feat(reserved_vram): 添加DynamicVRAM兼容性支持，统一显存预留处理

- 新增工具函数统一管理显存预留和DynamicVRAM同步逻辑
- 适配ComfyUI官方set_extra_reserved_vram API，兼容旧版本构建
- 同步更新comfy-aimdo的simple vram headroom配置

README.md

@@ -37,3 +37,9 @@ new
 ![_$)59`(5~XN5OH7NM %WHU](https://github.com/user-attachments/assets/bb652d70-805b-452e-a522-f271c8c70bf4)
 
 ![image](https://github.com/user-attachments/assets/48f8ca7f-2a13-4ef5-a5bb-5f6ef9c974e3)
+
+2026-06-27 DynamicVRAM compatibility update
+
+- The node now updates ComfyUI DynamicVRAM / comfy-aimdo simple vram headroom when DynamicVRAM is enabled.
+- On ComfyUI builds that provide `model_management.set_extra_reserved_vram()`, the node uses that runtime API. On official builds without that API, it still updates `EXTRA_RESERVED_VRAM` and then syncs DynamicVRAM headroom from the node itself.
+- Auto mode can use torch CUDA memory info when NVML is unavailable or fails to initialize.

nodes.py

@@ -25,11 +25,11 @@ random.seed(time.time())
 reserved_vram_random_state = random.getstate()
 random.setstate(initial_random_state)
 
-def get_gpu_memory_info():
-    """获取GPU显存信息"""
-    if pynvml_installed and pynvml is not None:
-        try:
-            handle = pynvml.nvmlDeviceGetHandleByIndex(0)
+def get_gpu_memory_info():
+    """获取GPU显存信息"""
+    if pynvml_installed and pynvml is not None:
+        try:
+            handle = pynvml.nvmlDeviceGetHandleByIndex(0)
             memory_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
             total = memory_info.total / (1024 * 1024 * 1024)
             used = memory_info.used / (1024 * 1024 * 1024)
@@ -51,14 +51,44 @@ def get_gpu_memory_info():
             return total_gb, used_gb
     except Exception as e:
         print(f"[ReservedVRAM]获取GPU信息出错(torch): {e}")
-
-    return None, None
-
-def new_random_seed():
-    """生成一个新的随机种子"""
-    global reserved_vram_random_state
-    prev_random_state = random.getstate()
-    random.setstate(reserved_vram_random_state)
+
+    return None, None
+
+def set_reserved_vram(reserved_gb):
+    reserved_gb = max(0.0, float(reserved_gb))
+    reserved_vram = int(reserved_gb * 1024 * 1024 * 1024)
+    if hasattr(model_management, "set_extra_reserved_vram"):
+        model_management.set_extra_reserved_vram(reserved_gb)
+    else:
+        model_management.EXTRA_RESERVED_VRAM = reserved_vram
+    sync_dynamic_vram_headroom(reserved_vram)
+
+def sync_dynamic_vram_headroom(reserved_vram):
+    try:
+        import comfy.memory_management as memory_management
+
+        if not getattr(memory_management, "aimdo_enabled", False):
+            return
+
+        import comfy_aimdo.control as aimdo_control
+
+        if getattr(aimdo_control, "lib", None) is None:
+            return
+
+        try:
+            aimdo_control.init(simple_vram_headroom=int(reserved_vram))
+        except TypeError:
+            setter = getattr(aimdo_control.lib, "set_simple_vram_headroom", None)
+            if setter is not None:
+                setter(int(reserved_vram))
+    except Exception as e:
+        print(f"[ReservedVRAM]同步DynamicVRAM预留显存失败: {e}")
+
+def new_random_seed():
+    """生成一个新的随机种子"""
+    global reserved_vram_random_state
+    prev_random_state = random.getstate()
+    random.setstate(reserved_vram_random_state)
     seed = random.randint(1, 1125899906842624)
     reserved_vram_random_state = random.getstate()
     random.setstate(prev_random_state)
@@ -142,21 +172,21 @@ class ReservedVRAMSetter:
                 if auto_max_reserved > 0:
                     auto_reserved = min(auto_reserved, auto_max_reserved)
                     print(f'[ReservedVRAM]set EXTRA_RESERVED_VRAM={auto_reserved:.2f}GB (自动模式: 总显存={total:.2f}GB, 已用={used:.2f}GB, 最大限制值{auto_max_reserved:.2f}GB)')
-                else:
-                    print(f'[ReservedVRAM]set EXTRA_RESERVED_VRAM={auto_reserved:.2f}GB (自动模式: 总显存={total:.2f}GB, 已用={used:.2f}GB)')
-                model_management.EXTRA_RESERVED_VRAM = int(auto_reserved * 1024 * 1024 * 1024)
-                final_reserved_vram = round(auto_reserved, 2)
-            else:
-                manual_reserved = max(0, reserved)
-                model_management.EXTRA_RESERVED_VRAM = int(manual_reserved * 1024 * 1024 * 1024)
-                print(f'[ReservedVRAM]set EXTRA_RESERVED_VRAM={manual_reserved}GB (自动模式不可用，使用手动值)')
-                final_reserved_vram = round(manual_reserved, 2)
-        else:
-            # 手动模式
-            reserved = max(0, reserved)
-            model_management.EXTRA_RESERVED_VRAM = int(reserved * 1024 * 1024 * 1024)
-            print(f'[ReservedVRAM]set EXTRA_RESERVED_VRAM={reserved}GB (手动模式)，忽略最大限制值')
-            final_reserved_vram = round(reserved, 2)
+                else:
+                    print(f'[ReservedVRAM]set EXTRA_RESERVED_VRAM={auto_reserved:.2f}GB (自动模式: 总显存={total:.2f}GB, 已用={used:.2f}GB)')
+                set_reserved_vram(auto_reserved)
+                final_reserved_vram = round(auto_reserved, 2)
+            else:
+                manual_reserved = max(0, reserved)
+                set_reserved_vram(manual_reserved)
+                print(f'[ReservedVRAM]set EXTRA_RESERVED_VRAM={manual_reserved}GB (自动模式不可用，使用手动值)')
+                final_reserved_vram = round(manual_reserved, 2)
+        else:
+            # 手动模式
+            reserved = max(0, reserved)
+            set_reserved_vram(reserved)
+            print(f'[ReservedVRAM]set EXTRA_RESERVED_VRAM={reserved}GB (手动模式)，忽略最大限制值')
+            final_reserved_vram = round(reserved, 2)
 
         from comfy_execution.graph import ExecutionBlocker
         output_value = anything if anything is not None else ExecutionBlocker(None)