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import gradio as gr
import torch
from PIL.ImageDraw import Draw
from diffusers import StableDiffusionPipeline
from PIL import Image, ImageOps
# Load pipeline once
model_id = '/Users/tomerkeren/DeciDiffusion-1.0'
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = StableDiffusionPipeline.from_pretrained(model_id, custom_pipeline=model_id, torch_dtype=torch.float32)
pipe.unet = pipe.unet.from_pretrained(model_id, subfolder='flexible_unet', torch_dtype=torch.float32)
pipe = pipe.to(device)
def read_content(file_path: str) -> str:
"""read the content of target file
"""
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
return content
def predict(_prompt: str, _steps: int = 30, _seed: int = 42, _guidance_scale: float = 7.5, _negative_prompt: str = ""):
_negative_prompt = [_negative_prompt] if _negative_prompt else None
output = pipe(prompt=[_prompt],
negative_prompt=_negative_prompt,
num_inference_steps=int(_steps),
guidance_scale=_guidance_scale,
generator=torch.Generator(device).manual_seed(_seed),
)
output_image = output.images[0]
# Add border beneath the image with Deci logo + prompt
if len(_prompt) > 52:
_prompt = _prompt[:52] + "..."
original_image_height = output_image.size[1]
output_image = ImageOps.expand(output_image, border=(0, 0, 0, 64), fill='white')
deci_logo = Image.open('https://huggingface.co/spaces/Deci/DeciDiffusion-v1-0/resolve/main/deci_logo_white.png')
output_image.paste(deci_logo, (0, original_image_height))
Draw(output_image).text((deci_logo.size[0], original_image_height), _prompt, (127, 127, 127))
return output_image
css = '''
.gradio-container {
max-width: 1100px !important;
background-image: url(https://huggingface.co/spaces/Deci/Deci-DeciDiffusionClean/resolve/main/background-image.png);
background-size: cover;
background-position: center center;
background-repeat: no-repeat;
}
.footer {margin-bottom: 45px;margin-top: 35px !important;text-align: center;border-bottom: 1px solid #e5e5e5}
.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
.dark .footer {border-color: #303030}
.dark .footer>p {background: #0b0f19}
.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
'''
demo = gr.Blocks(css=css, elem_id="total-container")
with demo:
gr.HTML(read_content("header.html"))
with gr.Row():
with gr.Column():
with gr.Row(mobile_collapse=False, equal_height=True):
prompt = gr.Textbox(placeholder="Your prompt", show_label=False, elem_id="prompt", autofocus=True, lines=3, )
with gr.Accordion(label="Advanced Settings", open=False):
with gr.Row(mobile_collapse=False, equal_height=True):
steps = gr.Slider(value=30, minimum=15, maximum=50, step=1, label="steps", interactive=True)
seed = gr.Slider(value=42, minimum=1, maximum=100, step=1, label="seed", interactive=True)
guidance_scale = gr.Slider(value=7.5, minimum=1, maximum=15, step=0.1, label='guidance_scale', interactive=True)
with gr.Row(mobile_collapse=False, equal_height=True):
negative_prompt = gr.Textbox(label="negative_prompt", placeholder="Your negative prompt",
info="what you don't want to see in the image", lines=3)
with gr.Row():
btn = gr.Button(value="Generate!", elem_id="run_button")
with gr.Column():
image_out = gr.Image(label="Output", elem_id="output-img", height=400)
btn.click(fn=predict,
inputs=[prompt, steps, seed, guidance_scale, negative_prompt],
outputs=[image_out],
api_name='run')
gr.HTML(
"""
<div class="footer">
<p>Model by <a href="https://deci.ai" style="text-decoration: underline;" target="_blank">Deci.ai</a> - Gradio Demo by 🤗 Hugging Face
</p>
</div>
"""
)
demo.queue(max_size=50).launch()
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