mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-10-31 03:20:40 +00:00
build: vendorize realesrgan
Removes lots of dependencies including problematic `grpcio` and `tb-nightly`
This commit is contained in:
parent
35ac8d64d7
commit
4bc78b9be5
3
Makefile
3
Makefile
@ -17,6 +17,8 @@ init: require_pyenv ## Setup a dev environment for local development.
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pip install --upgrade pip pip-tools
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pip-sync requirements-dev.txt
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pip install -e . --no-deps
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# the compiled requirements don't included OS specific subdependencies so we trigger those this way
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pip install `pip freeze | grep "^torch=="`
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@echo -e "\nEnvironment setup! ✨ 🍰 ✨ 🐍 \n\nCopy this path to tell PyCharm where your virtualenv is. You may have to click the refresh button in the pycharm file explorer.\n"
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@echo -e "\033[0;32m"
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@pyenv which python
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@ -86,7 +88,6 @@ vendor_openai_clip:
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revendorize: vendorize_kdiffusion
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make vendorize REPO=git@github.com:openai/CLIP.git PKG=clip COMMIT=d50d76daa670286dd6cacf3bcd80b5e4823fc8e1
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make af
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vendorize_clipseg:
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@ -3,11 +3,11 @@ from functools import lru_cache
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import numpy as np
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import torch
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from PIL import Image
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from realesrgan import RealESRGANer
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from imaginairy.model_manager import get_cached_url_path
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from imaginairy.utils import get_device
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from imaginairy.vendored.basicsr.rrdbnet_arch import RRDBNet
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from imaginairy.vendored.realesrgan import RealESRGANer
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@lru_cache
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354
imaginairy/vendored/realesrgan.py
Normal file
354
imaginairy/vendored/realesrgan.py
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@ -0,0 +1,354 @@
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import math
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import os
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import queue
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import threading
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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 torch.nn import functional as F
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from imaginairy.model_manager import get_cached_url_path
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ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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class RealESRGANer:
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"""A helper class for upsampling images with RealESRGAN.
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Args:
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scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
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model_path (str): The path to the pretrained model. It can be urls (will first download it automatically).
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model (nn.Module): The defined network. Default: None.
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tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
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input images into tiles, and then process each of them. Finally, they will be merged into one image.
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0 denotes for do not use tile. Default: 0.
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tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
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pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
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half (float): Whether to use half precision during inference. Default: False.
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"""
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def __init__(
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self,
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scale,
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model_path,
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dni_weight=None,
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model=None,
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tile=0,
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tile_pad=10,
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pre_pad=10,
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half=False,
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device=None,
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gpu_id=None,
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):
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self.scale = scale
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self.tile_size = tile
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self.tile_pad = tile_pad
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self.pre_pad = pre_pad
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self.mod_scale = None
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self.half = half
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# initialize model
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if gpu_id:
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self.device = (
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torch.device(f"cuda:{gpu_id}" if torch.cuda.is_available() else "cpu")
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if device is None
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else device
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)
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else:
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self.device = (
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torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if device is None
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else device
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)
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if isinstance(model_path, list):
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# dni
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assert len(model_path) == len(
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dni_weight
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), "model_path and dni_weight should have the save length."
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loadnet = self.dni(model_path[0], model_path[1], dni_weight)
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else:
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# if the model_path starts with https, it will first download models to the folder: weights
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if model_path.startswith("https://"):
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model_path = get_cached_url_path(model_path)
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loadnet = torch.load(model_path, map_location=torch.device("cpu"))
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# prefer to use params_ema
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if "params_ema" in loadnet:
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keyname = "params_ema"
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else:
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keyname = "params"
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model.load_state_dict(loadnet[keyname], strict=True)
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model.eval()
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self.model = model.to(self.device)
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if self.half:
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self.model = self.model.half()
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def dni(self, net_a, net_b, dni_weight, key="params", loc="cpu"):
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"""Deep network interpolation.
