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import spaces |
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import gradio as gr |
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import time |
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import torch |
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from diffusers import ( |
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DDPMScheduler, |
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AutoPipelineForText2Image, |
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AutoencoderTiny, |
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) |
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import oneflow as flow |
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from onediff.infer_compiler import oneflow_compile |
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BASE_MODEL = "stabilityai/sdxl-turbo" |
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device = "cuda" |
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vae = AutoencoderTiny.from_pretrained( |
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'madebyollin/taesdxl', |
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use_safetensors=True, |
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torch_dtype=torch.float16, |
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).to('cuda') |
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base_pipe = AutoPipelineForText2Image.from_pretrained( |
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BASE_MODEL, |
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vae=vae, |
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torch_dtype=torch.float16, |
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variant="fp16", |
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use_safetensors=True, |
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) |
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base_pipe.to(device) |
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base_pipe = base_pipe.to(device, silence_dtype_warnings=True) |
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base_pipe.scheduler = DDPMScheduler.from_pretrained( |
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BASE_MODEL, |
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subfolder="scheduler", |
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) |
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base_pipe.unet = oneflow_compile(base_pipe.unet) |
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def create_demo() -> gr.Blocks: |
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@spaces.GPU(duration=10) |
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def text_to_image( |
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prompt:str, |
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steps:int, |
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): |
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run_task_time = 0 |
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time_cost_str = '' |
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run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) |
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generated_image = base_pipe( |
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prompt=prompt, |
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num_inference_steps=steps, |
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).images[0] |
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run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str) |
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return generated_image |
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def get_time_cost(run_task_time, time_cost_str): |
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now_time = int(time.time()*1000) |
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if run_task_time == 0: |
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time_cost_str = 'start' |
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else: |
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if time_cost_str != '': |
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time_cost_str += f'-->' |
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time_cost_str += f'{now_time - run_task_time}' |
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run_task_time = now_time |
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return run_task_time, time_cost_str |
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with gr.Blocks() as demo: |
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with gr.Row(): |
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with gr.Column(): |
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prompt = gr.Textbox(label="Prompt", placeholder="Write a prompt here", lines=2, value="A beautiful sunset over the city") |
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with gr.Column(): |
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steps = gr.Slider(label="Inference Steps", min=1, max=30, step=1, value=5) |
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g_btn = gr.Button("Generate") |
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with gr.Row(): |
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generated_image = gr.Image(label="Generated Image", type="pil", interactive=False) |
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g_btn.click( |
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fn=text_to_image, |
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inputs=[prompt, steps], |
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outputs=[generated_image], |
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) |
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return demo |
