nodes/ModelTC-ComfyUI-Lightx2vWrapper
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refactor: remove enable_teacache option and improve error handling in coefficient calculation; update input types and tooltips for clarity in nodes.py

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This commit is contained in:
GACLove
2025-07-15 23:27:19 +08:00
parent 89c51e4b33
commit 2c10ad5c60
2 changed files with 59 additions and 53 deletions
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bridge.py
+5 -8
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@@ -107,7 +107,6 @@ class LightX2VDefaultConfig:
"patch_size": [1, 2, 2],
# ========== Feature Caching (TeaCache) ==========
"feature_caching": "NoCaching",
"enable_teacache": False,
"teacache_thresh": 0.26,
"coefficients": None, # Auto-calculated
"use_ret_steps": False,
@@ -199,7 +198,9 @@ class CoefficientCalculator:
if coeffs:
return coeffs[0] if use_ret_steps else coeffs[1]
return None
raise ValueError(
f"No coefficients found for task: {task}, model_size: {model_size}, resolution: {resolution}, use_ret_steps: {use_ret_steps}"
)
class ModularConfigManager:
@@ -281,21 +282,17 @@ class ModularConfigManager:
if config.get("enable", False):
updates["feature_caching"] = "Tea"
updates["enable_teacache"] = True
updates["teacache_thresh"] = config.get("threshold", 0.26)
updates["use_ret_steps"] = config.get("cache_key_steps_only", False)
updates["use_ret_steps"] = config.get("use_ret_steps", False)
# Auto-calculate coefficients
task = model_info.get("task", "t2v")
model_size = "14b" if "14b" in model_info.get("model_cls", "") else "1.3b"
resolution = (model_info.get("target_width", 832), model_info.get("target_height", 480))
coeffs = CoefficientCalculator.get_coefficients(task, model_size, resolution, updates["use_ret_steps"])
if coeffs:
updates["coefficients"] = coeffs
updates["coefficients"] = coeffs
else:
updates["feature_caching"] = "NoCaching"
updates["enable_teacache"] = False
return updates
nodes.py
+54 -45
View File
@@ -23,17 +23,17 @@ class LightX2VInferenceConfig:
def INPUT_TYPES(cls):
return {
"required": {
"model_cls": (["wan2.1", "hunyuan"], {"default": "wan2.1", "tooltip": "模型类型"}),
"model_path": ("STRING", {"default": "", "tooltip": "模型路径"}),
"task": (["t2v", "i2v"], {"default": "t2v", "tooltip": "任务类型：文本到视频或图像到视频"}),
"infer_steps": ("INT", {"default": 40, "min": 1, "max": 100, "tooltip": "推理步数"}),
"seed": ("INT", {"default": 42, "min": -1, "max": 2**32 - 1, "tooltip": "随机种子，-1为随机"}),
"cfg_scale": ("FLOAT", {"default": 5.0, "min": 1.0, "max": 10.0, "step": 0.1, "tooltip": "CFG引导强度"}),
"sample_shift": ("INT", {"default": 5, "min": 0, "max": 10, "tooltip": "采样偏移"}),
"height": ("INT", {"default": 480, "min": 64, "max": 2048, "step": 8, "tooltip": "视频高度"}),
"width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "视频宽度"}),
"video_length": ("INT", {"default": 81, "min": 16, "max": 120, "tooltip": "视频帧数"}),
"fps": ("INT", {"default": 16, "min": 8, "max": 30, "tooltip": "每秒帧数"}),
"model_cls": (["wan2.1", "wan2.1_audio", "wan2.1_distill", "hunyuan"], {"default": "wan2.1", "tooltip": "Model type"}),
"model_path": ("STRING", {"default": "", "tooltip": "Model path"}),
"task": (["t2v", "i2v"], {"default": "t2v", "tooltip": "Task type: text-to-video or image-to-video"}),
"infer_steps": ("INT", {"default": 40, "min": 1, "max": 100, "tooltip": "Inference steps"}),
"seed": ("INT", {"default": 42, "min": -1, "max": 2**32 - 1, "tooltip": "Random seed, -1 for random"}),
