print

app.py

@@ -6,7 +6,7 @@ from PIL import Image
 from diffusers import LCMScheduler, AutoPipelineForImage2Image
 from diffusers.utils import load_image
 import math
-
+import time
 model_id = "segmind/Segmind-Vega"
 adapter_id = "segmind/Segmind-VegaRT"
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -23,12 +23,16 @@ def resize(value,img):
     return img
 
 def infer(source_img, prompt, steps, seed, Strength):
+    start = time.time()
     generator = torch.Generator(device).manual_seed(seed)  
     if int(steps * Strength) < 1:
         steps = math.ceil(1 / max(0.10, Strength))
     source_image = resize(512, source_img)
     source_image.save('source.png')
     image = pipe(prompt, image=source_image, strength=Strength, guidance_scale=0.0, num_inference_steps=steps).images[0]
+    end = time.time()
+    print("步数",steps)
+    print("时间",end-start)
     return image
 
 gr.Interface(fn=infer, inputs=[