File size: 2,890 Bytes
5d7514b |
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 |
import random
import string
import time
import cv2
import numpy as np
import torch
from backend import memory_management
def prepare_free_memory(aggressive=False):
if aggressive:
memory_management.unload_all_models()
print("Cleanup all memory...")
return
memory_management.free_memory(memory_required=memory_management.minimum_inference_memory(), device=memory_management.get_torch_device())
print("Cleanup minimal inference memory...")
def apply_circular_forge(model, tiling_enabled=False):
if not model.is_webui_legacy_model():
return
if model.tiling_enabled == tiling_enabled:
return
model.tiling_enabled = tiling_enabled
unet: torch.nn.Module = model.forge_objects.unet.model.diffusion_model
for layer in [layer for layer in unet.modules() if isinstance(layer, torch.nn.Conv2d)]:
layer.padding_mode = "circular" if tiling_enabled else "zeros"
print(f"Tiling: {tiling_enabled}")
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(255.0 * 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 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
|