hugging-apps/echo-memory

1import torch2from einops import repeat3from PIL import Image4import numpy as np5 6 7class ResidualDenseBlock(torch.nn.Module):8 9    def __init__(self, num_feat=64, num_grow_ch=32):10        super(ResidualDenseBlock, self).__init__()11        self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)12        self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)13        self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)14        self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)15        self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)16        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)17 18    def forward(self, x):19        x1 = self.lrelu(self.conv1(x))20        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))21        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))22        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))23        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))24        return x5 * 0.2 + x25 26 27class RRDB(torch.nn.Module):28 29    def __init__(self, num_feat, num_grow_ch=32):30        super(RRDB, self).__init__()31        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)32        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)33        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)34 35    def forward(self, x):36        out = self.rdb1(x)37        out = self.rdb2(out)38        out = self.rdb3(out)39        return out * 0.2 + x40 41 42class RRDBNet(torch.nn.Module):43 44    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, **kwargs):45        super(RRDBNet, self).__init__()46        self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)47        self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)])48        self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)49        # upsample50        self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)51        self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)52        self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1)53        self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)54        self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True)55 56    def forward(self, x):57        feat = x58        feat = self.conv_first(feat)59        body_feat = self.conv_body(self.body(feat))60        feat = feat + body_feat61        # upsample62        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")63        feat = self.lrelu(self.conv_up1(feat))64        feat = repeat(feat, "B C H W -> B C (H 2) (W 2)")65        feat = self.lrelu(self.conv_up2(feat))66        out = self.conv_last(self.lrelu(self.conv_hr(feat)))67        return out68    69    @staticmethod70    def state_dict_converter():71        return RRDBNetStateDictConverter()72    73 74class RRDBNetStateDictConverter:75    def __init__(self):76        pass77 78    def from_diffusers(self, state_dict):79        return state_dict, {"upcast_to_float32": True}80    81    def from_civitai(self, state_dict):82        return state_dict, {"upcast_to_float32": True}83 84 85class ESRGAN(torch.nn.Module):86    def __init__(self, model):87        super().__init__()88        self.model = model89 90    @staticmethod91    def from_model_manager(model_manager):92        return ESRGAN(model_manager.fetch_model("esrgan"))93 94    def process_image(self, image):95        image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1)96        return image97    98    def process_images(self, images):99        images = [self.process_image(image) for image in images]100        images = torch.stack(images)101        return images102    103    def decode_images(self, images):104        images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8)105        images = [Image.fromarray(image) for image in images]106        return images107    108    @torch.no_grad()109    def upscale(self, images, batch_size=4, progress_bar=lambda x:x):110        if not isinstance(images, list):111            images = [images]112            is_single_image = True113        else:114            is_single_image = False115 116        # Preprocess117        input_tensor = self.process_images(images)118 119        # Interpolate120        output_tensor = []121        for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)):122            batch_id_ = min(batch_id + batch_size, input_tensor.shape[0])123            batch_input_tensor = input_tensor[batch_id: batch_id_]124            batch_input_tensor = batch_input_tensor.to(125                device=self.model.conv_first.weight.device,126                dtype=self.model.conv_first.weight.dtype)127            batch_output_tensor = self.model(batch_input_tensor)128            output_tensor.append(batch_output_tensor.cpu())129        130        # Output131        output_tensor = torch.concat(output_tensor, dim=0)132 133        # To images134        output_images = self.decode_images(output_tensor)135        if is_single_image:136            output_images = output_images[0]137        return output_images138