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import os
import gradio as gr
from transformers import AutoModelForCausalLM
from optimum.intel.openvino import OVStableDiffusionPipeline
import torch
# 定義模型 ID 與存儲路徑
model_id = "Kouki79/Realistic_Vision6_LCM"
export_path = "exported_model_openvino_int8"
# 設定圖片大小
HIGH = 1024
WIDTH = 512
print("🔍 檢查 OpenVINO 模型是否已存在...")
if not os.path.exists(export_path) or not os.listdir(export_path):
print("⚠️ 尚未轉換 OpenVINO 8-bit 模型，開始轉換...")
# 轉換 Hugging Face 模型為 OpenVINO 8-bit
model = OVStableDiffusionPipeline.from_pretrained(
model_id,
export=True, # 自動轉換為 OpenVINO
device="CPU",
precision="int8", # 啟用 8-bit 量化
)
# 儲存轉換後的 OpenVINO 8-bit 模型
model.save_pretrained(export_path)
print(f"✅ 轉換完成！OpenVINO 8-bit 模型已儲存至 '{export_path}'")
else:
print(f"✅ 發現已轉換的 OpenVINO 8-bit 模型：'{export_path}'")
# 載入 OpenVINO 8-bit 模型
print("🔄 載入 OpenVINO 8-bit 模型...")
pipe = OVStableDiffusionPipeline.from_pretrained(
export_path,
compile=True, # 編譯模型以提高效能
device="CPU",
safety_checker=None,
torch_dtype=torch.uint8
)
print("✅ OpenVINO 模型載入完成！")
# 設定推論函數
def infer(prompt):
print(f"🖼️ 生成圖片: {prompt}")
image = pipe(
prompt=f",hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic,",
negative_prompt="EasyNegative, cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly,",
width=WIDTH,
height=HIGH,
guidance_scale=1.0,
num_inference_steps=6,
num_images_per_prompt=1,
).images[0]
return image
# Gradio UI 設定
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# {model_id.split('/')[1]} {WIDTH}x{HIGH}
Running on OpenVINO (8-bit).
""")
with gr.Row():
prompt = gr.Textbox(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Generate", scale=0)
result = gr.Image(label="Result", show_label=False)
run_button.click(
fn=infer,
inputs=[prompt],
outputs=[result]
)
print("🚀 啟動 Gradio Web UI...")
demo.queue().launch()
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