add image url support

requirements.txt

@@ -2,3 +2,4 @@ git+https://github.com/finegrain-ai/refiners@299217f45ab788bb7e670bcafb37a789a05
 gradio_imageslider==0.0.20
 spaces==0.28.3
 numpy<2.0.0
+requests

src/app.py

@@ -6,6 +6,8 @@ import torch
 def my_arange(*args, **kwargs):
     return torch.arange(*args, **kwargs)
 
+from io import BytesIO
+import PIL.Image
 
 torch.arange = my_arange
 
@@ -17,8 +19,9 @@ from huggingface_hub import hf_hub_download
 from PIL import Image
 from refiners.fluxion.utils import manual_seed
 from refiners.foundationals.latent_diffusion import Solver, solvers
-
+import requests
 from enhancer import ESRGANUpscaler, ESRGANUpscalerCheckpoints
+import time
 
 TITLE = """
 Image Enhancer
@@ -101,9 +104,28 @@ DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 enhancer.to(device=DEVICE, dtype=DTYPE)
 
 
+
+class calculateDuration:
+    def __init__(self, activity_name=""):
+        self.activity_name = activity_name
+
+    def __enter__(self):
+        self.start_time = time.time()
+        return self
+    
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.end_time = time.time()
+        self.elapsed_time = self.end_time - self.start_time
+        if self.activity_name:
+            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
+        else:
+            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
+
+
 @spaces.GPU
 def process(
     input_image: Image.Image,
+    image_url:str,
     prompt: str = "masterpiece, best quality, highres",
     negative_prompt: str = "worst quality, low quality, normal quality",
     seed: int = 42,
@@ -118,33 +140,43 @@ def process(
     solver: str = "DDIM",
 ) -> tuple[Image.Image, Image.Image]:
     manual_seed(seed)
+    
+    if image_url:
+        # fetch image from url
+        with calculateDuration("Download Image"):
+            print("start to fetch image from url", image_url)
+            response = requests.get(image_url)
+            response.raise_for_status()
+            input_image = PIL.Image.open(BytesIO(response.content))
+            print("fetch image success")
+
     print("start", prompt, upscale_factor)
     solver_type: type[Solver] = getattr(solvers, solver)
-
-    enhanced_image = enhancer.upscale(
-        image=input_image,
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        upscale_factor=upscale_factor,
-        controlnet_scale=controlnet_scale,
-        controlnet_scale_decay=controlnet_decay,
-        condition_scale=condition_scale,
-        tile_size=(tile_height, tile_width),
-        denoise_strength=denoise_strength,
-        num_inference_steps=num_inference_steps,
-        loras_scale=LORA_SCALES,
-        solver_type=solver_type,
-    )
-    print("finish")
+    with calculateDuration("enhancer"):
+        enhanced_image = enhancer.upscale(
+            image=input_image,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            upscale_factor=upscale_factor,
+            controlnet_scale=controlnet_scale,
+            controlnet_scale_decay=controlnet_decay,
+            condition_scale=condition_scale,
+            tile_size=(tile_height, tile_width),
+            denoise_strength=denoise_strength,
+            num_inference_steps=num_inference_steps,
+            loras_scale=LORA_SCALES,
+            solver_type=solver_type,
+        )
+        print("enhancer finish")
     return [input_image, enhanced_image]
 
-
 with gr.Blocks() as demo:
     gr.HTML(TITLE)
 
     with gr.Row():
         with gr.Column():
             input_image = gr.Image(type="pil", label="Input Image")
+            image_url = gr.Textbox(label="Image Url", placeholder="Enter image URL here (optional)")
             run_button = gr.ClearButton(components=None, value="Enhance Image")
         with gr.Column():
             output_slider = ImageSlider(label="Generate image", type="pil", slider_color="pink")
@@ -232,6 +264,7 @@ with gr.Blocks() as demo:
         fn=process,
         inputs=[
             input_image,
+            image_url,
             prompt,
             negative_prompt,
             seed,