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https://github.com/modelscope/DiffSynth-Studio.git
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119 lines
4.5 KiB
Python
119 lines
4.5 KiB
Python
import torch
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from einops import repeat
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from PIL import Image
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import numpy as np
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class ResidualDenseBlock(torch.nn.Module):
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def __init__(self, num_feat=64, num_grow_ch=32):
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super(ResidualDenseBlock, self).__init__()
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self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
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self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
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self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
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self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x):
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x1 = self.lrelu(self.conv1(x))
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x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
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x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
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x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
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x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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return x5 * 0.2 + x
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class RRDB(torch.nn.Module):
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def __init__(self, num_feat, num_grow_ch=32):
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super(RRDB, self).__init__()
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self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
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self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
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self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)
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def forward(self, x):
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out = self.rdb1(x)
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out = self.rdb2(out)
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out = self.rdb3(out)
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return out * 0.2 + x
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class RRDBNet(torch.nn.Module):
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def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32):
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super(RRDBNet, self).__init__()
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self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
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self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)])
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self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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# upsample
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self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)
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self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
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self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x):
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feat = x
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feat = self.conv_first(feat)
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body_feat = self.conv_body(self.body(feat))
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feat = feat + body_feat
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# upsample
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feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
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feat = self.lrelu(self.conv_up1(feat))
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feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")
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feat = self.lrelu(self.conv_up2(feat))
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out = self.conv_last(self.lrelu(self.conv_hr(feat)))
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return out
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class ESRGAN(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.model = model
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@staticmethod
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def from_pretrained(model_path):
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model = RRDBNet()
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state_dict = torch.load(model_path, map_location="cpu")["params_ema"]
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model.load_state_dict(state_dict)
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model.eval()
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return ESRGAN(model)
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def process_image(self, image):
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image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1)
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return image
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def process_images(self, images):
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images = [self.process_image(image) for image in images]
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images = torch.stack(images)
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return images
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def decode_images(self, images):
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images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8)
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images = [Image.fromarray(image) for image in images]
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return images
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@torch.no_grad()
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def upscale(self, images, batch_size=4, progress_bar=lambda x:x):
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# Preprocess
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input_tensor = self.process_images(images)
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# Interpolate
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output_tensor = []
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for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)):
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batch_id_ = min(batch_id + batch_size, input_tensor.shape[0])
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batch_input_tensor = input_tensor[batch_id: batch_id_]
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batch_input_tensor = batch_input_tensor.to(
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device=self.model.conv_first.weight.device,
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dtype=self.model.conv_first.weight.dtype)
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batch_output_tensor = self.model(batch_input_tensor)
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output_tensor.append(batch_output_tensor.cpu())
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# Output
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output_tensor = torch.concat(output_tensor, dim=0)
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# To images
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output_images = self.decode_images(output_tensor)
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return output_images
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