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Update swinir.py
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C43H66N12O12S2
AUTOMATIC1111
parent
5f71ecfe6f
commit
d8ed699839
@ -1,61 +1,85 @@
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import sys
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import traceback
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import cv2
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from collections import OrderedDict
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import os
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import requests
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from collections import namedtuple
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import contextlib
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import numpy as np
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from PIL import Image
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import torch
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import modules.images
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from modules.shared import cmd_opts, opts, device
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from modules.swinir_arch import SwinIR as net
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precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
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def load_model(task = "realsr", large_model = True, model_path="C:/sd/ESRGANn/4x-large.pth", scale=4):
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precision_scope = (
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torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
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)
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def load_model(filename, scale=4):
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model = net(
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upscale=scale,
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in_chans=3,
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img_size=64,
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window_size=8,
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img_range=1.0,
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depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
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embed_dim=240,
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num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
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mlp_ratio=2,
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upsampler="nearest+conv",
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resi_connection="3conv",
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)
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pretrained_model = torch.load(filename)
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model.load_state_dict(pretrained_model["params_ema"], strict=True)
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if not cmd_opts.no_half:
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model = model.half()
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return model
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def load_models(dirname):
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for file in os.listdir(dirname):
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path = os.path.join(dirname, file)
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model_name, extension = os.path.splitext(file)
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if extension != ".pt" and extension != ".pth":
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continue
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try:
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modules.shared.sd_upscalers.append(UpscalerSwin("McSwinnySwin"))
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modules.shared.sd_upscalers.append(UpscalerSwin(path, model_name))
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except Exception:
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print(f"Error loading ESRGAN model", file=sys.stderr)
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print(f"Error loading SwinIR model: {path}", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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if not large_model:
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# use 'nearest+conv' to avoid block artifacts
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model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
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img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
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mlp_ratio=2, upsampler='nearest+conv', resi_connection='1conv')
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else:
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# larger model size; use '3conv' to save parameters and memory; use ema for GAN training
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model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
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img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240,
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num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
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mlp_ratio=2, upsampler='nearest+conv', resi_connection='3conv')
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pretrained_model = torch.load(model_path)
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model.load_state_dict(pretrained_model["params_ema"], strict=True)
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return model.half().to(device)
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def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, window_size = 8, scale = 4):
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def upscale(
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img,
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model,
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tile=opts.GAN_tile,
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tile_overlap=opts.GAN_tile_overlap,
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window_size=8,
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scale=4,
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):
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img = np.array(img)
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img = img[:, :, ::-1]
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img = np.moveaxis(img, 2, 0) / 255
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img = torch.from_numpy(img).float()
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img = img.unsqueeze(0).to(device)
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model = load_model()
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with torch.no_grad(), precision_scope("cuda"):
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_, _, h_old, w_old = img.size()
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h_pad = (h_old // window_size + 1) * window_size - h_old
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w_pad = (w_old // window_size + 1) * window_size - w_old
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img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, :h_old + h_pad, :]
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img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w_old + w_pad]
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img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
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img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
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output = inference(img, model, tile, tile_overlap, window_size, scale)
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output = output[..., :h_old * scale, :w_old * scale]
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output = output[..., : h_old * scale, : w_old * scale]
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output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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if output.ndim == 3:
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output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
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output = np.transpose(
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output[[2, 1, 0], :, :], (1, 2, 0)
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) # CHW-RGB to HCW-BGR
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output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
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return Image.fromarray(output, 'RGB')
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return Image.fromarray(output, "RGB")
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def inference(img, model, tile, tile_overlap, window_size, scale):
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@ -66,27 +90,34 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
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sf = scale
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stride = tile - tile_overlap
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h_idx_list = list(range(0, h-tile, stride)) + [h-tile]
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w_idx_list = list(range(0, w-tile, stride)) + [w-tile]
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E = torch.zeros(b, c, h*sf, w*sf, dtype=torch.half, device=device).type_as(img)
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h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
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w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
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E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
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W = torch.zeros_like(E, dtype=torch.half, device=device)
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for h_idx in h_idx_list:
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for w_idx in w_idx_list:
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in_patch = img[..., h_idx:h_idx+tile, w_idx:w_idx+tile]
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in_patch = img[..., h_idx : h_idx + tile, w_idx : w_idx + tile]
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out_patch = model(in_patch)
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out_patch_mask = torch.ones_like(out_patch)
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E[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch)
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W[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch_mask)
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E[
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..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
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].add_(out_patch)
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W[
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..., h_idx * sf : (h_idx + tile) * sf, w_idx * sf : (w_idx + tile) * sf
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].add_(out_patch_mask)
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output = E.div_(W)
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return output
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class UpscalerSwin(modules.images.Upscaler):
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def __init__(self, title):
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def __init__(self, filename, title):
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self.name = title
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self.model = load_model(filename)
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def do_upscale(self, img):
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img = upscale(img)
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model = self.model.to(device)
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img = upscale(img, model)
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return img
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