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``Paper: Deep Network Interpolation for Continuous Imagery Effect Transition``
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"""
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net_a = torch.load(net_a, map_location=torch.device(loc))
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net_b = torch.load(net_b, map_location=torch.device(loc))
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for k, v_a in net_a[key].items():
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net_a[key][k] = dni_weight[0] * v_a + dni_weight[1] * net_b[key][k]
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return net_a
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def pre_process(self, img):
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"""Pre-process, such as pre-pad and mod pad, so that the images can be divisible."""
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img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
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self.img = img.unsqueeze(0).to(self.device)
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if self.half:
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self.img = self.img.half()
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# pre_pad
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if self.pre_pad != 0:
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self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), "reflect")
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# mod pad for divisible borders
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if self.scale == 2:
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self.mod_scale = 2
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elif self.scale == 1:
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self.mod_scale = 4
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if self.mod_scale is not None:
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self.mod_pad_h, self.mod_pad_w = 0, 0
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_, _, h, w = self.img.size()
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if h % self.mod_scale != 0:
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self.mod_pad_h = self.mod_scale - h % self.mod_scale
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if w % self.mod_scale != 0:
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self.mod_pad_w = self.mod_scale - w % self.mod_scale
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self.img = F.pad(
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self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), "reflect"
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)
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def process(self):
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# model inference
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self.output = self.model(self.img)
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def tile_process(self):
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"""It will first crop input images to tiles, and then process each tile.
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Finally, all the processed tiles are merged into one images.
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Modified from: https://github.com/ata4/esrgan-launcher
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"""
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batch, channel, height, width = self.img.shape
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output_height = height * self.scale
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output_width = width * self.scale
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output_shape = (batch, channel, output_height, output_width)
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# start with black image
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self.output = self.img.new_zeros(output_shape)
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tiles_x = math.ceil(width / self.tile_size)
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tiles_y = math.ceil(height / self.tile_size)
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# loop over all tiles
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for y in range(tiles_y):
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for x in range(tiles_x):
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# extract tile from input image
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ofs_x = x * self.tile_size
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ofs_y = y * self.tile_size
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# input tile area on total image
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input_start_x = ofs_x
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input_end_x = min(ofs_x + self.tile_size, width)
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input_start_y = ofs_y
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input_end_y = min(ofs_y + self.tile_size, height)
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# input tile area on total image with padding
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input_start_x_pad = max(input_start_x - self.tile_pad, 0)
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input_end_x_pad = min(input_end_x + self.tile_pad, width)
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input_start_y_pad = max(input_start_y - self.tile_pad, 0)
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input_end_y_pad = min(input_end_y + self.tile_pad, height)
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# input tile dimensions
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input_tile_width = input_end_x - input_start_x
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input_tile_height = input_end_y - input_start_y
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tile_idx = y * tiles_x + x + 1
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input_tile = self.img[
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:,
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:,
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input_start_y_pad:input_end_y_pad,
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input_start_x_pad:input_end_x_pad,
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]
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# upscale tile
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try:
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with torch.no_grad():
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output_tile = self.model(input_tile)
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except RuntimeError as error:
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print("Error", error)
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print(f"\tTile {tile_idx}/{tiles_x * tiles_y}")
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# output tile area on total image
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output_start_x = input_start_x * self.scale
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output_end_x = input_end_x * self.scale
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output_start_y = input_start_y * self.scale
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output_end_y = input_end_y * self.scale
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# output tile area without padding
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output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
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output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
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output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
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output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
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# put tile into output image
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self.output[
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:, :, output_start_y:output_end_y, output_start_x:output_end_x
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] = output_tile[
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:,
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:,
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output_start_y_tile:output_end_y_tile,
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output_start_x_tile:output_end_x_tile,
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]
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def post_process(self):
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# remove extra pad
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if self.mod_scale is not None:
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_, _, h, w = self.output.size()
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self.output = self.output[
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:,
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:,
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0 : h - self.mod_pad_h * self.scale,
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0 : w - self.mod_pad_w * self.scale,
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]
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# remove prepad
