555 lines
21 KiB
Python
555 lines
21 KiB
Python
"""Modular ComfyUI nodes for LightX2V without presets."""
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import asyncio
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import gc
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import logging
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import os
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import tempfile
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from typing import Any, Dict
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import numpy as np
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import torch
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from comfy.utils import ProgressBar
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from PIL import Image
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from .bridge import ModularConfigManager, get_available_attn_ops, get_available_quant_ops
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from .lightx2v.lightx2v.infer import init_runner
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class LightX2VInferenceConfig:
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"""Basic inference configuration node."""
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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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"model_cls": (["wan2.1", "wan2.1_audio", "wan2.1_distill", "hunyuan"], {"default": "wan2.1", "tooltip": "Model type"}),
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"model_path": ("STRING", {"default": "", "tooltip": "Model path"}),
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"task": (["t2v", "i2v"], {"default": "t2v", "tooltip": "Task type: text-to-video or image-to-video"}),
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"infer_steps": ("INT", {"default": 40, "min": 1, "max": 100, "tooltip": "Inference steps"}),
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"seed": ("INT", {"default": 42, "min": -1, "max": 2**32 - 1, "tooltip": "Random seed, -1 for random"}),
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"cfg_scale": ("FLOAT", {"default": 5.0, "min": 1.0, "max": 10.0, "step": 0.1, "tooltip": "CFG guidance strength"}),
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"sample_shift": ("INT", {"default": 5, "min": 0, "max": 10, "tooltip": "Sample shift"}),
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"height": ("INT", {"default": 480, "min": 64, "max": 2048, "step": 8, "tooltip": "Video height"}),
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"width": ("INT", {"default": 832, "min": 64, "max": 2048, "step": 8, "tooltip": "Video width"}),
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"video_length": ("INT", {"default": 81, "min": 16, "max": 120, "tooltip": "Video frame count"}),
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"fps": ("INT", {"default": 16, "min": 8, "max": 30, "tooltip": "Model output frame rate (cannot be changed)"}),
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},
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"optional": {
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"denoising_steps": (
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"STRING",
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{
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"default": "",
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"tooltip": "Custom denoising steps for distillation models (comma-separated, e.g., '999,750,500,250'). Leave empty to use model defaults.",
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},
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),
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},
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}
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RETURN_TYPES = ("INFERENCE_CONFIG",)
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RETURN_NAMES = ("inference_config",)
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FUNCTION = "create_config"
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CATEGORY = "LightX2V/Config"
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def create_config(
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self, model_cls, model_path, task, infer_steps, seed, cfg_scale, sample_shift, height, width, video_length, fps, denoising_steps=""
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):
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"""Create basic inference configuration."""
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config = {
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"model_cls": model_cls,
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"model_path": model_path,
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"task": task,
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"infer_steps": infer_steps,
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"seed": seed if seed != -1 else np.random.randint(0, 2**32 - 1),
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"cfg_scale": cfg_scale,
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"sample_shift": sample_shift,
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"height": height,
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"width": width,
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"video_length": video_length,
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"fps": fps,
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}
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if denoising_steps and denoising_steps.strip():
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try:
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steps_list = [int(s.strip()) for s in denoising_steps.split(",")]
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config["denoising_step_list"] = steps_list
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config["infer_steps"] = len(steps_list)
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except ValueError:
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pass
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return (config,)
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class LightX2VTeaCache:
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"""TeaCache configuration node."""
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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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"enable": ("BOOLEAN", {"default": False, "tooltip": "Enable TeaCache feature caching"}),
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"threshold": (
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"FLOAT",
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{
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"default": 0.26,
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"min": 0.0,
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"max": 1.0,
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"step": 0.01,
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"tooltip": "Cache threshold, lower values provide more speedup: 0.1 ~2x speedup, 0.2 ~3x speedup",
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},
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),
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"use_ret_steps": ("BOOLEAN", {"default": False, "tooltip": "Only cache key steps to balance quality and speed"}),
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}
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}
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RETURN_TYPES = ("TEACACHE_CONFIG",)
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RETURN_NAMES = ("teacache_config",)
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FUNCTION = "create_config"
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CATEGORY = "LightX2V/Config"
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def create_config(self, enable, threshold, use_ret_steps):
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"""Create TeaCache configuration."""
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config = {
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"enable": enable,
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"threshold": threshold,
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"use_ret_steps": use_ret_steps,
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}
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return (config,)
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class LightX2VQuantization:
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"""Quantization configuration node."""
