✨ feat(automation): 新增无人值守自动化运营模块

- 新增完整的自动化运营模块，包含一键评论、一键发布和定时调度功能
- 【一键评论】自动搜索高赞笔记，AI分析内容并生成个性化评论进行引流
- 【一键发布】随机选择主题和风格，AI生成文案，SD生成图片并自动发布到小红书
- 【定时调度】支持随机时间间隔的自动评论和发布，模拟真人操作节奏降低风险
- 新增自动化日志系统，实时记录操作状态和结果
- 在UI中新增“自动运营”标签页，提供完整的配置和操作界面

📝 docs(prompts): 优化SD提示词生成模板

- 更新文案生成提示词，适配JuggernautXL模型并优化质量描述
- 新增详细的质量词、光影、风格、构图和细节要求
- 移除括号权重语法，改为英文逗号分隔的描述方式
- 优化反向提示词，针对SDXL模型进行适配和增强

🔧 chore(config): 更新安全令牌配置

- 更新config.json中的xsec_token为新的安全令牌值

⚡️ perf(sd): 优化Stable Diffusion服务参数

- 增加SD服务超时时间至900秒，适应高质量图片生成
- 优化文生图和图生图的默认参数，适配JuggernautXL模型
- 新增采样器和调度器参数配置，提升图片生成质量
- 优化默认反向提示词，针对SDXL模型进行专门优化

config.json

@@ -21,5 +21,5 @@
             "base_url": "https://wolfai.top/v1"
         }
     ],
-    "xsec_token": "ABS1TagQqhCpZmeNlq0VoCfNEyI6Q83GJzjTGJvzEAq5I="
+    "xsec_token": "ABdAEbqP9ScgelmyolJxsnpCr_e645SCpnub2dLZJc4Ck="
 }

llm_service.py

@@ -26,7 +26,13 @@ PROMPT_COPYWRITING = """
 3. 结尾必须有 5 个以上相关话题标签(#)。
 
 【绘图 Prompt】：
-生成对应的 Stable Diffusion 英文提示词，强调：masterpiece, best quality, 8k, soft lighting, ins style。
+生成对应的 Stable Diffusion 英文提示词，适配 JuggernautXL 模型，强调：
+- 质量词：masterpiece, best quality, ultra detailed, 8k uhd, high resolution
+- 光影：natural lighting, soft shadows, studio lighting, golden hour 等（根据场景选择）
+- 风格：photorealistic, cinematic, editorial photography, ins style
+- 构图：dynamic angle, depth of field, bokeh 等
+- 细节：detailed skin texture, sharp focus, vivid colors
+注意：不要使用括号权重语法，直接用英文逗号分隔描述。
 
 返回 JSON 格式：
 {"title": "...", "content": "...", "sd_prompt": "...", "tags": ["标签1", "标签2", ...]}
@@ -105,7 +111,11 @@ PROMPT_COPY_WITH_REFERENCE = """
 3. 结尾有 5 个以上话题标签(#)。
 
 【绘图 Prompt】：
-生成 Stable Diffusion 英文提示词。
+生成 Stable Diffusion 英文提示词，适配 JuggernautXL 模型：
+- 必含质量词：masterpiece, best quality, ultra detailed, 8k uhd
+- 风格：photorealistic, cinematic, editorial photography
+- 光影和细节：natural lighting, sharp focus, vivid colors, detailed skin texture
+- 用英文逗号分隔，不用括号权重语法。
 
 返回 JSON 格式：
 {{"title": "...", "content": "...", "sd_prompt": "...", "tags": ["标签1", "标签2", ...]}}

main.py

@@ -10,6 +10,9 @@ import time
 import logging
 import platform
 import subprocess
+import threading
+import random
+from datetime import datetime
 from PIL import Image
 import matplotlib
 import matplotlib.pyplot as plt
@@ -881,6 +884,306 @@ def fetch_my_profile(user_id, xsec_token, mcp_url):
         return f"❌ {e}", "", None, None, None
 
