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/genconfig_to_diffusers_kwargs.md

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+---
+name: genconfig_to_diffusers_kwargs
+kind: function
+lang: py
+domain: ml
+version: "1.0.0"
+purity: pure
+signature: "def genconfig_to_diffusers_kwargs(cfg: GenerationConfig) -> dict"
+description: "Convierte un GenerationConfig al dict de kwargs listo para pipe(**kwargs) de diffusers. Mapea prompt, steps, cfg_scale, width, height. LoRAs y sampler se aplican antes de la llamada; generator=None para que el caller setee torch.Generator por separado."
+tags: [ml, diffusers, generation, converter, pure]
+params:
+  - name: cfg
+    desc: "Instancia de GenerationConfig con los parametros de generacion validados."
+output: "dict con claves prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, generator (None). Listo para desempaquetar con pipe(**kwargs)."
+uses_functions: []
+uses_types:
+  - generation_config_py_ml
+returns: []
+returns_optional: false
+error_type: ""
+imports: []
+tested: true
+tests:
+  - "kwargs contiene todas las claves requeridas"
+  - "negative_prompt None se pasa tal cual"
+  - "steps y cfg_scale se mapean a num_inference_steps y guidance_scale"
+  - "generator siempre es None"
+test_file_path: "python/functions/ml/tests/test_genconfig_to_diffusers_kwargs.py"
+file_path: "python/functions/ml/genconfig_to_diffusers_kwargs.py"
+---
+
+## Ejemplo
+
+```python
+from ml.genconfig_to_diffusers_kwargs import genconfig_to_diffusers_kwargs
+from ml.generation_config import GenerationConfig
+from ml.model_ref import ModelRef
+
+cfg = GenerationConfig(
+    prompt="a dog in the park",
+    seed=42,
+    steps=30,
+    cfg_scale=7.5,
+    sampler="euler_a",
+    width=512,
+    height=512,
+    model=ModelRef(name="runwayml/stable-diffusion-v1-5", model_type="sd15"),
+)
+
+kwargs = genconfig_to_diffusers_kwargs(cfg)
+# kwargs["num_inference_steps"] == 30
+# kwargs["guidance_scale"] == 7.5
+# kwargs["generator"] is None
+
+# El caller asigna el generator:
+# kwargs["generator"] = torch.Generator(device=device).manual_seed(cfg.seed)
+# image = pipe(**kwargs).images[0]
+```
+
+## Notas
+
+Funcion pura: sin I/O, sin torch, sin imports opcionales en tiempo de ejecucion.
+Los LoRAs se aplican via `pipe.load_lora_weights(lora.path, adapter_name=...)` antes
+de la llamada. El scheduler/sampler se configura via `pipe.scheduler = ...` tambien
+antes. Ambos no tienen mapping directo a kwargs de `__call__`.