feat: refine quantization configuration in bridge.py; enhance model class handling in ModularConfigManager; update lora model retrieval in model_utils.py; add cfg_scale2 parameter in LightX2VInferenceConfig

bridge.py

@@ -105,7 +105,6 @@ class LightX2VDefaultConfig:
         "coefficients": None,
         "use_ret_steps": False,
         # Quantization
-        "dit_quant_scheme": DEFAULT_QUANTIZATION_SCHEMES["dit"],
         "t5_quant_scheme": DEFAULT_QUANTIZATION_SCHEMES["t5"],
         "clip_quant_scheme": DEFAULT_QUANTIZATION_SCHEMES["clip"],
         "adapter_quant_scheme": DEFAULT_QUANTIZATION_SCHEMES["adapter"],
@@ -301,12 +300,16 @@ class ModularConfigManager:
 
         self._update_from_config(updates, config, basic_mappings)
 
-        # CFG特殊处理
         if "cfg_scale" in config:
             updates["sample_guide_scale"] = config["cfg_scale"]
             updates["enable_cfg"] = config["cfg_scale"] != 1.0
 
-        # 注意力类型配置
+        if config["model_cls"] == "wan2.2_moe":
+            updates["sample_guide_scale"] = [config["cfg_scale2"], config["cfg_scale2"]]
+            updates["boundary"] = 0.9
+        if "wan2.2" in config["model_cls"]:
+            updates["use_image_encoder"] = False
+
         attention_type = config.get("attention_type", LightX2VDefaultConfig.DEFAULT_ATTENTION_TYPE)
         for attn_key in ["attention_type", "self_attn_1_type", "cross_attn_1_type", "cross_attn_2_type"]:
             updates[attn_key] = attention_type
@@ -364,9 +367,6 @@ class ModularConfigManager:
         updates = {}
         defaults = LightX2VDefaultConfig.DEFAULT_QUANTIZATION_SCHEMES
 
-        print("config", config)
-
-        # 获取量化方案
         dit_scheme = config.get("dit_quant_scheme", defaults["dit"])
         t5_scheme = config.get("t5_quant_scheme", defaults["t5"])
         clip_scheme = config.get("clip_quant_scheme", defaults["clip"])
@@ -398,7 +398,6 @@ class ModularConfigManager:
         """Apply memory optimization settings."""
         updates = {}
 
-        # 直接配置项映射
         direct_mappings = {
             "enable_rotary_chunk": "rotary_chunk",
             "clean_cuda_cache": "clean_cuda_cache",

model_utils.py

@@ -45,16 +45,26 @@ def support_model_cls_list() -> List[str]:
     ]
 
 
-def scan_loras() -> List[str]:
+def get_loras_models(model_path: Path) -> List[str]:
     loras = []
-    base_path = get_model_base_path()
-    loras_path = base_path / "loras"
-
-    if loras_path.exists():
-        for item in loras_path.iterdir():
+    if model_path.exists():
+        for item in model_path.iterdir():
             if item.is_file():
                 if item.suffix.lower() in [".safetensors", ".pt", ".pth", ".ckpt"]:
                     loras.append(item.name)
+    return loras
+
+
+def scan_loras() -> List[str]:
+    base_path = get_model_base_path()
+    loras_path = base_path / "loras"
+    loras = get_loras_models(loras_path)
+
+    models_base = folder_paths.models_dir
+    loras_path = Path(models_base) / "loras"
+
+    loras2 = get_loras_models(loras_path)
+    loras.extend(loras2)
 
     loras.sort()
 
@@ -82,6 +92,13 @@ def get_lora_full_path(lora_name: str) -> str:
 
     if lora_path.exists():
         return str(lora_path)
+
+    models_base = folder_paths.models_dir
+    loras_path = Path(models_base) / "loras" / lora_name
+
+    if loras_path.exists():
+        return str(loras_path)
+
     return ""
 
 

nodes.py

@@ -85,6 +85,16 @@ class LightX2VInferenceConfig:
                         "tooltip": "CFG guidance strength",
                     },
                 ),
+                "cfg_scale2": (
+                    "FLOAT",
+                    {
+                        "default": 5.0,
+                        "min": 1.0,
+                        "max": 10.0,
+                        "step": 0.1,
+                        "tooltip": "CFG guidance, lower noise when model cls is Wan2.2 MoE",
+                    },
+                ),
                 "sample_shift": (
                     "INT",
                     {"default": 5, "min": 0, "max": 10, "tooltip": "Sample shift"},
@@ -133,7 +143,7 @@ class LightX2VInferenceConfig:
                     },
                 ),
                 "resize_mode": (
-                    ["adaptive", "keep_ratio_fixed_area", "fixed_min_area", "fixed_max_area", "fixed_shape"],
+                    ["adaptive", "keep_ratio_fixed_area", "fixed_min_area", "fixed_max_area", "fixed_shape", "fixed_min_side"],
                     {
                         "default": "adaptive",
                         "tooltip": "Adaptive resize input image to target aspect ratio",
@@ -143,7 +153,7 @@ class LightX2VInferenceConfig:
                     "STRING",
                     {
                         "default": "720p",
-                        "tooltip": "Fixed shape for input image, e.g., '720p', '480p', when resize_mode is 'keep_ratio_fixed_area'",
+                        "tooltip": "Fixed shape for input image, e.g., '720p', '480p', when resize_mode is 'keep_ratio_fixed_area' or 'fixed_min_side'",
                     },
                 ),
                 "segment_length": (
@@ -187,6 +197,7 @@ class LightX2VInferenceConfig:
         infer_steps,
         seed,
         cfg_scale,
+        cfg_scale2,
         sample_shift,
         height,
         width,
@@ -230,6 +241,7 @@ class LightX2VInferenceConfig:
             "infer_steps": infer_steps,
             "seed": seed if seed != -1 else np.random.randint(0, 2**32 - 1),
             "cfg_scale": cfg_scale,
+            "cfg_scale2": cfg_scale2,
             "sample_shift": sample_shift,
             "height": height,
             "width": width,