Create server.py

server.py

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+from flask import Flask, request, jsonify
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+import torch
+
+app = Flask(__name__)
+
+# ** Hafif SD Turbo Modeli ve LoRA Yükleme **
+base_model = "stabilityai/sd-turbo"  # Hafif model
+lora_model = "maria26/Floor_Plan_LoRA"
+
+pipe = StableDiffusionPipeline.from_pretrained(
+    base_model, torch_dtype=torch.float16, safety_checker=None
+)
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+# LoRA'yı yükle
+pipe.load_lora_weights(lora_model)
+
+# Eğer GPU yetmezse CPU'ya geçir
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe.to(device)
+
+@app.route('/generate', methods=['POST'])
+def generate():
+    data = request.json
+    prompt = data.get("prompt", "a simple architectural floor plan")
+
+    try:
+        image = pipe(prompt).images[0]
+        image_path = "static/output.png"
+        image.save(image_path)
+        return jsonify({"status": "success", "image_url": image_path})
+    except Exception as e:
+        return jsonify({"status": "error", "message": str(e)})
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=5000, debug=True)