Add upscaler

lib/__init__.py

@@ -1,2 +1,3 @@
 from .inference import generate
 from .loader import Loader
+from .upscaler import RealESRGAN

lib/inference.py

@@ -91,7 +91,8 @@ def generate(
     increment_seed=True,
     deepcache_interval=1,
     tome_ratio=0,
-    log: Callable[[str], None] = None,
+    scale=1,
+    Info: Callable[[str], None] = None,
     Error=Exception,
 ):
     if not torch.cuda.is_available():
@@ -118,12 +119,13 @@ def generate(
     with torch.inference_mode():
         start = time.perf_counter()
         loader = Loader()
-        pipe = loader.load(
+        pipe, upscaler = loader.load(
             model,
             scheduler,
             karras,
             taesd,
             deepcache_interval,
+            scale,
             DTYPE,
             DEVICE,
         )
@@ -167,6 +169,7 @@ def generate(
             with token_merging(pipe, tome_ratio=tome_ratio):
                 try:
                     image = pipe(
+                        output_type="np" if scale > 1 else "pil",
                         num_inference_steps=inference_steps,
                         negative_prompt_embeds=neg_embeds,
                         guidance_scale=guidance_scale,
@@ -175,6 +178,8 @@ def generate(
                         height=height,
                         width=width,
                     ).images[0]
+                    if scale > 1:
+                        image = upscaler.predict(image)
                     images.append((image, str(current_seed)))
                 finally:
                     if not ZERO_GPU:
@@ -188,6 +193,6 @@ def generate(
             loader.pipe = None
 
         diff = time.perf_counter() - start
-        if log:
-            log(f"Generated {len(images)} image{'s' if len(images) > 1 else ''} in {diff:.2f}s")
+        if Info:
+            Info(f"Generated {len(images)} image{'s' if len(images) > 1 else ''} in {diff:.2f}s")
         return images

lib/loader.py

@@ -15,6 +15,8 @@ from diffusers import (
 from diffusers.models import AutoencoderKL, AutoencoderTiny
 from torch._dynamo import OptimizedModule
 
+from .upscaler import RealESRGAN
+
 ZERO_GPU = (
     os.environ.get("SPACES_ZERO_GPU", "").lower() == "true"
     or os.environ.get("SPACES_ZERO_GPU", "") == "1"
@@ -38,8 +40,17 @@ class Loader:
         if cls._instance is None:
             cls._instance = super(Loader, cls).__new__(cls)
             cls._instance.pipe = None
+            cls._instance.upscaler = None
         return cls._instance
 
+    def _load_upscaler(self, device=None, scale=4):
+        same_scale = self.upscaler is not None and self.upscaler.scale == scale
+        if scale == 1:
+            self.upscaler = None
+        if scale > 1 and not same_scale:
+            self.upscaler = RealESRGAN(device=device, scale=scale)
+            self.upscaler.load_weights()
+
     def _load_deepcache(self, interval=1):
         has_deepcache = hasattr(self.pipe, "deepcache")
 
@@ -82,7 +93,7 @@ class Loader:
                 model=model,
             )
 
-    def load(self, model, scheduler, karras, taesd, deepcache_interval, dtype, device):
+    def load(self, model, scheduler, karras, taesd, deepcache_interval, scale, dtype, device):
         model_lower = model.lower()
 
         schedulers = {
@@ -145,7 +156,9 @@ class Loader:
                     self.pipe.scheduler = schedulers[scheduler](**scheduler_kwargs)
                 self._load_vae(model_lower, taesd, variant)
                 self._load_deepcache(interval=deepcache_interval)
-                return self.pipe
+                self._load_upscaler(device=device, scale=scale)
+                torch.cuda.empty_cache()
+                return self.pipe, self.upscaler
             else:
                 print(f"Unloading {model_name.lower()}...")
                 self.pipe = None
@@ -161,6 +174,6 @@ class Loader:
         )
         self._load_vae(model_lower, taesd, variant)
         self._load_deepcache(interval=deepcache_interval)
-
+        self._load_upscaler(device=device, scale=scale)
         torch.cuda.empty_cache()
-        return self.pipe
+        return self.pipe, self.upscaler