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if self.pre_pad != 0:
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_, _, h, w = self.output.size()
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self.output = self.output[
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:,
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:,
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0 : h - self.pre_pad * self.scale,
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0 : w - self.pre_pad * self.scale,
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]
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return self.output
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@torch.no_grad()
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def enhance(self, img, outscale=None, alpha_upsampler="realesrgan"):
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h_input, w_input = img.shape[0:2]
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# img: numpy
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img = img.astype(np.float32)
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if np.max(img) > 256: # 16-bit image
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max_range = 65535
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print("\tInput is a 16-bit image")
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else:
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max_range = 255
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img = img / max_range
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if len(img.shape) == 2: # gray image
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img_mode = "L"
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
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elif img.shape[2] == 4: # RGBA image with alpha channel
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img_mode = "RGBA"
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alpha = img[:, :, 3]
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img = img[:, :, 0:3]
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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if alpha_upsampler == "realesrgan":
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alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
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else:
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img_mode = "RGB"
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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# ------------------- process image (without the alpha channel) ------------------- #
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self.pre_process(img)
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if self.tile_size > 0:
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self.tile_process()
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else:
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self.process()
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output_img = self.post_process()
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output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
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if img_mode == "L":
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output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
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# ------------------- process the alpha channel if necessary ------------------- #
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if img_mode == "RGBA":
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if alpha_upsampler == "realesrgan":
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self.pre_process(alpha)
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if self.tile_size > 0:
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self.tile_process()
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else:
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self.process()
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output_alpha = self.post_process()
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output_alpha = (
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output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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)
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output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
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output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
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else: # use the cv2 resize for alpha channel
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h, w = alpha.shape[0:2]
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output_alpha = cv2.resize(
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alpha,
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(w * self.scale, h * self.scale),
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interpolation=cv2.INTER_LINEAR,
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)
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# merge the alpha channel
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output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
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output_img[:, :, 3] = output_alpha
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# ------------------------------ return ------------------------------ #
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if max_range == 65535: # 16-bit image
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output = (output_img * 65535.0).round().astype(np.uint16)
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else:
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output = (output_img * 255.0).round().astype(np.uint8)
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if outscale is not None and outscale != float(self.scale):
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output = cv2.resize(
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output,
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(
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int(w_input * outscale),
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int(h_input * outscale),
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),
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interpolation=cv2.INTER_LANCZOS4,
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)
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return output, img_mode
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class PrefetchReader(threading.Thread):
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"""Prefetch images.
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Args:
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img_list (list[str]): A image list of image paths to be read.
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num_prefetch_queue (int): Number of prefetch queue.
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"""
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def __init__(self, img_list, num_prefetch_queue):
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super().__init__()
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self.que = queue.Queue(num_prefetch_queue)
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self.img_list = img_list
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def run(self):
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for img_path in self.img_list:
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img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
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self.que.put(img)
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self.que.put(None)
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def __next__(self):
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next_item = self.que.get()
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if next_item is None:
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raise StopIteration
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return next_item
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def __iter__(self):
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return self
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class IOConsumer(threading.Thread):
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def __init__(self, opt, que, qid):
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super().__init__()
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self._queue = que
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self.qid = qid
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self.opt = opt
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def run(self):
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while True:
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msg = self._queue.get()
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if isinstance(msg, str) and msg == "quit":
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break
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output = msg["output"]
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save_path = msg["save_path"]
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cv2.imwrite(save_path, output)
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print(f"IO worker {self.qid} is done.")
