✨ feat(sd): 新增图片生成质量预设模式并优化换脸功能

- 新增三档生成质量预设【快速/标准/精细】，针对 SDXL 模型优化参数
- 新增 `SD_PRESETS` 配置字典和 `get_sd_preset` 工具函数
- 为 `generate_images` 函数和 `txt2img` 方法添加 `quality_mode` 参数支持
- 在 Gradio UI 中添加生成模式选择器，并实现参数联动预览
- 优化换脸头像处理逻辑，支持多种输入格式并增强日志记录
- 调整默认绘图参数以匹配预设，并更新相关函数调用

♻️ refactor(sd): 重构 ReActor 换脸 API 调用参数

- 更新 `_build_reactor_payload` 方法参数列表以匹配最新 API
- 将部分字符串参数（如日志级别、性别检测）调整为整数类型
- 优化参数默认值，如提高 CodeFormer 权重至 0.8

main.py

@@ -19,7 +19,7 @@ import matplotlib.pyplot as plt
 
 from config_manager import ConfigManager, OUTPUT_DIR
 from llm_service import LLMService
-from sd_service import SDService, DEFAULT_NEGATIVE, FACE_IMAGE_PATH
+from sd_service import SDService, DEFAULT_NEGATIVE, FACE_IMAGE_PATH, SD_PRESET_NAMES, get_sd_preset
 from mcp_client import MCPClient, get_mcp_client
 
 # ================= matplotlib 中文字体配置 =================
@@ -309,22 +309,37 @@ def generate_copy(model, topic, style):
         return "", "", "", "", f"❌ 生成失败: {e}"
 
 
-def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_swap_on, face_img):
+def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_swap_on, face_img, quality_mode):
-    """生成图片（可选 ReActor 换脸）"""
+    """生成图片（可选 ReActor 换脸，支持质量模式预设）"""
     if not model:
         return None, [], "❌ 未选择 SD 模型"
     try:
         svc = SDService(sd_url)
         # 判断是否启用换脸
         face_image = None
-        if face_swap_on and face_img is not None:
+        if face_swap_on:
-            if isinstance(face_img, Image.Image):
+            # Gradio 可能传 PIL.Image / numpy.ndarray / 文件路径 / None
-                face_image = face_img
+            if face_img is not None:
-            elif isinstance(face_img, str) and os.path.isfile(face_img):
+                if isinstance(face_img, Image.Image):
-                face_image = Image.open(face_img).convert("RGB")
+                    face_image = face_img
-        if face_swap_on and face_image is None:
+                elif isinstance(face_img, str) and os.path.isfile(face_img):
-            # 尝试从默认路径加载
+                    face_image = Image.open(face_img).convert("RGB")
-            face_image = SDService.load_face_image()
+                else:
+                    # numpy array 等其他格式
+                    try:
+                        import numpy as np
+                        if isinstance(face_img, np.ndarray):
+                            face_image = Image.fromarray(face_img).convert("RGB")
+                            logger.info("头像从 numpy array 转换为 PIL Image")
+                    except Exception as e:
+                        logger.warning("头像格式转换失败 (%s): %s", type(face_img).__name__, e)
+            # 如果 UI 没传有效头像，从本地文件加载
+            if face_image is None:
+                face_image = SDService.load_face_image()
+            if face_image is not None:
+                logger.info("换脸头像已就绪: %dx%d", face_image.width, face_image.height)
+            else:
+                logger.warning("换脸已启用但未找到有效头像")
 
         images = svc.txt2img(
             prompt=prompt,
@@ -333,9 +348,11 @@ def generate_images(sd_url, prompt, neg_prompt, model, steps, cfg_scale, face_sw
             steps=int(steps),
             cfg_scale=float(cfg_scale),
             face_image=face_image,
+            quality_mode=quality_mode,
         )
+        preset = get_sd_preset(quality_mode)
         swap_hint = " (已换脸)" if face_image else ""
-        return images, images, f"✅ 生成 {len(images)} 张图片{swap_hint}"
+        return images, images, f"✅ 生成 {len(images)} 张图片{swap_hint} [{quality_mode}]"
     except Exception as e:
         logger.error("图片生成失败: %s", e)
         return None, [], f"❌ 绘图失败: {e}"
@@ -1976,7 +1993,9 @@ def auto_publish_once(topics_str, mcp_url, sd_url_val, sd_model_name, model, fac
                 _auto_log_append("🎭 换脸已启用")
             else:
                 _auto_log_append("⚠️ 换脸已启用但未找到头像，跳过换脸")
-        images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name, face_image=face_image)
+        images = sd_svc.txt2img(prompt=sd_prompt, model=sd_model_name,
+                               face_image=face_image,
+                               quality_mode="快速 (约30秒)")
         if not images:
             _record_error()
             return "❌ 图片生成失败：没有返回图片"
@@ -2529,12 +2548,18 @@ with gr.Blocks(
 
