Update app.py

app.py

@@ -20,10 +20,6 @@ import cyper
 from image_gen_aux import UpscaleWithModel
 import torch
 
-import preallocate_cuda_memory as pc
-mc = pc.MemoryController(0)  # 0 is the GPU index
-mc.occupy_all_available_memory()
-
 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
@@ -118,7 +114,7 @@ def scheduler_swap_callback(pipeline, step_index, timestep, callback_kwargs):
      #   pipe.vae = vae_a
      #   pipe.unet = unet_a
         torch.backends.cudnn.deterministic = False
-        pipe.unet.set_default_attn_processor()
+        #pipe.unet.set_default_attn_processor()
         print("-- swapping scheduler --")
       #  pipeline.scheduler = heun_scheduler
         #pipe.scheduler.set_timesteps(num_inference_steps*.70)
@@ -213,7 +209,7 @@ FTP_PASS = "GoogleBez12!"
 def scheduler_swap_callback(pipeline, step_index, timestep, callback_kwargs):
         # adjust the batch_size of prompt_embeds according to guidance_scale
     if step_index == int(pipeline.num_timesteps * 0.1):
-        print("-- swapping scheduler --")
+        print("-- swapping torch modes --")
       #  pipeline.scheduler = euler_scheduler
         torch.set_float32_matmul_precision("high")
        # pipe.vae = vae_b
@@ -240,7 +236,7 @@ def scheduler_swap_callback(pipeline, step_index, timestep, callback_kwargs):
      #   pipe.vae = vae_a
      #   pipe.unet = unet_a
         torch.backends.cudnn.deterministic = False
-        print("-- swapping scheduler --")
+        print("-- swapping torch modes --")
       #  pipeline.scheduler = heun_scheduler
         #pipe.scheduler.set_timesteps(num_inference_steps*.70)
       #  print(f"-- setting step {pipeline.num_timesteps * 0.9} --")
@@ -292,7 +288,7 @@ def generate_30(
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     seed = random.randint(0, MAX_SEED)
-  #  generator = torch.Generator(device='cuda').manual_seed(seed)
+    generator = torch.Generator(device='cuda').manual_seed(seed)
     options = {
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
@@ -301,7 +297,7 @@ def generate_30(
         "height": height,
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
-   #     "generator": generator,
+        "generator": generator,
         "output_type": "pil",
         "callback_on_step_end": pyx.scheduler_swap_callback
     }
@@ -343,7 +339,7 @@ def generate_60(
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     seed = random.randint(0, MAX_SEED)
-  #  generator = torch.Generator(device='cuda').manual_seed(seed)
+    generator = torch.Generator(device='cuda').manual_seed(seed)
     options = {
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
@@ -352,7 +348,7 @@ def generate_60(
         "height": height,
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
-  #      "generator": generator,
+        "generator": generator,
         "output_type": "pil",
         "callback_on_step_end": scheduler_swap_callback
     }
@@ -384,7 +380,7 @@ def generate_90(
     progress=gr.Progress(track_tqdm=True)  # Add progress as a keyword argument
 ):
     seed = random.randint(0, MAX_SEED)
-  #  generator = torch.Generator(device='cuda').manual_seed(seed)
+    generator = torch.Generator(device='cuda').manual_seed(seed)
     options = {
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
@@ -393,7 +389,7 @@ def generate_90(
         "height": height,
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
- #       "generator": generator,
+        "generator": generator,
         "output_type": "pil",
         "callback_on_step_end": scheduler_swap_callback
     }