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@classmethod
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def INPUT_TYPES(cls):
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# Get available quantization backends
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available_ops = get_available_quant_ops()
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quant_backends = []
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for op_name, is_available in available_ops:
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if is_available:
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quant_backends.append(op_name)
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# Always have at least one option
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if not quant_backends:
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quant_backends = ["none"]
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return {
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"required": {
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"dit_precision": (["bf16", "int8", "fp8"], {"default": "bf16", "tooltip": "DIT model quantization precision"}),
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"t5_precision": (["bf16", "int8", "fp8"], {"default": "bf16", "tooltip": "T5 encoder quantization precision"}),
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"clip_precision": (["fp16", "int8", "fp8"], {"default": "fp16", "tooltip": "CLIP encoder quantization precision"}),
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"quant_backend": (quant_backends, {"default": quant_backends[0], "tooltip": "Quantization computation backend"}),
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"sensitive_layers_precision": (
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["fp32", "bf16"],
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{"default": "fp32", "tooltip": "Sensitive layers (normalization and embedding) precision"},
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),
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}
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}
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RETURN_TYPES = ("QUANT_CONFIG",)
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RETURN_NAMES = ("quantization_config",)
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FUNCTION = "create_config"
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CATEGORY = "LightX2V/Config"
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def create_config(self, dit_precision, t5_precision, clip_precision, quant_backend, sensitive_layers_precision):
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"""Create quantization configuration."""
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config = {
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"dit_precision": dit_precision,
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"t5_precision": t5_precision,
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"clip_precision": clip_precision,
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"quant_backend": quant_backend,
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"sensitive_layers_precision": sensitive_layers_precision,
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}
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return (config,)
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class LightX2VMemoryOptimization:
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"""Memory optimization configuration node."""
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@classmethod
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def INPUT_TYPES(cls):
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# Get available attention types
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available_attn = get_available_attn_ops()
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attn_types = []
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for op_name, is_available in available_attn:
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if is_available:
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attn_types.append(op_name)
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# Always include fallback
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if "torch_sdpa" not in attn_types:
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attn_types.append("torch_sdpa")
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return {
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"required": {
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"optimization_level": (
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["none", "low", "medium", "high", "extreme"],
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{"default": "none", "tooltip": "Memory optimization level, higher levels save more memory but may affect speed"},
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),
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"attention_type": (attn_types, {"default": attn_types[0], "tooltip": "Attention mechanism type"}),
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},
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"optional": {
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# GPU optimization
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"enable_rotary_chunk": ("BOOLEAN", {"default": False, "tooltip": "Enable rotary encoding chunking"}),
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"rotary_chunk_size": ("INT", {"default": 100, "min": 100, "max": 10000, "step": 100}),
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"clean_cuda_cache": ("BOOLEAN", {"default": False, "tooltip": "Clean CUDA cache promptly"}),
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# CPU offloading
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"enable_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Enable CPU offloading"}),
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"offload_granularity": (["block", "phase"], {"default": "phase", "tooltip": "Offload granularity"}),
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"offload_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
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# Module management
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"lazy_load": ("BOOLEAN", {"default": False, "tooltip": "Lazy load model"}),
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"unload_after_inference": ("BOOLEAN", {"default": False, "tooltip": "Unload modules after inference"}),
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},
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}
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RETURN_TYPES = ("MEMORY_CONFIG",)
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RETURN_NAMES = ("memory_config",)
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FUNCTION = "create_config"
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CATEGORY = "LightX2V/Config"
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def create_config(
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self,
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optimization_level,
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attention_type,
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enable_rotary_chunk=False,
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rotary_chunk_size=100,
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clean_cuda_cache=False,
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enable_cpu_offload=False,
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offload_granularity="phase",
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offload_ratio=1.0,
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lazy_load=False,
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unload_after_inference=False,
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):
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"""Create memory optimization configuration."""
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config = {
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"optimization_level": optimization_level,
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"attention_type": attention_type,
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"enable_rotary_chunk": enable_rotary_chunk,
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"rotary_chunk_size": rotary_chunk_size,
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"clean_cuda_cache": clean_cuda_cache,
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"enable_cpu_offload": enable_cpu_offload,
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"offload_granularity": offload_granularity,
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"offload_ratio": offload_ratio,
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"lazy_load": lazy_load,
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"unload_after_inference": unload_after_inference,
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}
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return (config,)
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class LightX2VLightweightVAE:
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"""Lightweight VAE configuration node."""
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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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"use_tiny_vae": ("BOOLEAN", {"default": False, "tooltip": "Use lightweight VAE to accelerate decoding"}),
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"use_tiling_vae": ("BOOLEAN", {"default": False, "tooltip": "Use VAE tiling inference to reduce VRAM usage"}),
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}
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}
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RETURN_TYPES = ("VAE_CONFIG",)
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RETURN_NAMES = ("vae_config",)
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FUNCTION = "create_config"
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CATEGORY = "LightX2V/Config"
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def create_config(self, use_tiny_vae, use_tiling_vae):
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"""Create VAE configuration."""
