137 lines
4.2 KiB
Python
137 lines
4.2 KiB
Python
# !/usr/bin/env python
|
||
# -*- coding: UTF-8 -*-
|
||
import os
|
||
import torch
|
||
import gc
|
||
import comfy.model_management as mm
|
||
from PIL import Image
|
||
import numpy as np
|
||
from comfy.utils import common_upscale
|
||
from datetime import datetime
|
||
import folder_paths
|
||
import soundfile as sf
|
||
import uuid
|
||
cur_path = os.path.dirname(os.path.abspath(__file__))
|
||
|
||
def audio2path(audio,):
|
||
unique_id = uuid.uuid4().hex[:8]
|
||
audio_file = os.path.join(folder_paths.get_temp_directory(), f"audio_refer_temp_{unique_id}.wav")
|
||
waveform = audio["waveform"].squeeze(0)
|
||
waveform_np = waveform.cpu().numpy() if hasattr(waveform, 'cpu') else waveform.numpy()
|
||
|
||
# 3. 格式转换：torchaudio 格式为 (channels, samples)，soundfile 需要 (samples, channels)
|
||
# 如果是单声道音频 (1, samples)，转置后变成 (samples, 1)，符合 soundfile 的单声道要求
|
||
if waveform_np.ndim == 2:
|
||
waveform_np = waveform_np.T
|
||
|
||
sf.write(audio_file, waveform_np, audio["sample_rate"])
|
||
|
||
return audio_file
|
||
def auto_match(num_frames):
|
||
vae_scale_factor_temporal = 4
|
||
k = round((num_frames + vae_scale_factor_temporal - 1) / (vae_scale_factor_temporal * vae_scale_factor_temporal))
|
||
k = max(k, 1)
|
||
corrected_num_frames = (vae_scale_factor_temporal * vae_scale_factor_temporal) * k - (vae_scale_factor_temporal - 1)
|
||
if corrected_num_frames != num_frames:
|
||
print(f"[LongCat Video] Auto-corrected num_frames from {num_frames} to {corrected_num_frames} to satisfy temporal alignment (16k - 3).")
|
||
num_frames = corrected_num_frames
|
||
return num_frames
|
||
|
||
def clear_comfyui_cache():
|
||
cf_models=mm.loaded_models()
|
||
try:
|
||
for pipe in cf_models:
|
||
pipe.unpatch_model(device_to=torch.device("cpu"))
|
||
except: pass
|
||
mm.soft_empty_cache()
|
||
torch.cuda.empty_cache()
|
||
max_gpu_memory = torch.cuda.max_memory_allocated()
|
||
print(f"After Max GPU memory allocated: {max_gpu_memory / 1000 ** 3:.2f} GB")
|
||
|
||
def gc_cleanup():
|
||
gc.collect()
|
||
torch.cuda.empty_cache()
|
||
|
||
|
||
def phi2narry(img):
|
||
img = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0)
|
||
return img
|
||
|
||
def tensor2image(tensor):
|
||
tensor = tensor.cpu()
|
||
image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy()
|
||
image = Image.fromarray(image_np, mode='RGB')
|
||
return image
|
||
|
||
def tensor2pillist(tensor_in):
|
||
d1, _, _, _ = tensor_in.size()
|
||
if d1 == 1:
|
||
img_list = [tensor2image(tensor_in)]
|
||
else:
|
||
tensor_list = torch.chunk(tensor_in, chunks=d1)
|
||
img_list=[tensor2image(i) for i in tensor_list]
|
||
return img_list
|
||
|
||
def tensor2pillist_upscale(tensor_in,width,height):
|
||
d1, _, _, _ = tensor_in.size()
|
||
if d1 == 1:
|
||
img_list = [nomarl_upscale(tensor_in,width,height)]
|
||
else:
|
||
tensor_list = torch.chunk(tensor_in, chunks=d1)
|
||
img_list=[nomarl_upscale(i,width,height) for i in tensor_list]
|
||
return img_list
|
||
|
||
def tensor2list(tensor_in,width,height):
|
||
if tensor_in is None:
|
||
return None
|
||
d1, _, _, _ = tensor_in.size()
|
||
if d1 == 1:
|
||
tensor_list = [tensor_upscale(tensor_in,width,height)]
|
||
else:
|
||
tensor_list_ = torch.chunk(tensor_in, chunks=d1)
|
||
tensor_list=[tensor_upscale(i,width,height) for i in tensor_list_]
|
||
return tensor_list
|
||
|
||
def tensor_upscale(tensor, width, height):
|
||
samples = tensor.movedim(-1, 1)
|
||
samples = common_upscale(samples, width, height, "bilinear", "center")
|
||
samples = samples.movedim(1, -1)
|
||
return samples
|
||
|
||
def nomarl_upscale(img, width, height):
|
||
samples = img.movedim(-1, 1)
|
||
img = common_upscale(samples, width, height, "bilinear", "center")
|
||
samples = img.movedim(1, -1)
|
||
img = tensor2image(samples)
|
||
return img
|
||
|
||
|
||
def map_0_1_to_neg1_1(t):
|
||
|
||
if not torch.is_tensor(t):
|
||
t = torch.tensor(t)
|
||
t = t.float()
|
||
|
||
try:
|
||
vmax = float(t.max())
|
||
except Exception:
|
||
vmax = 1.0
|
||
if vmax > 2.0:
|
||
t = t / 255.0
|
||
try:
|
||
vmin = float(t.min())
|
||
vmax = float(t.max())
|
||
except Exception:
|
||
vmin, vmax = -1.0, 1.0
|
||
if vmin >= 0.0 and vmax <= 1.1:
|
||
t = t * 2.0 - 1.0
|
||
return t
|
||
|
||
def map_neg1_1_to_0_1(t):
|
||
if not torch.is_tensor(t):
|
||
t = torch.tensor(t)
|
||
t = t.float()
|
||
t = (t + 1.0) * 0.5
|
||
t = t.clamp(0.0, 1.0)
|
||
return t
|