Update app.py

app.py

@@ -430,7 +430,8 @@ CHECKPOINTS = ESRGANUpscalerCheckpoints(
 
 device = DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 DTYPE = dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
-enhancer = ESRGANUpscaler(checkpoints=CHECKPOINTS, device="cpu", dtype=DTYPE)
+
+enhancer = ESRGANUpscaler(checkpoints=CHECKPOINTS, device=device, dtype=DTYPE).to(device)
 
 # logging
 
@@ -450,8 +451,8 @@ MAX_SEED = np.iinfo(np.int32).max
 # precision data
 
 seq=512
-image_steps=25
-img_accu=3.5
+image_steps=30
+img_accu=9.0
 
 # ui data
 
@@ -523,6 +524,7 @@ torch.cuda.empty_cache()
 
 # functionality
 
+@spaces.GPU(duration=300)
 def upscaler(
     input_image: Image.Image,
     prompt: str = "Masterpiece, Best Quality, Hyper-Realistic, Super-Realistic.",
@@ -593,7 +595,7 @@ def _summarize(text):
     gen = model.generate(
         toks,
         length_penalty=2.0,
-        num_beams=4,
+        num_beams=8,
         early_stopping=True,
         max_length=512
     )
@@ -1286,7 +1288,7 @@ def translate(txt,to_lang="en",from_lang="auto"):
     log(f'RET translate with translation as {translation}')
     return translation.lower()
 
-@spaces.GPU(duration=300)
+@spaces.GPU(duration=100)
 def handle_generation(h,w,d):
 
     log(f'CALL handle_generate')
@@ -1334,7 +1336,7 @@ def handle_generation(h,w,d):
             output_type="pil",
             guidance_scale=img_accu,
             num_images_per_prompt=1,
-            num_inference_steps=30,
+            num_inference_steps=image_steps,
             max_sequence_length=seq,
             generator=torch.Generator(device).manual_seed(random.randint(0, MAX_SEED))
         ).images[0]