examples

app.py

@@ -4,12 +4,12 @@ import torch
 from diffusers import DiffusionPipeline, AutoencoderKL
 from PIL import Image
 from torchvision import transforms
-import numpy as np
 import tempfile
 import os
+import time
 import uuid
 
-TORCH_COMPILE = os.getenv("TORCH_COMPILE", "0") == "1"
+LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
@@ -25,8 +25,6 @@ pipe = DiffusionPipeline.from_pretrained(
     vae=vae,
 )
 pipe = pipe.to(device)
-if TORCH_COMPILE:
-    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
 
 
 def load_and_process_image(pil_image):
@@ -73,6 +71,7 @@ def predict(
     padded_image = pad_image(input_image).resize((1024, 1024)).convert("RGB")
     image_lr = load_and_process_image(padded_image).to(device)
     generator = torch.manual_seed(seed)
+    last_time = time.time()
     images = pipe(
         prompt,
         negative_prompt=negative_prompt,
@@ -90,8 +89,9 @@ def predict(
         sigma=0.8,
         multi_decoder=True,
         show_image=False,
-        lowvram=True,
+        lowvram=LOW_MEMORY,
     )
+    print(f"Time taken: {time.time() - last_time}")
     images_path = tempfile.mkdtemp()
     paths = []
     uuid_name = uuid.uuid4()
@@ -149,7 +149,7 @@ with gr.Blocks(css=css) as demo:
                 "./examples/lara.jpeg",
                 "photography of lara croft 8k high definition award winning",
                 "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
-                1415535897932,
+                1415926535897932,
                 2,
             ],
             [

gradio_cached_examples/14/log.csv

@@ -1,4 +1,4 @@
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