Update app.py

app.py

@@ -3,20 +3,20 @@ from diffusers import LCMScheduler, AutoPipelineForText2Image,DDPMScheduler
 from PIL import Image
 import numpy as np
 import gradio as gr
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+adapter_id = "ksyint/teu_lora"
 
+pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float32, variant="fp16")
+pipe.scheduler = DDPMScheduler.from_config(pipe.scheduler.config)
+pipe.to("cpu")
 
-def main(prompt,num_steps):
-    num_steps=int(num_steps)
-   
-    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-    adapter_id = "ksyint/teu_lora"
-
-    pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float32, variant="fp16")
-    pipe.scheduler = DDPMScheduler.from_config(pipe.scheduler.config)
-    pipe.to("cpu")
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
 
-    pipe.load_lora_weights(adapter_id)
-    pipe.fuse_lora()
+def main(prompt):
+    num_steps=60
+   
+    
 
     image = pipe(prompt=prompt, num_inference_steps=num_steps, guidance_scale=5.0,strength=5.0).images[0]
     #gc.collect()
@@ -24,7 +24,7 @@ def main(prompt,num_steps):
     return image
 
 
-iface = gr.Interface(fn=main, inputs=["text", "text"], outputs="image", title="Text to Image Generation", 
+iface = gr.Interface(fn=main, inputs=["text"], outputs="image", title="Text to Image Generation", 
              description="Generate images based on textual prompts.")
 if __name__ == "__main__":