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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=20, 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() |