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import gradio as gr | |
import requests | |
import os | |
from PIL import Image | |
from io import BytesIO | |
import base64 | |
def error_str(error, title="Error"): | |
return f"""#### {title} | |
{error}""" if error else "" | |
def inference(prompt, guidance, steps, image_size="Landscape", seed=0, img=None, strength=0.5, neg_prompt="", disable_auto_prompt_correction=False): | |
try: | |
response = requests.post(os.environ["BACKEND"], json={ | |
"data": [ | |
prompt, | |
guidance, | |
steps, | |
image_size, | |
seed, | |
img, | |
strength, | |
neg_prompt, | |
disable_auto_prompt_correction, | |
] | |
}).json() | |
data = response["data"] | |
image=Image.open(BytesIO(base64.b64decode(data[0].split(',')[1]))) | |
return image,data[1],data[2] | |
except Exception as e: | |
print(error_str(e)) | |
return None, "Error", "Error" | |
css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem} | |
""" | |
with gr.Blocks(css=css) as demo: | |
gr.HTML( | |
f""" | |
<div class="main-div"> | |
<div> | |
<h1>ChatEmi Beta デモ</h1> | |
</div> | |
<p> | |
個人情報などは入れないでください。 | |
</p> | |
<p> | |
サンプルプロンプト1:黒い髪の美少女の顔アップ | |
</p> | |
<p> | |
サンプルプロンプト2:白い髪の男性の上半身 | |
</p> | |
</div> | |
""" | |
) | |
with gr.Row(): | |
with gr.Column(scale=55): | |
with gr.Group(): | |
with gr.Row(): | |
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]") | |
generate = gr.Button(value="Generate") | |
image_out = gr.Image(height=1024,width=1024) | |
error_output = gr.Markdown() | |
with gr.Column(scale=45): | |
with gr.Tab("Options"): | |
with gr.Group(): | |
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") | |
disable_auto_prompt_correction = gr.Checkbox(label="Disable auto prompt corretion.") | |
with gr.Row(): | |
image_size=gr.Radio(["Portrait","Landscape","Square"]) | |
image_size.show_label=False | |
image_size.value="Square" | |
with gr.Row(): | |
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=25) | |
steps = gr.Slider(label="Steps", value=8, minimum=2, maximum=30, step=1) | |
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) | |
prompt_display= gr.Textbox(label="Upsampled prompt", interactive=False) | |
with gr.Tab("Image to image"): | |
with gr.Group(): | |
image = gr.Image(label="Image", height=256, tool="editor", type="pil") | |
strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) | |
inputs = [prompt, guidance, steps, image_size, seed, image, strength, neg_prompt, disable_auto_prompt_correction] | |
outputs = [image_out, error_output, prompt_display] | |
prompt.submit(inference, inputs=inputs, outputs=outputs) | |
generate.click(inference, inputs=inputs, outputs=outputs) | |
demo.queue(concurrency_count=1) | |
demo.launch() |