Update .gitignore to ignore large files && Adapt to obtaining resources within Docker.

.gitignore

@@ -2,6 +2,7 @@ __pycache__/
 *.py[cod]
 /output/
 /input/
+/cache/
 !/input/example.png
 /models/
 /temp/

comfy/model_management.py

@@ -32,6 +32,26 @@ import comfy.memory_management
 import comfy.utils
 import comfy.quant_ops
 
+
+def get_container_ram_total():
+    cgroup_v2_limit = "/sys/fs/cgroup/memory.max"
+    cgroup_v1_limit = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
+    try:
+        if os.path.exists(cgroup_v2_limit):
+            with open(cgroup_v2_limit, "r") as f:
+                limit = int(f.read().strip())
+        elif os.path.exists(cgroup_v1_limit):
+            with open(cgroup_v1_limit, "r") as f:
+                limit = int(f.read().strip())
+        else:
+            limit = psutil.virtual_memory().total
+        if limit == 9223372036854771712:
+            limit = psutil.virtual_memory().total
+        return limit
+    except:
+        return psutil.virtual_memory().total
+
+
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
     NO_VRAM = 1     #Very low vram: enable all the options to save vram
@@ -211,11 +231,13 @@ def get_total_memory(dev=None, torch_total_too=False):
         dev = get_torch_device()
 
     if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
-        mem_total = psutil.virtual_memory().total
+        # 核心修改：读取容器内存
+        mem_total = get_container_ram_total()
         mem_total_torch = mem_total
     else:
+        # GPU逻辑保持不变
         if directml_enabled:
-            mem_total = 1024 * 1024 * 1024 #TODO
+            mem_total = 1024 * 1024 * 1024
             mem_total_torch = mem_total
         elif is_intel_xpu():
             stats = torch.xpu.memory_stats(dev)
@@ -254,7 +276,7 @@ def mac_version():
         return None
 
 total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
-total_ram = psutil.virtual_memory().total / (1024 * 1024)
+total_ram = get_total_memory(torch.device("cpu")) / (1024 * 1024)
 logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
 
 try:

comfy_execution/caching.py

@@ -383,7 +383,38 @@ class RAMPressureCache(LRUCache):
 
     def poll(self, ram_headroom):
         def _ram_gb():
-            return psutil.virtual_memory().available / (1024**3)
+            """读取容器可用内存（GB），优先从 cgroup v2/v1 读取"""
+            # === 容器内存检测核心逻辑 ===
+            # 1. 尝试读取 cgroup v2 内存限制（Docker/K8s 主流）
+            cgroup_mem_limit_path = "/sys/fs/cgroup/memory.max"
+            cgroup_mem_usage_path = "/sys/fs/cgroup/memory.current"
+            
+            # 兼容 cgroup v1
+            if not os.path.exists(cgroup_mem_limit_path):
+                cgroup_mem_limit_path = "/sys/fs/cgroup/memory/memory.limit_in_bytes"
+                cgroup_mem_usage_path = "/sys/fs/cgroup/memory/memory.usage_in_bytes"
+
+            # 读取内存限制（字节）
+            try:
+                with open(cgroup_mem_limit_path, "r") as f:
+                    mem_limit = int(f.read().strip())
+                    # cgroup v1 中 limit_in_bytes 为 9223372036854771712 表示无限制， fallback 到系统内存
+                    if mem_limit == 9223372036854771712:
+                        mem_limit = psutil.virtual_memory().total
+            except (FileNotFoundError, ValueError):
+                mem_limit = psutil.virtual_memory().total
+
+            # 读取已使用内存（字节）
+            try:
+                with open(cgroup_mem_usage_path, "r") as f:
+                    mem_used = int(f.read().strip())
+            except (FileNotFoundError, ValueError):
+                mem_used = psutil.virtual_memory().total - psutil.virtual_memory().available
+
+            # 计算可用内存（GB）
+            mem_available = mem_limit - mem_used
+            return mem_available / (1024**3)
+        # === 替换结束 ===
 
         if _ram_gb() > ram_headroom:
             return

styles/default.csv

@@ -0,0 +1,3 @@
+name,prompt,negative_prompt
+❌Low Token,,"embedding:EasyNegative, NSFW, Cleavage, Pubic Hair, Nudity, Naked, censored"
+✅Line Art / Manga,"(Anime Scene, Toonshading, Satoshi Kon, Ken Sugimori, Hiromu Arakawa:1.2), (Anime Style, Manga Style:1.3), Low detail, sketch, concept art, line art, webtoon, manhua, hand drawn, defined lines, simple shades, minimalistic, High contrast, Linear compositions, Scalable artwork, Digital art, High Contrast Shadows, glow effects, humorous illustration, big depth of field, Masterpiece, colors, concept art, trending on artstation, Vivid colors, dramatic",