Create app.py

app.py

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+import torch
+from diffusers import LCMScheduler, AutoPipelineForText2Image,DDPMScheduler
+from PIL import Image
+import numpy as np
+import gradio as gr
+import gc 
+
+
+def main(prompt):
+   
+    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    adapter_id = "lorabase"
+
+    pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float32, variant="fp16")
+    pipe.scheduler = DDPMScheduler.from_config(pipe.scheduler.config)
+    pipe.to("cuda")
+
+    pipe.load_lora_weights(adapter_id)
+    pipe.fuse_lora()
+
+    image = pipe(prompt=prompt, num_inference_steps=60, guidance_scale=7.0,strength=5.0).images[0]
+    #gc.collect()
+    #torch.cuda.empty_cache()
+    return image
+
+
+iface = gr.Interface(fn=main, inputs="text", outputs="image", title="Text to Image Generation", 
+             description="Generate images based on textual prompts.")
+
+iface.launch(  debug=False,share=True,
+                         server_name="0.0.0.0",
+                         server_port=8433) #iface.launch( show_error=True, server_name='0.0.0.0' , share=True)
+
+
+#interface.launch(server_name=“0.0.0.0”, server_port=7860)
+