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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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import os |
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import json |
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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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os.system("python3 -m pip --no-cache-dir install --pre nexfort -f https://github.com/siliconflow/nexfort_releases/releases/expanded_assets/torch2.4.1_cu121") |
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os.system("git clone https://github.com/siliconflow/onediff.git") |
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os.system("cd onediff && python3 -m pip install -e .") |
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os.system("cd onediff_diffusers_extensions && python3 -m pip install -e .") |
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from onediffx import compile_pipe |
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def nexfort_compile(torch_module: torch.nn.Module): |
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options = json.loads('{"mode": "max-autotune:cudagraphs", "dynamic": true}') |
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return compile_pipe(torch_module, backend="nexfort", options=options, fuse_qkv_projections=True) |
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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 = compile_pipe(base_pipe) |
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def create_demo() -> gr.Blocks: |
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@spaces.GPU(duration=30) |
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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, time_cost_str |
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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(minimum=1, maximum=100, value=5, step=1, label="Num Steps") |
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g_btn = gr.Button("Generate") |
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with gr.Row(): |
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with gr.Column(): |
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generated_image = gr.Image(label="Generated Image", type="pil", interactive=False) |
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with gr.Column(): |
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time_cost = gr.Textbox(label="Time Cost", lines=1, 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, time_cost], |
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) |
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return demo |