                     gr.Markdown("---")
                     gr.Markdown("### 🎨 绘图参数")
-                    with gr.Accordion("高级设置", open=False):
+                    quality_mode = gr.Radio(
+                        SD_PRESET_NAMES,
+                        label="生成模式",
+                        value="标准 (约1分钟)",
+                        info="快速≈30s 标准≈1min 精细≈2-3min (SDXL)",
+                    )
+                    with gr.Accordion("高级设置 (覆盖预设)", open=False):
                         neg_prompt = gr.Textbox(
                             label="反向提示词", value=DEFAULT_NEGATIVE, lines=2,
                         )
-                        steps = gr.Slider(15, 50, value=25, step=1, label="步数")
+                        steps = gr.Slider(8, 50, value=20, step=1, label="步数")
-                        cfg_scale = gr.Slider(1, 15, value=7, step=0.5, label="CFG Scale")
+                        cfg_scale = gr.Slider(1, 15, value=5.5, step=0.5, label="CFG Scale")
                     btn_gen_img = gr.Button("🎨 第二步：生成图片", variant="primary")
 
                 # 中栏：文案编辑
@@ -3093,10 +3118,21 @@ with gr.Blocks(
         outputs=[res_title, res_content, res_prompt, res_tags, status_bar],
     )
 
+    # 生成模式切换 → 同步更新步数/CFG预览
+    def on_quality_mode_change(mode):
+        p = get_sd_preset(mode)
+        return p["steps"], p["cfg_scale"]
+
+    quality_mode.change(
+        fn=on_quality_mode_change,
+        inputs=[quality_mode],
+        outputs=[steps, cfg_scale],
+    )
+
     btn_gen_img.click(
         fn=generate_images,
         inputs=[sd_url, res_prompt, neg_prompt, sd_model, steps, cfg_scale,
-                face_swap_toggle, face_image_preview],
+                face_swap_toggle, face_image_preview, quality_mode],
         outputs=[gallery, state_images, status_bar],
     )
 

sd_service.py

@@ -16,6 +16,46 @@ SD_TIMEOUT = 1800  # 图片生成可能需要较长时间
 # 头像文件默认保存路径
 FACE_IMAGE_PATH = os.path.join(os.path.dirname(__file__), "my_face.png")
 