 
+# ==================================================
+#              自动化运营模块
+# ==================================================
+
+# 自动化状态
+_auto_running = threading.Event()
+_auto_thread: threading.Thread | None = None
+_auto_log: list[str] = []
+
+DEFAULT_TOPICS = [
+    "春季穿搭", "通勤穿搭", "约会穿搭", "显瘦穿搭", "平价好物",
+    "护肤心得", "妆容教程", "好物分享", "生活好物", "减脂餐分享",
+    "居家好物", "收纳技巧", "咖啡探店", "书单推荐", "旅行攻略",
+]
+
+DEFAULT_STYLES = ["好物种草", "干货教程", "情绪共鸣", "生活Vlog", "测评避雷"]
+
+DEFAULT_COMMENT_KEYWORDS = [
+    "穿搭", "美食", "护肤", "好物推荐", "旅行", "生活日常", "减脂",
+]
+
+
+def _auto_log_append(msg: str):
+    """记录自动化日志"""
+    ts = datetime.now().strftime("%H:%M:%S")
+    entry = f"[{ts}] {msg}"
+    _auto_log.append(entry)
+    if len(_auto_log) > 500:
+        _auto_log[:] = _auto_log[-300:]
+    logger.info("[自动化] %s", msg)
+
+
+def _auto_comment_with_log(keywords_str, mcp_url, model, persona_text):
+    """一键评论 + 同步刷新日志"""
+    msg = auto_comment_once(keywords_str, mcp_url, model, persona_text)
+    return msg, get_auto_log()
+
+
+def auto_comment_once(keywords_str, mcp_url, model, persona_text):
+    """一键评论：自动搜索高赞笔记 → AI生成评论 → 发送"""
+    try:
+        keywords = [k.strip() for k in keywords_str.split(",") if k.strip()] if keywords_str else DEFAULT_COMMENT_KEYWORDS
+        keyword = random.choice(keywords)
+        _auto_log_append(f"🔍 搜索关键词: {keyword}")
+
+        client = get_mcp_client(mcp_url)
+
+        # 搜索高赞笔记
+        entries = client.search_feeds_parsed(keyword, sort_by="最多点赞")
+        if not entries:
+            _auto_log_append("⚠️ 搜索无结果，尝试推荐列表")
+            entries = client.list_feeds_parsed()
+        if not entries:
+            return "❌ 未找到任何笔记"
+
+        # 过滤掉自己的笔记
+        my_uid = cfg.get("my_user_id", "")
+        if my_uid:
+            filtered = [e for e in entries if e.get("user_id") != my_uid]
+            if filtered:
+                entries = filtered
+
+        # 从前10个中随机选择
+        target = random.choice(entries[:min(10, len(entries))])
+        feed_id = target["feed_id"]
+        xsec_token = target["xsec_token"]
+        title = target.get("title", "未知")
+        _auto_log_append(f"🎯 选中: {title[:30]} (@{target.get('author', '未知')})")
+
+        if not feed_id or not xsec_token:
+            return "❌ 笔记缺少必要参数 (feed_id/xsec_token)"
+
+        # 模拟浏览延迟
+        time.sleep(random.uniform(2, 5))
+
+        # 加载笔记详情
+        result = client.get_feed_detail(feed_id, xsec_token, load_all_comments=True)
+        if "error" in result:
+            return f"❌ 加载笔记失败: {result['error']}"
+
+        full_text = result.get("text", "")
+        if "评论" in full_text:
+            parts = full_text.split("评论", 1)
+            content_part = parts[0].strip()[:600]
+            comments_part = ("评论" + parts[1])[:800] if len(parts) > 1 else ""
+        else:
+            content_part = full_text[:500]
+            comments_part = ""
+
+        # AI 生成评论
+        api_key, base_url, _ = _get_llm_config()
+        if not api_key:
+            return "❌ LLM 未配置，请先在全局设置中配置提供商"
+
+        svc = LLMService(api_key, base_url, model)
+        comment = svc.generate_proactive_comment(
+            persona_text, title, content_part, comments_part
+        )
+        _auto_log_append(f"💬 生成评论: {comment[:60]}...")
+
+        # 随机等待后发送
+        time.sleep(random.uniform(3, 8))
+        result = client.post_comment(feed_id, xsec_token, comment)
+        resp_text = result.get("text", "")
+        _auto_log_append(f"📡 MCP 响应: {resp_text[:200]}")
+
+        if "error" in result:
+            _auto_log_append(f"❌ 评论发送失败: {result['error']}")
+            return f"❌ 评论发送失败: {result['error']}"
+
+        # 检查是否真正成功
+        if "成功" not in resp_text and "success" not in resp_text.lower() and not resp_text:
+            _auto_log_append(f"⚠️ 评论可能未成功，MCP 原始响应: {result}")
+            return f"⚠️ 评论状态不确定，请手动检查\nMCP 响应: {resp_text[:300]}\n📝 评论: {comment}"
+
+        _auto_log_append(f"✅ 评论已发送到「{title[:20]}」")
+        return f"✅ 已评论「{title[:25]}」\n📝 评论: {comment}\n\n💡 小红书可能有内容审核延迟，请稍等 1-2 分钟后查看"
+
+    except Exception as e:
+        _auto_log_append(f"❌ 一键评论异常: {e}")
+        return f"❌ 评论失败: {e}"
+
+
+def _auto_publish_with_log(topics_str, mcp_url, sd_url_val, sd_model_name, model):
+    """一键发布 + 同步刷新日志"""
+    msg = auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model)
+    return msg, get_auto_log()
+
+
+def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model):
+    """一键发布：自动生成文案 → 生成图片 → 发布到小红书"""
+    try:
+        topics = [t.strip() for t in topics_str.split(",") if t.strip()] if topics_str else DEFAULT_TOPICS
+        topic = random.choice(topics)
+        style = random.choice(DEFAULT_STYLES)
+        _auto_log_append(f"📝 主题: {topic} | 风格: {style}")
+
+        # 生成文案
+        api_key, base_url, _ = _get_llm_config()
+        if not api_key:
+            return "❌ LLM 未配置，请先在全局设置中配置提供商"
+
+        svc = LLMService(api_key, base_url, model)
+        data = svc.generate_copy(topic, style)
+        title = (data.get("title", "") or "")[:20]
+        content = data.get("content", "")
+        sd_prompt = data.get("sd_prompt", "")
+        tags = data.get("tags", [])
+
+        if not title:
