Update app.py

app.py

@@ -3,8 +3,6 @@ import numpy as np
 import random
 from diffusers import DiffusionPipeline
 import torch
-from PIL import Image, ImageDraw, ImageFont
-
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -27,39 +25,20 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-
+        
     generator = torch.Generator().manual_seed(seed)
-
+    
     image = pipe(
-        prompt=style_prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator
-    ).images[0]
-
-    # Ensure image is in uint8 format
-    image = (255 * np.clip(image, 0, 1)).astype(np.uint8)
-
-    # Convert the image to PIL format for overlaying the watermark
-    pil_image = Image.fromarray(image)
-
-    # Add watermark
-    watermark_text = "Bibou.jpeg"
-    font = ImageFont.truetype("arial.ttf", size=30)  # Adjust font and size as needed
-    draw = ImageDraw.Draw(pil_image)
-    text_width, text_height = draw.textsize(watermark_text, font=font)
-    margin = 10
-    opacity = 0.6
-    draw.text((pil_image.width - text_width - margin, pil_image.height - text_height - margin), watermark_text, font=font, fill=(255, 255, 255, int(255 * opacity)))
-
-    # Convert back to numpy array for Gradio display
-    watermarked_image = np.array(pil_image)
-
-    return watermarked_image
-
+        prompt = style_prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
+    ).images[0] 
+    
+    return image