Попытка оптимизировать модель для более быстрого расчёта

pyproject.toml

@@ -5,7 +5,6 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "imageio>=2.37.3",
     "numpy>=2.4.4",
     "omegaconf>=2.3.0",
     "opencv-python>=4.13.0.92",

src/interpolator.py

@@ -29,6 +29,7 @@ class ModelRunner:
             ckpt_path (Path): Path to model checkpoint in .pth format
             device (torch.device): Device to load the model on
         """
+        torch.set_float32_matmul_precision("high")
         omega_config = OmegaConf.load(config)
         network_config: DictConfig = omega_config.network
         logging.info(
@@ -39,7 +40,7 @@ class ModelRunner:
         model.load_state_dict(checkpoint["state_dict"])
         model = model.to(get_device())
         model.eval()
-        self.model = model
+        self.model = torch.compile(model, mode="max-autotune")
 
 
 def get_vram_available(device: torch.device) -> int:
@@ -76,65 +77,46 @@ class ImageInterpolator:
         self.device = device
         self.anchor = anchor
         self.vram_available = get_vram_available(device)
+        self._scale = None
+        self._padder = None
         self.embt = torch.tensor(1 / 2).float().view(1, 1, 1, 1).to(device)
         self.model_runner = model_runner
         logging.debug(
             f"Initialized ImageInterpolator with device: {device}, anchor: {anchor}, available VRAM: {self.vram_available} bytes"
         )
 
-    def interpolate(self, image1: np.ndarray, image2: np.ndarray) -> np.ndarray:
+    def interpolate(self, image1: torch.Tensor, image2: torch.Tensor) -> torch.Tensor:
-        """
+        interpolated = self.model_runner.model(
-        Interpolates between two images and saves the result.
+            image1, image2, self.embt, scale_factor=self._scale, eval=True
-        Args:
+        )["imgt_pred"]
-            image1 (Path): Path to the first input image (only png and jpg formats are supported)
+        if not self._padder:
-            image2 (Path): Path to the second input image (only png and jpg formats are supported)
+            raise NotImplemented("Padder not implemented")
-            output_path (Path): Path to save the interpolated image (only png and jpg formats are supported)
+        return self._padder.unpad(interpolated)[0]
-        """
-        logging.debug(f"Reading images: {image1} and {image2}")
-        tensor1 = img2tensor(image1).to(self.device)
-        tensor2 = img2tensor(image2).to(self.device)
-        logging.debug(
-            f"Image shapes after conversion to tensors: {tensor1.shape}, {tensor2.shape}"
-        )
-        tensor1, tensor2 = check_dim_and_resize(tensor1, tensor2)
-        logging.debug(f"Image shapes after resizing: {tensor1.shape}, {tensor2.shape}")
-        h, w = tensor1.shape[2], tensor1.shape[3]
-        logging.debug(f"Interpolating images of size: {h}x{w}")
 
+    def make_tensor(self, img: np.ndarray) -> torch.Tensor:
+        tensor = img2tensor(img).to(self.device)
+        h, w = tensor.shape[2], tensor.shape[3]
         scale = self.scale(h, w)
-        logging.debug(f"Calculated scale factor: {scale:.2f}")
         padding = int(16 / scale)
-        logging.debug(f"Calculated padding: {padding} pixels")
+        if self._padder is None:
-        padder = InputPadder(tensor1.shape, divisor=padding)
+            self._padder = InputPadder(tensor.shape, padding)
-        tensor1_padded, tensor2_padded = padder.pad(tensor1, tensor2)
+        return self._padder.pad(tensor)[0]
-        logging.debug(
-            f"Image shapes after padding: {tensor1_padded.shape}, {tensor2_padded.shape}"
-        )
-
-        tensor1_padded = tensor1_padded.to(self.device)
-        tensor2_padded = tensor2_padded.to(self.device)
-        logging.debug("Running model inference for interpolation")
-        with torch.no_grad():
-            interpolated = self.model_runner.model(
-                tensor1_padded, tensor2_padded, self.embt, scale_factor=scale, eval=True
-            )["imgt_pred"]
-        logging.debug(f"Interpolated image shape before unpadding: {interpolated.shape}")
-        (interpolated,) = padder.unpad(interpolated)
-        logging.debug(f"Interpolated image shape after unpadding: {interpolated.shape}")
-        return tensor2img(interpolated.cpu())
 
     def scale(self, height: int, width: int) -> float:
-        scale = (
+        if self._scale is None:
-            self.anchor.resolution
+            scale = (
-            / (height * width)
+                self.anchor.resolution
-            * np.sqrt(
+                / (height * width)
-                (self.vram_available - self.anchor.memory_bias) / self.anchor.memory
+                * np.sqrt(
+                    (self.vram_available - self.anchor.memory_bias) / self.anchor.memory
+                )
             )
-        )
+            scale = 1 if scale > 1 else scale
-        scale = 1 if scale > 1 else scale
+            scale = 1 / np.floor(1 / np.sqrt(scale) * 16) * 16
-        scale = 1 / np.floor(1 / np.sqrt(scale) * 16) * 16
+            if scale < 1:
-        if scale < 1:
+                logging.debug(
-            logging.info(
+                    f"Due to the limited VRAM, the video will be scaled by {scale:.2f}"
-                f"Due to the limited VRAM, the video will be scaled by {scale:.2f}"
+                )
-            )
+            self._scale = float(scale)
-        return scale
+            logging.info(f"Calculated scale factor: {self._scale:.2f}")
+        return self._scale

