让 AI 和你的程序 调用本地引擎

{
  "prompt": "雨夜的老式公交站台，一位七十岁的中国老爷爷撑着黑伞，电影感，真实摄影风格",
  "width": 1344, "height": 768,
  "seed": 12345,
  "ref_images": [],
  "sample_params": { "sample_method": "euler", "sample_steps": 4, "guidance": { "txt_cfg": 1.0 } },
  "vae_tiling_params": { "enabled": true },
  "output_format": "png"
}

{
  "prompt": "场景：雨夜老式公交站台…… 正文：固定镜头，缓慢推近。老爷爷望着远处，小声说：“来了。” 无背景音乐 无字幕",
  "width": 1280, "height": 704,
  "video_frames": 121, "fps": 24,
  "seed": 12345,
  "model_type": "ltx2_25_22B_distilled",
  "init_image": null, "end_image": null, "ref_images": null,
  "sample_params": { "sample_method": "euler", "sample_steps": 8, "guidance": { "txt_cfg": 1.0 } },
  "vae_tiling_params": { "enabled": true, "temporal_tiling": true, "tile_size_x": 16, "tile_size_y": 16,
                         "extra_tiling_args": "temporal_tile_frames=2,temporal_tile_overlap=1" },
  "output_format": "webm"
}

{ "video": "data:video/mp4;base64,....", "spatial": "flashvsr*1.5", "temporal": "", "seed": 1 }

{ "path": "D:\\分镜\\shot01.png", "prompt": "雨夜的老式公交站台……", "seed": 12345, "model": "flux" }

import base64, json, os, time, urllib.request

MGR, API = "http://127.0.0.1:7860", "http://127.0.0.1:7861"
HDR = {"Content-Type": "application/json", "Origin": "http://localhost"}

def call(url, body=None):
    req = urllib.request.Request(url, data=None if body is None else json.dumps(body).encode(),
                                 headers=HDR, method="GET" if body is None else "POST")
    with urllib.request.urlopen(req, timeout=60) as r:
        return json.loads(r.read())

def use_model(m):                      # flux / zimage / ltx
    st = call(MGR + "/status")
    if st["model"] != m or st["state"] != "ready":
        call(MGR + "/start?model=" + m, {})
        while call(MGR + "/status")["state"] != "ready":
            print("模型载入中……"); time.sleep(5)

def wait(job_id):
    while True:
        s = call(API + "/sdcpp/v1/jobs/" + job_id)
        if s["status"] == "completed": return s["result"]
        if s["status"] in ("failed", "cancelled"): raise RuntimeError(s.get("error") or s["status"])
        time.sleep(2)

def to_studio(path, prompt, seed, model):   # 登记到收件箱：作品自动出现在本地创作台（需要新版引擎）
    try: call(MGR + "/inbox", {"path": os.path.abspath(path), "prompt": prompt, "seed": seed, "model": model})
    except Exception: pass

def image(prompt, path, w=1344, h=768, seed=1):
    use_model("flux")
    job = call(API + "/sdcpp/v1/img_gen", {
        "prompt": prompt, "width": w, "height": h, "seed": seed, "ref_images": [],
        "sample_params": {"sample_method": "euler", "sample_steps": 4, "guidance": {"txt_cfg": 1.0}},
        "vae_tiling_params": {"enabled": True}, "output_format": "png"})
    res = wait(job["id"])
    open(path, "wb").write(base64.b64decode(res["images"][0]["b64_json"]))
    to_studio(path, prompt, seed, "flux")
    print("图片已保存：", path)

def video(prompt, path, seconds=5, w=1280, h=704, seed=1):
    use_model("ltx")
    job = call(API + "/sdcpp/v1/vid_gen", {
        "prompt": prompt, "width": w, "height": h, "seed": seed, "fps": 24,
        "video_frames": seconds * 24 + 1, "model_type": "ltx2_25_22B_distilled",
        "init_image": None, "end_image": None, "ref_images": None,
        "sample_params": {"sample_method": "euler", "sample_steps": 8, "guidance": {"txt_cfg": 1.0}},
        "vae_tiling_params": {"enabled": True, "temporal_tiling": True, "tile_size_x": 16, "tile_size_y": 16,
                              "extra_tiling_args": "temporal_tile_frames=2,temporal_tile_overlap=1"},
        "output_format": "webm"})
    res = wait(job["id"])
    with urllib.request.urlopen(urllib.request.Request(API + res["video_url"], headers=HDR)) as r:
        open(path, "wb").write(r.read())
    to_studio(path, prompt, seed, "ltx")
    print("视频已保存：", path)

if __name__ == "__main__":
    image("雨夜的老式公交站台，一位七十岁的中国老爷爷撑着黑伞，电影感，真实摄影风格", "test.png")
    # video("雨夜公交站台，老爷爷望着远处，小声说：“来了。” 无背景音乐 无字幕", "test.mp4")