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.gitignore

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+/.venv/

Dockerfile

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+# Use an Ubuntu-based image with Python 3.10
+FROM python:3.10-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install UV for faster dependency installation
+RUN pip install uv
+
+# Copy requirements and install
+COPY requirements.txt .
+RUN uv pip install --system -r requirements.txt
+
+# Copy application code
+COPY app.py .
+
+# Download pre-converted OpenVINO model
+RUN python -c "from optimum.intel.openvino import OVStableDiffusionPipeline; \
+    OVStableDiffusionPipeline.from_pretrained('rupeshs/hyper-sd-sdxl-1-step-openvino-int8', ov_config={'CACHE_DIR': ''})"
+
+# Expose port 5000
+EXPOSE 5000
+
+# Command to run the Flask app
+CMD ["python", "app.py"]

app.py

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+import os
+from flask import Flask, request, jsonify, send_file
+from optimum.intel.openvino.modeling_diffusion import OVStableDiffusionPipeline
+from PIL import Image
+import io
+import torch
+
+app = Flask(__name__)
+
+# Load the pre-converted OpenVINO SDXL model
+model_id = "rupeshs/hyper-sd-sdxl-1-step-openvino-int8"
+pipeline = OVStableDiffusionPipeline.from_pretrained(
+    model_id,
+    ov_config={"CACHE_DIR": ""},
+    device="CPU"
+)
+
+# Ensure Tiny Auto Encoder is enabled to reduce memory usage
+pipeline.enable_tiny_auto_encoder()
+
+@app.route('/generate', methods=['POST'])
+def generate_image():
+    try:
+        # Get prompt from request
+        data = request.get_json()
+        prompt = data.get('prompt', 'A futuristic cityscape at sunset, cyberpunk style, 8k')
+        width = data.get('width', 512)
+        height = data.get('height', 512)
+        num_inference_steps = data.get('num_inference_steps', 1)
+        guidance_scale = data.get('guidance_scale', 1.0)
+
+        # Generate image
+        image = pipeline(
+            prompt=prompt,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale
+        ).images[0]
+
+        # Save image to a bytes buffer
+        img_io = io.BytesIO()
+        image.save(img_io, 'PNG')
+        img_io.seek(0)
+
+        return send_file(
+            img_io,
+            mimetype='image/png',
+            as_attachment=True,
+            download_name='generated_image.png'
+        )
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)

requirements.txt

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+flask
+optimum[openvino]
+diffusers
+transformers
+accelerate
+opencv-python-headless
+torch
+pillow