src/networks/AMT_G.py

@@ -177,14 +177,11 @@ class Model(nn.Module):
         )
 
         if scale_factor != 1.0:
-            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (
+            factor = 1.0 / scale_factor
-                1.0 / scale_factor
+            up_flow0_1 = resize(up_flow0_1, factor) * factor
-            )
+            up_flow1_1 = resize(up_flow1_1, factor) * factor
-            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (
+            mask = resize(mask, factor)
-                1.0 / scale_factor
+            img_res = resize(img_res, factor)
-            )
-            mask = resize(mask, scale_factor=(1.0 / scale_factor))
-            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
 
         # Merge multiple predictions
         imgt_pred = multi_flow_combine(

src/networks/AMT_L.py

@@ -142,14 +142,11 @@ class Model(nn.Module):
         )
 
         if scale_factor != 1.0:
-            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (
+            factor = 1.0 / scale_factor
-                1.0 / scale_factor
+            up_flow0_1 = resize(up_flow0_1, factor) * factor
-            )
+            up_flow1_1 = resize(up_flow1_1, factor) * factor
-            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (
+            mask = resize(mask, factor)
-                1.0 / scale_factor
+            img_res = resize(img_res, factor)
-            )
-            mask = resize(mask, scale_factor=(1.0 / scale_factor))
-            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
 
         # Merge multiple predictions
         imgt_pred = multi_flow_combine(

src/networks/AMT_S.py

@@ -140,14 +140,11 @@ class Model(nn.Module):
         )
 
         if scale_factor != 1.0:
-            up_flow0_1 = resize(up_flow0_1, scale_factor=(1.0 / scale_factor)) * (
+            factor = 1.0 / scale_factor
-                1.0 / scale_factor
+            up_flow0_1 = resize(up_flow0_1, factor) * factor
-            )
+            up_flow1_1 = resize(up_flow1_1, factor) * factor
-            up_flow1_1 = resize(up_flow1_1, scale_factor=(1.0 / scale_factor)) * (
+            mask = resize(mask, factor)
-                1.0 / scale_factor
+            img_res = resize(img_res, factor)
-            )
-            mask = resize(mask, scale_factor=(1.0 / scale_factor))
-            img_res = resize(img_res, scale_factor=(1.0 / scale_factor))
 
         # Merge multiple predictions
         imgt_pred = multi_flow_combine(

src/runner.py

@@ -4,10 +4,12 @@ from typing import TYPE_CHECKING
 
 from cv2 import imwrite
 import tqdm
+import torch
 
 from .config import presets
 from .utils.fs import FileSystem
 from .utils.video import VideoMaker
+from .utils.torch import tensor2img
 from .interpolator import (
     ImageInterpolator,
     Anchor,
@@ -63,8 +65,11 @@ def init_anchor(device: "torch.device") -> Anchor:
     if device.type in ("cpu", "mps"):
         return Anchor(resolution=8192 * 8192, memory=1, memory_bias=0)
     elif device.type == "cuda":
+        # return Anchor(
+        #     resolution=1024 * 512, memory=1500 * 1024**2, memory_bias=2500 * 1024**2
+        # )
         return Anchor(
-            resolution=1024 * 512, memory=1500 * 1024**2, memory_bias=2500 * 1024**2
+            resolution=1280 * 720, memory=6500 * 1024**2, memory_bias=7500 * 1024**2
         )
     else:
         raise Exception(f"Unsupported device type: {device.type}")
@@ -108,64 +113,35 @@ class InterpolationPipeline:
         logging.info(f"Video FPS: {fps}")
         fps *= 2  # Doubling FPS
         width, height = self.video_maker.get_size(video_path)
-        for frames in self.video_maker.video_to_frames_generator(
+        with torch.autocast(self.device.type, torch.float16):
-            video_path, self.fs.frames_path, chunk_seconds
+            with torch.no_grad():
-        ):
+                prev_tensor = None
-            logging.info(f"Processing frames: {len(frames)}")
+                for idx, frames in enumerate(
-            if prev_frames:
+                    self.video_maker.video_to_frames_generator(
-                img1 = prev_frames[-1]
+                        video_path, self.fs.frames_path, chunk_seconds
-                img2 = frames[0]
+                    )
-                img1_2 = self.interpolator.interpolate(img1, img2)
+                ):
-                interpolated_frames.append(img1_2)
+                    interpolated_frames: list["np.ndarray"] = []
-                generator = self._frame_generator(prev_frames, interpolated_frames)
+                    for frame in tqdm.tqdm(frames):
-                part_path = self.fs.video_part_path / f"video_{part:08d}.mp4"
+                        tensor = self.interpolator.make_tensor(frame)
-                self.video_maker.images_to_video_pipeline(
+                        if prev_tensor is None:
-                    generator, part_path, width, height, fps
+                            prev_tensor = tensor
-                )
+                            continue
-                interpolated_frames = []
+                        interpolated_frames.append(
-                logging.info(f"Finished processing part {part:08d}")
+                            tensor2img(
-                part += 1
+                                self.interpolator.interpolate(
-            for i in tqdm.tqdm(
+                                    prev_tensor, tensor
-                range(len(frames) - 1),
+                                )
-                desc=f"Processing video frames {part + 1} / {total_parts}",
+                            )
-            ):
+                        )
-                img1 = frames[i]
+                        prev_tensor = tensor
-                img2 = frames[i + 1]
-                img1_2 = self.interpolator.interpolate(img1, img2)
-                interpolated_frames.append(img1_2)
-            prev_frames = frames
 