"cfg_scale": ("FLOAT", {"default": 5.0, "min": 1.0, "max": 10.0, "step": 0.1, "tooltip": "CFG guidance strength"}),
"sample_shift": ("INT", {"default": 5, "min": 0, "max": 10, "tooltip": "Sample shift"}),
"height": ("INT", {"default": 480, "min": 64, "max": 2048, "step": 8, "tooltip": "Video height"}),
"width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Video width"}),
"video_length": ("INT", {"default": 81, "min": 16, "max": 120, "tooltip": "Video frame count"}),
"fps": ("INT", {"default": 16, "min": 8, "max": 30, "tooltip": "Model output frame rate (cannot be changed)"}),
}
}
@@ -67,12 +67,18 @@ class LightX2VTeaCache:
def INPUT_TYPES(cls):
return {
"required": {
"enable": ("BOOLEAN", {"default": False, "tooltip": "启用TeaCache特征缓存"}),
"enable": ("BOOLEAN", {"default": False, "tooltip": "Enable TeaCache feature caching"}),
"threshold": (
"FLOAT",
{"default": 0.26, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "缓存阈值，越低加速越多：0.1约2倍加速，0.2约3倍加速"},
{
"default": 0.26,
"min": 0.0,
"max": 1.0,
"step": 0.01,
"tooltip": "Cache threshold, lower values provide more speedup: 0.1 ~2x speedup, 0.2 ~3x speedup",
},
),
"cache_key_steps_only": ("BOOLEAN", {"default": False, "tooltip": "只缓存关键步骤以平衡质量和速度"}),
"use_ret_steps": ("BOOLEAN", {"default": False, "tooltip": "Only cache key steps to balance quality and speed"}),
}
}
@@ -81,12 +87,12 @@ class LightX2VTeaCache:
FUNCTION = "create_config"
CATEGORY = "LightX2V/Config"
def create_config(self, enable, threshold, cache_key_steps_only):
def create_config(self, enable, threshold, use_ret_steps):
"""Create TeaCache configuration."""
config = {
"enable": enable,
"threshold": threshold,
"cache_key_steps_only": cache_key_steps_only,
"use_ret_steps": use_ret_steps,
}
return (config,)
@@ -110,11 +116,14 @@ class LightX2VQuantization:
return {
"required": {
"dit_precision": (["bf16", "int8", "fp8"], {"default": "bf16", "tooltip": "DIT模型量化精度"}),
"t5_precision": (["bf16", "int8", "fp8"], {"default": "bf16", "tooltip": "T5编码器量化精度"}),
"clip_precision": (["fp16", "int8", "fp8"], {"default": "fp16", "tooltip": "CLIP编码器量化精度"}),
"quant_backend": (quant_backends, {"default": quant_backends[0], "tooltip": "量化计算后端"}),
"sensitive_layers_precision": (["fp32", "bf16"], {"default": "fp32", "tooltip": "敏感层（归一化和嵌入层）精度"}),
"dit_precision": (["bf16", "int8", "fp8"], {"default": "bf16", "tooltip": "DIT model quantization precision"}),
"t5_precision": (["bf16", "int8", "fp8"], {"default": "bf16", "tooltip": "T5 encoder quantization precision"}),
"clip_precision": (["fp16", "int8", "fp8"], {"default": "fp16", "tooltip": "CLIP encoder quantization precision"}),
"quant_backend": (quant_backends, {"default": quant_backends[0], "tooltip": "Quantization computation backend"}),
"sensitive_layers_precision": (
["fp32", "bf16"],
{"default": "fp32", "tooltip": "Sensitive layers (normalization and embedding) precision"},
),
}
}
@@ -156,22 +165,22 @@ class LightX2VMemoryOptimization:
"required": {
"optimization_level": (
["none", "low", "medium", "high", "extreme"],
{"default": "none", "tooltip": "内存优化级别，越高越省内存但可能影响速度"},
{"default": "none", "tooltip": "Memory optimization level, higher levels save more memory but may affect speed"},
),
"attention_type": (attn_types, {"default": attn_types[0], "tooltip": "注意力机制类型"}),
"attention_type": (attn_types, {"default": attn_types[0], "tooltip": "Attention mechanism type"}),
},
"optional": {
# GPU optimization
"enable_rotary_chunk": ("BOOLEAN", {"default": False, "tooltip": "启用旋转编码分块"}),
