from optimum.intel import OVStableDiffusionPipeline # Changed from OVPipelineForText2Image from openvino import Core, Model # Updated to use non-deprecated openvino module import gradio as gr # Load the model model_id = "AIFunOver/FLUX.1-dev-openvino-fp16" try: model = OVStableDiffusionPipeline.from_pretrained( model_id, export=True, # Enable export to OpenVINO IR if needed device="AUTO" # Use AUTO for flexible device selection ) # Save the model after export to avoid re-conversion model.save_pretrained("./flux1_dev_openvino_fp16") except Exception as e: print(f"Error loading model: {e}") exit(1) # Define the image generation function def generate_image(prompt, num_inference_steps=50, guidance_scale=7.5): try: # Generate image using the model result = model( prompt=prompt, num_inference_steps=int(num_inference_steps), # Ensure integer input guidance_scale=float(guidance_scale), # Ensure float input height=512, width=512 ) image = result.images[0] # Extract the generated image return image except Exception as e: return f"Error generating image: {e}" # Set up Gradio interface interface = gr.Interface( fn=generate_image, inputs=[ gr.Textbox(label="Prompt", placeholder="Enter your image description here..."), gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=50, step=1), gr.Slider(label="Guidance Scale", minimum=1.0, maximum=20.0, value=7.5, step=0.1) ], outputs=gr.Image(label="Generated Image"), title="FLUX.1-dev Image Generation with OpenVINO", description="Enter a text prompt to generate an image using the FLUX.1-dev model optimized with OpenVINO." ) # Launch the Gradio app if __name__ == "__main__": try: interface.launch(server_name="0.0.0.0", server_port=7860) # Explicit server settings except Exception as e: print(f"Error launching Gradio interface: {e}")