lib/upscaler.py

@@ -0,0 +1,317 @@
+# BSD 3-Clause License
+#
+# Copyright (c) 2021, Sberbank AI
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+import einops
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+from torch import nn as nn
+from torch.nn import functional as F
+from torch.nn import init as init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+# https://huggingface.co/ai-forever/Real-ESRGAN
+HF_MODELS = {
+    2: {
+        "repo_id": "ai-forever/Real-ESRGAN",
+        "filename": "RealESRGAN_x2.pth",
+    },
+    4: {
+        "repo_id": "ai-forever/Real-ESRGAN",
+        "filename": "RealESRGAN_x4.pth",
+    },
+    # 8: {
+    #     "repo_id": "ai-forever/Real-ESRGAN",
+    #     "filename": "RealESRGAN_x8.pth",
+    # },
+}
+
+
+def pad_reflect(image, pad_size):
+    # fmt: off
+    image_size = image.shape
+    height, width = image_size[:2]
+    new_image = np.zeros([height + pad_size * 2, width + pad_size * 2, image_size[2]]).astype(np.uint8)
+    new_image[pad_size:-pad_size, pad_size:-pad_size, :] = image
+    new_image[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0)    # top
+    new_image[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0)    # bottom
+    new_image[:, 0:pad_size, :] = np.flip(new_image[:, pad_size : pad_size * 2, :], axis=1)    # left
+    new_image[:, -pad_size:, :] = np.flip(new_image[:, -pad_size * 2 : -pad_size, :], axis=1)  # right
+    return new_image
+    # fmt: on
+
+
+def unpad_image(image, pad_size):
+    return image[pad_size:-pad_size, pad_size:-pad_size, :]
+
+
+def pad_patch(image_patch, padding_size, channel_last=True):
+    if channel_last:
+        return np.pad(
+            image_patch,
+            ((padding_size, padding_size), (padding_size, padding_size), (0, 0)),
+            "edge",
+        )
+    else:
+        return np.pad(
+            image_patch,
+            ((0, 0), (padding_size, padding_size), (padding_size, padding_size)),
+            "edge",
+        )
+
+
+def unpad_patches(image_patches, padding_size):
+    return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
+
+
+def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
+    xmax, ymax, _ = image_array.shape
+    x_remainder = xmax % patch_size
+    y_remainder = ymax % patch_size
+
+    # modulo here is to avoid extending of patch_size instead of 0
+    x_extend = (patch_size - x_remainder) % patch_size
+    y_extend = (patch_size - y_remainder) % patch_size
+
+    # make sure the image is divisible into regular patches
+    extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), "edge")
+
+    # add padding around the image to simplify computations
+    padded_image = pad_patch(extended_image, padding_size, channel_last=True)
+
+    patches = []
+    xmax, ymax, _ = padded_image.shape
+    x_lefts = range(padding_size, xmax - padding_size, patch_size)
+    y_tops = range(padding_size, ymax - padding_size, patch_size)
+
+    for x in x_lefts:
+        for y in y_tops:
+            x_left = x - padding_size
+            y_top = y - padding_size
+            x_right = x + patch_size + padding_size
+            y_bottom = y + patch_size + padding_size
+            patch = padded_image[x_left:x_right, y_top:y_bottom, :]
+            patches.append(patch)
+    return np.array(patches), padded_image.shape
+
+
+def stitch_together(patches, padded_image_shape, target_shape, padding_size=4):
+    xmax, ymax, _ = padded_image_shape
+    patches = unpad_patches(patches, padding_size)
+    patch_size = patches.shape[1]
+    n_patches_per_row = ymax // patch_size
+    complete_image = np.zeros((xmax, ymax, 3))
+
+    row = -1
+    col = 0
+    for i in range(len(patches)):
+        if i % n_patches_per_row == 0:
+            row += 1
+            col = 0
+        complete_image[
+            row * patch_size : (row + 1) * patch_size, col * patch_size : (col + 1) * patch_size, :
+        ] = patches[i]
+        col += 1
+    return complete_image[0 : target_shape[0], 0 : target_shape[1], :]
+
+
+@torch.no_grad()
+def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
+    if not isinstance(module_list, list):
+        module_list = [module_list]
+    for module in module_list:
+        for m in module.modules():
+            if isinstance(m, nn.Conv2d):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, nn.Linear):
+                init.kaiming_normal_(m.weight, **kwargs)
+                m.weight.data *= scale
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+            elif isinstance(m, _BatchNorm):
+                init.constant_(m.weight, 1)
+                if m.bias is not None:
+                    m.bias.data.fill_(bias_fill)
+
+
+def make_layer(basic_block, num_basic_block, **kwarg):
+    layers = []
+    for _ in range(num_basic_block):
+        layers.append(basic_block(**kwarg))
+    return nn.Sequential(*layers)
+
+
+def pixel_unshuffle(x, scale):
+    _, _, h, w = x.shape
