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"cells": [
{
"cell_type": "markdown",
"id": "37de3483",
"metadata": {},
"source": [
"# PixelForge with Z-Image Turbo (Colab Test)\n",
"Test stärkere Modelle mit kostenloser GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0d963b3",
"metadata": {},
"outputs": [],
"source": [
"# 1. Install dependencies\n",
"!pip install -q torch diffusers transformers accelerate numpy pillow requests -U\n",
"!pip install -q flask flask-cors python-dotenv\n",
"print(\"✓ Dependencies installed\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f93fdb6",
"metadata": {},
"outputs": [],
"source": [
"# 2. Check GPU\n",
"import torch\n",
"print(f\"GPU Available: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")\n",
" print(f\"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1d69f6e",
"metadata": {},
"outputs": [],
"source": [
"# 3. Download & Load Model (beispiel: FLUX oder ähnlich)\n",
"# Optional: Ersetze mit deinem Z-Image Turbo Modell\n",
"\n",
"from diffusers import StableDiffusionPipeline\n",
"import torch\n",
"\n",
"MODEL_ID = \"segmind/tiny-sd\" # oder dein Z-Image Turbo\n",
"print(f\"Loading {MODEL_ID}...\")\n",
"\n",
"pipe = StableDiffusionPipeline.from_pretrained(\n",
" MODEL_ID,\n",
" torch_dtype=torch.float16,\n",
" safety_checker=None,\n",
" requires_safety_checker=False\n",
")\n",
"pipe = pipe.to(\"cuda\")\n",
"print(\"✓ Model loaded\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7575df6",
"metadata": {},
"outputs": [],
"source": [
"# 4. Test Generation\n",
"prompt = \"A beautiful futuristic city at sunset\"\n",
"print(f\"Generating: {prompt}\")\n",
"\n",
"image = pipe(\n",
" prompt,\n",
" height=512,\n",
" width=512,\n",
" num_inference_steps=20,\n",
" guidance_scale=7.5\n",
").images[0]\n",
"\n",
"image.save(\"/tmp/test_output.png\")\n",
"print(\"✓ Image generated: /tmp/test_output.png\")\n",
"image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c84cc93",
"metadata": {},
"outputs": [],
"source": [
"# 5. Start API Server (optional: expose to PixelForge)\n",
"from flask import Flask, request, jsonify\n",
"import json\n",
"import base64\n",
"from io import BytesIO\n",
"\n",
"app = Flask(__name__)\n",
"\n",
"@app.route('/health', methods=['GET'])\n",
"def health():\n",
" return jsonify({\"status\": \"ok\", \"model\": MODEL_ID, \"gpu\": torch.cuda.get_device_name(0)})\n",
"\n",
"@app.route('/generate', methods=['POST'])\n",
"def generate():\n",
" data = request.json\n",
" prompt = data.get('prompt', 'a test image')\n",
" steps = data.get('steps', 20)\n",
" guidance = data.get('guidance', 7.5)\n",
" \n",
" print(f\"Generating: {prompt}\")\n",
" image = pipe(\n",
" prompt,\n",
" height=512,\n",
" width=512,\n",
" num_inference_steps=steps,\n",
" guidance_scale=guidance\n",
" ).images[0]\n",
" \n",
" # Convert to base64\n",
" buffered = BytesIO()\n",
" image.save(buffered, format=\"PNG\")\n",
" img_base64 = base64.b64encode(buffered.getvalue()).decode()\n",
" \n",
" return jsonify({\n",
" \"success\": True,\n",
" \"image\": img_base64,\n",
" \"prompt\": prompt\n",
" })\n",
"\n",
"# Ngrok tunnel (kostenlos)\n",
"from pyngrok import ngrok\n",
"ngrok.set_auth_token(\"DEIN_NGROK_TOKEN\") # Gratis auf ngrok.com registrieren\n",
"\n",
"print(\"Starting API server...\")\n",
"public_url = ngrok.connect(5000)\n",
"print(f\"Public URL: {public_url}\")\n",
"print(\"Use this URL in PixelForge config!\")\n",
"\n",
"app.run(port=5000)"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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