mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-16 08:12:12 +03:00
93 lines
4.0 KiB
Python
93 lines
4.0 KiB
Python
import sys
|
|
import traceback
|
|
import cv2
|
|
from collections import OrderedDict
|
|
import os
|
|
import requests
|
|
from collections import namedtuple
|
|
import numpy as np
|
|
from PIL import Image
|
|
import torch
|
|
import modules.images
|
|
from modules.shared import cmd_opts, opts, device
|
|
from modules.swinir_arch import SwinIR as net
|
|
precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
|
|
def load_model(task = "realsr", large_model = True, model_path="C:/sd/ESRGANn/4x-large.pth", scale=4):
|
|
|
|
try:
|
|
modules.shared.sd_upscalers.append(UpscalerSwin("McSwinnySwin"))
|
|
except Exception:
|
|
print(f"Error loading ESRGAN model", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
if not large_model:
|
|
# use 'nearest+conv' to avoid block artifacts
|
|
model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
|
|
img_range=1., depths=[6, 6, 6, 6, 6, 6], embed_dim=180, num_heads=[6, 6, 6, 6, 6, 6],
|
|
mlp_ratio=2, upsampler='nearest+conv', resi_connection='1conv')
|
|
else:
|
|
# larger model size; use '3conv' to save parameters and memory; use ema for GAN training
|
|
model = net(upscale=scale, in_chans=3, img_size=64, window_size=8,
|
|
img_range=1., depths=[6, 6, 6, 6, 6, 6, 6, 6, 6], embed_dim=240,
|
|
num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
|
|
mlp_ratio=2, upsampler='nearest+conv', resi_connection='3conv')
|
|
|
|
pretrained_model = torch.load(model_path)
|
|
model.load_state_dict(pretrained_model["params_ema"], strict=True)
|
|
|
|
return model.half().to(device)
|
|
|
|
def upscale(img, tile=opts.ESRGAN_tile, tile_overlap=opts.ESRGAN_tile_overlap, window_size = 8, scale = 4):
|
|
img = np.array(img)
|
|
img = img[:, :, ::-1]
|
|
img = np.moveaxis(img, 2, 0) / 255
|
|
img = torch.from_numpy(img).float()
|
|
img = img.unsqueeze(0).to(device)
|
|
model = load_model()
|
|
with torch.no_grad(), precision_scope("cuda"):
|
|
_, _, h_old, w_old = img.size()
|
|
h_pad = (h_old // window_size + 1) * window_size - h_old
|
|
w_pad = (w_old // window_size + 1) * window_size - w_old
|
|
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, :h_old + h_pad, :]
|
|
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w_old + w_pad]
|
|
output = inference(img, model, tile, tile_overlap, window_size, scale)
|
|
output = output[..., :h_old * scale, :w_old * scale]
|
|
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
|
|
if output.ndim == 3:
|
|
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
|
|
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
|
|
return Image.fromarray(output, 'RGB')
|
|
|
|
|
|
def inference(img, model, tile, tile_overlap, window_size, scale):
|
|
# test the image tile by tile
|
|
b, c, h, w = img.size()
|
|
tile = min(tile, h, w)
|
|
assert tile % window_size == 0, "tile size should be a multiple of window_size"
|
|
sf = scale
|
|
|
|
stride = tile - tile_overlap
|
|
h_idx_list = list(range(0, h-tile, stride)) + [h-tile]
|
|
w_idx_list = list(range(0, w-tile, stride)) + [w-tile]
|
|
E = torch.zeros(b, c, h*sf, w*sf, dtype=torch.half, device=device).type_as(img)
|
|
W = torch.zeros_like(E, dtype=torch.half, device=device)
|
|
|
|
for h_idx in h_idx_list:
|
|
for w_idx in w_idx_list:
|
|
in_patch = img[..., h_idx:h_idx+tile, w_idx:w_idx+tile]
|
|
out_patch = model(in_patch)
|
|
out_patch_mask = torch.ones_like(out_patch)
|
|
|
|
E[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch)
|
|
W[..., h_idx*sf:(h_idx+tile)*sf, w_idx*sf:(w_idx+tile)*sf].add_(out_patch_mask)
|
|
output = E.div_(W)
|
|
|
|
return output
|
|
|
|
class UpscalerSwin(modules.images.Upscaler):
|
|
def __init__(self, title):
|
|
self.name = title
|
|
|
|
def do_upscale(self, img):
|
|
img = upscale(img)
|
|
return img
|