwargs` omite generator=None; esta funcion lo reemplaza
+con `torch.Generator(device=device).manual_seed(seed)`.
+
+Import lazy de torch — ImportError descriptivo si no instalado.