-        generator = self._frame_generator(prev_frames, interpolated_frames)
+                    generator = self._frame_generator(frames, interpolated_frames)
-        part_path = self.fs.video_part_path / f"video_{part:08d}.mp4"
+                    part_path = self.fs.video_part_path / f"video_{idx:08d}.mp4"
-        self.video_maker.images_to_video_pipeline(
+                    self.video_maker.images_to_video_pipeline(
-            generator, part_path, width, height, fps
+                        generator, part_path, width, height, fps
-        )
+                    )
-        logging.info(f"Finished processing part {part:08d}")
+                self._merge_video_parts(self.fs.output_path / output_video)
-        self._merge_video_parts(self.fs.output_path / output_video)
-        logging.info(
-            f"Video interpolation completed. Output saved to: {self.fs.output_path / output_video}"
-        )
-
-    def _save_images(
-        self,
-        source: tuple["np.ndarray", ...],
-        interpolated: list["np.ndarray"],
-    ):
-        logging.info("Saving images...")
-        self.fs.clear_directory(self.fs.moved_path)
-        index = 0
-        for i, frame in enumerate(source):
-            name = self.fs.moved_path / f"img_{index:08d}.png"
-            index += 1
-            imwrite(name, frame)
-            if i < len(interpolated):
-                name = self.fs.moved_path / f"img_{index:08d}.png"
-                index += 1
-                imwrite(name, interpolated[i])
-        logging.info("Success...")
-
-    def _merge_frames_to_video(self, output_video: Path, fps: float):
-        self.video_maker.images_to_video(self.fs.moved_path, output_video, fps)
 
     def _merge_video_parts(self, output_video: Path):
         self.video_maker.concatenate_videos(self.fs.video_part_path, output_video)
@@ -176,10 +152,17 @@ class InterpolationPipeline:
         source: tuple["np.ndarray", ...],
         interpolated: list["np.ndarray"],
     ):
-        for i, frame in enumerate(source):
+        if len(source) == len(interpolated):
+            first = interpolated
+            second = source
+        else:
+            first = source
+            second = interpolated
+
+        for i, frame in enumerate(first):
             yield frame
-            if i < len(interpolated):
+            if i < len(second):
-                yield interpolated[i]
+                yield second[i]
 
 
 def run(

src/utils/padder.py

@@ -21,10 +21,7 @@ class InputPadder:
         ]
 
     def pad(self, *inputs: "torch.Tensor"):
-        if len(inputs) == 1:
+        return [F.pad(x, self._pad, mode="replicate") for x in inputs]
-            return F.pad(inputs[0], self._pad, mode="replicate")
-        else:
-            return [F.pad(x, self._pad, mode="replicate") for x in inputs]
 
     def unpad(self, *inputs: "torch.Tensor"):
         return [self._unpad(x) for x in inputs]

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 
 [[package]]
@@ -7,7 +7,6 @@ name = "amt-apple"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
-    { name = "imageio" },
     { name = "numpy" },
     { name = "omegaconf" },
     { name = "opencv-python" },
@@ -17,7 +16,6 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
-    { name = "imageio", specifier = ">=2.37.3" },
     { name = "numpy", specifier = ">=2.4.4" },
     { name = "omegaconf", specifier = ">=2.3.0" },
     { name = "opencv-python", specifier = ">=4.13.0.92" },
@@ -127,19 +125,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d5/1f/5f4a3cd9e4440e9d9bc78ad0a91a1c8d46b4d429d5239ebe6793c9fe5c41/fsspec-2026.3.0-py3-none-any.whl", hash = "sha256:d2ceafaad1b3457968ed14efa28798162f1638dbb5d2a6868a2db002a5ee39a4", size = 202595, upload-time = "2026-03-27T19:11:13.595Z" },
 ]
 
