check spaces zero

app.py

@@ -16,6 +16,7 @@ import time
 import cv2
 import numpy as np
 
+IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
@@ -42,10 +43,13 @@ pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
     use_safetensors=True,
     scheduler=scheduler,
 )
-# pipe.enable_xformers_memory_efficient_attention()
-# pipe.enable_model_cpu_offload()
+if not IS_SPACES_ZERO:
+    pipe.enable_xformers_memory_efficient_attention()
+    pipe.enable_model_cpu_offload()
+
 pipe.enable_vae_tiling()
 
+
 compel = Compel(
     tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
     text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
@@ -54,6 +58,9 @@ compel = Compel(
 )
 pipe = pipe.to(device)
 
+if not IS_SPACES_ZERO:
+    apply_hidiffusion(pipe)
+
 
 def pad_image(image):
     w, h = image.size
@@ -87,7 +94,8 @@ def predict(
     controlnet_end=1.0,
     progress=gr.Progress(track_tqdm=True),
 ):
-    apply_hidiffusion(pipe)
+    if IS_SPACES_ZERO:
+        apply_hidiffusion(pipe)
     if input_image is None:
         raise gr.Error("Please upload an image.")
     padded_image = pad_image(input_image).resize((1024, 1024)).convert("RGB")