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