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@ -2,9 +2,11 @@ black
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coverage
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isort
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ruff
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pycln
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pylama
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pylint
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pytest
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pytest-randomly
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pytest-sugar
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responses
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wheel
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@ -4,10 +4,6 @@
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#
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# pip-compile --output-file=requirements-dev.txt --resolver=backtracking requirements-dev.in setup.py
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#
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absl-py==1.3.0
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# via tb-nightly
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addict==2.4.0
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# via basicsr
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aiohttp==3.8.3
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# via fsspec
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aiosignal==1.3.1
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@ -22,14 +18,8 @@ attrs==22.2.0
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# via
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# aiohttp
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# pytest
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basicsr==1.4.2
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# via
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# gfpgan
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# realesrgan
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black==22.12.0
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# via -r requirements-dev.in
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cachetools==5.2.1
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# via google-auth
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certifi==2022.12.7
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# via requests
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charset-normalizer==2.1.1
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@ -41,6 +31,7 @@ click==8.1.3
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# black
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# click-shell
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# imaginAIry (setup.py)
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# typer
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click-shell==2.1
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# via imaginAIry (setup.py)
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contourpy==1.0.6
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@ -58,9 +49,7 @@ einops==0.6.0
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exceptiongroup==1.1.0
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# via pytest
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facexlib==0.2.5
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# via
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# gfpgan
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# realesrgan
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# via imaginAIry (setup.py)
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fairscale==0.4.13
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# via imaginAIry (setup.py)
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filelock==3.9.0
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@ -82,18 +71,6 @@ ftfy==6.1.1
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# via
