791 lines
25 KiB
Python
791 lines
25 KiB
Python
"""Modular ComfyUI nodes for LightX2V without presets."""
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import gc
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import hashlib
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import json
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import logging
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import os
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import tempfile
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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 (
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ModularConfigManager,
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get_available_attn_ops,
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get_available_quant_ops,
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)
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from .lightx2v.lightx2v.infer import init_runner
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from .model_utils import (
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get_lora_full_path,
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get_model_full_path,
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scan_loras,
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scan_models,
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)
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class LightX2VInferenceConfig:
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@classmethod
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def INPUT_TYPES(cls):
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available_models = scan_models()
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return {
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"required": {
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"model_cls": (
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["wan2.1", "wan2.1_audio", "wan2.1_distill", "hunyuan"],
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{"default": "wan2.1", "tooltip": "Model type"},
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),
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"model_name": (
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available_models,
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{
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"default": available_models[0],
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"tooltip": "Select model from available models",
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},
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),
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"task": (
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["t2v", "i2v"],
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{
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"default": "t2v",
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"tooltip": "Task type: text-to-video or image-to-video",
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},
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),
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"infer_steps": (
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"INT",
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{"default": 40, "min": 1, "max": 100, "tooltip": "Inference steps"},
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),
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"seed": (
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"INT",
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{
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"default": 42,
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"min": -1,
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"max": 2**32 - 1,
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"tooltip": "Random seed, -1 for random",
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},
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),
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"cfg_scale": (
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"FLOAT",
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{
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"default": 5.0,
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"min": 1.0,
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"max": 10.0,
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"step": 0.1,
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"tooltip": "CFG guidance strength",
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},
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),
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"sample_shift": (
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"INT",
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{"default": 5, "min": 0, "max": 10, "tooltip": "Sample shift"},
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),
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"height": (
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"INT",
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{
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"default": 1280,
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"min": 64,
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"max": 2048,
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"step": 8,
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"tooltip": "Video height",
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},
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),
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"width": (
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"INT",
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{
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"default": 720,
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"min": 64,
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"max": 2048,
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"step": 8,
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"tooltip": "Video width",
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},
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),
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"duration": (
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"FLOAT",
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{
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"default": 5.0,
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"min": 1.0,
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"max": 999,
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"step": 0.1,
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"tooltip": "Video duration in seconds",
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},
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),
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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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"adaptive_resize": (
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"BOOLEAN",
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{
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"default": False,
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"tooltip": "Adaptive resize input image to target aspect ratio",
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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,
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model_cls,
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model_name,
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task,
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infer_steps,
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seed,
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cfg_scale,
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sample_shift,
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height,
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width,
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duration,
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denoising_steps="",
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adaptive_resize=False,
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):
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"""Create basic inference configuration."""
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model_path = get_model_full_path(model_name)
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if model_cls == "hunyuan":
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fps = 24
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else:
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fps = 16
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video_length = int(round(duration * fps))
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if video_length < 16:
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video_length = 16
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remainder = (video_length - 1) % 4
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if remainder != 0:
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video_length = video_length + (4 - remainder)
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# TODO(xxx):
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use_31_block = True
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if "wan2.1_audio" in [model_cls]:
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video_length = 81
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use_31_block = False
