| | import torch |
| | from einops import repeat |
| | from PIL import Image |
| | import numpy as np |
| |
|
| |
|
| | class ResidualDenseBlock(torch.nn.Module): |
| |
|
| | def __init__(self, num_feat=64, num_grow_ch=32): |
| | super(ResidualDenseBlock, self).__init__() |
| | self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) |
| | self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) |
| | self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) |
| | self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) |
| | self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) |
| | self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| |
|
| | def forward(self, x): |
| | x1 = self.lrelu(self.conv1(x)) |
| | x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) |
| | x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) |
| | x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) |
| | x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) |
| | return x5 * 0.2 + x |
| |
|
| |
|
| | class RRDB(torch.nn.Module): |
| |
|
| | def __init__(self, num_feat, num_grow_ch=32): |
| | super(RRDB, self).__init__() |
| | self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) |
| | self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) |
| | self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) |
| |
|
| | def forward(self, x): |
| | out = self.rdb1(x) |
| | out = self.rdb2(out) |
| | out = self.rdb3(out) |
| | return out * 0.2 + x |
| |
|
| |
|
| | class RRDBNet(torch.nn.Module): |
| |
|
| | def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs): |
| | super(RRDBNet, self).__init__() |
| | self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) |
| | self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)]) |
| | self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) |
| | |
| | self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) |
| | self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) |
| | self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) |
| | self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) |
| | self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) |
| |
|
| | def forward(self, x): |
| | feat = x |
| | feat = self.conv_first(feat) |
| | body_feat = self.conv_body(self.body(feat)) |
| | feat = feat + body_feat |
| | |
| | feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") |
| | feat = self.lrelu(self.conv_up1(feat)) |
| | feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") |
| | feat = self.lrelu(self.conv_up2(feat)) |
| | out = self.conv_last(self.lrelu(self.conv_hr(feat))) |
| | return out |
| | |
| | @staticmethod |
| | def state_dict_converter(): |
| | return RRDBNetStateDictConverter() |
| | |
| |
|
| | class RRDBNetStateDictConverter: |
| | def __init__(self): |
| | pass |
| |
|
| | def from_diffusers(self, state_dict): |
| | return state_dict, {"upcast_to_float32": True} |
| | |
| | def from_civitai(self, state_dict): |
| | return state_dict, {"upcast_to_float32": True} |
| |
|
| |
|
| | class ESRGAN(torch.nn.Module): |
| | def __init__(self, model): |
| | super().__init__() |
| | self.model = model |
| |
|
| | @staticmethod |
| | def from_model_manager(model_manager): |
| | return ESRGAN(model_manager.fetch_model("esrgan")) |
| |
|
| | def process_image(self, image): |
| | image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1) |
| | return image |
| | |
| | def process_images(self, images): |
| | images = [self.process_image(image) for image in images] |
| | images = torch.stack(images) |
| | return images |
| | |
| | def decode_images(self, images): |
| | images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) |
| | images = [Image.fromarray(image) for image in images] |
| | return images |
| | |
| | @torch.no_grad() |
| | def upscale(self, images, batch_size=4, progress_bar=lambda x:x): |
| | if not isinstance(images, list): |
| | images = [images] |
| | is_single_image = True |
| | else: |
| | is_single_image = False |
| |
|
| | |
| | input_tensor = self.process_images(images) |
| |
|
| | |
| | output_tensor = [] |
| | for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): |
| | batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) |
| | batch_input_tensor = input_tensor[batch_id: batch_id_] |
| | batch_input_tensor = batch_input_tensor.to( |
| | device=self.model.conv_first.weight.device, |
| | dtype=self.model.conv_first.weight.dtype) |
| | batch_output_tensor = self.model(batch_input_tensor) |
| | output_tensor.append(batch_output_tensor.cpu()) |
| | |
| | |
| | output_tensor = torch.concat(output_tensor, dim=0) |
| |
|
| | |
| | output_images = self.decode_images(output_tensor) |
| | if is_single_image: |
| | output_images = output_images[0] |
| | return output_images |
| |
|
