From e472383acbb9e07dca311abe5fb16ee2675e410a Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Wed, 27 Dec 2023 11:04:33 +0200
Subject: Refactor esrgan_upscale to more generic upscale_with_model

---
 modules/upscaler_utils.py | 66 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 66 insertions(+)
 create mode 100644 modules/upscaler_utils.py

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
new file mode 100644
index 00000000..8bdda51c
--- /dev/null
+++ b/modules/upscaler_utils.py
@@ -0,0 +1,66 @@
+import logging
+from typing import Callable
+
+import numpy as np
+import torch
+import tqdm
+from PIL import Image
+
+from modules import devices, images
+
+logger = logging.getLogger(__name__)
+
+
+def upscale_without_tiling(model, img: Image.Image):
+    img = np.array(img)
+    img = img[:, :, ::-1]
+    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
+    img = torch.from_numpy(img).float()
+    img = img.unsqueeze(0).to(devices.device_esrgan)
+    with torch.no_grad():
+        output = model(img)
+    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
+    output = 255. * np.moveaxis(output, 0, 2)
+    output = output.astype(np.uint8)
+    output = output[:, :, ::-1]
+    return Image.fromarray(output, 'RGB')
+
+
+def upscale_with_model(
+    model: Callable[[torch.Tensor], torch.Tensor],
+    img: Image.Image,
+    *,
+    tile_size: int,
+    tile_overlap: int = 0,
+    desc="tiled upscale",
+) -> Image.Image:
+    if tile_size <= 0:
+        logger.debug("Upscaling %s without tiling", img)
+        output = upscale_without_tiling(model, img)
+        logger.debug("=> %s", output)
+        return output
+
+    grid = images.split_grid(img, tile_size, tile_size, tile_overlap)
+    newtiles = []
+
+    with tqdm.tqdm(total=grid.tile_count, desc=desc) as p:
+        for y, h, row in grid.tiles:
+            newrow = []
+            for x, w, tile in row:
+                logger.debug("Tile (%d, %d) %s...", x, y, tile)
+                output = upscale_without_tiling(model, tile)
+                scale_factor = output.width // tile.width
+                logger.debug("=> %s (scale factor %s)", output, scale_factor)
+                newrow.append([x * scale_factor, w * scale_factor, output])
+                p.update(1)
+            newtiles.append([y * scale_factor, h * scale_factor, newrow])
+
+    newgrid = images.Grid(
+        newtiles,
+        tile_w=grid.tile_w * scale_factor,
+        tile_h=grid.tile_h * scale_factor,
+        image_w=grid.image_w * scale_factor,
+        image_h=grid.image_h * scale_factor,
+        overlap=grid.overlap * scale_factor,
+    )
+    return images.combine_grid(newgrid)
-- 
cgit v1.2.3


From 8100e901ab0c5b04d289eebb722c8a653b8beef1 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 30 Dec 2023 22:41:53 +0300
Subject: fix error with RealESRGAN model failing to upscale fp32 image

---
 modules/upscaler_utils.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 8bdda51c..39f78a0b 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -16,9 +16,13 @@ def upscale_without_tiling(model, img: Image.Image):
     img = img[:, :, ::-1]
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(devices.device_esrgan)
+
+    model_weight = next(iter(model.parameters()))
+    img = img.unsqueeze(0).to(device=model_weight.device, dtype=model_weight.dtype)
+
     with torch.no_grad():
         output = model(img)
+
     output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
     output = 255. * np.moveaxis(output, 0, 2)
     output = output.astype(np.uint8)
-- 
cgit v1.2.3


From 3be90740316f8fbb950b31d440458a5e8ed4beb3 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 31 Dec 2023 00:43:41 +0300
Subject: fix for the previous fix.

---
 modules/upscaler_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 39f78a0b..dde5d7ad 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -17,7 +17,7 @@ def upscale_without_tiling(model, img: Image.Image):
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
 
-    model_weight = next(iter(model.parameters()))
+    model_weight = next(iter(model.model.parameters()))
     img = img.unsqueeze(0).to(device=model_weight.device, dtype=model_weight.dtype)
 
     with torch.no_grad():
-- 
cgit v1.2.3


From 777af661a21821994993df3ef566b01df2bb61a0 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sun, 31 Dec 2023 00:09:51 +0200
Subject: Be more clear about Spandrel model nomenclature

---
 modules/upscaler_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index dde5d7ad..174c9bc3 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -6,7 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import devices, images
+from modules import images
 
 logger = logging.getLogger(__name__)
 
-- 
cgit v1.2.3


From 6f86b62a1be7993073ba3a789d522e0b8870605a Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sat, 30 Dec 2023 22:53:49 +0200
Subject: Deduplicate tiled inference code from SwinIR/ScuNET