-[[package]]
-name = "imageio"
-version = "2.37.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "pillow" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/b1/84/93bcd1300216ea50811cee96873b84a1bebf8d0489ffaf7f2a3756bab866/imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451", size = 389673, upload-time = "2026-03-09T11:31:12.573Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/49/fa/391e437a34e55095173dca5f24070d89cbc233ff85bf1c29c93248c6588d/imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0", size = 317646, upload-time = "2026-03-09T11:31:10.771Z" },
-]
-
 [[package]]
 name = "jinja2"
 version = "3.1.6"
@@ -474,75 +459,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e9/a5/1be1516390333ff9be3a9cb648c9f33df79d5096e5884b5df71a588af463/opencv_python-4.13.0.92-cp37-abi3-win_amd64.whl", hash = "sha256:423d934c9fafb91aad38edf26efb46da91ffbc05f3f59c4b0c72e699720706f5", size = 40212062, upload-time = "2026-02-05T07:02:12.724Z" },
 ]
 
-[[package]]
-name = "pillow"
-version = "12.1.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/1f/42/5c74462b4fd957fcd7b13b04fb3205ff8349236ea74c7c375766d6c82288/pillow-12.1.1.tar.gz", hash = "sha256:9ad8fa5937ab05218e2b6a4cff30295ad35afd2f83ac592e68c0d871bb0fdbc4", size = 46980264, upload-time = "2026-02-11T04:23:07.146Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/07/d3/8df65da0d4df36b094351dce696f2989bec731d4f10e743b1c5f4da4d3bf/pillow-12.1.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ab323b787d6e18b3d91a72fc99b1a2c28651e4358749842b8f8dfacd28ef2052", size = 5262803, upload-time = "2026-02-11T04:20:47.653Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/71/5026395b290ff404b836e636f51d7297e6c83beceaa87c592718747e670f/pillow-12.1.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:adebb5bee0f0af4909c30db0d890c773d1a92ffe83da908e2e9e720f8edf3984", size = 4657601, upload-time = "2026-02-11T04:20:49.328Z" },
-    { url = "https://files.pythonhosted.org/packages/b1/2e/1001613d941c67442f745aff0f7cc66dd8df9a9c084eb497e6a543ee6f7e/pillow-12.1.1-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:bb66b7cc26f50977108790e2456b7921e773f23db5630261102233eb355a3b79", size = 6234995, upload-time = "2026-02-11T04:20:51.032Z" },
-    { url = "https://files.pythonhosted.org/packages/07/26/246ab11455b2549b9233dbd44d358d033a2f780fa9007b61a913c5b2d24e/pillow-12.1.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aee2810642b2898bb187ced9b349e95d2a7272930796e022efaf12e99dccd293", size = 8045012, upload-time = "2026-02-11T04:20:52.882Z" },
-    { url = "https://files.pythonhosted.org/packages/b2/8b/07587069c27be7535ac1fe33874e32de118fbd34e2a73b7f83436a88368c/pillow-12.1.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:a0b1cd6232e2b618adcc54d9882e4e662a089d5768cd188f7c245b4c8c44a397", size = 6349638, upload-time = "2026-02-11T04:20:54.444Z" },
-    { url = "https://files.pythonhosted.org/packages/ff/79/6df7b2ee763d619cda2fb4fea498e5f79d984dae304d45a8999b80d6cf5c/pillow-12.1.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7aac39bcf8d4770d089588a2e1dd111cbaa42df5a94be3114222057d68336bd0", size = 7041540, upload-time = "2026-02-11T04:20:55.97Z" },
-    { url = "https://files.pythonhosted.org/packages/2c/5e/2ba19e7e7236d7529f4d873bdaf317a318896bac289abebd4bb00ef247f0/pillow-12.1.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ab174cd7d29a62dd139c44bf74b698039328f45cb03b4596c43473a46656b2f3", size = 6462613, upload-time = "2026-02-11T04:20:57.542Z" },
-    { url = "https://files.pythonhosted.org/packages/03/03/31216ec124bb5c3dacd74ce8efff4cc7f52643653bad4825f8f08c697743/pillow-12.1.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:339ffdcb7cbeaa08221cd401d517d4b1fe7a9ed5d400e4a8039719238620ca35", size = 7166745, upload-time = "2026-02-11T04:20:59.196Z" },
-    { url = "https://files.pythonhosted.org/packages/1f/e7/7c4552d80052337eb28653b617eafdef39adfb137c49dd7e831b8dc13bc5/pillow-12.1.1-cp312-cp312-win32.whl", hash = "sha256:5d1f9575a12bed9e9eedd9a4972834b08c97a352bd17955ccdebfeca5913fa0a", size = 6328823, upload-time = "2026-02-11T04:21:01.385Z" },