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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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"video_duration": duration,
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"adaptive_resize": adaptive_resize,
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"use_31_block": use_31_block,
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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": (
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"BOOLEAN",
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{"default": False, "tooltip": "Enable TeaCache feature caching"},
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),
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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": (
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"BOOLEAN",
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{
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"default": False,
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"tooltip": "Only cache key steps to balance quality and speed",
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},
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),
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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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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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@classmethod
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def INPUT_TYPES(cls):
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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": (
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["bf16", "int8", "fp8"],
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{"default": "bf16", "tooltip": "DIT model quantization precision"},
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),
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"t5_precision": (
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["bf16", "int8", "fp8"],
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{"default": "bf16", "tooltip": "T5 encoder quantization precision"},
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),
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"clip_precision": (
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["fp16", "int8", "fp8"],
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{
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"default": "fp16",
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"tooltip": "CLIP encoder quantization precision",
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},
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),
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"quant_backend": (
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quant_backends,
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{
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"default": quant_backends[0],
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"tooltip": "Quantization computation backend",
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},
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),
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"sensitive_layers_precision": (
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["fp32", "bf16"],
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{
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"default": "fp32",
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"tooltip": "Sensitive layers (normalization and embedding) precision",
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},
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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(
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self,
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dit_precision,
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t5_precision,
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clip_precision,
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quant_backend,
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sensitive_layers_precision,
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):
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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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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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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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{
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"default": "none",
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"tooltip": "Memory optimization level, higher levels save more memory but may affect speed",
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},
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),
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"attention_type": (
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attn_types,
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{"default": attn_types[0], "tooltip": "Attention mechanism type"},
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),
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},
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"optional": {
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# GPU optimization
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"enable_rotary_chunk": (
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"BOOLEAN",
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{"default": False, "tooltip": "Enable rotary encoding chunking"},
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),
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"rotary_chunk_size": (
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"INT",
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{"default": 100, "min": 100, "max": 10000, "step": 100},
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),
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"clean_cuda_cache": (
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"BOOLEAN",
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{"default": False, "tooltip": "Clean CUDA cache promptly"},
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),
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# CPU offloading
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"enable_cpu_offload": (
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"BOOLEAN",
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{"default": False, "tooltip": "Enable CPU offloading"},
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),
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"offload_granularity": (
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["block", "phase"],
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{"default": "phase", "tooltip": "Offload granularity"},
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),
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"offload_ratio": (
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"FLOAT",
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{"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1},
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),
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# Module management
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"lazy_load": (
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"BOOLEAN",
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{"default": False, "tooltip": "Lazy load model"},
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),
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"unload_after_inference": (
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"BOOLEAN",
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{"default": False, "tooltip": "Unload modules after inference"},
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),
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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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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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@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": (
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"BOOLEAN",
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{
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"default": False,
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"tooltip": "Use lightweight VAE to accelerate decoding",
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},
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),
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"use_tiling_vae": (
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"BOOLEAN",
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{
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"default": False,
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"tooltip": "Use VAE tiling inference to reduce VRAM usage",
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},
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),
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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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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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@classmethod
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def INPUT_TYPES(cls):
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available_loras = scan_loras()
