Update app.py

app.py

@@ -100,17 +100,13 @@ def realesrgan(img, model_name, face_enhance):
         print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
     else:
         # Save restored image and return it to the output Image component
-        if img_mode == 'RGBA':  # RGBA images should be saved in png format
-            extension = 'jpg'
-        else:
-            extension = 'jpg'
-
+        extension = 'jpg'
         out_filename = f"output_{rnd_string(16)}.{extension}"
         cv2.imwrite(out_filename, output)
         global last_file
         last_file = out_filename
         return out_filename
-
+        
 
 def rnd_string(x):
     """Returns a string of 'x' random characters
@@ -176,7 +172,7 @@ def main():
         
         gr.Markdown(
             """# <div align="center"> Upscale image </div> 
-        Here I demo my self-trained models. The models with their corresponding infos can be found on [my github repo](https://github.com/phhofm/models).
+        Here I demo some of my self-trained models (only those trained on the SRVGGNet or RRDBNet archs). All my self-trained models can be found on the [openmodeldb](https://openmodeldb.info/?q=Helaman&sort=date-desc) or on [my github repo](https://github.com/phhofm/models).
         """
         )
 
@@ -217,7 +213,25 @@ def main():
     4xLSDIRplusC - upscale a jpg compressed photo 4x   
     4xLSDIRplusR - upscale a degraded photo 4x (too strong, best used for interpolation like 4xLSDIRplusN (or C) 75% 4xLSDIRplusR 25% to add little degradation handling to the previous one)    
 
+    *Models that I trained that are not featured here, but available on [openmodeldb](https://openmodeldb.info/?q=Helaman&sort=date-desc) or on [github](https://github.com/phhofm/models):*
+    4xNomos8kSCHAT-L - Photo upscaler (handles little bit of jpg compression and blur), [HAT-L](https://github.com/XPixelGroup/HAT) model (good output but very slow since huge)  
+    4xNomos8kSCHAT-S - Photo upscaler (handles little bit of jpg compression and blur), [HAT-S](https://github.com/XPixelGroup/HAT) model  
+    4xNomos8kSCSRFormer - Photo upscaler (handles little bit of jpg compression and blur), [SRFormer](https://github.com/HVision-NKU/SRFormer) base model (also good and slow since also big model)    
+    2xHFA2kAVCOmniSR - Anime frame upscaler that handles AVC (h264) video compression, [OmniSR](https://github.com/Francis0625/Omni-SR) model  
+    4xHFA2kAVCSRFormer_light - Anime frame upscaler that handles AVC (h264) video compression, [SRFormer](https://github.com/HVision-NKU/SRFormer) lightweight model  
+    2xHFA2kAVCEDSR_M - Anime frame upscaler that handles AVC (h264) video compression, [EDSR-M](https://github.com/LimBee/NTIRE2017) model  
+    2xHFA2kAVCCompact - Anime frame upscaler that handles AVC (h264) video compression, [SRVGGNet](https://github.com/xinntao/Real-ESRGAN) (also called Real-ESRGAN Compact) model  
+    4xHFA2kLUDVAESwinIR_light - Anime image upscaler that handles various realistic degradations, [SwinIR](https://github.com/JingyunLiang/SwinIR) light model  
+    4xHFA2kLUDVAEGRL_small - Anime image upscaler that handles various realistic degradations, [GRL](https://github.com/ofsoundof/GRL-Image-Restoration) small model  
+    4xHFA2kLUDVAESRFormer_light - Anime image upscaler that handles various realistic degradations, [SRFormer](https://github.com/HVision-NKU/SRFormer) light model  
+    4xLexicaHAT - An AI generated image upscaler, does not handle any degradations, [HAT](https://github.com/XPixelGroup/HAT) base model  
+    2xLexicaSwinIR - An AI generated image upscaler, does not handle any degradations, [SwinIR](https://github.com/JingyunLiang/SwinIR) base model  
+    2xLexicaRRDBNet - An AI generated image upscaler, does not handle any degradations, RRDBNet base model  
+    2xLexicaRRDBNet_Sharp - An AI generated image upscaler with sharper outputs, does not handle any degradations, RRDBNet base model  
+    4xHFA2kLUDVAESAFMN - dropped model since there were artifacts on the outputs when training with SAFMN arch  
+
     *The following are not models I had trained, but rather interpolations I had created, they are available on my [repo](https://github.com/phhofm/models) and can be tried out locally with chaiNNer:*  
+    4xLSDIRplus (4xLSDIRplusC + 4xLSDIRplusR)
     4xLSDIRCompact3 (4xLSDIRCompactC3 + 4xLSDIRCompactR3)  
     4xLSDIRCompact2 (4xLSDIRCompactC2 + 4xLSDIRCompactR2)  
     4xInt-Ultracri (UltraSharp + Remacri)