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# imaginAIry (setup.py)
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# open-clip-torch
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future==0.18.2
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# via basicsr
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gfpgan==1.3.8
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# via realesrgan
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google-auth==2.15.0
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# via
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||||
# google-auth-oauthlib
|
||||
# tb-nightly
|
||||
google-auth-oauthlib==0.4.6
|
||||
# via tb-nightly
|
||||
grpcio==1.51.1
|
||||
# via tb-nightly
|
||||
huggingface-hub==0.11.1
|
||||
# via
|
||||
# diffusers
|
||||
@ -105,9 +82,7 @@ idna==3.4
|
||||
# requests
|
||||
# yarl
|
||||
imageio==2.24.0
|
||||
# via
|
||||
# imaginAIry (setup.py)
|
||||
# scikit-image
|
||||
# via imaginAIry (setup.py)
|
||||
importlib-metadata==6.0.0
|
||||
# via diffusers
|
||||
iniconfig==2.0.0
|
||||
@ -122,18 +97,12 @@ kornia==0.6.9
|
||||
# via imaginAIry (setup.py)
|
||||
lazy-object-proxy==1.9.0
|
||||
# via astroid
|
||||
libcst==0.4.9
|
||||
# via pycln
|
||||
lightning-utilities==0.5.0
|
||||
# via pytorch-lightning
|
||||
llvmlite==0.39.1
|
||||
# via numba
|
||||
lmdb==1.4.0
|
||||
# via
|
||||
# basicsr
|
||||
# gfpgan
|
||||
markdown==3.4.1
|
||||
# via tb-nightly
|
||||
markupsafe==2.1.1
|
||||
# via werkzeug
|
||||
matplotlib==3.6.2
|
||||
# via filterpy
|
||||
mccabe==0.7.0
|
||||
@ -145,48 +114,37 @@ multidict==6.0.4
|
||||
# aiohttp
|
||||
# yarl
|
||||
mypy-extensions==0.4.3
|
||||
# via black
|
||||
networkx==3.0
|
||||
# via scikit-image
|
||||
# via
|
||||
# black
|
||||
# typing-inspect
|
||||
numba==0.56.4
|
||||
# via facexlib
|
||||
numpy==1.23.5
|
||||
# via
|
||||
# basicsr
|
||||
# contourpy
|
||||
# diffusers
|
||||
# facexlib
|
||||
# fairscale
|
||||
# filterpy
|
||||
# gfpgan
|
||||
# imageio
|
||||
# imaginAIry (setup.py)
|
||||
# matplotlib
|
||||
# numba
|
||||
# opencv-python
|
||||
# pytorch-lightning
|
||||
# pywavelets
|
||||
# realesrgan
|
||||
# scikit-image
|
||||
# scipy
|
||||
# tb-nightly
|
||||
# tensorboardx
|
||||
# tifffile
|
||||
# torchmetrics
|
||||
# torchvision
|
||||
# transformers
|
||||
oauthlib==3.2.2
|
||||
# via requests-oauthlib
|
||||
omegaconf==2.3.0
|
||||
# via imaginAIry (setup.py)
|
||||
open-clip-torch==2.9.2
|
||||
# via imaginAIry (setup.py)
|
||||
opencv-python==4.7.0.68
|
||||
# via
|
||||
# basicsr
|
||||
# facexlib
|
||||
# gfpgan
|
||||
# realesrgan
|
||||
# imaginAIry (setup.py)
|
||||
packaging==23.0
|
||||
# via
|
||||
# huggingface-hub
|
||||
@ -196,21 +154,19 @@ packaging==23.0
|
||||
# pytest
|
||||
# pytest-sugar
|
||||
# pytorch-lightning
|
||||
# scikit-image
|
||||
# torchmetrics
|
||||
# transformers
|
||||
pathspec==0.10.3
|
||||
# via black
|
||||
pathspec==0.9.0
|
||||
# via
|
||||
# black
|
||||
# pycln
|
||||
pillow==9.4.0
|
||||
# via
|
||||
# basicsr
|
||||
# diffusers
|
||||
# facexlib
|
||||
# imageio
|
||||
# imaginAIry (setup.py)
|
||||
# matplotlib
|
||||
# realesrgan
|
||||
# scikit-image
|
||||
# torchvision
|
||||
platformdirs==2.6.2
|
||||
# via
|
||||
@ -222,16 +178,11 @@ protobuf==3.20.1
|
||||
# via
|
||||
# imaginAIry (setup.py)
|
||||
# open-clip-torch
|
||||
# tb-nightly
|
||||
# tensorboardx
|
||||
psutil==5.9.4
|
||||
# via imaginAIry (setup.py)
|
||||
pyasn1==0.4.8
|
||||
# via
|
||||
# pyasn1-modules
|
||||
# rsa
|
||||
pyasn1-modules==0.2.8
|
||||
# via google-auth
|
||||
pycln==2.1.2
|
||||
# via -r requirements-dev.in
|
||||
pycodestyle==2.10.0
|
||||
# via pylama
|
||||
pydocstyle==6.2.3
|
||||
@ -257,19 +208,15 @@ python-dateutil==2.8.2
|
||||
# via matplotlib
|
||||
pytorch-lightning==1.8.6
|
||||
# via imaginAIry (setup.py)
|
||||
pywavelets==1.4.1
|
||||
# via scikit-image
|
||||
pyyaml==6.0
|
||||
# via
|
||||
# basicsr
|
||||
# gfpgan
|
||||
# huggingface-hub
|
||||
# libcst
|
||||
# omegaconf
|
||||