+            return "❌ 文案生成失败：无标题"
+        _auto_log_append(f"📄 文案: {title}")
+
+        # 生成图片
+        if not sd_url_val or not sd_model_name:
+            return "❌ SD WebUI 未连接或未选择模型，请先在全局设置中连接"
+
+        sd_svc = SDService(sd_url_val)
+        images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name)
+        if not images:
+            return "❌ 图片生成失败：没有返回图片"
+        _auto_log_append(f"🎨 已生成 {len(images)} 张图片")
+
+        # 保存图片到临时目录
+        temp_dir = os.path.join(OUTPUT_DIR, "_temp_publish")
+        os.makedirs(temp_dir, exist_ok=True)
+        image_paths = []
+        ts = int(time.time())
+        for idx, img in enumerate(images):
+            if isinstance(img, Image.Image):
+                path = os.path.abspath(os.path.join(temp_dir, f"auto_{ts}_{idx}.png"))
+                img.save(path)
+                image_paths.append(path)
+
+        if not image_paths:
+            return "❌ 图片保存失败"
+
+        # 发布到小红书
+        client = get_mcp_client(mcp_url)
+        result = client.publish_content(
+            title=title, content=content, images=image_paths, tags=tags
+        )
+        if "error" in result:
+            _auto_log_append(f"❌ 发布失败: {result['error']}")
+            return f"❌ 发布失败: {result['error']}"
+
+        _auto_log_append(f"🚀 发布成功: {title}")
+        return f"✅ 发布成功！\n📌 标题: {title}\n{result.get('text', '')}"
+
+    except Exception as e:
+        _auto_log_append(f"❌ 一键发布异常: {e}")
+        return f"❌ 发布失败: {e}"
+
+
+def _scheduler_loop(comment_enabled, publish_enabled,
+                    comment_min, comment_max, publish_min, publish_max,
+                    keywords, topics, mcp_url, sd_url_val, sd_model_name,
+                    model, persona_text):
+    """后台定时调度循环"""
+    _auto_log_append("🤖 自动化调度器已启动")
+
+    # 首次执行的随机延迟
+    next_comment = time.time() + random.randint(10, 60)
+    next_publish = time.time() + random.randint(30, 120)
+
+    while _auto_running.is_set():
+        now = time.time()
+
+        # 自动评论
+        if comment_enabled and now >= next_comment:
+            try:
+                _auto_log_append("--- 🔄 执行自动评论 ---")
+                msg = auto_comment_once(keywords, mcp_url, model, persona_text)
+                _auto_log_append(msg)
+            except Exception as e:
+                _auto_log_append(f"❌ 自动评论异常: {e}")
+            interval = random.randint(int(comment_min) * 60, int(comment_max) * 60)
+            next_comment = time.time() + interval
+            _auto_log_append(f"⏰ 下次评论: {interval // 60} 分钟后")
+
+        # 自动发布
+        if publish_enabled and now >= next_publish:
+            try:
+                _auto_log_append("--- 🔄 执行自动发布 ---")
+                msg = auto_publish_once(topics, mcp_url, sd_url_val, sd_model_name, model)
+                _auto_log_append(msg)
+            except Exception as e:
+                _auto_log_append(f"❌ 自动发布异常: {e}")
+            interval = random.randint(int(publish_min) * 60, int(publish_max) * 60)
+            next_publish = time.time() + interval
+            _auto_log_append(f"⏰ 下次发布: {interval // 60} 分钟后")
+
+        # 每5秒检查一次停止信号
+        for _ in range(5):
+            if not _auto_running.is_set():
+                break
+            time.sleep(1)
+
+    _auto_log_append("🛑 自动化调度器已停止")
+
+
+def start_scheduler(comment_on, publish_on, c_min, c_max, p_min, p_max,
+                    keywords, topics, mcp_url, sd_url_val, sd_model_name,
+                    model, persona_text):
+    """启动定时自动化"""
+    global _auto_thread
+    if _auto_running.is_set():
+        return "⚠️ 调度器已在运行中，请先停止"
+
+    if not comment_on and not publish_on:
+        return "❌ 请至少启用一项自动化功能（评论或发布）"
+
+    api_key, _, _ = _get_llm_config()
+    if not api_key:
+        return "❌ LLM 未配置，请先在全局设置中配置提供商"
+
+    _auto_running.set()
+    _auto_thread = threading.Thread(
+        target=_scheduler_loop,
+        args=(comment_on, publish_on,
+              c_min, c_max, p_min, p_max,
+              keywords, topics, mcp_url, sd_url_val, sd_model_name,
+              model, persona_text),
+        daemon=True,
+    )
+    _auto_thread.start()
+
+    parts = []
+    if comment_on:
+        parts.append(f"评论 (每 {int(c_min)}-{int(c_max)} 分钟)")
+    if publish_on:
+        parts.append(f"发布 (每 {int(p_min)}-{int(p_max)} 分钟)")
+
+    _auto_log_append(f"调度器已启动: {' + '.join(parts)}")
+    return f"✅ 自动化已启动 🟢\n任务: {' | '.join(parts)}\n\n💡 点击「刷新日志」查看实时进度"
+
+
+def stop_scheduler():
+    """停止定时自动化"""
+    if not _auto_running.is_set():
+        return "⚠️ 调度器未在运行"
+    _auto_running.clear()
+    _auto_log_append("⏹️ 收到停止信号，等待当前任务完成...")
+    return "🛑 调度器停止中...当前任务完成后将完全停止"
+
+
+def get_auto_log():
+    """获取自动化运行日志"""
+    if not _auto_log:
+        return "📋 暂无日志\n\n💡 点击「一键评论」「一键发布」或启动定时后日志将在此显示"
+    return "\n".join(_auto_log[-80:])
+
+
+def get_scheduler_status():
+    """获取调度器运行状态"""
+    if _auto_running.is_set():
+        return "🟢 **调度器运行中**"
+    return "⚪ **调度器未运行**"
+
+
 # ==================================================
 #              UI 构建
 # ==================================================
@@ -1294,6 +1597,101 @@ with gr.Blocks(
                 label="笔记数据明细",
             )
 
