comfyui-context

ComfyUI 上下文查询

查询 ComfyUI 当前状态：系统、模型、队列、节点类型。 所有操作为只读，不修改任何内容。

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预注入变量

直接使用，无需 import： nodes, folder_paths, client, L (L.model_management)

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1. 系统信息

stats = client.get_system_stats()
print(f"系统信息: {stats}")

# GPU/VRAM 信息
mm = L.model_management
if mm:
    total = mm.get_total_memory() / (1024**3)
    free = mm.get_free_memory() / (1024**3)
    print(f"VRAM: {free:.1f}GB free / {total:.1f}GB total")

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2. 列出可用模型

# Checkpoints（主模型）
ckpts = folder_paths.get_filename_list("checkpoints")
print(f"Checkpoints ({len(ckpts)}):")
for c in ckpts:
    print(f"  {c}")

# LoRA
loras = folder_paths.get_filename_list("loras")
print(f"\nLoRAs ({len(loras)}):")
for l in loras:
    print(f"  {l}")

# VAE
vaes = folder_paths.get_filename_list("vae")
print(f"\nVAEs ({len(vaes)}):")
for v in vaes:
    print(f"  {v}")

可查询的模型类型

folder_paths 参数	说明
"checkpoints"	Stable Diffusion 主模型
"loras"	LoRA 模型
"vae"	VAE 模型
"controlnet"	ControlNet 模型
"clip"	CLIP 模型
"clip_vision"	CLIP Vision 模型
"upscale_models"	超分辨率模型
"embeddings"	Textual Inversion embeddings
"hypernetworks"	Hypernetwork 模型

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3. 队列状态

queue = client.get_queue()
running = queue.get("queue_running", [])
pending = queue.get("queue_pending", [])
print(f"运行中: {len(running)}")
print(f"排队中: {len(pending)}")

# 取消当前任务
# client.cancel_current()

# 清空队列
# client.clear_queue()

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4. 列出所有可用节点类型

all_nodes = sorted(nodes.NODE_CLASS_MAPPINGS.keys())
print(f"可用节点类型 ({len(all_nodes)}):")
for name in all_nodes:
    print(f"  {name}")

按关键词搜索节点

keyword = "sampler"  # 修改为需要搜索的关键词
matches = [n for n in nodes.NODE_CLASS_MAPPINGS.keys() if keyword.lower() in n.lower()]
print(f"包含 '{keyword}' 的节点:")
for m in matches:
    print(f"  {m}")

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5. 查询节点参数 Schema

# 查询 KSampler 的输入参数定义
class_name = "KSampler"
NodeClass = nodes.NODE_CLASS_MAPPINGS[class_name]
input_types = NodeClass.INPUT_TYPES()

print(f"=== {class_name} ===")
print(f"RETURN_TYPES: {NodeClass.RETURN_TYPES}")
print(f"FUNCTION: {NodeClass.FUNCTION}")
print(f"CATEGORY: {NodeClass.CATEGORY}")

print("\nRequired inputs:")
for name, spec in input_types.get("required", {}).items():
    print(f"  {name}: {spec}")

if "optional" in input_types:
    print("\nOptional inputs:")
    for name, spec in input_types.get("optional", {}).items():
        print(f"  {name}: {spec}")