Update app.py

app.py

@@ -1,13 +1,14 @@
-import random
 import gradio as gr
 import numpy as np
+import random
 import spaces
+import sourcecode
 import torch
 import torchvision.transforms as transforms
 from PIL import Image
 from torchmetrics.functional.image import structural_similarity_index_measure as ssim
 from transformers import CLIPModel, CLIPProcessor
-import sourcecode
+
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_path = "czl/stable-diffusion-v1-5"
@@ -90,7 +91,6 @@ def infer(
     ).to(device)
     embed_pairs = zip(interpolated_prompt_embeds, interpolated_negative_prompts_embeds)
     embed_pairs_list = list(embed_pairs)
-    # offset step by -1
     prompt_embeds, negative_prompt_embeds = embed_pairs_list[interpolation_step - 1]
     preprocess_input = transforms.Compose(
         [transforms.ToTensor(), transforms.Resize((512, 512))]
@@ -117,7 +117,6 @@ def infer(
         return image, seed, "Unsafe content detected", "Unsafe content detected"
     else:
         image = images_list.images[0]
-
     pred_image = transforms.ToTensor()(image).unsqueeze(0)
     ssim_score = ssim(pred_image, input_img_tensor).item()
     real_inputs = clip_processor(