Update app.py

app.py

@@ -61,7 +61,7 @@ checkpoint = "microsoft/Phi-3.5-mini-instruct"
 vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
 #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16")
 
-pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16").to(torch.bfloat16)
+pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16") #.to(torch.bfloat16)
 #pipe = StableDiffusion3Pipeline.from_pretrained("ford442/stable-diffusion-3.5-medium-bf16").to(torch.device("cuda:0"))
 #pipe = StableDiffusion3Pipeline.from_pretrained("ford442/RealVis_Medium_1.0b_bf16", torch_dtype=torch.bfloat16)
 #pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium", token=hftoken, torch_dtype=torch.float32, device_map='balanced')
@@ -117,7 +117,7 @@ def filter_text(text,phraseC):
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 4096
 
-@spaces.GPU(duration=80)
+@spaces.GPU(duration=90)
 def infer(
     prompt,
     negative_prompt,
@@ -132,7 +132,7 @@ def infer(
     progress=gr.Progress(track_tqdm=True),
 ):
     upscaler_2.to(torch.device('cpu'))
-    pipe.to(torch.device('cuda'))
+    pipe.to(device=device, dtype=torch.bfloat16)
     torch.set_float32_matmul_precision("highest")
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)