Update app.py

app.py

@@ -12,7 +12,7 @@ import torch
 from diffusers import StableDiffusionXLPipeline, KDPM2AncestralDiscreteScheduler, AutoencoderKL
 
 DESCRIPTION = """
-# Proteus V0.1
+# Proteus ```V0.1```
 
 Model by [dataautogpt3](https://huggingface.co/dataautogpt3)
 
@@ -80,18 +80,17 @@ def generate(
     seed = int(randomize_seed_fn(seed, randomize_seed))
 
     if not use_negative_prompt:
-        negative_prompt = None  # type: ignore
-    with torch.no_grad():
-        images = pipe(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            width=width,
-            height=height,
-            guidance_scale=guidance_scale,
-            num_inference_steps=25,
-            num_images_per_prompt=1,
-            output_type="pil",
-        ).images
+        negative_prompt = ""  # type: ignore
+    images = pipe(
+        prompt=f'''{prompt}, best quality, HD, "*aesthetic*"''',
+        negative_prompt=f"{negative_prompt}, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image",
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=20,
+        num_images_per_prompt=1,
+        output_type="pil",
+    ).images
 
     image_paths = [save_image(img) for img in images]
     print(image_paths)