+        # -------- Tab 6: 自动运营 --------
+        with gr.Tab("🤖 自动运营"):
+            gr.Markdown(
+                "### 🤖 无人值守自动化运营\n"
+                "> 一键评论引流 + 一键内容发布 + 随机定时全自动\n\n"
+                "⚠️ **注意**: 请确保已连接 LLM、SD WebUI 和 MCP 服务"
+            )
+
+            with gr.Row():
+                # 左栏: 一键操作
+                with gr.Column(scale=1):
+                    gr.Markdown("#### 💬 一键智能评论")
+                    gr.Markdown(
+                        "> 自动搜索高赞笔记 → AI 分析内容 → 生成评论 → 发送\n"
+                        "每次随机选关键词搜索，从结果中随机选笔记"
+                    )
+                    auto_comment_keywords = gr.Textbox(
+                        label="评论关键词池 (逗号分隔)",
+                        value="穿搭, 美食, 护肤, 好物推荐, 旅行, 生活日常",
+                        placeholder="关键词1, 关键词2, ...",
+                    )
+                    btn_auto_comment = gr.Button(
+                        "💬 一键评论 (单次)", variant="primary", size="lg",
+                    )
+                    auto_comment_result = gr.Markdown("")
+
+                    gr.Markdown("---")
+                    gr.Markdown("#### 🚀 一键智能发布")
+                    gr.Markdown(
+                        "> 随机选主题+风格 → AI 生成文案 → SD 生成图片 → 自动发布"
+                    )
+                    auto_publish_topics = gr.Textbox(
+                        label="主题池 (逗号分隔)",
+                        value="春季穿搭, 通勤穿搭, 显瘦穿搭, 平价好物, 护肤心得, 好物分享",
+                        placeholder="主题1, 主题2, ...",
+                    )
+                    btn_auto_publish = gr.Button(
+                        "🚀 一键发布 (单次)", variant="primary", size="lg",
+                    )
+                    auto_publish_result = gr.Markdown("")
+
+                # 右栏: 定时自动化
+                with gr.Column(scale=1):
+                    gr.Markdown("#### ⏰ 随机定时自动化")
+                    gr.Markdown(
+                        "> 设置时间间隔后启动，系统将在随机时间自动执行\n"
+                        "> 模拟真人操作节奏，降低被检测风险"
+                    )
+                    sched_status = gr.Markdown("⚪ **调度器未运行**")
+
+                    with gr.Group():
+                        sched_comment_on = gr.Checkbox(
+                            label="✅ 启用自动评论", value=True,
+                        )
+                        with gr.Row():
+                            sched_c_min = gr.Number(
+                                label="评论最小间隔(分钟)", value=15, minimum=5,
+                            )
+                            sched_c_max = gr.Number(
+                                label="评论最大间隔(分钟)", value=45, minimum=10,
+                            )
+
+                    with gr.Group():
+                        sched_publish_on = gr.Checkbox(
+                            label="✅ 启用自动发布", value=True,
+                        )
+                        with gr.Row():
+                            sched_p_min = gr.Number(
+                                label="发布最小间隔(分钟)", value=60, minimum=30,
+                            )
+                            sched_p_max = gr.Number(
+                                label="发布最大间隔(分钟)", value=180, minimum=60,
+                            )
+
+                    with gr.Row():
+                        btn_start_sched = gr.Button(
+                            "▶️ 启动定时", variant="primary", size="lg",
+                        )
+                        btn_stop_sched = gr.Button(
+                            "⏹️ 停止定时", variant="stop", size="lg",
+                        )
+                    sched_result = gr.Markdown("")
+
+            gr.Markdown("---")
+            gr.Markdown("#### 📋 运行日志")
+            with gr.Row():
+                btn_refresh_log = gr.Button("🔄 刷新日志", size="sm")
+                btn_clear_log = gr.Button("🗑️ 清空日志", size="sm")
+            auto_log_display = gr.TextArea(
+                label="自动化运行日志",
+                value="📋 暂无日志\n\n💡 执行操作后日志将在此显示",
+                lines=15,
+                interactive=False,
+            )
+
     # ==================================================
     #              事件绑定
     # ==================================================
@@ -1482,6 +1880,41 @@ with gr.Blocks(
         outputs=[data_status, profile_card, chart_interact, chart_notes, notes_detail],
     )
 
