Update app.py

app.py

@@ -205,6 +205,8 @@ def main():
     4xLSDIRCompactC3 - upscale a jpg compressed photo 4x, fast (SRVGGNetCompact)  
     4xLSDIRCompactR3 - upscale a degraded photo 4x, fast (SRVGGNetCompact) (too strong, best used for interpolation like 4xLSDIRCompactN (or C) 75% 4xLSDIRCompactR3 25% to add little degradation handling to the previous one)  
 
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     *RRDBNet models - in general more powerful than SRVGGNetCompact, but very slow in this demo*  
     4xNomos8kSC - use for upscaling photos 4x or can also be tried out on anime   
     4xHFA2k - use for upscaling anime images 4x   
@@ -213,11 +215,14 @@ 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  
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+    *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  
+    2xHFA2kAVCOmniSR_Sharp - Anime frame upscaler that handles AVC (h264) video compression with sharper outputs, [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  
@@ -228,7 +233,9 @@ def main():
     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  
+    4xHFA2kLUDVAESAFMN - dropped model since there were artifacts on the outputs when training with [SAFMN](https://github.com/sunny2109/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)