Update app.py

app.py

@@ -15,15 +15,13 @@ import os
 from image_gen_aux import UpscaleWithModel
 from huggingface_hub import hf_hub_download
 
+import cyper
+
 #from diffusers import SD3Transformer2DModel, AutoencoderKL
 #from models.transformer_sd3 import SD3Transformer2DModel
 #from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
-from PIL import Image
 
-FTP_HOST = "1ink.us"
-FTP_USER = "ford442"
-FTP_PASS = "GoogleBez12!"
-FTP_DIR = "1ink.us/stable_diff/"  # Remote directory on FTP server
+from PIL import Image
 
 torch.backends.cuda.matmul.allow_tf32 = False
 torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
@@ -36,6 +34,16 @@ torch.backends.cudnn.benchmark = False
 
 hftoken = os.getenv("HF_AUTH_TOKEN") 
 
+
+code = r'''
+import torch
+import paramiko
+import os
+FTP_HOST = os.getenv("FTP_HOST")
+FTP_USER = os.getenv("FTP_USER")
+FTP_PASS = os.getenv("FTP_PASS")
+FTP_DIR = os.getenv("FTP_DIR")
+
 def upload_to_ftp(filename):
     try:
         transport = paramiko.Transport((FTP_HOST, 22))
@@ -48,6 +56,10 @@ def upload_to_ftp(filename):
         print(f"Uploaded {filename} to FTP server")
     except Exception as e:
         print(f"FTP upload error: {e}")
+        
+'''
+
+pyx = cyper.inline(code, fast_indexing=True, directives=dict(boundscheck=False, wraparound=False, language_level=3))
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 torch_dtype = torch.bfloat16
@@ -68,10 +80,7 @@ pipe = StableDiffusion3Pipeline.from_pretrained(
 )
 
 #pipe.to(device=device, dtype=torch.bfloat16)
-
-#pipe.enable_model_cpu_offload()
 pipe.to(device)
-#pipe.to(device=device, dtype=torch.bfloat16)
 
 upscaler_2 = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device('cpu'))
 
@@ -79,7 +88,7 @@ MAX_SEED = np.iinfo(np.int32).max
 
 MAX_IMAGE_SIZE = 4096
 
-@spaces.GPU(duration=30)
+@spaces.GPU(duration=40)
 def infer_30(
     prompt,
     negative_prompt_1,
@@ -110,21 +119,22 @@ def infer_30(
             max_sequence_length=512
     ).images[0]
     print('-- got image --')
-    sd35_path = f"sd35l_{seed}.png"
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    sd35_path = f"sd35l_{timestamp}.png"
     sd_image.save(sd35_path,optimize=False,compress_level=0)
-    upload_to_ftp(sd35_path)
+    pyx.upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
     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 --')
     downscale2 = upscale2.resize((upscale2.width // 4, upscale2.height // 4),Image.LANCZOS)
-    upscale_path = f"sd35l_upscale_{seed}.png"
+    upscale_path = f"sd35l_upscale_{timestamp}.png"
     downscale2.save(upscale_path,optimize=False,compress_level=0)
-    upload_to_ftp(upscale_path)
+    pyx.upload_to_ftp(upscale_path)
     return sd_image, prompt
 
-@spaces.GPU(duration=60)
+@spaces.GPU(duration=70)
 def infer_60(
     prompt,
     negative_prompt_1,
@@ -155,21 +165,22 @@ def infer_60(
             max_sequence_length=512
     ).images[0]
     print('-- got image --')
-    sd35_path = f"sd35l_{seed}.png"
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    sd35_path = f"sd35l_{timestamp}.png"
     sd_image.save(sd35_path,optimize=False,compress_level=0)
-    upload_to_ftp(sd35_path)
+    pyx.upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
     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 --')
     downscale2 = upscale2.resize((upscale2.width // 4, upscale2.height // 4),Image.LANCZOS)
-    upscale_path = f"sd35l_upscale_{seed}.png"
+    upscale_path = f"sd35l_upscale_{timestamp}.png"
     downscale2.save(upscale_path,optimize=False,compress_level=0)
-    upload_to_ftp(upscale_path)
+    pyx.upload_to_ftp(upscale_path)
     return sd_image, prompt
 
-@spaces.GPU(duration=90)
+@spaces.GPU(duration=100)
 def infer_90(
     prompt,
     negative_prompt_1,
@@ -200,21 +211,22 @@ def infer_90(
             max_sequence_length=512
     ).images[0]
     print('-- got image --')
-    sd35_path = f"sd35l_{seed}.png"
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    sd35_path = f"sd35l_{timestamp}.png"
     sd_image.save(sd35_path,optimize=False,compress_level=0)
-    upload_to_ftp(sd35_path)
+    pyx.upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
     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 --')
     downscale2 = upscale2.resize((upscale2.width // 4, upscale2.height // 4),Image.LANCZOS)
-    upscale_path = f"sd35l_upscale_{seed}.png"
+    upscale_path = f"sd35l_upscale_{timestamp}.png"
     downscale2.save(upscale_path,optimize=False,compress_level=0)
-    upload_to_ftp(upscale_path)
+    pyx.upload_to_ftp(upscale_path)
     return sd_image, prompt
 
-@spaces.GPU(duration=100)
+@spaces.GPU(duration=110)
 def infer_100(
     prompt,
     negative_prompt_1,
@@ -245,18 +257,19 @@ def infer_100(
             max_sequence_length=512
     ).images[0]
     print('-- got image --')
-    sd35_path = f"sd35l_{seed}.png"
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    sd35_path = f"sd35l_{timestamp}.png"
     sd_image.save(sd35_path,optimize=False,compress_level=0)
-    upload_to_ftp(sd35_path)
+    pyx.upload_to_ftp(sd35_path)
     #  pipe.unet.to('cpu')
     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 --')
     downscale2 = upscale2.resize((upscale2.width // 4, upscale2.height // 4),Image.LANCZOS)
-    upscale_path = f"sd35l_upscale_{seed}.png"
+    upscale_path = f"sd35l_upscale_{timestamp}.png"
     downscale2.save(upscale_path,optimize=False,compress_level=0)
-    upload_to_ftp(upscale_path)
+    pyx.upload_to_ftp(upscale_path)
     return sd_image, prompt
     
 css = """