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import gradio as gr
from PIL import Image
from io import BytesIO
import torch
import os
from diffusers import DiffusionPipeline, DDIMScheduler
MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD')
has_cuda = torch.cuda.is_available()
device = torch.device('cpu' if not has_cuda else 'cuda')
pipe = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
safety_checker=None,
use_auth_token=MY_SECRET_TOKEN,
custom_pipeline="imagic_stable_diffusion",
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
).to(device)
#generator = torch.Generator("cuda").manual_seed(0)
def infer(prompt, init_image):
res = pipe.train(
prompt,
init_image,
guidance_scale=7.5,
num_inference_steps=50)
res = pipe(alpha=1)
return res.images[0]
title = """
<div style="text-align: center; max-width: 650px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
"
>
<h1 style="font-weight: 900; margin-bottom: 7px;">
Imagic Stable Diffusion • Community Pipeline
</h1>
</div>
<p style="margin-bottom: 10px; font-size: 94%">
Text-Based Real Image Editing with Diffusion Models
</p>
</div>
"""
article = """
"""
css = '''
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
'''
with gr.Blocks(css=css) as block:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
prompt_input = gr.Textbox()
image_init = gr.Image(source="upload", type="filepath")
submit_btn = gr.Button("Submit")
image_output = gr.Image()
#gr.HTML(article)
submit_btn.click(fn=infer, inputs=[prompt_input,image_init], outputs=[image_output])
block.queue(max_size=32,concurrency_count=20).launch(show_api=False) |