xsl
b61ea6f33b
接口5改造（生发机制与接口2一致，按性别分流）： - generate_hairline_pngs: 生发图来源从 _grow_from_texture(局部inpaint) 改为按性别分流——female走 generate_grow_results_swap(swapHair+Flux整帧重绘)， male走 generate_grow_results(ComfyUI add_hair) - 新增参数 redraw_max_side/unet_name/v2_defaults(female路径透传) - 接口5 handler 加 flux_model/redraw_max_side Form参数 - 保留接口5独有输出: 3档叠图(middle/high/low)/中心点/face_measure - 已验证: female日志出现"接口2女 管线降分辨率max_side=640"+swap+Flux; male走add_hair.json; generate_grow_image=false正确跳过生发 统一ComfyUI重绘提示词: - "填充遮罩区域的头发，皮肤加一点磨皮，再加一点美颜" → "填充遮罩区域的头发" - 覆盖: _REDRAW_PROMPT/_DEFAULT_PROMPT常量 + app.py各接口Form默认 + 工作流JSON节点60(add_hair/0716add-hair-api/hair_repaint) + 全部测试页输入框
78 lines
3.1 KiB
Python
78 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""Benchmark ComfyUI hair-inpaint workflow across model / dtype / steps."""
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import io, time, sys
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import requests
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import numpy as np
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from PIL import Image, ImageFilter
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import app as A
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COMFY = "http://127.0.0.1:8188"
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def prep_and_upload():
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image = Image.open("用来重绘.jpg").convert("RGB")
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mask_img = Image.open("用来重绘.png").convert("RGBA")
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mask_data = np.max(np.array(mask_img), axis=2)
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m = Image.fromarray(mask_data, mode="L")
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if m.size != image.size:
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m = m.resize(image.size, Image.LANCZOS)
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m = m.filter(ImageFilter.GaussianBlur(radius=4))
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alpha = Image.eval(m, lambda x: 255 - x)
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r, g, b = image.split()
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rgba = Image.merge("RGBA", (r, g, b, alpha))
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buf = io.BytesIO(); rgba.save(buf, format="PNG"); buf.seek(0)
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up = requests.post(f"{COMFY}/upload/image",
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files={"image": ("hair_input.png", buf, "image/png")}).json()
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return up["name"]
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def run_once(fname, model, dtype, steps):
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wf = A.build_workflow(fname, "填充遮罩区域的头发")
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wf["16"]["inputs"]["unet_name"] = model
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wf["16"]["inputs"]["weight_dtype"] = dtype
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wf["1"]["inputs"]["steps"] = steps
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r = requests.post(f"{COMFY}/prompt", json={"prompt": wf}).json()
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if "prompt_id" not in r:
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raise RuntimeError(f"submit failed: {str(r)[:300]}")
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pid = r["prompt_id"]
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deadline = time.time() + 180
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while time.time() < deadline:
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time.sleep(0.1)
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h = requests.get(f"{COMFY}/history/{pid}").json()
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if pid not in h:
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continue
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st = h[pid].get("status", {})
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if st.get("status_str") == "error":
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for m in st.get("messages", []):
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if m[0] == "execution_error":
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raise RuntimeError(str(m[1])[:300])
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raise RuntimeError("execution error")
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if "17" in h[pid].get("outputs", {}):
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ts = {mm[0]: mm[1].get("timestamp") for mm in st["messages"]}
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return (ts["execution_success"] - ts["execution_start"]) / 1000.0
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raise TimeoutError("run exceeded 180s")
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CONFIGS = [
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("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn", 6, "9B fp8 (当前)"),
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("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 6, "9B fp8-fast"),
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("flux2.0/flux-2-klein-9b-fp8.safetensors", "fp8_e4m3fn_fast", 4, "9B fp8-fast s4"),
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("flux-2-klein-4b-fp8.safetensors", "fp8_e4m3fn", 6, "4B fp8"),
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("flux-2-klein-4b-fp8.safetensors", "fp8_e4m3fn_fast", 6, "4B fp8-fast"),
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("flux-2-klein-4b-fp8.safetensors", "fp8_e4m3fn_fast", 4, "4B fp8-fast s4"),
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]
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fname = prep_and_upload()
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print("input uploaded:", fname)
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print(f"{'配置':<22}{'warmup':>10}{'run1':>10}{'run2':>10}{'best':>10}")
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for model, dtype, steps, label in CONFIGS:
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times = []
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for i in range(3): # 1 warmup + 2 measured
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try:
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t = run_once(fname, model, dtype, steps)
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except Exception as e:
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t = float('nan'); print("ERR", label, e)
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times.append(t)
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best = min(times[1:])
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print(f"{label:<22}{times[0]:>9.2f}s{times[1]:>9.2f}s{times[2]:>9.2f}s{best:>9.2f}s")
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