这页ppt太丑了 ，帮我优化一下 ，做成两个纵轴 ，倍数 和 时间 ，时间不断缩减，提速倍数 上升，...

python
import matplotlib.pyplot as plt

# Data from the PPT
methods = [
    "Baseline", "quant", "attn kernel", "TeaCache", "FreeCFG",
    "Our Cache", "Token prune", "VAE compile", "VAE mask", "c/s + async"
]
time_seconds = [220, 202, 190, 58, 36.5, 23.7, 16.9, 11.3, 7.2, 7.2]
speedup = [1, 1.09, 1.16, 3.76, 6.02, 9.27, 13.05, 19.52, 30.66, 30.66]

# Create figure and axis with two y-axes
fig, ax1 = plt.subplots(figsize=(10, 6))

ax2 = ax1.twinx()

# Plot time on primary y-axis
ax1.plot(methods, time_seconds, marker='o', color='tab:blue', label='时间 (s)')
ax1.set_ylabel('时间 (s)', color='tab:blue')
ax1.tick_params(axis='y', labelcolor='tab:blue')

# Plot speedup on secondary y-axis
ax2.plot(methods, speedup, marker='s', color='tab:red', label='倍数 (x)')
ax2.set_ylabel('倍数 (x)', color='tab:red')
ax2.tick_params(axis='y', labelcolor='tab:red')

# Title and grid
plt.title("优化方案效果：时间缩短 vs 提速倍数")
ax1.grid(True, linestyle='--', alpha=0.5)

# Rotate x labels for readability
plt.xticks(rotation=30)

plt.tight_layout()
plt.show()

python
import matplotlib.pyplot as plt

# 数据
methods = [
    "Baseline", "quant", "attn kernel", "TeaCache", "FreeCFG",
    "Our Cache", "Token prune", "VAE compile", "VAE mask", "c/s + async"
]
time_seconds = [220, 202, 190, 58, 37, 24, 17, 11, 7, 7]  # 近似延迟时间
speedup = [1, 1.09, 1.16, 3.76, 6.02, 9.27, 13.05, 19.52, 30.66, 30.66]

fig, ax1 = plt.subplots(figsize=(10,6))

# 左轴: 延迟时间
ax1.plot(methods, time_seconds, marker="o", linestyle="-", label="延迟 (s)")
ax1.set_xlabel("优化方法")
ax1.set_ylabel("延迟时间 (s)", color="blue")
ax1.tick_params(axis="y", labelcolor="blue")

# 右轴: 提速倍数
ax2 = ax1.twinx()
ax2.plot(methods, speedup, marker="s", linestyle="--", color="red", label="提速倍数")
ax2.set_ylabel("提速倍数 (x)", color="red")
ax2.tick_params(axis="y", labelcolor="red")

# 阶段区域背景
ax1.axvspan(-0.5, 2.5, color="lightblue", alpha=0.2, label="DIT优化")
ax1.axvspan(2.5, 7.5, color="lightgreen", alpha=0.2, label="VAE优化")
ax1.axvspan(7.5, 9.5, color="lightyellow", alpha=0.3, label="架构优化")

# 标题与图例
plt.title("优化方案：延迟缩减与提速倍数提升")
fig.tight_layout()

plt.show()