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Swarmui_Lightning/on_start.sh

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+#!/bin/bash
+
+# This script runs every time your Studio starts, from your home directory.
+echo "Preparando carpetas temporales..."
+python ~/symb.py
+
+# Logs from previous runs can be found in ~/.lightning_studio/logs/
+
+# aseg\u00farate de que la ruta sea correcta desde la ra\u00edz de tu studio
+
+# LD_PRELOAD se establece para esta \u00fanica ejecuci\u00f3n, y el '&' lo env\u00eda al fondo.
+
+
+# List files under fast_load that need to load quickly on start (e.g. model checkpoints).
+#
+# ! fast_load
+# <your file here>
+
+# Add your startup commands below.
+#
+# Example: streamlit run my_app.py
+# Example: gradio my_app.py

Swarmui_Lightning/symb.py

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+# setup_civitai.py
+import os
+import subprocess
+from pathlib import Path
+
+# ---------- configuración ----------
+TOKEN_CIVITAI = "4487c60c8a71cbfe100a7a03ad9e6ae9"
+STORE_FILE    = Path.home() / ".civitai_token.pkl"   # sustituto de %store
+# -----------------------------------
+
+def _store(var: str, val: str) -> None:
+    """Simula %store var val (persistente entre kernels)."""
+    import pickle, builtins
+    # escribe en el archivo
+    data = {var: val}
+    STORE_FILE.write_bytes(pickle.dumps(data))
+    # opcional: también lo deja disponible en builtins para este kernel
+    setattr(builtins, var, val)
+    print(f"✅ Variable '{var}' guardada en {STORE_FILE} (persistente).")
+
+def _run(cmd: str) -> None:
+    """Ejecuta un comando shell y muestra la salida."""
+    print(f"+ {cmd}")
+    subprocess.run(cmd, shell=True, check=False)
+
+def preparar():
+    """Función principal: guarda el token y crea enlaces."""
+    if not TOKEN_CIVITAI.strip():
+        print("⚠️ No se ingresó token.")
+        return
+    _store("token_civitai", TOKEN_CIVITAI)
+
+    # limpieza y enlaces
+    cmds = [
+        "rm -rf /teamspace/studios/this_studio/.cache",
+        "rm -rf /teamspace/studios/this_studio/tmp ~/tmp",
+        "ln -vs /tmp ~/tmp",
+        "rm -rf /teamspace/studios/this_studio/SwarmUI/Models/Stable-Diffusion/tmp_models",
+        "mkdir -p /tmp/models",
+        "ln -vs /tmp/models /teamspace/studios/this_studio/SwarmUI/Models/Stable-Diffusion/tmp_models",
+        "rm -rf /teamspace/studios/this_studio/SwarmUI/Models/Lora/tmp_lora",
+        "mkdir -p /tmp/lora",
+        "ln -vs /tmp/lora /teamspace/studios/this_studio/SwarmUI/Models/Lora/tmp_lora",
+        "rm -rf /teamspace/studios/this_studio/SwarmUI/Models/diffusion_models",
+        "mkdir -p /tmp/diffusion_models",
+        "ln -vs /tmp/diffusion_models /teamspace/studios/this_studio/SwarmUI/Models/diffusion_models",
+        "rm -rf /teamspace/studios/this_studio/SwarmUI/Models/text_encoders",
+        "mkdir -p /tmp/text_encoders",
+        "ln -vs /tmp/text_encoders /teamspace/studios/this_studio/SwarmUI/Models/text_encoders",
+        "rm -rf /teamspace/studios/this_studio/SwarmUI/Models/unet",
+        "mkdir -p /tmp/unet",
+        "ln -vs /tmp/unet /teamspace/studios/this_studio/SwarmUI/Models/unet",
+        "rm -rf /teamspace/studios/this_studio/SwarmUI/Models/controlnet",
+        "mkdir -p /tmp/controlnet",
+        "ln -vs /tmp/controlnet /teamspace/studios/this_studio/SwarmUI/Models/controlnet",
+    ]
+    for c in cmds:
+        _run(c)
+    print("🎉 Entorno listo.")
+
+if __name__ == "__main__":
+    preparar()