Update app.py

app.py

@@ -274,14 +274,10 @@ import torch
 import paramiko
 import os
 
-#FTP_HOST = os.getenv("FTP_HOST")
+FTP_HOST = os.getenv("FTP_HOST")
 FTP_USER = os.getenv("FTP_USER")
-#FTP_PASS = os.getenv("FTP_PASS")
+FTP_PASS = os.getenv("FTP_PASS")
 FTP_DIR = os.getenv("FTP_DIR")
-FTP_HOST = "1ink.us"
-#FTP_USER = "ford442"
-FTP_PASS = "GoogleBez12!"
-#FTP_DIR = "1ink.us/stable_diff/"  # Remote directory on FTP server
 
 def scheduler_swap_callback(pipeline, step_index, timestep, callback_kwargs):
         # adjust the batch_size of prompt_embeds according to guidance_scale
@@ -342,7 +338,6 @@ def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
         f.write(f"Steps: {num_inference_steps} \n")
         f.write(f"Guidance Scale: {guidance_scale} \n")
         f.write(f"SPACE SETUP: \n")
-        f.write(f"Model Scheduler: Euler_a all_custom before cuda \n")
         f.write(f"Model VAE: sdxl-vae-bf16\n")
         f.write(f"To cuda and bfloat \n")
     return filename
@@ -351,7 +346,7 @@ def uploadNote(prompt,num_inference_steps,guidance_scale,timestamp):
 
 pyx = cyper.inline(code, fast_indexing=True, directives=dict(boundscheck=False, wraparound=False, language_level=3))
 
-@spaces.GPU(duration=30)
+@spaces.GPU(duration=35)
 def generate_30(
     prompt: str,
     negative_prompt: str = "",
@@ -362,7 +357,7 @@ def generate_30(
     guidance_scale: float = 4,
     num_inference_steps: int = 125,
     use_resolution_binning: bool = True, 
-    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
+    progress=gr.Progress(track_tqdm=True)
 ):
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)
@@ -383,9 +378,7 @@ def generate_30(
     images = []
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
     filename = pyx.uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
-    #upload_to_ftp(filename) 
     pyx.upload_to_ftp(filename) 
-    #uploadNote(prompt,num_inference_steps,guidance_scale,timestamp)
     batch_options = options.copy()
     with torch.inference_mode():
         rv_image = pipe(**batch_options).images[0]
@@ -403,7 +396,7 @@ def generate_30(
     os.symlink(sd_image_path, unique_name)  
     return [unique_name]
 
-@spaces.GPU(duration=60)
+@spaces.GPU(duration=65)
 def generate_60(
     prompt: str,
     negative_prompt: str = "",
@@ -414,7 +407,7 @@ def generate_60(
     guidance_scale: float = 4,
     num_inference_steps: int = 125,
     use_resolution_binning: bool = True, 
-    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
+    progress=gr.Progress(track_tqdm=True)
 ):
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)
@@ -445,7 +438,7 @@ def generate_60(
     os.symlink(sd_image_path, unique_name)  
     return [unique_name]
     
-@spaces.GPU(duration=90)
+@spaces.GPU(duration=95)
 def generate_90(
     prompt: str,
     negative_prompt: str = "",
@@ -456,7 +449,7 @@ def generate_90(
     guidance_scale: float = 4,
     num_inference_steps: int = 125,
     use_resolution_binning: bool = True, 
-    progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
+    progress=gr.Progress(track_tqdm=True)
 ):
     seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device='cuda').manual_seed(seed)