Try to run on ZERO

app.py

@@ -1,11 +1,17 @@
-import random
 import cv2
 import numpy as np
 import torch
 import gradio as gr
+import random
+import spaces
 
 from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline
 
+DESCRIPTION='''
+This uses code lifted almost verbatim from
+[Outpainting II - Differential Diffusion](https://huggingface.co/blog/OzzyGT/outpainting-differential-diffusion).
+'''
+
 xlp_kwargs = {
     'custom_pipeline': 'pipeline_stable_diffusion_xl_differential_img2img'
 }
@@ -17,9 +23,12 @@ if torch.cuda.is_available():
 else:
     device = 'cpu'
     device_dtype = torch.float32
+    DESCRIPTION+='''
 
-xlp_kwargs['torch_dtype'] = device_dtype
+This Space appears to be running on a CPU; it will take hours to get results.  You may [duplicate this space](https://huggingface.co/spaces/clinteroni/outpainting-demo?duplicate=true) and pay for an upgraded runtime instead.
+    '''
 
+xlp_kwargs['torch_dtype'] = device_dtype
 
 def merge_images(original, new_image, offset, direction):
     if direction in ["left", "right"]:
@@ -173,54 +182,56 @@ def image_resize(image, new_size=1024):
 
     return image
 
+@spaces.GPU
+def outpaint(pil_image, direction='right', times_to_expand=4):
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+
+    pipeline = StableDiffusionXLPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-base-1.0",
+        **xlp_kwargs
+    ).to(device)
+    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
+        pipeline.scheduler.config, use_karras_sigmas=True)
+
+    pipeline.load_ip_adapter(
+        "h94/IP-Adapter",
+        subfolder="sdxl_models",
+        weight_name=[
+            "ip-adapter-plus_sdxl_vit-h.safetensors",
+        ],
+        image_encoder_folder="models/image_encoder",
+    )
+    pipeline.set_ip_adapter_scale(0.1)
+
+    def generate_image(prompt, negative_prompt, image, mask, ip_adapter_image, seed: int = None):
+        if seed is None:
+            seed = random.randint(0, 2**32 - 1)
 
-pipeline = StableDiffusionXLPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0",
-    **xlp_kwargs
-).to(device)
-pipeline.scheduler = DPMSolverMultistepScheduler.from_config(
-    pipeline.scheduler.config, use_karras_sigmas=True)
+        generator = torch.Generator(device="cpu").manual_seed(seed)
 
-pipeline.load_ip_adapter(
-    "h94/IP-Adapter",
-    subfolder="sdxl_models",
-    weight_name=[
-        "ip-adapter-plus_sdxl_vit-h.safetensors",
-    ],
-    image_encoder_folder="models/image_encoder",
-)
-pipeline.set_ip_adapter_scale(0.1)
-
-
-def generate_image(prompt, negative_prompt, image, mask, ip_adapter_image, seed: int = None):
-    if seed is None:
-        seed = random.randint(0, 2**32 - 1)
-
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-
-    image = pipeline(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        width=1024,
-        height=1024,
-        guidance_scale=4.0,
-        num_inference_steps=25,
-        original_image=image,
-        image=image,
-        strength=1.0,
-        map=mask,
-        generator=generator,
-        ip_adapter_image=[ip_adapter_image],
-        output_type="np",
-    ).images[0]
-
-    image = (image * 255).astype(np.uint8)
-    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        image = pipeline(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=1024,
+            height=1024,
+            guidance_scale=4.0,
+            num_inference_steps=25,
+            original_image=image,
+            image=image,
+            strength=1.0,
+            map=mask,
+            generator=generator,
+            ip_adapter_image=[ip_adapter_image],
+            output_type="np",
+        ).images[0]
 
-    return image
+        image = (image * 255).astype(np.uint8)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+        return image
 
 
-def outpaint(pil_image, direction='right', times_to_expand=4):
     prompt = ""
     negative_prompt = ""
     inpaint_mask_color = 50  # lighter use more of the Telea inpainting
@@ -272,14 +283,8 @@ gradio_app = gr.Interface(
     ],
     outputs=[gr.Image(label="Processed Image")],
     title="Outpainting with differential diffusion demo",
-    description='''
-        # Outpainting with differential diffusion demo
-        This uses code lifted almost verbatim from
-        [Outpainting II - Differential Diffusion](https://huggingface.co/blog/OzzyGT/outpainting-differential-diffusion).
-
-        If this Space is running on a CPU, it will take hours to get results.  You may [duplicate this space](https://huggingface.co/spaces/clinteroni/outpainting-demo?duplicate=true) and pay for an upgraded runtime instead.
-        '''
+    description=DESCRIPTION
 )
 
 if __name__ == "__main__":
-    gradio_app.launch()
+    gradio_app.queue(max_size=20).launch()