#!/usr/bin/env python3
"""Steps sweep + resolution test on the working 9B fp8-fast config."""
import io, time
import requests
import numpy as np
from PIL import Image, ImageFilter
import app as A

COMFY = "http://127.0.0.1:8188"
MODEL = "flux2.0/flux-2-klein-9b-fp8.safetensors"
DTYPE = "fp8_e4m3fn_fast"


def upload(scale=1.0):
    image = Image.open("用来重绘.jpg").convert("RGB")
    mask_img = Image.open("用来重绘.png").convert("RGBA")
    if scale != 1.0:
        w, h = image.size
        w, h = int(w * scale) // 8 * 8, int(h * scale) // 8 * 8
        image = image.resize((w, h), Image.LANCZOS)
    mask_data = np.max(np.array(mask_img), axis=2)
    m = Image.fromarray(mask_data, mode="L")
    if m.size != image.size:
        m = m.resize(image.size, Image.LANCZOS)
    m = m.filter(ImageFilter.GaussianBlur(radius=4))
    alpha = Image.eval(m, lambda x: 255 - x)
    r, g, b = image.split()
    rgba = Image.merge("RGBA", (r, g, b, alpha))
    buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
    up = requests.post(f"{COMFY}/upload/image",
                       files={"image": ("hair_input.png", buf, "image/png")}).json()
    return up["name"], image.size


def run(fname, steps):
    wf = A.build_workflow(fname, "填充遮罩区域的头发")
    wf["16"]["inputs"]["unet_name"] = MODEL
    wf["16"]["inputs"]["weight_dtype"] = DTYPE
    wf["1"]["inputs"]["steps"] = steps
    pid = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()["prompt_id"]
    dl = time.time() + 120
    while time.time() < dl:
        time.sleep(0.1)
        h = requests.get(f"{COMFY}/history/{pid}").json()
        if pid in h and "17" in h[pid].get("outputs", {}):
            ts = {m[0]: m[1].get("timestamp") for m in h[pid]["status"]["messages"]}
            return (ts["execution_success"] - ts["execution_start"]) / 1000.0
    return float("nan")


print("=== 步数扫描 (9B fp8-fast, 原分辨率 1024x775) ===", flush=True)
fname, sz = upload(1.0)
run(fname, 6)  # warmup
res = {}
for s in [2, 3, 4, 6, 8]:
    t = min(run(fname, s), run(fname, s))
    res[s] = t
    print(f"  steps={s}: {t:.2f}s", flush=True)
# derive per-step cost & fixed overhead via two points
per = (res[8] - res[2]) / 6
fixed = res[2] - per * 2
print(f"  -> 每步 ~{per:.3f}s, 固定开销(VAE/编码/colormatch/加载) ~{fixed:.2f}s", flush=True)

print("\n=== 分辨率影响 (steps=4) ===", flush=True)
for scale in [1.0, 0.75, 0.6]:
    fn, s2 = upload(scale)
    run(fn, 4)  # warmup
    t = min(run(fn, 4), run(fn, 4))
    print(f"  {s2[0]}x{s2[1]} ({s2[0]*s2[1]/1e6:.2f}MP): {t:.2f}s", flush=True)