| 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): |
| |
| 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 |
|
|
