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import random |
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import string |
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import time |
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import cv2 |
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import numpy as np |
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import torch |
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from backend import memory_management |
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def prepare_free_memory(aggressive=False): |
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if aggressive: |
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memory_management.unload_all_models() |
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print("Cleanup all memory...") |
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return |
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memory_management.free_memory(memory_required=memory_management.minimum_inference_memory(), device=memory_management.get_torch_device()) |
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print("Cleanup minimal inference memory...") |
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def apply_circular_forge(model, tiling_enabled=False): |
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if not model.is_webui_legacy_model(): |
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return |
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if model.tiling_enabled == tiling_enabled: |
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return |
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model.tiling_enabled = tiling_enabled |
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unet: torch.nn.Module = model.forge_objects.unet.model.diffusion_model |
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for layer in [layer for layer in unet.modules() if isinstance(layer, torch.nn.Conv2d)]: |
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layer.padding_mode = "circular" if tiling_enabled else "zeros" |
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print(f"Tiling: {tiling_enabled}") |
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def HWC3(x): |
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assert x.dtype == np.uint8 |
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if x.ndim == 2: |
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x = x[:, :, None] |
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assert x.ndim == 3 |
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H, W, C = x.shape |
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assert C == 1 or C == 3 or C == 4 |
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if C == 3: |
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return x |
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if C == 1: |
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return np.concatenate([x, x, x], axis=2) |
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if C == 4: |
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color = x[:, :, 0:3].astype(np.float32) |
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alpha = x[:, :, 3:4].astype(np.float32) / 255.0 |
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y = color * alpha + 255.0 * (1.0 - alpha) |
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y = y.clip(0, 255).astype(np.uint8) |
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return y |
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def generate_random_filename(extension=".txt"): |
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timestamp = time.strftime("%Y%m%d-%H%M%S") |
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random_string = "".join(random.choices(string.ascii_lowercase + string.digits, k=5)) |
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filename = f"{timestamp}-{random_string}{extension}" |
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return filename |
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@torch.no_grad() |
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@torch.inference_mode() |
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def pytorch_to_numpy(x): |
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return [np.clip(255.0 * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x] |
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@torch.no_grad() |
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@torch.inference_mode() |
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def numpy_to_pytorch(x): |
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y = x.astype(np.float32) / 255.0 |
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y = y[None] |
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y = np.ascontiguousarray(y.copy()) |
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y = torch.from_numpy(y).float() |
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return y |
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def pad64(x): |
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return int(np.ceil(float(x) / 64.0) * 64 - x) |
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def safer_memory(x): |
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return np.ascontiguousarray(x.copy()).copy() |
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def resize_image_with_pad(img, resolution): |
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H_raw, W_raw, _ = img.shape |
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k = float(resolution) / float(min(H_raw, W_raw)) |
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interpolation = cv2.INTER_CUBIC if k > 1 else cv2.INTER_AREA |
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H_target = int(np.round(float(H_raw) * k)) |
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W_target = int(np.round(float(W_raw) * k)) |
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img = cv2.resize(img, (W_target, H_target), interpolation=interpolation) |
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H_pad, W_pad = pad64(H_target), pad64(W_target) |
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img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode="edge") |
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def remove_pad(x): |
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return safer_memory(x[:H_target, :W_target]) |
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return safer_memory(img_padded), remove_pad |
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