---
 modules/upscaler_utils.py | 72 ++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 71 insertions(+), 1 deletion(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 174c9bc3..8e413854 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -6,7 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import images
+from modules import images, shared
 
 logger = logging.getLogger(__name__)
 
@@ -68,3 +68,73 @@ def upscale_with_model(
         overlap=grid.overlap * scale_factor,
     )
     return images.combine_grid(newgrid)
+
+
+def tiled_upscale_2(
+    img,
+    model,
+    *,
+    tile_size: int,
+    tile_overlap: int,
+    scale: int,
+    device,
+    desc="Tiled upscale",
+):
+    # Alternative implementation of `upscale_with_model` originally used by
+    # SwinIR and ScuNET.  It differs from `upscale_with_model` in that tiling and
+    # weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
+    # Pillow space without weighting.
+    b, c, h, w = img.size()
+    tile_size = min(tile_size, h, w)
+
+    if tile_size <= 0:
+        logger.debug("Upscaling %s without tiling", img.shape)
+        return model(img)
+
+    stride = tile_size - tile_overlap
+    h_idx_list = list(range(0, h - tile_size, stride)) + [h - tile_size]
+    w_idx_list = list(range(0, w - tile_size, stride)) + [w - tile_size]
+    result = torch.zeros(
+        b,
+        c,
+        h * scale,
+        w * scale,
+        device=device,
+    ).type_as(img)
+    weights = torch.zeros_like(result)
+    logger.debug("Upscaling %s to %s with tiles", img.shape, result.shape)
+    with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc) as pbar:
+        for h_idx in h_idx_list:
+            if shared.state.interrupted or shared.state.skipped:
+                break
+
+            for w_idx in w_idx_list:
+                if shared.state.interrupted or shared.state.skipped:
+                    break
+
+                in_patch = img[
+                    ...,
+                    h_idx : h_idx + tile_size,
+                    w_idx : w_idx + tile_size,
+                ]
+                out_patch = model(in_patch)
+
+                result[
+                    ...,
+                    h_idx * scale : (h_idx + tile_size) * scale,
+                    w_idx * scale : (w_idx + tile_size) * scale,
+                ].add_(out_patch)
+
+                out_patch_mask = torch.ones_like(out_patch)
+
+                weights[
+                    ...,
+                    h_idx * scale : (h_idx + tile_size) * scale,
+                    w_idx * scale : (w_idx + tile_size) * scale,
+                ].add_(out_patch_mask)
+
+                pbar.update(1)
+
+    output = result.div_(weights)
+
+    return output
-- 
cgit v1.2.3


From 5768afc776a66bb94e77a9c1daebeea58fa731d5 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sun, 31 Dec 2023 00:20:30 +0200
Subject: Add utility to inspect a model's parameters (to get dtype/device)

---
 modules/upscaler_utils.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 8e413854..c60e3beb 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -7,6 +7,7 @@ import tqdm
 from PIL import Image
 
 from modules import images, shared
+from modules.torch_utils import get_param
 
 logger = logging.getLogger(__name__)
 
@@ -17,8 +18,8 @@ def upscale_without_tiling(model, img: Image.Image):
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
 
-    model_weight = next(iter(model.model.parameters()))
-    img = img.unsqueeze(0).to(device=model_weight.device, dtype=model_weight.dtype)
+    param = get_param(model)
+    img = img.unsqueeze(0).to(device=param.device, dtype=param.dtype)
 
     with torch.no_grad():
         output = model(img)
-- 
cgit v1.2.3


From a70dfb64a86b9b6d869deffdb0ffebe980365473 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sun, 31 Dec 2023 22:38:30 +0300
Subject: change import statements for #14478

---
 modules/upscaler_utils.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index c60e3beb..f5cb92d5 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -6,8 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import images, shared
-from modules.torch_utils import get_param
+from modules import images, shared, torch_utils
 
 logger = logging.getLogger(__name__)
 
@@ -18,7 +17,7 @@ def upscale_without_tiling(model, img: Image.Image):
     img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
     img = torch.from_numpy(img).float()
 
-    param = get_param(model)
+    param = torch_utils.get_param(model)
     img = img.unsqueeze(0).to(device=param.device, dtype=param.dtype)
 
     with torch.no_grad():
-- 
cgit v1.2.3


From 1341b2208185cd89b0019bda2df63b406ec0cb5e Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Tue, 2 Jan 2024 06:47:26 +0300
Subject: add an option to hide upscaling progressbar

---
 modules/upscaler_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index f5cb92d5..9379f512 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -47,7 +47,7 @@ def upscale_with_model(
     grid = images.split_grid(img, tile_size, tile_size, tile_overlap)
     newtiles = []
 
