|
|
import random |
|
|
import string |
|
|
import time |
|
|
|
|
|
import cv2 |
|
|
import numpy as np |
|
|
import torch |
|
|
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") |
|
|
|
|
|
|
|
|
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" |
|
|
|
|
|
|
|
|
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): |
|
|
|
|
|
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()) |
|
|
|
