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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}") |