# pycln
|
||||
# pytorch-lightning
|
||||
# timm
|
||||
# transformers
|
||||
realesrgan==0.3.0
|
||||
# via imaginAIry (setup.py)
|
||||
regex==2022.10.31
|
||||
# via
|
||||
# diffusers
|
||||
@ -277,58 +224,34 @@ regex==2022.10.31
|
||||
# transformers
|
||||
requests==2.28.1
|
||||
# via
|
||||
# basicsr
|
||||
# diffusers
|
||||
# fsspec
|
||||
# huggingface-hub
|
||||
# imaginAIry (setup.py)
|
||||
# requests-oauthlib
|
||||
# responses
|
||||
# tb-nightly
|
||||
# torchvision
|
||||
# transformers
|
||||
requests-oauthlib==1.3.1
|
||||
# via google-auth-oauthlib
|
||||
responses==0.22.0
|
||||
# via -r requirements-dev.in
|
||||
rsa==4.9
|
||||
# via google-auth
|
||||
ruff==0.0.215
|
||||
# via -r requirements-dev.in
|
||||
safetensors==0.2.7
|
||||
# via imaginAIry (setup.py)
|
||||
scikit-image==0.19.3
|
||||
# via basicsr
|
||||
scipy==1.10.0
|
||||
# via
|
||||
# basicsr
|
||||
# facexlib
|
||||
# filterpy
|
||||
# gfpgan
|
||||
# scikit-image
|
||||
# torchdiffeq
|
||||
sentencepiece==0.1.97
|
||||
# via open-clip-torch
|
||||
six==1.16.0
|
||||
# via
|
||||
# google-auth
|
||||
# python-dateutil
|
||||
# via python-dateutil
|
||||
snowballstemmer==2.2.0
|
||||
# via pydocstyle
|
||||
tb-nightly==2.12.0a20230107
|
||||
# via
|
||||
# basicsr
|
||||
# gfpgan
|
||||
tensorboard-data-server==0.6.1
|
||||
# via tb-nightly
|
||||
tensorboard-plugin-wit==1.8.1
|
||||
# via tb-nightly
|
||||
tensorboardx==2.5.1
|
||||
# via pytorch-lightning
|
||||
termcolor==2.2.0
|
||||
# via pytest-sugar
|
||||
tifffile==2022.10.10
|
||||
# via scikit-image
|
||||
timm==0.6.12
|
||||
# via imaginAIry (setup.py)
|
||||
tokenizers==0.13.2
|
||||
@ -341,18 +264,17 @@ tomli==2.0.1
|
||||
# pylint
|
||||
# pytest
|
||||
tomlkit==0.11.6
|
||||
# via pylint
|
||||
# via
|
||||
# pycln
|
||||
# pylint
|
||||
torch==1.13.1
|
||||
# via
|
||||
# basicsr
|
||||
# facexlib
|
||||
# fairscale
|
||||
# gfpgan
|
||||
# imaginAIry (setup.py)
|
||||
# kornia
|
||||
# open-clip-torch
|
||||
# pytorch-lightning
|
||||
# realesrgan
|
||||
# timm
|
||||
# torchdiffeq
|
||||
# torchmetrics
|
||||
@ -365,52 +287,46 @@ torchmetrics==0.11.0
|
||||
# pytorch-lightning
|
||||
torchvision==0.14.1
|
||||
# via
|
||||
# basicsr
|
||||
# facexlib
|
||||
# gfpgan
|
||||
# imaginAIry (setup.py)
|
||||
# open-clip-torch
|
||||
# realesrgan
|
||||
# timm
|
||||
tqdm==4.64.1
|
||||
# via
|
||||
# basicsr
|
||||
# facexlib
|
||||
# gfpgan
|
||||
# huggingface-hub
|
||||
# imaginAIry (setup.py)
|
||||
# open-clip-torch
|
||||
# pytorch-lightning
|
||||
# realesrgan
|
||||
# transformers
|
||||
transformers==4.25.1
|
||||
# via imaginAIry (setup.py)
|
||||
typer==0.7.0
|
||||
# via pycln
|
||||
types-toml==0.10.8.1
|
||||
# via responses
|
||||
typing-extensions==4.4.0
|
||||
# via
|
||||
# astroid
|
||||
# huggingface-hub
|
||||
# libcst
|
||||
# lightning-utilities
|
||||
# pytorch-lightning
|
||||
# torch
|
||||
# torchvision
|
||||
# typing-inspect
|
||||
typing-inspect==0.8.0
|
||||
# via libcst
|
||||
urllib3==1.26.13
|
||||
# via
|
||||
# requests
|
||||
# responses
|
||||
wcwidth==0.2.5
|
||||
# via ftfy
|
||||
werkzeug==2.2.2
|
||||
# via tb-nightly
|
||||
wheel==0.38.4
|
||||
# via tb-nightly
|
||||
# via -r requirements-dev.in
|
||||
wrapt==1.14.1
|
||||
# via astroid
|
||||
yapf==0.32.0
|
||||
# via
|
||||
# basicsr
|
||||
# gfpgan
|
||||
yarl==1.8.2
|
||||
# via aiohttp
|
||||
zipp==3.11.0
|
||||
|
3
setup.py
3
setup.py
@ -37,6 +37,7 @@ setup(
|
||||
"click",
|
||||
"click-shell",
|
||||
"protobuf != 3.20.2, != 3.19.5",
|
||||
"facexlib",
|
||||
"fairscale>=0.4.4", # for vendored blip
|
||||
"ftfy", # for vendored clip
|
||||
"torch>=1.2.0",
|
||||
@ -49,6 +50,7 @@ setup(
|
||||
"pytorch-lightning>=1.4.2",
|
||||
"omegaconf>=2.1.1",
|
||||
"open-clip-torch",
|
||||
"opencv-python",
|
||||
"requests",
|
||||
"einops>=0.3.0",
|
||||
"safetensors",
|
||||
@ -58,6 +60,5 @@ setup(
|
||||
"torchmetrics>=0.6.0",
|
||||
"torchvision>=0.13.1",
|
||||
"kornia>=0.6",
|
||||
"realesrgan",
|
||||
],
|
||||
)
|
||||
|
Loading…
Reference in New Issue
Block a user