import os import subprocess from pathlib import Path import gradio as gr import torch from demo import SdmCompressionDemo dest_path_config = Path('checkpoints/BK-SDM-Small_iter50000/unet/config.json') dest_path_torch_ckpt = Path('checkpoints/BK-SDM-Small_iter50000/unet/diffusion_pytorch_model.bin') BK_SDM_CONFIG_URL: str = os.getenv('BK_SDM_CONFIG_URL', None) BK_SDM_TORCH_CKPT_URL: str = os.getenv('BK_SDM_TORCH_CKPT_URL', None) assert BK_SDM_CONFIG_URL is not None assert BK_SDM_TORCH_CKPT_URL is not None subprocess.call( f"wget --no-check-certificate -O {dest_path_config} {BK_SDM_CONFIG_URL}", shell=True ) subprocess.call( f"wget --no-check-certificate -O {dest_path_torch_ckpt} {BK_SDM_TORCH_CKPT_URL}", shell=True ) if __name__ == "__main__": device = 'cuda' if torch.cuda.is_available() else 'cpu' servicer = SdmCompressionDemo(device) example_list = servicer.get_example_list() with gr.Blocks(theme='nota-ai/theme') as demo: gr.Markdown(Path('docs/header.md').read_text()) gr.Markdown(Path('docs/description.md').read_text()) with gr.Row(): with gr.Column(variant='panel', scale=30): text = gr.Textbox(label="Input Prompt", max_lines=5, placeholder="Enter your prompt") with gr.Row().style(equal_height=True): generate_original_button = gr.Button(value="Generate with Original Model", variant="primary") generate_compressed_button = gr.Button(value="Generate with Compressed Model", variant="primary") with gr.Accordion("Advanced Settings", open=False): negative = gr.Textbox(label=f'Negative Prompt', placeholder=f'Enter aspects to remove (e.g., {"low quality"})') with gr.Row(): guidance_scale = gr.Slider(label="Guidance Scale", value=7.5, minimum=4, maximum=11, step=0.5) steps = gr.Slider(label="Denoising Steps", value=25, minimum=10, maximum=75, step=5) seed = gr.Slider(0, 999999, label='Random Seed', value=1234, step=1) with gr.Tab("Example Prompts"): examples = gr.Examples(examples=example_list, inputs=[text]) with gr.Column(variant='panel',scale=35): # Define original model output components gr.Markdown('

Original Stable Diffusion 1.4

') original_model_output = gr.Image(label="Original Model") with gr.Row().style(equal_height=True): with gr.Column(): original_model_test_time = gr.Textbox(value="", label="Inference Time (sec)") original_model_params = gr.Textbox(value=servicer.get_sdm_params(servicer.pipe_original), label="# Parameters") original_model_error = gr.Markdown() with gr.Column(variant='panel',scale=35): # Define compressed model output components gr.Markdown('

Compressed Stable Diffusion (Ours)

') compressed_model_output = gr.Image(label="Compressed Model") with gr.Row().style(equal_height=True): with gr.Column(): compressed_model_test_time = gr.Textbox(value="", label="Inference Time (sec)") compressed_model_params = gr.Textbox(value=servicer.get_sdm_params(servicer.pipe_compressed), label="# Parameters") compressed_model_error = gr.Markdown() inputs = [text, negative, guidance_scale, steps, seed] # Click the generate button for original model original_model_outputs = [original_model_output, original_model_error, original_model_test_time] text.submit(servicer.infer_original_model, inputs=inputs, outputs=original_model_outputs) generate_original_button.click(servicer.infer_original_model, inputs=inputs, outputs=original_model_outputs) # Click the generate button for compressed model compressed_model_outputs = [compressed_model_output, compressed_model_error, compressed_model_test_time] text.submit(servicer.infer_compressed_model, inputs=inputs, outputs=compressed_model_outputs) generate_compressed_button.click(servicer.infer_compressed_model, inputs=inputs, outputs=compressed_model_outputs) gr.Markdown(Path('docs/footer.md').read_text()) demo.queue(concurrency_count=1) # demo.launch() demo.launch()