import gradio as gr import torch from diffusers import FluxPipeline from huggingface_hub import HfApi import spaces import random """ This application uses the Flux.1 Lite model: @article{flux1-lite, title={Flux.1 Lite: Distilling Flux1.dev for Efficient Text-to-Image Generation}, author={Daniel Verdú, Javier Martín}, email={dverdu@freepik.com, javier.martin@freepik.com}, year={2024}, } """ @spaces.GPU(duration=70) def initialize_model(): model_id = "Freepik/flux.1-lite-8B-alpha" pipe = FluxPipeline.from_pretrained( model_id, torch_dtype=torch.bfloat16 ).to("cuda") return pipe @spaces.GPU(duration=70) def generate_image( prompt, guidance_scale=3.5, width=1024, height=1024 ): try: # Initialize model within the GPU context pipe = initialize_model() # Generate random seed seed = random.randint(1, 1000000) with torch.inference_mode(): image = pipe( prompt=prompt, generator=torch.Generator(device="cuda").manual_seed(seed), num_inference_steps=25, # Fixed steps guidance_scale=guidance_scale, height=height, width=width, ).images[0] return image except Exception as e: print(f"Error during image generation: {str(e)}") raise e # Create the Gradio interface demo = gr.Interface( fn=generate_image, inputs=[ gr.Textbox( label="Prompt", placeholder="Enter your image description here...", value="a glass cup with beer, inside the beer a scuba diver, with a beautiful sunset background" ), gr.Slider( minimum=1, maximum=20, value=3.5, label="Guidance Scale", step=0.5 ), gr.Slider( minimum=128, maximum=1024, value=1024, label="Width", step=64 ), gr.Slider( minimum=128, maximum=1024, value=1024, label="Height", step=64 ) ], outputs=gr.Image(type="pil", label="Generated Image"), title="Freepix Flux.1-lite-8B-alpha Model (Zero-GPU)", description="Generate images using Freepik's Flux model with Zero-GPU allocation. Using 25 fixed steps and random seed for each generation.", examples=[ ["A close-up image of a green alien with fluorescent skin in the middle of a dark purple forest", 3.5, 1024, 1024], ["a glass cup with beer, inside the beer a scuba diver, with a beautiful sunset backgroudn", 3.5, 1024, 1024] ] # Properly closed the examples list ) # Properly closed the Interface parenthesis # Launch the app if __name__ == "__main__": demo.launch()