-    with tqdm.tqdm(total=grid.tile_count, desc=desc) as p:
+    with tqdm.tqdm(total=grid.tile_count, desc=desc, disable=not shared.opts.enable_upscale_progressbar) as p:
         for y, h, row in grid.tiles:
             newrow = []
             for x, w, tile in row:
@@ -103,7 +103,7 @@ def tiled_upscale_2(
     ).type_as(img)
     weights = torch.zeros_like(result)
     logger.debug("Upscaling %s to %s with tiles", img.shape, result.shape)
-    with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc) as pbar:
+    with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc, disable=not shared.opts.enable_upscale_progressbar) as pbar:
         for h_idx in h_idx_list:
             if shared.state.interrupted or shared.state.skipped:
                 break
-- 
cgit v1.2.3


From cf14a6a7aaf8ccb40552990785d5c9e400d93610 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sun, 31 Dec 2023 16:11:18 +0200
Subject: Refactor upscale_2 helper out of ScuNET/SwinIR; make sure devices are
 right

---
 modules/upscaler_utils.py | 89 ++++++++++++++++++++++++++++++++++++-----------
 1 file changed, 69 insertions(+), 20 deletions(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 9379f512..e4c63f09 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -11,23 +11,40 @@ from modules import images, shared, torch_utils
 logger = logging.getLogger(__name__)
 
 
-def upscale_without_tiling(model, img: Image.Image):
-    img = np.array(img)
-    img = img[:, :, ::-1]
-    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
-    img = torch.from_numpy(img).float()
-
+def pil_image_to_torch_bgr(img: Image.Image) -> torch.Tensor:
+    img = np.array(img.convert("RGB"))
+    img = img[:, :, ::-1]  # flip RGB to BGR
+    img = np.transpose(img, (2, 0, 1))  # HWC to CHW
+    img = np.ascontiguousarray(img) / 255  # Rescale to [0, 1]
+    return torch.from_numpy(img)
+
+
+def torch_bgr_to_pil_image(tensor: torch.Tensor) -> Image.Image:
+    if tensor.ndim == 4:
+        # If we're given a tensor with a batch dimension, squeeze it out
+        # (but only if it's a batch of size 1).
+        if tensor.shape[0] != 1:
+            raise ValueError(f"{tensor.shape} does not describe a BCHW tensor")
+        tensor = tensor.squeeze(0)
+    assert tensor.ndim == 3, f"{tensor.shape} does not describe a CHW tensor"
+    # TODO: is `tensor.float().cpu()...numpy()` the most efficient idiom?
+    arr = tensor.float().cpu().clamp_(0, 1).numpy()  # clamp
+    arr = 255.0 * np.moveaxis(arr, 0, 2)  # CHW to HWC, rescale
+    arr = arr.astype(np.uint8)
+    arr = arr[:, :, ::-1]  # flip BGR to RGB
+    return Image.fromarray(arr, "RGB")
+
+
+def upscale_pil_patch(model, img: Image.Image) -> Image.Image:
+    """
+    Upscale a given PIL image using the given model.
+    """
     param = torch_utils.get_param(model)
-    img = img.unsqueeze(0).to(device=param.device, dtype=param.dtype)
 
     with torch.no_grad():
-        output = model(img)
-
-    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-    output = 255. * np.moveaxis(output, 0, 2)
-    output = output.astype(np.uint8)
-    output = output[:, :, ::-1]
-    return Image.fromarray(output, 'RGB')
+        tensor = pil_image_to_torch_bgr(img).unsqueeze(0)  # add batch dimension
+        tensor = tensor.to(device=param.device, dtype=param.dtype)
+        return torch_bgr_to_pil_image(model(tensor))
 
 
 def upscale_with_model(
@@ -40,7 +57,7 @@ def upscale_with_model(
 ) -> Image.Image:
     if tile_size <= 0:
         logger.debug("Upscaling %s without tiling", img)
-        output = upscale_without_tiling(model, img)
+        output = upscale_pil_patch(model, img)
         logger.debug("=> %s", output)
         return output
 
@@ -52,7 +69,7 @@ def upscale_with_model(
             newrow = []
             for x, w, tile in row:
                 logger.debug("Tile (%d, %d) %s...", x, y, tile)
-                output = upscale_without_tiling(model, tile)
+                output = upscale_pil_patch(model, tile)
                 scale_factor = output.width // tile.width
                 logger.debug("=> %s (scale factor %s)", output, scale_factor)
                 newrow.append([x * scale_factor, w * scale_factor, output])
@@ -71,19 +88,22 @@ def upscale_with_model(
 
 
 def tiled_upscale_2(
-    img,
+    img: torch.Tensor,
     model,
     *,
     tile_size: int,
     tile_overlap: int,
     scale: int,
-    device,
     desc="Tiled upscale",
 ):
     # Alternative implementation of `upscale_with_model` originally used by
     # SwinIR and ScuNET.  It differs from `upscale_with_model` in that tiling and
     # weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
     # Pillow space without weighting.
+
+    # Grab the device the model is on, and use it.
+    device = torch_utils.get_param(model).device
+
     b, c, h, w = img.size()
     tile_size = min(tile_size, h, w)
 
