ZeroGPU to T4

app.py

@@ -1,22 +1,21 @@
 import torch
 import random
-import spaces
+#import spaces  ## For ZeroGPU
 import gradio as gr
-from PIL import Image
 from diffusers import AutoPipelineForText2Image
 from diffusers.utils import load_image
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=dtype)
+pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=dtype).to("cuda") ## For ZeroGPU no .to("cuda")
 pipe.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
 pipe.to(device)
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
     if randomize_seed:
         seed = random.randint(0, 2000)
     return seed
-    
-@spaces.GPU()
+print("piped")
+#@spaces.GPU() ## For ZeroGPU
 def create_image(image_pil,
                  prompt,
                  n_prompt,
@@ -43,9 +42,9 @@ def create_image(image_pil,
                 "up": {"block_0": [0.0, control_scale, 0.0]},
             }
         pipe.set_ip_adapter_scale(scale)
-
+    
     style_image = load_image(image_pil)
-    generator = torch.Generator().manual_seed(randomize_seed_fn(seed, True))
+    generator = torch.Generator(device="cpu").manual_seed(randomize_seed_fn(seed, True))  ## For ZeroGPU no device="cpu"
     
     
     image = pipe(
@@ -57,6 +56,7 @@ def create_image(image_pil,
         generator=generator,
     ).images[0]
     
+    
     return image
     
     
@@ -146,4 +146,4 @@ with block:
     
     gr.Markdown(article)
 
-block.launch(show_error=True)
+block.launch(show_error=True)