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#!/usr/bin/env python
from __future__ import annotations
import os
import shlex
import subprocess
import gradio as gr
import numpy as np
import torch
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if os.getenv('SYSTEM') == 'spaces':
subprocess.run(
shlex.split(
'pip install git+https://github.com/modelscope/modelscope.git@refs/pull/173/head'
))
DESCRIPTION = '# [ModelScope Chinese text2image (tiny)](https://www.modelscope.cn/models/damo/cv_diffusion_text-to-image-synthesis_tiny/summary)'
SPACE_ID = os.getenv('SPACE_ID')
if SPACE_ID is not None:
DESCRIPTION += f'<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
pipe = pipeline(Tasks.text_to_image_synthesis,
'damo/cv_diffusion_text-to-image-synthesis_tiny')
def run(
text: str,
seed: int,
num_steps_generator: int,
num_steps_upscaler1: int,
num_steps_upscaler2: int,
guidance_scale: float,
) -> np.ndarray:
torch.manual_seed(seed)
results = pipe({
'text': text,
'solver': 'ddim',
'generator_ddim_timesteps': num_steps_generator,
'upsampler_256_ddim_timesteps': num_steps_upscaler1,
'upsampler_1024_ddim_timesteps': num_steps_upscaler2,
'generator_guide_scale': guidance_scale,
})
return results['output_imgs'][0]
examples = [
['中国山水画', 0, 250, 50, 20, 5.0],
]
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
text = gr.Text(label='Prompt')
seed = gr.Slider(label='Seed',
minimum=0,
maximum=100000,
value=0,
step=1,
randomize=True)
run_button = gr.Button('Run')
with gr.Accordion('Advanced options', open=False):
num_steps_generator = gr.Slider(label='Steps (Generator)',
minimum=1,
maximum=1000,
value=250,
step=1)
num_steps_upscaler1 = gr.Slider(
label='Steps (Upscaler 64=>256)',
minimum=1,
maximum=50,
value=50,
step=1)
num_steps_upscaler2 = gr.Slider(
label='Steps (Upscaler 256=>1024)',
minimum=1,
maximum=20,
value=20,
step=1)
guidance_scale = gr.Slider(label='Guidance scale',
minimum=0,
maximum=100,
value=5.0,
step=0.1)
with gr.Column():
result = gr.Image(label='Output')
inputs = [
text,
seed,
num_steps_generator,
num_steps_upscaler1,
num_steps_upscaler2,
guidance_scale,
]
with gr.Row():
gr.Examples(examples=examples,
inputs=inputs,
outputs=result,
fn=run,
cache_examples=True)
text.submit(fn=run, inputs=inputs, outputs=result)
run_button.click(fn=run, inputs=inputs, outputs=result)
demo.queue(api_open=False).launch()
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