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config = {
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"use_tiny_vae": use_tiny_vae,
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"use_tiling_vae": use_tiling_vae,
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}
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return (config,)
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class LightX2VLoRALoader:
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"""LoRA loader node that can be chained."""
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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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"lora_path": ("STRING", {"default": "", "tooltip": "Path to the LoRA file"}),
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"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1, "tooltip": "LoRA strength"}),
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},
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"optional": {
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"lora_chain": ("LORA_CHAIN", {"tooltip": "Previous LoRA chain to append to"}),
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},
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}
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RETURN_TYPES = ("LORA_CHAIN",)
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RETURN_NAMES = ("lora_chain",)
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FUNCTION = "load_lora"
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CATEGORY = "LightX2V/LoRA"
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def load_lora(self, lora_path, strength, lora_chain=None):
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"""Load LoRA and chain with previous LoRAs."""
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# Initialize or extend the LoRA chain
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if lora_chain is None:
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lora_chain = []
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else:
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# Make a copy to avoid modifying the input
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lora_chain = lora_chain.copy()
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# Add new LoRA configuration
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if lora_path and lora_path.strip():
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lora_config = {"path": lora_path.strip(), "strength": strength}
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lora_chain.append(lora_config)
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return (lora_chain,)
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class LightX2VConfigCombiner:
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"""Combines all configuration nodes into a single config object."""
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def __init__(self):
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self.config_manager = ModularConfigManager()
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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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"inference_config": ("INFERENCE_CONFIG", {"tooltip": "Basic inference configuration"}),
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},
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"optional": {
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"teacache_config": ("TEACACHE_CONFIG", {"tooltip": "TeaCache configuration"}),
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"quantization_config": ("QUANT_CONFIG", {"tooltip": "Quantization configuration"}),
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"memory_config": ("MEMORY_CONFIG", {"tooltip": "Memory optimization configuration"}),
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"vae_config": ("VAE_CONFIG", {"tooltip": "VAE configuration"}),
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"lora_chain": ("LORA_CHAIN", {"tooltip": "LoRA chain configuration"}),
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},
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}
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RETURN_TYPES = ("COMBINED_CONFIG",)
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RETURN_NAMES = ("combined_config",)
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FUNCTION = "combine_configs"
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CATEGORY = "LightX2V/Config"
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def combine_configs(
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self,
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inference_config,
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teacache_config=None,
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quantization_config=None,
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memory_config=None,
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vae_config=None,
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lora_chain=None,
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):
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"""Combine all configurations into a single config object."""
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# Collect all configurations
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configs = {
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"inference": inference_config,
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}
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if teacache_config:
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configs["teacache"] = teacache_config
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if quantization_config:
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configs["quantization"] = quantization_config
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if memory_config:
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configs["memory"] = memory_config
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if vae_config:
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configs["vae"] = vae_config
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# Build final configuration
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config = self.config_manager.build_final_config(configs)
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# Add LoRA configurations if provided
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if lora_chain:
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config.lora_configs = lora_chain
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return (config,)
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class LightX2VModularInference:
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"""Modular inference node that uses a combined configuration."""
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def __init__(self):
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self._current_runner = None
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self._current_config_hash = None
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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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"combined_config": ("COMBINED_CONFIG", {"tooltip": "Combined configuration from config combiner"}),
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"prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "Generation prompt"}),
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"negative_prompt": ("STRING", {"multiline": True, "default": "", "tooltip": "Negative prompt"}),
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},
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"optional": {
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"image": ("IMAGE", {"tooltip": "Input image for i2v task"}),
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"audio": ("AUDIO", {"tooltip": "Input audio for audio-driven generation"}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("images",)
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FUNCTION = "generate"
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CATEGORY = "LightX2V/Inference"
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def _get_config_hash(self, config) -> str:
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"""Generate a hash for configuration to detect changes."""
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import hashlib
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import json
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# Only hash model-related configs
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relevant_configs = {
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"model_cls": getattr(config, "model_cls", None),
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"model_path": getattr(config, "model_path", None),
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"dit_quantized": getattr(config, "dit_quantized", False),
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"t5_quantized": getattr(config, "t5_quantized", False),
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"lazy_load": getattr(config, "lazy_load", False),
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}
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config_str = json.dumps(relevant_configs, sort_keys=True)
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return hashlib.md5(config_str.encode()).hexdigest()
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def generate(
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self,
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combined_config,
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prompt,
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negative_prompt,
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image=None,
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audio=None,
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**kwargs,
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):
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"""Generate video using combined configuration."""