+# ==================== 生成质量预设 ====================
+# 针对 JuggernautXL (SDXL) 优化的三档参数
+SD_PRESETS = {
+    "快速 (约30秒)": {
+        "steps": 12,
+        "cfg_scale": 5.0,
+        "width": 768,
+        "height": 1024,
+        "sampler_name": "Euler a",
+        "scheduler": "Normal",
+        "batch_size": 2,
+    },
+    "标准 (约1分钟)": {
+        "steps": 20,
+        "cfg_scale": 5.5,
+        "width": 832,
+        "height": 1216,
+        "sampler_name": "DPM++ 2M",
+        "scheduler": "Karras",
+        "batch_size": 2,
+    },
+    "精细 (约2-3分钟)": {
+        "steps": 35,
+        "cfg_scale": 6.0,
+        "width": 832,
+        "height": 1216,
+        "sampler_name": "DPM++ 2M SDE",
+        "scheduler": "Karras",
+        "batch_size": 2,
+    },
+}
+
+SD_PRESET_NAMES = list(SD_PRESETS.keys())
+
+
+def get_sd_preset(name: str) -> dict:
+    """获取生成预设参数，默认返回'标准'"""
+    return SD_PRESETS.get(name, SD_PRESETS["标准 (约1分钟)"])
+
+
 # 默认反向提示词（针对 JuggernautXL / SDXL 优化，偏向东方审美）
 DEFAULT_NEGATIVE = (
     "nsfw, nudity, lowres, bad anatomy, bad hands, text, error, missing fingers, "
@@ -97,24 +137,23 @@ class SDService:
                     1,              # 10: upscaler visibility
                     False,          # 11: swap in source
                     True,           # 12: swap in generated
-                    "Minimum",      # 13: log level
+                    1,              # 13: console log level (0=min, 1=med, 2=max)
-                    "No",           # 14: gender detection (source)
+                    0,              # 14: gender detection source (0=No)
-                    "No",           # 15: gender detection (target)
+                    0,              # 15: gender detection target (0=No)
                     False,          # 16: save original
-                    0.6,            # 17: CodeFormer weight (fidelity)
+                    0.8,            # 17: CodeFormer weight (0=max effect, 1=min)
-                    True,           # 18: source hash check
+                    False,          # 18: source hash check
                     False,          # 19: target hash check
                     "CUDA",         # 20: execution provider
                     True,           # 21: face mask correction
-                    "Image(s)",     # 22: select source type
+                    0,              # 22: select source (0=Image, 1=FaceModel, 2=Folder)
-                    "None",         # 23: face model name
+                    "",             # 23: face model filename (when #22=1)
-                    "",             # 24: source folder
+                    "",             # 24: source folder path (when #22=2)
-                    None,           # 25: multiple source images
+                    None,           # 25: skip for API
                     False,          # 26: random image
                     False,          # 27: force upscale
-                    0.5,            # 28: detection threshold
+                    0.6,            # 28: face detection threshold
-                    0,              # 29: max faces (0 = no limit)
+                    2,              # 29: max faces to detect (0=unlimited)
-                    "tab_single",   # 30: tab
                 ],
             }
         }
@@ -164,36 +203,44 @@ class SDService:
         prompt: str,
         negative_prompt: str = DEFAULT_NEGATIVE,
         model: str = None,
-        steps: int = 30,
+        steps: int = None,
-        cfg_scale: float = 5.0,
+        cfg_scale: float = None,
-        width: int = 832,
+        width: int = None,
-        height: int = 1216,
+        height: int = None,
-        batch_size: int = 2,
+        batch_size: int = None,
         seed: int = -1,
-        sampler_name: str = "DPM++ 2M",
+        sampler_name: str = None,
-        scheduler: str = "Karras",
+        scheduler: str = None,
         face_image: Image.Image = None,
+        quality_mode: str = None,
     ) -> list[Image.Image]:
         """文生图（参数针对 JuggernautXL 优化）
         
         Args:
             face_image: 头像 PIL Image，传入后自动启用 ReActor 换脸
+            quality_mode: 预设模式名，如 '快速 (约30秒)' / '标准 (约1分钟)' / '精细 (约2-3分钟)'
+                          传入后自动应用预设参数，其余参数可覆盖
         """
         if model:
             self.switch_model(model)
 
+        # 加载预设作为基底，再用显式参数覆盖
+        preset = get_sd_preset(quality_mode) if quality_mode else get_sd_preset("标准 (约1分钟)")
         payload = {
             "prompt": prompt,
             "negative_prompt": negative_prompt,
-            "steps": steps,
+            "steps": steps if steps is not None else preset["steps"],
-            "cfg_scale": cfg_scale,
+            "cfg_scale": cfg_scale if cfg_scale is not None else preset["cfg_scale"],
-            "width": width,
+            "width": width if width is not None else preset["width"],
-            "height": height,
+            "height": height if height is not None else preset["height"],
-            "batch_size": batch_size,
+            "batch_size": batch_size if batch_size is not None else preset["batch_size"],
             "seed": seed,
-            "sampler_name": sampler_name,
+            "sampler_name": sampler_name if sampler_name is not None else preset["sampler_name"],
-            "scheduler": scheduler,
+            "scheduler": scheduler if scheduler is not None else preset["scheduler"],
         }
+        logger.info("SD 生成参数: steps=%s, cfg=%.1f, %dx%d, sampler=%s",
+                    payload['steps'], payload['cfg_scale'],
+                    payload['width'], payload['height'], payload['sampler_name'])
 
         # 如果提供了头像，通过 ReActor 换脸
         if face_image is not None: