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import gradio as gr
from optimum.intel import OVDiffusionPipeline
from transformers import AutoTokenizer
from threading import Lock
import warnings
# Suppress deprecation warnings (optional)
warnings.filterwarnings("ignore", category=DeprecationWarning)
# Load the pipeline globally
model_id = "OpenVINO/FLUX.1-schnell-int4-ov"
pipeline = OVDiffusionPipeline.from_pretrained(model_id, device="CPU")
# Explicitly load and configure the tokenizer
# FLUX.1-schnell uses a T5-based encoder, typically t5-v1_1-xl or similar
tokenizer = AutoTokenizer.from_pretrained("google/t5-v1_1-xl", use_fast=True, add_prefix_space=True)
pipeline.text_encoder_2.tokenizer = tokenizer # Assign to the T5 encoder
lock = Lock()
# Define the image generation function
def generate_image(prompt):
with lock:
# Reduce num_inference_steps for faster inference to avoid timeouts
image = pipeline(prompt, num_inference_steps=2, guidance_scale=3.5).images[0]
return image
# Create the Gradio interface
interface = gr.Interface(
fn=generate_image,
inputs=gr.Textbox(label="Enter your prompt", placeholder="e.g., A futuristic cityscape at sunset"),
outputs=gr.Image(label="Generated Image"),
title="FLUX.1-Schnell (OpenVINO INT4) Image Generator",
description="Generate images from text prompts using FLUX.1-schnell optimized for CPU with OpenVINO.",
examples=[["A serene mountain landscape"], ["A cyberpunk city at night"]],
cache_examples=False
)
# Launch the interface
if __name__ == "__main__":
interface.launch(server_name="0.0.0.0", server_port=7860) # Explicitly set for Spaces |