+    assert h % scale == 0 and w % scale == 0, "Height and width must be divisible by scale"
+    return einops.rearrange(
+        x,
+        "b c (h s1) (w s2) -> b (c s1 s2) h w",
+        s1=scale,
+        s2=scale,
+    )
+
+
+class ResidualDenseBlock(nn.Module):
+    def __init__(self, num_feat=64, num_grow_ch=32):
+        super(ResidualDenseBlock, self).__init__()
+        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
+        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
+        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+        default_init_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
+
+    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  # scale the residual by a factor of 0.2
+
+
+class RRDB(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  # scale the residual by a factor of 0.2
+
+
+class RRDBNet(nn.Module):
+    def __init__(self, num_in_ch, num_out_ch, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
+        super(RRDBNet, self).__init__()
+        self.scale = scale
+        if scale == 2:
+            num_in_ch = num_in_ch * 4
+        elif scale == 1:
+            num_in_ch = num_in_ch * 16
+        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
+        self.body = make_layer(RRDB, num_block, num_feat=num_feat, num_grow_ch=num_grow_ch)
+        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        if scale == 8:
+            self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
+        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
+        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x):
+        if self.scale == 2:
+            feat = pixel_unshuffle(x, scale=2)
+        elif self.scale == 1:
+            feat = pixel_unshuffle(x, scale=4)
+        else:
+            feat = x
+        feat = self.conv_first(feat)
+        body_feat = self.conv_body(self.body(feat))
+        feat = feat + body_feat
+        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode="nearest")))
+        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode="nearest")))
+        if self.scale == 8:
+            feat = self.lrelu(self.conv_up3(F.interpolate(feat, scale_factor=2, mode="nearest")))
+        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
+        return out
+
+
+class RealESRGAN:
+    def __init__(self, device, scale=4):
+        self.device = device
+        self.scale = scale
+        self.model = RRDBNet(
+            num_in_ch=3,
+            num_out_ch=3,
+            num_feat=64,
+            num_block=23,
+            num_grow_ch=32,
+            scale=scale,
+        )
+
+    def load_weights(self):
+        assert self.scale in [2, 4, 8], "You can download models only with scales: 2, 4, 8"
+        config = HF_MODELS[self.scale]
+        cache_path = hf_hub_download(config["repo_id"], filename=config["filename"])
+        loadnet = torch.load(cache_path)
+        if "params" in loadnet:
+            self.model.load_state_dict(loadnet["params"], strict=True)
+        elif "params_ema" in loadnet:
+            self.model.load_state_dict(loadnet["params_ema"], strict=True)
+        else:
+            self.model.load_state_dict(loadnet, strict=True)
+        self.model.eval().to(device=self.device)
+
+    @torch.cuda.amp.autocast()
+    def predict(self, lr_image, batch_size=4, patches_size=192, padding=24, pad_size=15):
+        scale = self.scale
+        if not isinstance(lr_image, np.ndarray):
+            lr_image = np.array(lr_image)
+        if lr_image.min() < 0.0:
+            lr_image = (lr_image + 1.0) / 2.0
+        if lr_image.max() <= 1.0:
+            lr_image = lr_image * 255.0
+        lr_image = pad_reflect(lr_image, pad_size)
+        patches, p_shape = split_image_into_overlapping_patches(
+            lr_image,
+            patch_size=patches_size,
+            padding_size=padding,
+        )
+        patches = torch.Tensor(patches / 255.0)
+        image = einops.rearrange(patches, "b h w c -> b c h w").to(device=self.device)
+
+        with torch.inference_mode():
+            res = self.model(image[0:batch_size])
+            for i in range(batch_size, image.shape[0], batch_size):
+                res = torch.cat((res, self.model(image[i : i + batch_size])), 0)
+
+        sr_image = einops.rearrange(res.clamp(0, 1), "b c h w -> b h w c").cpu().numpy()
+        padded_size_scaled = tuple(np.multiply(p_shape[0:2], scale)) + (3,)
+        scaled_image_shape = tuple(np.multiply(lr_image.shape[0:2], scale)) + (3,)
+        sr_image = stitch_together(
+            sr_image,
+            padded_image_shape=padded_size_scaled,
+            target_shape=scaled_image_shape,
+            padding_size=padding * scale,
+        )
+        sr_image = (sr_image * 255).astype(np.uint8)
+        sr_image = unpad_image(sr_image, pad_size * scale)
+        sr_image = Image.fromarray(sr_image)
+        return sr_image