+    # ---- Tab 6: 自动运营 ----
+    btn_auto_comment.click(
+        fn=_auto_comment_with_log,
+        inputs=[auto_comment_keywords, mcp_url, llm_model, persona],
+        outputs=[auto_comment_result, auto_log_display],
+    )
+    btn_auto_publish.click(
+        fn=_auto_publish_with_log,
+        inputs=[auto_publish_topics, mcp_url, sd_url, sd_model, llm_model],
+        outputs=[auto_publish_result, auto_log_display],
+    )
+    btn_start_sched.click(
+        fn=start_scheduler,
+        inputs=[sched_comment_on, sched_publish_on,
+                sched_c_min, sched_c_max, sched_p_min, sched_p_max,
+                auto_comment_keywords, auto_publish_topics,
+                mcp_url, sd_url, sd_model, llm_model, persona],
+        outputs=[sched_result],
+    )
+    btn_stop_sched.click(
+        fn=stop_scheduler,
+        inputs=[],
+        outputs=[sched_result],
+    )
+    btn_refresh_log.click(
+        fn=lambda: (get_auto_log(), get_scheduler_status()),
+        inputs=[],
+        outputs=[auto_log_display, sched_status],
+    )
+    btn_clear_log.click(
+        fn=lambda: (_auto_log.clear() or "📋 日志已清空"),
+        inputs=[],
+        outputs=[auto_log_display],
+    )
+
     # ---- 启动时自动刷新 SD ----
     app.load(fn=connect_sd, inputs=[sd_url], outputs=[sd_model, status_bar])
 