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return {
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"required": {
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"lora_name": (
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available_loras,
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{
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"default": available_loras[0],
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"tooltip": "Select LoRA from available LoRAs",
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},
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),
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"strength": (
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"FLOAT",
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{
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"default": 1.0,
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"min": 0.0,
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"max": 2.0,
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"step": 0.1,
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"tooltip": "LoRA strength",
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},
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),
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},
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"optional": {
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"lora_chain": (
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"LORA_CHAIN",
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{"tooltip": "Previous LoRA chain to append to"},
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),
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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_name, strength, lora_chain=None):
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if lora_chain is None:
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lora_chain = []
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else:
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lora_chain = lora_chain.copy()
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lora_path = get_lora_full_path(lora_name)
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if lora_path:
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lora_config = {"path": lora_path, "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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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": (
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"INFERENCE_CONFIG",
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{"tooltip": "Basic inference configuration"},
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),
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},
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"optional": {
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"teacache_config": (
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"TEACACHE_CONFIG",
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{"tooltip": "TeaCache configuration"},
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),
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"quantization_config": (
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"QUANT_CONFIG",
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{"tooltip": "Quantization configuration"},
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),
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"memory_config": (
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"MEMORY_CONFIG",
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{"tooltip": "Memory optimization configuration"},
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),
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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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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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config = self.config_manager.build_final_config(configs)
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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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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": (
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"COMBINED_CONFIG",
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{"tooltip": "Combined configuration from config combiner"},
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),
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"prompt": (
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"STRING",
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{"multiline": True, "default": "", "tooltip": "Generation prompt"},
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),
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|
"negative_prompt": (
|
|
"STRING",
|
|
{"multiline": True, "default": "", "tooltip": "Negative prompt"},
|
|
),
|
|
},
|
|
"optional": {
|
|
"image": ("IMAGE", {"tooltip": "Input image for i2v task"}),
|
|
"audio": (
|
|
"AUDIO",
|
|
{"tooltip": "Input audio for audio-driven generation"},
|
|
),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "AUDIO")
|
|
RETURN_NAMES = ("images", "audio")
|
|
FUNCTION = "generate"
|
|
CATEGORY = "LightX2V/Inference"
|
|
|
|
def _get_config_hash(self, config) -> str:
|
|
relevant_configs = {
|
|
"model_cls": getattr(config, "model_cls", None),
|
|
"model_path": getattr(config, "model_path", None),
|
|
"dit_quantized": getattr(config, "dit_quantized", False),
|
|
"t5_quantized": getattr(config, "t5_quantized", False),
|
|
"clip_quantized": getattr(config, "clip_quantized", False),
|
|
"lora_configs": getattr(config, "lora_configs", None),
|
|
"lazy_load": getattr(config, "lazy_load", False),
|
|
}
|
|
|
|
config_str = json.dumps(relevant_configs, sort_keys=True)
|
|
return hashlib.md5(config_str.encode()).hexdigest()
|
|
|
|
def generate(
|
|
self,
|
|
combined_config,
|
|
prompt,
|
|
negative_prompt,
|
|
image=None,
|
|
audio=None,
|
|
**kwargs,
|
|
):
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
|
if "DTYPE" not in os.environ:
|
|
os.environ["DTYPE"] = "BF16"
|
|
if "ENABLE_GRAPH_MODE" not in os.environ:
|
|
os.environ["ENABLE_GRAPH_MODE"] = "false"
|
|
if "ENABLE_PROFILING_DEBUG" not in os.environ:
|
|
os.environ["ENABLE_PROFILING_DEBUG"] = "true"
|
|
|
|
config = combined_config
|
|
|
|
config.prompt = prompt
|
|
config.negative_prompt = negative_prompt
|
|
|
|
if config.task == "i2v" and image is None:
|
|
raise ValueError("i2v task requires input image")
|
|
|
|
temp_files = []
|
|
|
|
try:
|
|
if config.task == "i2v" and image is not None:
|
|
image_np = (image[0].cpu().numpy() * 255).astype(np.uint8)
|
|
pil_image = Image.fromarray(image_np)
|
|
|
|
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
|
|
pil_image.save(tmp.name)
|
|
config.image_path = tmp.name
|
|
temp_files.append(tmp.name)
|
|
logging.info(f"Image saved to {tmp.name}")
|
|
|
|
if (
|
|
audio is not None
|
|
and hasattr(config, "model_cls")
|
|
and "audio" in config.model_cls
|
|
):
|
|
if (
|
|
isinstance(audio, dict)
|
|
and "waveform" in audio
|
|
and "sample_rate" in audio
|
|
):
|
|
waveform = audio["waveform"]
|
|
sample_rate = audio["sample_rate"]
|
|
|
|
# Handle different waveform shapes
|
|
if isinstance(waveform, torch.Tensor):
|
|
if waveform.dim() == 3: # [batch, channels, samples]
|
|
waveform = waveform[0] # Take first batch
|
|
if waveform.dim() == 2: # [channels, samples]
|
|
# Convert to [samples, channels] for wav file
|
|
waveform = waveform.transpose(0, 1)
|
|
waveform = waveform.cpu().numpy()
|
|
elif isinstance(audio, tuple) and len(audio) == 2:
|
|
# Legacy format support
|
|
waveform, sample_rate = audio
|
|
if isinstance(waveform, torch.Tensor):
|
|
waveform = waveform.cpu().numpy()
|
|
else:
|
|
raise ValueError(f"Unsupported audio format: {type(audio)}")
|
|
|
|
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
|
|
try:
|
|
import scipy.io.wavfile as wavfile
|
|
except ImportError:
|
|
import wave
|
|
|
|
with wave.open(tmp.name, "wb") as wav_file:
|
|
wav_file.setnchannels(
|
|
1 if waveform.ndim == 1 else waveform.shape[-1]
|
|
)
|
|
wav_file.setsampwidth(2) # 16-bit
|
|
wav_file.setframerate(sample_rate)
|
|
if waveform.dtype != np.int16:
|
|
waveform = (waveform * 32767).astype(np.int16)
|
|
wav_file.writeframes(waveform.tobytes())
|
|
else:
|
|
if waveform.ndim == 1:
|
|
wavfile.write(tmp.name, sample_rate, waveform)
|
|
else:
|
|
if waveform.shape[0] < waveform.shape[1]:
|
|
waveform = waveform.T
|
|
wavfile.write(tmp.name, sample_rate, waveform)
|
|
|
|
config.audio_path = tmp.name
|
|
temp_files.append(tmp.name)
|
|
|
|
logging.info(f"Audio saved to {tmp.name}")
|
|
|
|
config_hash = self._get_config_hash(config)
|
|
needs_reinit = (
|
|
self._current_runner is None
|
|
or self._current_config_hash != config_hash
|
|
or getattr(config, "lazy_load", False)
|
|
)
|
|
|
|
if needs_reinit:
|
|
if self._current_runner is not None:
|
|
del self._current_runner
|
|
torch.cuda.empty_cache()
|
|
gc.collect()
|
|
|
|
self._current_runner = init_runner(config)
|
|
self._current_config_hash = config_hash
|
|
else:
|
|
if hasattr(self._current_runner, "config"):
|
|
self._current_runner.config = config
|
|
|
|
progress = ProgressBar(100)
|
|
|
|
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)
|
|
|
|
images, audio = self._current_runner.run_pipeline(save_video=False)
|
|
|
|
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()
|
|
|
|
return (images, audio)
|
|
|
|
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",
|
|
}
|