"enable_rotary_chunk": ("BOOLEAN", {"default": False, "tooltip": "Enable rotary encoding chunking"}),
"rotary_chunk_size": ("INT", {"default": 100, "min": 100, "max": 10000, "step": 100}),
"clean_cuda_cache": ("BOOLEAN", {"default": False, "tooltip": "及时清理CUDA缓存"}),
"clean_cuda_cache": ("BOOLEAN", {"default": False, "tooltip": "Clean CUDA cache promptly"}),
# CPU offloading
"enable_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "启用CPU卸载"}),
"offload_granularity": (["block", "phase"], {"default": "phase", "tooltip": "卸载粒度"}),
"enable_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Enable CPU offloading"}),
"offload_granularity": (["block", "phase"], {"default": "phase", "tooltip": "Offload granularity"}),
"offload_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
# Module management
"lazy_load": ("BOOLEAN", {"default": False, "tooltip": "延迟加载模型"}),
"unload_after_inference": ("BOOLEAN", {"default": False, "tooltip": "推理后卸载模块"}),
"lazy_load": ("BOOLEAN", {"default": False, "tooltip": "Lazy load model"}),
"unload_after_inference": ("BOOLEAN", {"default": False, "tooltip": "Unload modules after inference"}),
},
}
@@ -216,8 +225,8 @@ class LightX2VLightweightVAE:
def INPUT_TYPES(cls):
return {
"required": {
"use_tiny_vae": ("BOOLEAN", {"default": False, "tooltip": "使用轻量级VAE加速解码"}),
"use_tiling_vae": ("BOOLEAN", {"default": False, "tooltip": "使用VAE分块推理减少显存"}),
"use_tiny_vae": ("BOOLEAN", {"default": False, "tooltip": "Use lightweight VAE to accelerate decoding"}),
"use_tiling_vae": ("BOOLEAN", {"default": False, "tooltip": "Use VAE tiling inference to reduce VRAM usage"}),
}
}
@@ -247,16 +256,16 @@ class LightX2VModularInference:
def INPUT_TYPES(cls):
return {
"required": {
"inference_config": ("INFERENCE_CONFIG", {"tooltip": "基础推理配置"}),
"prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "生成提示词"}),
"negative_prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "负面提示词"}),
"inference_config": ("INFERENCE_CONFIG", {"tooltip": "Basic inference configuration"}),
"prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "Generation prompt"}),
"negative_prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "Negative prompt"}),
},
"optional": {
"image": ("IMAGE", {"tooltip": "i2v任务的输入图像"}),
"teacache_config": ("TEACACHE_CONFIG", {"tooltip": "TeaCache配置"}),
"quantization_config": ("QUANT_CONFIG", {"tooltip": "量化配置"}),
"memory_config": ("MEMORY_CONFIG", {"tooltip": "内存优化配置"}),
"vae_config": ("VAE_CONFIG", {"tooltip": "VAE配置"}),
"image": ("IMAGE", {"tooltip": "Input image for i2v task"}),
"teacache_config": ("TEACACHE_CONFIG", {"tooltip": "TeaCache configuration"}),
"quantization_config": ("QUANT_CONFIG", {"tooltip": "Quantization configuration"}),
"memory_config": ("MEMORY_CONFIG", {"tooltip": "Memory optimization configuration"}),
"vae_config": ("VAE_CONFIG", {"tooltip": "VAE configuration"}),
},
}
@@ -417,10 +426,10 @@ NODE_CLASS_MAPPINGS = {
}
NODE_DISPLAY_NAME_MAPPINGS = {
"LightX2VInferenceConfig": "LightX2V 推理配置",
"LightX2VTeaCache": "LightX2V TeaCache缓存",
"LightX2VQuantization": "LightX2V 低精度量化",
"LightX2VMemoryOptimization": "LightX2V 内存优化",
"LightX2VLightweightVAE": "LightX2V 轻量VAE",
"LightX2VModularInference": "LightX2V 模块化推理",
"LightX2VInferenceConfig": "LightX2V Inference Config",
"LightX2VTeaCache": "LightX2V TeaCache",
"LightX2VQuantization": "LightX2V Quantization",
"LightX2VMemoryOptimization": "LightX2V Memory Optimization",
"LightX2VLightweightVAE": "LightX2V Lightweight VAE",
"LightX2VModularInference": "LightX2V Modular Inference",
}