-    { url = "https://files.pythonhosted.org/packages/3d/17/688626d192d7261bbbf98846fc98995726bddc2c945344b65bec3a29d731/pillow-12.1.1-cp312-cp312-win_amd64.whl", hash = "sha256:21329ec8c96c6e979cd0dfd29406c40c1d52521a90544463057d2aaa937d66a6", size = 7033367, upload-time = "2026-02-11T04:21:03.536Z" },
-    { url = "https://files.pythonhosted.org/packages/ed/fe/a0ef1f73f939b0eca03ee2c108d0043a87468664770612602c63266a43c4/pillow-12.1.1-cp312-cp312-win_arm64.whl", hash = "sha256:af9a332e572978f0218686636610555ae3defd1633597be015ed50289a03c523", size = 2453811, upload-time = "2026-02-11T04:21:05.116Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/11/6db24d4bd7685583caeae54b7009584e38da3c3d4488ed4cd25b439de486/pillow-12.1.1-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:d242e8ac078781f1de88bf823d70c1a9b3c7950a44cdf4b7c012e22ccbcd8e4e", size = 4062689, upload-time = "2026-02-11T04:21:06.804Z" },
-    { url = "https://files.pythonhosted.org/packages/33/c0/ce6d3b1fe190f0021203e0d9b5b99e57843e345f15f9ef22fcd43842fd21/pillow-12.1.1-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:02f84dfad02693676692746df05b89cf25597560db2857363a208e393429f5e9", size = 4138535, upload-time = "2026-02-11T04:21:08.452Z" },
-    { url = "https://files.pythonhosted.org/packages/a0/c6/d5eb6a4fb32a3f9c21a8c7613ec706534ea1cf9f4b3663e99f0d83f6fca8/pillow-12.1.1-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:e65498daf4b583091ccbb2556c7000abf0f3349fcd57ef7adc9a84a394ed29f6", size = 3601364, upload-time = "2026-02-11T04:21:10.194Z" },
-    { url = "https://files.pythonhosted.org/packages/14/a1/16c4b823838ba4c9c52c0e6bbda903a3fe5a1bdbf1b8eb4fff7156f3e318/pillow-12.1.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:6c6db3b84c87d48d0088943bf33440e0c42370b99b1c2a7989216f7b42eede60", size = 5262561, upload-time = "2026-02-11T04:21:11.742Z" },
-    { url = "https://files.pythonhosted.org/packages/bb/ad/ad9dc98ff24f485008aa5cdedaf1a219876f6f6c42a4626c08bc4e80b120/pillow-12.1.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:8b7e5304e34942bf62e15184219a7b5ad4ff7f3bb5cca4d984f37df1a0e1aee2", size = 4657460, upload-time = "2026-02-11T04:21:13.786Z" },
-    { url = "https://files.pythonhosted.org/packages/9e/1b/f1a4ea9a895b5732152789326202a82464d5254759fbacae4deea3069334/pillow-12.1.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:18e5bddd742a44b7e6b1e773ab5db102bd7a94c32555ba656e76d319d19c3850", size = 6232698, upload-time = "2026-02-11T04:21:15.949Z" },
-    { url = "https://files.pythonhosted.org/packages/95/f4/86f51b8745070daf21fd2e5b1fe0eb35d4db9ca26e6d58366562fb56a743/pillow-12.1.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fc44ef1f3de4f45b50ccf9136999d71abb99dca7706bc75d222ed350b9fd2289", size = 8041706, upload-time = "2026-02-11T04:21:17.723Z" },
-    { url = "https://files.pythonhosted.org/packages/29/9b/d6ecd956bb1266dd1045e995cce9b8d77759e740953a1c9aad9502a0461e/pillow-12.1.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5a8eb7ed8d4198bccbd07058416eeec51686b498e784eda166395a23eb99138e", size = 6346621, upload-time = "2026-02-11T04:21:19.547Z" },
-    { url = "https://files.pythonhosted.org/packages/71/24/538bff45bde96535d7d998c6fed1a751c75ac7c53c37c90dc2601b243893/pillow-12.1.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47b94983da0c642de92ced1702c5b6c292a84bd3a8e1d1702ff923f183594717", size = 7038069, upload-time = "2026-02-11T04:21:21.378Z" },
-    { url = "https://files.pythonhosted.org/packages/94/0e/58cb1a6bc48f746bc4cb3adb8cabff73e2742c92b3bf7a220b7cf69b9177/pillow-12.1.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:518a48c2aab7ce596d3bf79d0e275661b846e86e4d0e7dec34712c30fe07f02a", size = 6460040, upload-time = "2026-02-11T04:21:23.148Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/57/9045cb3ff11eeb6c1adce3b2d60d7d299d7b273a2e6c8381a524abfdc474/pillow-12.1.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:a550ae29b95c6dc13cf69e2c9dc5747f814c54eeb2e32d683e5e93af56caa029", size = 7164523, upload-time = "2026-02-11T04:21:25.01Z" },
