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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() | |