Create app1.py

app1.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+import torch
+from PIL import Image
+
+# Load the pipeline
+prj_path = "jkcg/furniture-chair"
+model = "stabilityai/stable-diffusion-xl-base-1.0"
+pipe = DiffusionPipeline.from_pretrained(
+     model,
+     torch_dtype=torch.float32,  # Use float32 for CPU
+)
+pipe.to("cpu")  # Ensure the pipeline runs on CPU
+pipe.load_lora_weights(prj_path, weight_name="pytorch_lora_weights.safetensors")
+
+def generate_image(prompt, seed):
+    generator = torch.Generator("cpu").manual_seed(seed)
+    image = pipe(prompt=prompt, generator=generator).images[0]
+    return image
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt", value="photo of a furnichair-texx in an empty room"),
+        gr.Slider(label="Seed", minimum=0, maximum=10000, step=1, value=42)
+    ],
+    outputs=gr.Image(label="Generated Image")
+)
+
+# Launch the interface
+interface.launch(share=True)