Spaces:
Runtime error
Runtime error
File size: 4,296 Bytes
b1bd80d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import torch
import numpy as np
import os
import time
import random
import string
import cv2
from ldm_patched.modules import model_management
def prepare_free_memory(aggressive=False):
if aggressive:
model_management.unload_all_models()
print('Cleanup all memory.')
return
model_management.free_memory(memory_required=model_management.minimum_inference_memory(),
device=model_management.get_torch_device())
print('Cleanup minimal inference memory.')
return
def apply_circular_forge(model, tiling_enabled=False):
if model.tiling_enabled == tiling_enabled:
return
print(f'Tiling: {tiling_enabled}')
model.tiling_enabled = tiling_enabled
def flatten(el):
flattened = [flatten(children) for children in el.children()]
res = [el]
for c in flattened:
res += c
return res
layers = flatten(model)
for layer in [layer for layer in layers if 'Conv' in type(layer).__name__]:
layer.padding_mode = 'circular' if tiling_enabled else 'zeros'
return
def HWC3(x):
assert x.dtype == np.uint8
if x.ndim == 2:
x = x[:, :, None]
assert x.ndim == 3
H, W, C = x.shape
assert C == 1 or C == 3 or C == 4
if C == 3:
return x
if C == 1:
return np.concatenate([x, x, x], axis=2)
if C == 4:
color = x[:, :, 0:3].astype(np.float32)
alpha = x[:, :, 3:4].astype(np.float32) / 255.0
y = color * alpha + 255.0 * (1.0 - alpha)
y = y.clip(0, 255).astype(np.uint8)
return y
def generate_random_filename(extension=".txt"):
timestamp = time.strftime("%Y%m%d-%H%M%S")
random_string = ''.join(random.choices(string.ascii_lowercase + string.digits, k=5))
filename = f"{timestamp}-{random_string}{extension}"
return filename
@torch.no_grad()
@torch.inference_mode()
def pytorch_to_numpy(x):
return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x]
@torch.no_grad()
@torch.inference_mode()
def numpy_to_pytorch(x):
y = x.astype(np.float32) / 255.0
y = y[None]
y = np.ascontiguousarray(y.copy())
y = torch.from_numpy(y).float()
return y
def write_images_to_mp4(frame_list: list, filename=None, fps=6):
from modules.paths_internal import default_output_dir
video_folder = os.path.join(default_output_dir, 'svd')
os.makedirs(video_folder, exist_ok=True)
if filename is None:
filename = generate_random_filename('.mp4')
full_path = os.path.join(video_folder, filename)
try:
import av
except ImportError:
from launch import run_pip
run_pip(
"install imageio[pyav]",
"imageio[pyav]",
)
import av
options = {
"crf": str(23)
}
output = av.open(full_path, "w")
stream = output.add_stream('libx264', fps, options=options)
stream.width = frame_list[0].shape[1]
stream.height = frame_list[0].shape[0]
for img in frame_list:
frame = av.VideoFrame.from_ndarray(img)
packet = stream.encode(frame)
output.mux(packet)
packet = stream.encode(None)
output.mux(packet)
output.close()
return full_path
def pad64(x):
return int(np.ceil(float(x) / 64.0) * 64 - x)
def safer_memory(x):
# Fix many MAC/AMD problems
return np.ascontiguousarray(x.copy()).copy()
def resize_image_with_pad(img, resolution):
H_raw, W_raw, _ = img.shape
k = float(resolution) / float(min(H_raw, W_raw))
interpolation = cv2.INTER_CUBIC if k > 1 else cv2.INTER_AREA
H_target = int(np.round(float(H_raw) * k))
W_target = int(np.round(float(W_raw) * k))
img = cv2.resize(img, (W_target, H_target), interpolation=interpolation)
H_pad, W_pad = pad64(H_target), pad64(W_target)
img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode='edge')
def remove_pad(x):
return safer_memory(x[:H_target, :W_target])
return safer_memory(img_padded), remove_pad
def lazy_memory_management(model):
required_memory = model_management.module_size(model) + model_management.minimum_inference_memory()
model_management.free_memory(required_memory, device=model_management.get_torch_device())
return
|