|
|
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 |
|
|
|
