Update app.py

app.py

@@ -136,34 +136,38 @@ def upload_to_sftp(local_filepath):
 
         
 def calculate_new_dimensions(orig_w, orig_h):
-    if orig_w == 0 or orig_h == 0: return int(768), int(768)
+    if orig_w == 0 or orig_h == 0: return int(1024), int(1024)
     if orig_w >= orig_h:
-        new_h, new_w = 768, round((768 * (orig_w / orig_h)) / 32) * 32
+        new_h, new_w = 1024, round((1024 * (orig_w / orig_h)) / 32) * 32
     else:
-        new_w, new_h = 768, round((768 * (orig_h / orig_w)) / 32) * 32
+        new_w, new_h = 1024, round((1024 * (orig_h / orig_w)) / 32) * 32
     return int(max(256, min(new_h, MAX_IMAGE_SIZE))), int(max(256, min(new_w, MAX_IMAGE_SIZE)))
 
 
 def get_duration(*args, **kwargs):
     duration_ui = kwargs.get('duration_ui', 5.0)
-    if duration_ui > 7.0: return 120
+    if duration_ui > 7.0: return 110
     if duration_ui > 5.0: return 100
+    if duration_ui > 4.0: return 90
+    if duration_ui > 3.0: return 70
+    if duration_ui > 2.0: return 60
+    if duration_ui > 1.5: return 50
+    if duration_ui > 1.0: return 45
+    if duration_ui > 0.5: return 30
     return 90
 
-@spaces.GPU(duration=30)
+@spaces.GPU(duration=20)
 def superres_image(image_to_enhance: Image.Image):
     print("Doing super-resolution.")
-
     torch.backends.cuda.matmul.allow_tf32 = True
     torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
     torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
     torch.backends.cudnn.allow_tf32 = True
     torch.backends.cudnn.deterministic = True
     torch.set_float32_matmul_precision("high")
-
     
     with torch.no_grad():
-        upscale = upscaler(enhanced_image, tiling=True, tile_width=1024, tile_height=1024)
+        upscale = upscaler(image_to_enhance, tiling=True, tile_width=1024, tile_height=1024)
         enhanced_image = upscale.resize((upscale.width // 4, upscale.height // 4), Image.LANCZOS)
     return enhanced_image
             
@@ -181,10 +185,7 @@ def enhance_frame(prompt, image_to_enhance: Image.Image):
         torch.backends.cudnn.allow_tf32 = True
         torch.backends.cudnn.deterministic = True
         torch.set_float32_matmul_precision("high")
-
-
-        
-        enhanced_image = enhancer_pipeline(prompt=refine_prompt, image=image_to_enhance, strength=0.1, generator=generator, num_inference_steps=160).images[0]
+        enhanced_image = enhancer_pipeline(prompt=refine_prompt, image=image_to_enhance, strength=0.15, generator=generator, num_inference_steps=220).images[0]
         print("Frame enhancement successful.")
     except Exception as e:
         print(f"Error during frame enhancement: {e}")