Create app.py

app.py

@@ -0,0 +1,61 @@
+import gradio as gr
+import torch
+from optimum.intel import OVZImagePipeline
+
+# ===== Load model (only once) =====
+pipe = OVZImagePipeline.from_pretrained(
+    "hsuwill000/Z-Image-Turbo-ov",
+    device="cpu"
+)
+
+# ===== Inference function =====
+def generate_image(prompt, height, width, steps, seed):
+    if seed == -1:
+        generator = torch.Generator("cpu")
+    else:
+        generator = torch.Generator("cpu").manual_seed(int(seed))
+
+    image = pipe(
+        prompt=prompt,
+        height=int(height),
+        width=int(width),
+        num_inference_steps=int(steps),
+        guidance_scale=0.0,  # Turbo 必須 0
+        generator=generator,
+    ).images[0]
+
+    return image
+
+
+# ===== Default prompt =====
+default_prompt = """Cinematic portrait of a Japanese girl as Shinobu Kocho, realistic facial features, gradient purple compound eyes, intricate butterfly hair ornament, wearing silk haori with butterfly wing patterns, soft moonlight, hyper-realistic, depth of field, purple butterfly particles, 8k resolution, ethereal lighting"""
+
+
+# ===== Gradio UI =====
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚀 Z-Image Turbo (OpenVINO CPU)")
+
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", value=default_prompt, lines=5)
+
+            height = gr.Slider(256, 1024, value=512, step=64, label="Height")
+            width = gr.Slider(256, 1024, value=512, step=64, label="Width")
+
+            steps = gr.Slider(1, 20, value=9, step=1, label="Inference Steps")
+            seed = gr.Number(value=-1, label="Seed (-1 = random)")
+
+            run_btn = gr.Button("Generate")
+
+        with gr.Column():
+            output = gr.Image(label="Generated Image")
+
+    run_btn.click(
+        fn=generate_image,
+        inputs=[prompt, height, width, steps, seed],
+        outputs=output,
+    )
+
+# ===== Launch =====
+if __name__ == "__main__":
+    demo.launch()