sd_service.py

@@ -10,13 +10,16 @@ from PIL import Image
 
 logger = logging.getLogger(__name__)
 
-SD_TIMEOUT = 180  # 图片生成可能需要较长时间
+SD_TIMEOUT = 900  # 图片生成可能需要较长时间
 
-# 默认反向提示词
+# 默认反向提示词（针对 JuggernautXL / SDXL 优化）
 DEFAULT_NEGATIVE = (
-    "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, "
+    "nsfw, nudity, lowres, bad anatomy, bad hands, text, error, missing fingers, "
-    "extra digit, fewer digits, cropped, worst quality, low quality, "
+    "extra digit, fewer digits, cropped, worst quality, low quality, normal quality, "
-    "normal quality, jpeg artifacts, signature, watermark, blurry"
+    "jpeg artifacts, signature, watermark, blurry, deformed, mutated, disfigured, "
+    "ugly, duplicate, morbid, mutilated, poorly drawn face, poorly drawn hands, "
+    "extra limbs, fused fingers, too many fingers, long neck, username, "
+    "out of frame, distorted, oversaturated, underexposed, overexposed"
 )
 
 
@@ -61,14 +64,16 @@ class SDService:
         prompt: str,
         negative_prompt: str = DEFAULT_NEGATIVE,
         model: str = None,
-        steps: int = 25,
+        steps: int = 30,
-        cfg_scale: float = 7.0,
+        cfg_scale: float = 5.0,
-        width: int = 768,
+        width: int = 832,
-        height: int = 1024,
+        height: int = 1216,
         batch_size: int = 2,
         seed: int = -1,
+        sampler_name: str = "DPM++ 2M",
+        scheduler: str = "Karras",
     ) -> list[Image.Image]:
-        """文生图"""
+        """文生图（参数针对 JuggernautXL 优化）"""
         if model:
             self.switch_model(model)
 
@@ -81,6 +86,8 @@ class SDService:
             "height": height,
             "batch_size": batch_size,
             "seed": seed,
+            "sampler_name": sampler_name,
+            "scheduler": scheduler,
         }
 
         resp = requests.post(
@@ -101,11 +108,13 @@ class SDService:
         init_image: Image.Image,
         prompt: str,
         negative_prompt: str = DEFAULT_NEGATIVE,
-        denoising_strength: float = 0.6,
+        denoising_strength: float = 0.5,
-        steps: int = 25,
+        steps: int = 30,
-        cfg_scale: float = 7.0,
+        cfg_scale: float = 5.0,
+        sampler_name: str = "DPM++ 2M",
+        scheduler: str = "Karras",
     ) -> list[Image.Image]:
-        """图生图（参考图修改）"""
+        """图生图（参数针对 JuggernautXL 优化）"""
         # 将 PIL Image 转为 base64
         buf = io.BytesIO()
         init_image.save(buf, format="PNG")
@@ -120,6 +129,8 @@ class SDService:
             "cfg_scale": cfg_scale,
             "width": init_image.width,
             "height": init_image.height,
+            "sampler_name": sampler_name,
+            "scheduler": scheduler,
         }
 
         resp = requests.post(