Update app.py

app.py

@@ -22,23 +22,35 @@ pipe.fuse_lora()
 MAX_SEED = 2**32-1
 
 @spaces.GPU(duration=75)
-def generate_image(prompt, steps=28, seed=None, cfg_scale=3.5, width=1024, height=1024, lora_scale=1.00):
+def generate_image(prompt, steps=28, seed=None, cfg_scale=3.5, width=1024, height=1024, lora_scale=0.95):
+    if seed is None:
+        seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device="cuda").manual_seed(seed)
     
     image = pipe(
         prompt=prompt,
-        num_inference_steps=steps,
+        num_inference_steps=int(steps),
         guidance_scale=cfg_scale,
-        width=width,
-        height=height,
+        width=int(width),
+        height=int(height),
         generator=generator,
         joint_attention_kwargs={"scale": lora_scale},
     ).images[0]
     return image
 
-def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale):
+def run_lora(prompt, cfg_scale=3.5, steps=28, randomize_seed=True, seed=None, width=1024, height=1024, lora_scale=0.95):
+    # Handle the case when only prompt is provided (for Examples)
+    if isinstance(prompt, str) and all(param is None for param in [cfg_scale, steps, randomize_seed, seed, width, height, lora_scale]):
+        cfg_scale = 3.5
+        steps = 28
+        randomize_seed = True
+        seed = None
+        width = 1024
+        height = 1024
+        lora_scale = 0.95
+
     if randomize_seed or seed is None:
-       seed = random.randint(0, MAX_SEED)
+        seed = random.randint(0, MAX_SEED)
     
     image = generate_image(prompt, steps, seed, cfg_scale, width, height, lora_scale)
     return image, seed