Update app.py

app.py

@@ -199,7 +199,23 @@ def generate_30(
         options["use_resolution_binning"] = True
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
-    #with torch.no_grad():
+      # write note txt
+    filename= f'tst_{seed}.txt'
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    with open(filename, "w") as f:
+        f.write(f"Realvis 5.0: {seed} png\n")
+        f.write(f"Date/time: {timestamp} \n")
+        f.write(f"Prompt: {enhanced_prompt} \n")
+        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"Use Safetensors: no \n")
+        f.write(f"Use Model Dtype: no \n")
+        f.write(f"Model Scheduler: Euler_a custom \n")
+        f.write(f"Model VAE: stabilityai/sdxl-vae no dtype \n")
+        f.write(f"Model UNET: stabilityai no dtype \n")
+        f.write(f"Model to dtype - then to cuda \n")
+    upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
         batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
@@ -250,7 +266,23 @@ def generate_60(
         options["use_resolution_binning"] = True
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
-    #with torch.no_grad():
+      # write note txt
+    filename= f'tst_{seed}.txt'
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    with open(filename, "w") as f:
+        f.write(f"Realvis 5.0: {seed} png\n")
+        f.write(f"Date/time: {timestamp} \n")
+        f.write(f"Prompt: {enhanced_prompt} \n")
+        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"Use Safetensors: no \n")
+        f.write(f"Use Model Dtype: no \n")
+        f.write(f"Model Scheduler: Euler_a custom \n")
+        f.write(f"Model VAE: stabilityai/sdxl-vae no dtype \n")
+        f.write(f"Model UNET: stabilityai no dtype \n")
+        f.write(f"Model to dtype - then to cuda \n")
+    upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
         batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
@@ -301,7 +333,23 @@ def generate_90(
         options["use_resolution_binning"] = True
     images = []
     pipe.scheduler.set_timesteps(num_inference_steps,device)
-    #with torch.no_grad():
+      # write note txt
+    filename= f'tst_{seed}.txt'
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+    with open(filename, "w") as f:
+        f.write(f"Realvis 5.0: {seed} png\n")
+        f.write(f"Date/time: {timestamp} \n")
+        f.write(f"Prompt: {enhanced_prompt} \n")
+        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"Use Safetensors: no \n")
+        f.write(f"Use Model Dtype: no \n")
+        f.write(f"Model Scheduler: Euler_a custom \n")
+        f.write(f"Model VAE: stabilityai/sdxl-vae no dtype \n")
+        f.write(f"Model UNET: stabilityai no dtype \n")
+        f.write(f"Model to dtype - then to cuda \n")
+    upload_to_ftp(filename) 
     for i in range(0, num_images, BATCH_SIZE):
         batch_options = options.copy()
         batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]