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# Set environment variables
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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if "DTYPE" not in os.environ:
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os.environ["DTYPE"] = "BF16"
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if "ENABLE_GRAPH_MODE" not in os.environ:
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os.environ["ENABLE_GRAPH_MODE"] = "false"
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if "ENABLE_PROFILING_DEBUG" not in os.environ:
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os.environ["ENABLE_PROFILING_DEBUG"] = "true"
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config = combined_config
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config.prompt = prompt
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config.negative_prompt = negative_prompt
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if config.task == "i2v" and image is None:
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raise ValueError("i2v task requires input image")
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temp_files = []
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try:
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if config.task == "i2v" and image is not None:
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image_np = (image[0].cpu().numpy() * 255).astype(np.uint8)
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pil_image = Image.fromarray(image_np)
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with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
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pil_image.save(tmp.name)
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config.image_path = tmp.name
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temp_files.append(tmp.name)
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if audio is not None and hasattr(config, "model_cls") and "audio" in config.model_cls:
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if isinstance(audio, tuple) and len(audio) == 2:
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waveform, sample_rate = audio
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if isinstance(waveform, torch.Tensor):
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waveform = waveform.cpu().numpy()
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
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try:
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import scipy.io.wavfile as wavfile
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except ImportError:
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import wave
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with wave.open(tmp.name, "wb") as wav_file:
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wav_file.setnchannels(1 if waveform.ndim == 1 else waveform.shape[-1])
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wav_file.setsampwidth(2) # 16-bit
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wav_file.setframerate(sample_rate)
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if waveform.dtype != np.int16:
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waveform = (waveform * 32767).astype(np.int16)
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wav_file.writeframes(waveform.tobytes())
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else:
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if waveform.ndim == 1:
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wavfile.write(tmp.name, sample_rate, waveform)
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else:
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if waveform.shape[0] < waveform.shape[1]:
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waveform = waveform.T
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wavfile.write(tmp.name, sample_rate, waveform)
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config.audio_path = tmp.name
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temp_files.append(tmp.name)
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config_hash = self._get_config_hash(config)
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needs_reinit = self._current_runner is None or self._current_config_hash != config_hash or getattr(config, "lazy_load", False)
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if needs_reinit:
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if self._current_runner is not None:
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del self._current_runner
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torch.cuda.empty_cache()
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gc.collect()
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self._current_runner = init_runner(config)
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self._current_config_hash = config_hash
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else:
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if hasattr(self._current_runner, "config"):
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self._current_runner.config = config
|
|
|
|
total_steps = getattr(config, "infer_steps", 40)
|
|
progress = ProgressBar(total_steps)
|
|
|
|
def update_progress(current_step, total):
|
|
progress.update_absolute(current_step)
|
|
|
|
if hasattr(self._current_runner, "set_progress_callback"):
|
|
self._current_runner.set_progress_callback(update_progress)
|
|
|
|
if hasattr(self._current_runner, "run_pipeline"):
|
|
images = asyncio.run(self._current_runner.run_pipeline(save_video=False))
|
|
else:
|
|
images = self._current_runner()
|
|
|
|
if getattr(config, "unload_after_inference", False):
|
|
del self._current_runner
|
|
self._current_runner = None
|
|
self._current_config_hash = None
|
|
|
|
torch.cuda.empty_cache()
|
|
gc.collect()
|
|
|
|
images = (images + 1) / 2
|
|
images = images.squeeze(0).permute(1, 2, 3, 0).cpu()
|
|
images = torch.clamp(images, 0, 1)
|
|
|
|
return (images,)
|
|
|
|
except Exception as e:
|
|
logging.error(f"Error during inference: {e}")
|
|
raise
|
|
|
|
# finally:
|
|
# for temp_file in temp_files:
|
|
# if os.path.exists(temp_file):
|
|
# try:
|
|
# os.unlink(temp_file)
|
|
# except Exception:
|
|
# pass
|
|
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"LightX2VInferenceConfig": LightX2VInferenceConfig,
|
|
"LightX2VTeaCache": LightX2VTeaCache,
|
|
"LightX2VQuantization": LightX2VQuantization,
|
|
"LightX2VMemoryOptimization": LightX2VMemoryOptimization,
|
|
"LightX2VLightweightVAE": LightX2VLightweightVAE,
|
|
"LightX2VLoRALoader": LightX2VLoRALoader,
|
|
"LightX2VConfigCombiner": LightX2VConfigCombiner,
|
|
"LightX2VModularInference": LightX2VModularInference,
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"LightX2VInferenceConfig": "LightX2V Inference Config",
|
|
"LightX2VTeaCache": "LightX2V TeaCache",
|
|
"LightX2VQuantization": "LightX2V Quantization",
|
|
"LightX2VMemoryOptimization": "LightX2V Memory Optimization",
|
|
"LightX2VLightweightVAE": "LightX2V Lightweight VAE",
|
|
"LightX2VLoRALoader": "LightX2V LoRA Loader",
|
|
"LightX2VConfigCombiner": "LightX2V Config Combiner",
|
|
"LightX2VModularInference": "LightX2V Modular Inference",
|
|
}
|