@@ -100,7 +120,8 @@ def tiled_upscale_2(
         h * scale,
         w * scale,
         device=device,
-    ).type_as(img)
+        dtype=img.dtype,
+    )
     weights = torch.zeros_like(result)
     logger.debug("Upscaling %s to %s with tiles", img.shape, result.shape)
     with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc, disable=not shared.opts.enable_upscale_progressbar) as pbar:
@@ -112,11 +133,13 @@ def tiled_upscale_2(
                 if shared.state.interrupted or shared.state.skipped:
                     break
 
+                # Only move this patch to the device if it's not already there.
                 in_patch = img[
                     ...,
                     h_idx : h_idx + tile_size,
                     w_idx : w_idx + tile_size,
-                ]
+                ].to(device=device)
+
                 out_patch = model(in_patch)
 
                 result[
@@ -138,3 +161,29 @@ def tiled_upscale_2(
     output = result.div_(weights)
 
     return output
+
+
+def upscale_2(
+    img: Image.Image,
+    model,
+    *,
+    tile_size: int,
+    tile_overlap: int,
+    scale: int,
+    desc: str,
+):
+    """
+    Convenience wrapper around `tiled_upscale_2` that handles PIL images.
+    """
+    tensor = pil_image_to_torch_bgr(img).float().unsqueeze(0)  # add batch dimension
+
+    with torch.no_grad():
+        output = tiled_upscale_2(
+            tensor,
+            model,
+            tile_size=tile_size,
+            tile_overlap=tile_overlap,
+            scale=scale,
+            desc=desc,
+        )
+    return torch_bgr_to_pil_image(output)
-- 
cgit v1.2.3


From 7ad6899bf987a8ee615efbcfc99562457f89cd8b Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Tue, 2 Jan 2024 17:14:05 +0200
Subject: torch_bgr_to_pil_image: round, don't truncate

This matches what `realesrgan` does.
---
 modules/upscaler_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index e4c63f09..4f1417cf 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -30,7 +30,7 @@ def torch_bgr_to_pil_image(tensor: torch.Tensor) -> Image.Image:
     # TODO: is `tensor.float().cpu()...numpy()` the most efficient idiom?
     arr = tensor.float().cpu().clamp_(0, 1).numpy()  # clamp
     arr = 255.0 * np.moveaxis(arr, 0, 2)  # CHW to HWC, rescale
-    arr = arr.astype(np.uint8)
+    arr = arr.round().astype(np.uint8)
     arr = arr[:, :, ::-1]  # flip BGR to RGB
     return Image.fromarray(arr, "RGB")
 
-- 
cgit v1.2.3


From 62470ee23443cb2ad3943a152ccae26a689c86e1 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Wed, 3 Jan 2024 22:39:12 +0200
Subject: upscale_2: cast image to model's dtype

---
 modules/upscaler_utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'modules/upscaler_utils.py')

diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index e4c63f09..5db74877 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -94,6 +94,7 @@ def tiled_upscale_2(
     tile_size: int,
     tile_overlap: int,
     scale: int,
+    device: torch.device,
     desc="Tiled upscale",
 ):
     # Alternative implementation of `upscale_with_model` originally used by
@@ -101,9 +102,6 @@ def tiled_upscale_2(
     # weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
     # Pillow space without weighting.
 
-    # Grab the device the model is on, and use it.
-    device = torch_utils.get_param(model).device
-
     b, c, h, w = img.size()
     tile_size = min(tile_size, h, w)
 
@@ -175,7 +173,8 @@ def upscale_2(
     """
     Convenience wrapper around `tiled_upscale_2` that handles PIL images.
     """
-    tensor = pil_image_to_torch_bgr(img).float().unsqueeze(0)  # add batch dimension
+    param = torch_utils.get_param(model)
+    tensor = pil_image_to_torch_bgr(img).to(dtype=param.dtype).unsqueeze(0)  # add batch dimension
 
     with torch.no_grad():
         output = tiled_upscale_2(
@@ -185,5 +184,6 @@ def upscale_2(
             tile_overlap=tile_overlap,
             scale=scale,
             desc=desc,
+            device=param.device,
         )
     return torch_bgr_to_pil_image(output)
-- 
cgit v1.2.3