add RealPLKSR NomosWebPhoto and AnimeSharpV2 using spandrel engine

Signed-off-by: vladmandic <mandic00@live.com>

CHANGELOG.md

@@ -2,14 +2,19 @@
 
 ## Update for 2026-02-05
 
-Bugfix refresh
-
-- fix: add metadata restore to always-on scripts  
-- fix: improve wildcard weights parsing, thanks @Tillerz  
-- fix: `anima` model detection  
-- refactor: reorganize `cli` scripts  
-- upscalers: hqx, icbi  
-- ui: add CTD-NT64Light theme, thanks @resonantsky  
+- **UI**
+  - ui: add CTD-NT64Light theme, thanks @resonantsky  
+- **Internal**
+  - refactor: reorganize `cli` scripts  
+- **Upscalers**
+  - add support for [spandrel](https://github.com/chaiNNer-org/spandrel)  
+    upscaling engine with suport for new upscaling families  
+  - add two new ai upscalers: *RealPLKSR NomosWebPhoto* and *RealPLKSR AnimeSharpV2*  
+  - add two new interpolation methods: *HQX* and *ICB*  
+- **Fixes**
+  - fix: add metadata restore to always-on scripts  
+  - fix: improve wildcard weights parsing, thanks @Tillerz  
+  - fix: `anima` model detection  
 
 ## Update for 2026-02-04
 

modules/upscaler_spandrel.py

@@ -0,0 +1,55 @@
+import os
+import time
+from PIL import Image
+from modules.upscaler import Upscaler, UpscalerData
+from modules import devices, paths
+from modules.shared import log
+
+
+MODELS = {
+    "Spandrel 4x RealPLKSR NomosWebPhoto": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/4xNomosWebPhoto_RealPLKSR.safetensors",
+    "Spandrel 2x RealPLKSR AnimeSharpV2": "https://huggingface.co/vladmandic/sdnext-upscalers/resolve/main/2x-AnimeSharpV2_RPLKSR_Sharp.pth",
+}
+
+class UpscalerSpandrel(Upscaler):
+    def __init__(self, dirname=None): # pylint: disable=unused-argument
+        super().__init__(False)
+        self.name = "Spandrel"
+        self.model_path = os.path.join(paths.models_path, 'Spandrel')
+        self.user_path = os.path.join(paths.models_path, 'Spandrel')
+        self.selected = None
+        self.model = None
+        self.scalers = []
+        for model_name, model_path in MODELS.items():
+            scaler = UpscalerData(name=model_name, path=model_path, upscaler=self)
+            self.scalers.append(scaler)
+
+    def process(self, img: Image.Image) -> Image.Image:
+        import torchvision.transforms.functional as TF
+        tensor = TF.to_tensor(img).unsqueeze(0).to(devices.device)
+        img = img.convert('RGB')
+        t0 = time.time()
+        with devices.inference_context():
+            tensor = self.model(tensor)
+            tensor = tensor.clamp(0, 1).squeeze(0).cpu()
+        t1 = time.time()
+        upscaled = TF.to_pil_image(tensor)
+        log.debug(f'Upscale: name="{self.selected}" input={img.size} output={upscaled.size} time={t1 - t0:.2f}')
+        return upscaled
+
+    def do_upscale(self, img: Image, selected_model=None):
+        from installer import install
+        if selected_model is None:
+            return img
+        install('spandrel')
+        try:
+            import spandrel
+            if (self.model is None) or (self.selected != selected_model):
+                self.selected = selected_model
+                model = self.find_model(selected_model)
+                self.model = spandrel.ModelLoader().load_from_file(model.local_data_path)
+                self.model.to(devices.device).eval()
+            return self.process(img)
+        except Exception as e:
+            log.error(f'Spandrel: {e}')
+            return img

modules/upscaler_vae.py

@@ -1,3 +1,4 @@
+import time
 from PIL import Image
 from modules.upscaler import Upscaler, UpscalerData
 
@@ -30,12 +31,15 @@ class UpscalerAsymmetricVAE(Upscaler):
             self.vae.eval()
             self.selected = selected_model
             shared.log.debug(f'Upscaler load: selected="{self.selected}" vae="{repo_id}"')
+        t0 = time.time()
         img = img.resize((8 * (img.width // 8), 8 * (img.height // 8)), resample=Image.Resampling.LANCZOS).convert('RGB')
         tensor = (F.pil_to_tensor(img).unsqueeze(0) / 255.0).to(device=devices.device, dtype=devices.dtype)
         self.vae = self.vae.to(device=devices.device)
         tensor = self.vae(tensor).sample
         upscaled = F.to_pil_image(tensor.squeeze().clamp(0.0, 1.0).float().cpu())
         self.vae = self.vae.to(device=devices.cpu)
+        t1 = time.time()
+        shared.log.debug(f'Upscale: name="{self.selected}" input={img.size} output={upscaled.size} time={t1 - t0:.2f}')
         return upscaled
 
 
@@ -73,6 +77,7 @@ class UpscalerWanUpscale(Upscaler):
             self.selected = selected_model
             shared.log.debug(f'Upscaler load: selected="{self.selected}" encode="{repo_encode}" decode="{repo_decode}"')
 
+        t0 = time.time()
         self.vae_encode = self.vae_encode.to(device=devices.device)
         tensor = (F.pil_to_tensor(img).unsqueeze(0).unsqueeze(2) / 255.0).to(device=devices.device, dtype=devices.dtype)
         tensor = self.vae_encode.encode(tensor).latent_dist.mode()
@@ -84,4 +89,6 @@ class UpscalerWanUpscale(Upscaler):
         self.vae_decode.to(device=devices.cpu)
 
         upscaled = F.to_pil_image(tensor.squeeze().clamp(0.0, 1.0).float().cpu())
+        t1 = time.time()
+        shared.log.debug(f'Upscale: name="{self.selected}" input={img.size} output={upscaled.size} time={t1 - t0:.2f}')
         return upscaled

webui.py

@@ -39,6 +39,7 @@ import modules.upscaler
 import modules.upscaler_simple
 import modules.upscaler_vae
 import modules.upscaler_algo
+import modules.upscaler_spandrel
 import modules.extra_networks
 import modules.ui_extra_networks
 import modules.textual_inversion