use fastapi run with transformers too slow ( about 10s+), I have a GPU, how to solve the problem, my code is:

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from transformers import AutoProcessor, HunYuanVLForConditionalGeneration
from PIL import Image
import torch
import io
import os

os.environ["CUDA_VISIBLE_DEVICES"] = "1"

app = FastAPI(title="HunYuan OCR API")

def clean_repeated_substrings(text):
"""Clean repeated substrings in text"""
n = len(text)
if n < 8000:
return text
for length in range(2, n // 10 + 1):
candidate = text[-length:]
count = 0
i = n - length

    while i >= 0 and text[i:i + length] == candidate:
        count += 1
        i -= length

    if count >= 10:
        return text[:n - length * (count - 1)]

return text

---------- Load model & processor once (global) ----------

model_name = "/models/HunyuanOCR"

processor = AutoProcessor.from_pretrained(model_name, use_fast=False)
model = HunYuanVLForConditionalGeneration.from_pretrained(
model_name,
attn_implementation="eager",
dtype=torch.bfloat16,
device_map="auto"
)

device = next(model.parameters()).device

-------------------- API Route --------------------

@app .post("/ocr")
async def ocr_api(file: UploadFile = File(...)):
try:
# Read image bytes
img_bytes = await file.read()
image = Image.open(io.BytesIO(img_bytes))

    # Construct messages
    messages1 = [
        {"role": "system", "content": ""},
        {
            "role": "user",
            "content": [
                {"type": "image", "image": "uploaded_image"},
                {"type": "text", "text": "检测并识别图片中的文字，将文本坐标格式化输出。"},
            ],
        },
    ]
    messages = [messages1]

    texts = [
        processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
        for msg in messages
    ]

    # Prepare inputs
    inputs = processor(
        text=texts,
        images=image,
        padding=True,
        return_tensors="pt",
    ).to(device)

    # Generate
    with torch.no_grad():
        generated_ids = model.generate(
            **inputs, max_new_tokens=1024, do_sample=False
        )

    input_ids = inputs.input_ids
    generated_ids_trimmed = [
        out_ids[len(in_ids):] for in_ids, out_ids in zip(input_ids, generated_ids)
    ]

    output_text = processor.batch_decode(
        generated_ids_trimmed,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False
    )

    output_text = clean_repeated_substrings(output_text)

    return JSONResponse({"text": output_text})

except Exception as e:
    return JSONResponse({"error": str(e)}, status_code=500)