-    { url = "https://files.pythonhosted.org/packages/73/f2/9be9cb99f2175f0d4dbadd6616ce1bf068ee54a28277ea1bf1fbf729c250/pillow-12.1.1-cp313-cp313-win32.whl", hash = "sha256:a003d7422449f6d1e3a34e3dd4110c22148336918ddbfc6a32581cd54b2e0b2b", size = 6332552, upload-time = "2026-02-11T04:21:27.238Z" },
-    { url = "https://files.pythonhosted.org/packages/3f/eb/b0834ad8b583d7d9d42b80becff092082a1c3c156bb582590fcc973f1c7c/pillow-12.1.1-cp313-cp313-win_amd64.whl", hash = "sha256:344cf1e3dab3be4b1fa08e449323d98a2a3f819ad20f4b22e77a0ede31f0faa1", size = 7040108, upload-time = "2026-02-11T04:21:29.462Z" },
-    { url = "https://files.pythonhosted.org/packages/d5/7d/fc09634e2aabdd0feabaff4a32f4a7d97789223e7c2042fd805ea4b4d2c2/pillow-12.1.1-cp313-cp313-win_arm64.whl", hash = "sha256:5c0dd1636633e7e6a0afe7bf6a51a14992b7f8e60de5789018ebbdfae55b040a", size = 2453712, upload-time = "2026-02-11T04:21:31.072Z" },
-    { url = "https://files.pythonhosted.org/packages/19/2a/b9d62794fc8a0dd14c1943df68347badbd5511103e0d04c035ffe5cf2255/pillow-12.1.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0330d233c1a0ead844fc097a7d16c0abff4c12e856c0b325f231820fee1f39da", size = 5264880, upload-time = "2026-02-11T04:21:32.865Z" },
-    { url = "https://files.pythonhosted.org/packages/26/9d/e03d857d1347fa5ed9247e123fcd2a97b6220e15e9cb73ca0a8d91702c6e/pillow-12.1.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5dae5f21afb91322f2ff791895ddd8889e5e947ff59f71b46041c8ce6db790bc", size = 4660616, upload-time = "2026-02-11T04:21:34.97Z" },
-    { url = "https://files.pythonhosted.org/packages/f7/ec/8a6d22afd02570d30954e043f09c32772bfe143ba9285e2fdb11284952cd/pillow-12.1.1-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2e0c664be47252947d870ac0d327fea7e63985a08794758aa8af5b6cb6ec0c9c", size = 6269008, upload-time = "2026-02-11T04:21:36.623Z" },
-    { url = "https://files.pythonhosted.org/packages/3d/1d/6d875422c9f28a4a361f495a5f68d9de4a66941dc2c619103ca335fa6446/pillow-12.1.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:691ab2ac363b8217f7d31b3497108fb1f50faab2f75dfb03284ec2f217e87bf8", size = 8073226, upload-time = "2026-02-11T04:21:38.585Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/cd/134b0b6ee5eda6dc09e25e24b40fdafe11a520bc725c1d0bbaa5e00bf95b/pillow-12.1.1-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e9e8064fb1cc019296958595f6db671fba95209e3ceb0c4734c9baf97de04b20", size = 6380136, upload-time = "2026-02-11T04:21:40.562Z" },
-    { url = "https://files.pythonhosted.org/packages/7a/a9/7628f013f18f001c1b98d8fffe3452f306a70dc6aba7d931019e0492f45e/pillow-12.1.1-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:472a8d7ded663e6162dafdf20015c486a7009483ca671cece7a9279b512fcb13", size = 7067129, upload-time = "2026-02-11T04:21:42.521Z" },
-    { url = "https://files.pythonhosted.org/packages/1e/f8/66ab30a2193b277785601e82ee2d49f68ea575d9637e5e234faaa98efa4c/pillow-12.1.1-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:89b54027a766529136a06cfebeecb3a04900397a3590fd252160b888479517bf", size = 6491807, upload-time = "2026-02-11T04:21:44.22Z" },
-    { url = "https://files.pythonhosted.org/packages/da/0b/a877a6627dc8318fdb84e357c5e1a758c0941ab1ddffdafd231983788579/pillow-12.1.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:86172b0831b82ce4f7877f280055892b31179e1576aa00d0df3bb1bbf8c3e524", size = 7190954, upload-time = "2026-02-11T04:21:46.114Z" },
-    { url = "https://files.pythonhosted.org/packages/83/43/6f732ff85743cf746b1361b91665d9f5155e1483817f693f8d57ea93147f/pillow-12.1.1-cp313-cp313t-win32.whl", hash = "sha256:44ce27545b6efcf0fdbdceb31c9a5bdea9333e664cda58a7e674bb74608b3986", size = 6336441, upload-time = "2026-02-11T04:21:48.22Z" },
-    { url = "https://files.pythonhosted.org/packages/3b/44/e865ef3986611bb75bfabdf94a590016ea327833f434558801122979cd0e/pillow-12.1.1-cp313-cp313t-win_amd64.whl", hash = "sha256:a285e3eb7a5a45a2ff504e31f4a8d1b12ef62e84e5411c6804a42197c1cf586c", size = 7045383, upload-time = "2026-02-11T04:21:50.015Z" },
