159 lines
5.6 KiB
Python
159 lines
5.6 KiB
Python
import torch
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import comfy.model_management as model_management
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import gc
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import time
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class DDSamplingOptimizer:
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"""
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DD 采样速度优化器 - 极简版
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优化模型首次采样速度，减少第一次采样延迟
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通过预热模型和CLIP参数减少首次采样时的CUDA延迟
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"模型": ("MODEL",),
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"CLIP模型": ("CLIP",)
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}
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}
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RETURN_TYPES = ("MODEL", "CLIP",)
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RETURN_NAMES = ("优化模型", "优化CLIP",)
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FUNCTION = "optimize_sampling"
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CATEGORY = "🍺DD系列节点"
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def log_progress(self, message):
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"""输出进度信息"""
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print(f"[采样优化器] {message}")
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def warmup_model(self, model):
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"""模型预热核心方法 - 基于ComfyUI官方实现的优化方案"""
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try:
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# 对ModelPatcher类型的特殊处理
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if hasattr(model, "model") and model.model is not None:
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self.log_progress("通过model属性访问底层模型")
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# 访问内部模型的模块参数
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if hasattr(model.model, "modules"):
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self.log_progress("访问内部模型的modules")
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for module in model.model.modules():
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if hasattr(module, "_parameters"):
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for param in module._parameters.values():
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if param is not None:
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# 只是访问参数以触发CUDA预热
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_ = param.device
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break
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# 尝试访问model属性的参数
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self.log_progress("尝试通过model属性访问参数")
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try:
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for param in model.model.parameters():
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_ = param.device
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break
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self.log_progress("model参数访问成功")
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except Exception:
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pass
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# 如果上述方法失败，尝试直接访问模型
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elif hasattr(model, "modules"):
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self.log_progress("直接访问模型modules")
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for module in model.modules():
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if hasattr(module, "_parameters"):
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for param in module._parameters.values():
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if param is not None:
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_ = param.device
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break
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self.log_progress("模型基本优化完成")
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return True
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except Exception as e:
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self.log_progress(f"模型预热失败: {str(e)}")
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return False
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def warmup_clip(self, clip_model):
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"""CLIP模型预热核心方法 - 保留最可靠的方法"""
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self.log_progress("CLIP预热开始")
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try:
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with torch.no_grad():
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# 尝试最常见的CLIP方法
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if hasattr(clip_model, "encode_from_tokens") and hasattr(clip_model, "tokenize"):
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try:
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empty_tokens = clip_model.tokenize("")
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clip_model.encode_from_tokens(empty_tokens, return_pooled=True)
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return
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except Exception:
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pass
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# 备用方法
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if hasattr(clip_model, "encode"):
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try:
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clip_model.encode("a photo")
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return
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except Exception:
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pass
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# 最基本的方法 - 访问参数
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for param in clip_model.parameters():
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_ = param.device
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break
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self.log_progress("CLIP预热完成")
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except Exception as e:
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self.log_progress(f"CLIP预热失败: {str(e)}")
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def optimize_sampling(self, 模型, CLIP模型):
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"""
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优化模型首次采样速度 - 简化版
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Args:
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模型: 需要优化的扩散模型
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CLIP模型: CLIP文本编码器模型
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Returns:
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优化后的模型和CLIP
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"""
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try:
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start_time = time.time()
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self.log_progress("开始优化模型采样速度")
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# 清理CUDA缓存
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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# 执行模型预热
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self.log_progress("开始模型预热...")
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self.warmup_model(模型)
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# CLIP模型预热
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self.log_progress("开始CLIP模型预热...")
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self.warmup_clip(CLIP模型)
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# 清理缓存
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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# 统计时间
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elapsed_time = time.time() - start_time
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self.log_progress(f"采样优化完成，耗时 {elapsed_time:.2f} 秒")
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self.log_progress("后续采样过程应该不会出现明显的首次延迟")
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return (模型, CLIP模型)
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except Exception as e:
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self.log_progress(f"优化过程发生错误: {str(e)}")
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return (模型, CLIP模型)
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# 节点映射
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NODE_CLASS_MAPPINGS = {
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"DD-SamplingOptimizer": DDSamplingOptimizer
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}
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# 节点显示名称映射
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DD-SamplingOptimizer": "DD 采样优化器"
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}
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