Update app.py

app.py

@@ -34,7 +34,7 @@ from diffusers import AutoencoderKL
 #from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
 
 from PIL import Image
-'''
+
 torch.backends.cuda.matmul.allow_tf32 = False
 torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
 torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False
@@ -44,7 +44,7 @@ torch.backends.cudnn.benchmark = False
 torch.backends.cuda.preferred_blas_library="cublas"
 torch.backends.cuda.preferred_linalg_library="cusolver"
 torch.set_float32_matmul_precision("highest")
-'''
+
 hftoken = os.getenv("HF_AUTH_TOKEN") 
 
 # code = r'''
@@ -81,7 +81,7 @@ pipe = StableDiffusion3Pipeline.from_single_file(
     "https://huggingface.co/1inkus/sd35-large-UltraReal-bf16-DDUF/blob/main/sd3-bf16-large.dduf",
     #tokenizer_3=T5TokenizerFast.from_pretrained("ford442/stable-diffusion-3.5-large-bf16", add_prefix_space=False, use_fast=True, subfolder="tokenizer_3"),
     use_safetensors=True,
-    devive_map='cuda',
+    devive_map='auto',
 ) #.to(device=device)
 
 ###    pipe = StableDiffusion3Pipeline.from_pretrained(
@@ -113,7 +113,7 @@ pipe = StableDiffusion3Pipeline.from_single_file(
 #pipe.to(device=device) #, dtype=torch.bfloat16)
 #pipe.to(device)
 #pipe.vae=vaeX.to('cpu')
-upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device('cpu'))
+upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device('cuda'))
 
 MAX_SEED = np.iinfo(np.int32).max
 
@@ -159,7 +159,7 @@ def infer_30(
     #pyx.upload_to_ftp(sd35_path)
     upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
-    upscaler_2.to(torch.device('cuda'))
+    #upscaler_2.to(torch.device('cuda'))
     with torch.no_grad():
         upscale2 = upscaler_2(sd_image, tiling=True, tile_width=256, tile_height=256)
     print('-- got upscaled image --')
@@ -210,7 +210,7 @@ def infer_60(
     #pyx.upload_to_ftp(sd35_path)
     upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
-    upscaler_2.to(torch.device('cuda'))
+    #upscaler_2.to(torch.device('cuda'))
     with torch.no_grad():
         upscale2 = upscaler_2(sd_image, tiling=True, tile_width=256, tile_height=256)
     print('-- got upscaled image --')
@@ -261,7 +261,7 @@ def infer_90(
     #pyx.upload_to_ftp(sd35_path)
     upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
-    upscaler_2.to(torch.device('cuda'))
+    #upscaler_2.to(torch.device('cuda'))
     with torch.no_grad():
         upscale2 = upscaler_2(sd_image, tiling=True, tile_width=256, tile_height=256)
     print('-- got upscaled image --')
@@ -312,7 +312,7 @@ def infer_100(
     #pyx.upload_to_ftp(sd35_path)
     upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
-    upscaler_2.to(torch.device('cuda'))
+    #upscaler_2.to(torch.device('cuda'))
     with torch.no_grad():
         upscale2 = upscaler_2(sd_image, tiling=True, tile_width=256, tile_height=256)
     print('-- got upscaled image --')