-    { url = "https://files.pythonhosted.org/packages/a8/c6/f4fb24268d0c6908b9f04143697ea18b0379490cb74ba9e8d41b898bd005/pillow-12.1.1-cp313-cp313t-win_arm64.whl", hash = "sha256:cc7d296b5ea4d29e6570dabeaed58d31c3fea35a633a69679fb03d7664f43fb3", size = 2456104, upload-time = "2026-02-11T04:21:51.633Z" },
-    { url = "https://files.pythonhosted.org/packages/03/d0/bebb3ffbf31c5a8e97241476c4cf8b9828954693ce6744b4a2326af3e16b/pillow-12.1.1-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:417423db963cb4be8bac3fc1204fe61610f6abeed1580a7a2cbb2fbda20f12af", size = 4062652, upload-time = "2026-02-11T04:21:53.19Z" },
-    { url = "https://files.pythonhosted.org/packages/2d/c0/0e16fb0addda4851445c28f8350d8c512f09de27bbb0d6d0bbf8b6709605/pillow-12.1.1-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:b957b71c6b2387610f556a7eb0828afbe40b4a98036fc0d2acfa5a44a0c2036f", size = 4138823, upload-time = "2026-02-11T04:22:03.088Z" },
-    { url = "https://files.pythonhosted.org/packages/6b/fb/6170ec655d6f6bb6630a013dd7cf7bc218423d7b5fa9071bf63dc32175ae/pillow-12.1.1-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:097690ba1f2efdeb165a20469d59d8bb03c55fb6621eb2041a060ae8ea3e9642", size = 3601143, upload-time = "2026-02-11T04:22:04.909Z" },
-    { url = "https://files.pythonhosted.org/packages/59/04/dc5c3f297510ba9a6837cbb318b87dd2b8f73eb41a43cc63767f65cb599c/pillow-12.1.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:2815a87ab27848db0321fb78c7f0b2c8649dee134b7f2b80c6a45c6831d75ccd", size = 5266254, upload-time = "2026-02-11T04:22:07.656Z" },
-    { url = "https://files.pythonhosted.org/packages/05/30/5db1236b0d6313f03ebf97f5e17cda9ca060f524b2fcc875149a8360b21c/pillow-12.1.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:f7ed2c6543bad5a7d5530eb9e78c53132f93dfa44a28492db88b41cdab885202", size = 4657499, upload-time = "2026-02-11T04:22:09.613Z" },
-    { url = "https://files.pythonhosted.org/packages/6f/18/008d2ca0eb612e81968e8be0bbae5051efba24d52debf930126d7eaacbba/pillow-12.1.1-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:652a2c9ccfb556235b2b501a3a7cf3742148cd22e04b5625c5fe057ea3e3191f", size = 6232137, upload-time = "2026-02-11T04:22:11.434Z" },
-    { url = "https://files.pythonhosted.org/packages/70/f1/f14d5b8eeb4b2cd62b9f9f847eb6605f103df89ef619ac68f92f748614ea/pillow-12.1.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d6e4571eedf43af33d0fc233a382a76e849badbccdf1ac438841308652a08e1f", size = 8042721, upload-time = "2026-02-11T04:22:13.321Z" },
-    { url = "https://files.pythonhosted.org/packages/5a/d6/17824509146e4babbdabf04d8171491fa9d776f7061ff6e727522df9bd03/pillow-12.1.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b574c51cf7d5d62e9be37ba446224b59a2da26dc4c1bb2ecbe936a4fb1a7cb7f", size = 6347798, upload-time = "2026-02-11T04:22:15.449Z" },
-    { url = "https://files.pythonhosted.org/packages/d1/ee/c85a38a9ab92037a75615aba572c85ea51e605265036e00c5b67dfafbfe2/pillow-12.1.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a37691702ed687799de29a518d63d4682d9016932db66d4e90c345831b02fb4e", size = 7039315, upload-time = "2026-02-11T04:22:17.24Z" },
-    { url = "https://files.pythonhosted.org/packages/ec/f3/bc8ccc6e08a148290d7523bde4d9a0d6c981db34631390dc6e6ec34cacf6/pillow-12.1.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:f95c00d5d6700b2b890479664a06e754974848afaae5e21beb4d83c106923fd0", size = 6462360, upload-time = "2026-02-11T04:22:19.111Z" },
-    { url = "https://files.pythonhosted.org/packages/f6/ab/69a42656adb1d0665ab051eec58a41f169ad295cf81ad45406963105408f/pillow-12.1.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:559b38da23606e68681337ad74622c4dbba02254fc9cb4488a305dd5975c7eeb", size = 7165438, upload-time = "2026-02-11T04:22:21.041Z" },
-    { url = "https://files.pythonhosted.org/packages/02/46/81f7aa8941873f0f01d4b55cc543b0a3d03ec2ee30d617a0448bf6bd6dec/pillow-12.1.1-cp314-cp314-win32.whl", hash = "sha256:03edcc34d688572014ff223c125a3f77fb08091e4607e7745002fc214070b35f", size = 6431503, upload-time = "2026-02-11T04:22:22.833Z" },
