Create app.py

app.py

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+import gradio as gr
+from diffusers import DiffusionPipeline
+
+# Load the pipeline and LoRA weights
+
+def load_cust(base_model, models_sec):
+    pipeline = DiffusionPipeline.from_pretrained(base_model)
+    pipeline.load_lora_weights(models_sec)
+
+def generate_image(prompt, negative_prompt):
+    # Generate the image with the provided prompts
+    image = pipeline(prompt, negative_prompt=negative_prompt).images[0]
+    return image
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Text to Image Generation Custom models Demo")
+    prompt = gr.Textbox(label="Prompt", placeholder="Enter your text prompt here")
+    negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter your negative prompt here")
+    submit_button = gr.Button("Generate Image")
+    with gr.Accordion('load your custom models first'):
+        basem = gr.Textbox(label="your models adapter")
+        secondm = gr.Textbox(label="your main models")
+        exports = gr.Button("load your models")
+        exports.click(load_cust, inputs=[basem, secondm], outputs=[])
+    output_image = gr.Image(label="Generated Image")
+    submit_button.click(generate_image, inputs=[prompt, negative_prompt], outputs=output_image)
+
+# Launch the demo
+demo.launch()