-    { url = "https://files.pythonhosted.org/packages/40/72/4c245f7d1044b67affc7f134a09ea619d4895333d35322b775b928180044/pillow-12.1.1-cp314-cp314-win_amd64.whl", hash = "sha256:50480dcd74fa63b8e78235957d302d98d98d82ccbfac4c7e12108ba9ecbdba15", size = 7176748, upload-time = "2026-02-11T04:22:24.64Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/ad/8a87bdbe038c5c698736e3348af5c2194ffb872ea52f11894c95f9305435/pillow-12.1.1-cp314-cp314-win_arm64.whl", hash = "sha256:5cb1785d97b0c3d1d1a16bc1d710c4a0049daefc4935f3a8f31f827f4d3d2e7f", size = 2544314, upload-time = "2026-02-11T04:22:26.685Z" },
-    { url = "https://files.pythonhosted.org/packages/6c/9d/efd18493f9de13b87ede7c47e69184b9e859e4427225ea962e32e56a49bc/pillow-12.1.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:1f90cff8aa76835cba5769f0b3121a22bd4eb9e6884cfe338216e557a9a548b8", size = 5268612, upload-time = "2026-02-11T04:22:29.884Z" },
-    { url = "https://files.pythonhosted.org/packages/f8/f1/4f42eb2b388eb2ffc660dcb7f7b556c1015c53ebd5f7f754965ef997585b/pillow-12.1.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1f1be78ce9466a7ee64bfda57bdba0f7cc499d9794d518b854816c41bf0aa4e9", size = 4660567, upload-time = "2026-02-11T04:22:31.799Z" },
-    { url = "https://files.pythonhosted.org/packages/01/54/df6ef130fa43e4b82e32624a7b821a2be1c5653a5fdad8469687a7db4e00/pillow-12.1.1-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:42fc1f4677106188ad9a55562bbade416f8b55456f522430fadab3cef7cd4e60", size = 6269951, upload-time = "2026-02-11T04:22:33.921Z" },
-    { url = "https://files.pythonhosted.org/packages/a9/48/618752d06cc44bb4aae8ce0cd4e6426871929ed7b46215638088270d9b34/pillow-12.1.1-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:98edb152429ab62a1818039744d8fbb3ccab98a7c29fc3d5fcef158f3f1f68b7", size = 8074769, upload-time = "2026-02-11T04:22:35.877Z" },
-    { url = "https://files.pythonhosted.org/packages/c3/bd/f1d71eb39a72fa088d938655afba3e00b38018d052752f435838961127d8/pillow-12.1.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d470ab1178551dd17fdba0fef463359c41aaa613cdcd7ff8373f54be629f9f8f", size = 6381358, upload-time = "2026-02-11T04:22:37.698Z" },
-    { url = "https://files.pythonhosted.org/packages/64/ef/c784e20b96674ed36a5af839305f55616f8b4f8aa8eeccf8531a6e312243/pillow-12.1.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6408a7b064595afcab0a49393a413732a35788f2a5092fdc6266952ed67de586", size = 7068558, upload-time = "2026-02-11T04:22:39.597Z" },
-    { url = "https://files.pythonhosted.org/packages/73/cb/8059688b74422ae61278202c4e1ad992e8a2e7375227be0a21c6b87ca8d5/pillow-12.1.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:5d8c41325b382c07799a3682c1c258469ea2ff97103c53717b7893862d0c98ce", size = 6493028, upload-time = "2026-02-11T04:22:42.73Z" },
-    { url = "https://files.pythonhosted.org/packages/c6/da/e3c008ed7d2dd1f905b15949325934510b9d1931e5df999bb15972756818/pillow-12.1.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:c7697918b5be27424e9ce568193efd13d925c4481dd364e43f5dff72d33e10f8", size = 7191940, upload-time = "2026-02-11T04:22:44.543Z" },
-    { url = "https://files.pythonhosted.org/packages/01/4a/9202e8d11714c1fc5951f2e1ef362f2d7fbc595e1f6717971d5dd750e969/pillow-12.1.1-cp314-cp314t-win32.whl", hash = "sha256:d2912fd8114fc5545aa3a4b5576512f64c55a03f3ebcca4c10194d593d43ea36", size = 6438736, upload-time = "2026-02-11T04:22:46.347Z" },
-    { url = "https://files.pythonhosted.org/packages/f3/ca/cbce2327eb9885476b3957b2e82eb12c866a8b16ad77392864ad601022ce/pillow-12.1.1-cp314-cp314t-win_amd64.whl", hash = "sha256:4ceb838d4bd9dab43e06c363cab2eebf63846d6a4aeaea283bbdfd8f1a8ed58b", size = 7182894, upload-time = "2026-02-11T04:22:48.114Z" },
-    { url = "https://files.pythonhosted.org/packages/ec/d2/de599c95ba0a973b94410477f8bf0b6f0b5e67360eb89bcb1ad365258beb/pillow-12.1.1-cp314-cp314t-win_arm64.whl", hash = "sha256:7b03048319bfc6170e93bd60728a1af51d3dd7704935feb228c4d4faab35d334", size = 2546446, upload-time = "2026-02-11T04:22:50.342Z" },
-]
-
 [[package]]
 name = "pyyaml"
 version = "6.0.3"