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import json
import asyncio
import gradio as gr
import os
os.environ['CIVITAI_API_TOKEN'] = 'kunkun'
os.environ['FAL_KEY'] = 'Daisuki'
os.environ['CONF_PATH'] = './config.yaml'
from PIL import Image
import io
import base64
import httpx
from .base_config import init_instance
from .backend import TaskHandler
from .locales import _
class Gradio:
def __init__(self, host, port):
self.host = '127.0.0.1' if host == '0.0.0.0' else host
self.port = port
def get_caption(self, image):
caption = httpx.post(
f"http://{self.host}:{self.port}/tagger/v1/interrogate",
json=json.loads({"image": image}), timeout=600).json()
return caption
def format_caption_output(caption_result):
llm_text = caption_result.get("llm", '')
word_scores = "\n".join([f"{word}: {score}" for word, score in caption_result["caption"].items()])
word_ = ",".join([f"{word}" for word in caption_result["caption"].keys()])
return llm_text, word_scores, word_
async def create_gradio_interface(host, port):
gradio_api = Gradio(host, port)
from .api_server import api_instance
all_models = [i['title'] for i in await api_instance.get_sd_models()]
init_instance.logger.info(f"{_('Server is ready!')} Listen on {host}:{port}")
async def get_image(model, prompt, negative_prompt, width, height, cfg_scale, steps):
payload = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"width": width,
"height": height,
"steps": steps,
"cfg_scale": cfg_scale
}
task_handler = TaskHandler(payload, model_to_backend=model)
result = await task_handler.txt2img()
image_data = result.get("images")[0]
image = Image.open(io.BytesIO(base64.b64decode(image_data)))
return image
with gr.Blocks() as demo:
with gr.Tab("txt2img"):
with gr.Row():
with gr.Column():
model = gr.Dropdown(label="Model", choices=all_models)
prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
negative_prompt = gr.Textbox(label="Negative Prompt",
placeholder="Enter your negative prompt here...")
width = gr.Slider(label="Width", minimum=64, maximum=2048, step=1, value=512)
height = gr.Slider(label="Height", minimum=64, maximum=2048, step=1, value=512)
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=30, step=0.1, value=7.5)
steps = gr.Slider(label="Steps", minimum=1, maximum=200, step=1, value=20)
generate_button = gr.Button("Generate Image")
with gr.Column():
output_image = gr.Image(label="Generated Image")
generate_button.click(get_image, [model, prompt, negative_prompt, width, height, cfg_scale, steps],
output_image)
with gr.Tab("Caption"):
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Input Image")
caption_button = gr.Button("Get Caption")
with gr.Column():
llm_output = gr.Textbox(label="Natural Language Description")
word_output_ = gr.Textbox(label="Keywords", lines=6)
word_output = gr.Textbox(label="Keywords with Scores", lines=6)
caption_button.click(
lambda image: format_caption_output(gradio_api.get_caption(image)),
inputs=[input_image],
outputs=[llm_output, word_output, word_output_]
)
return demo
async def run_gradio(host, port):
interface = await create_gradio_interface(host, port)
interface.launch(server_name=host, server_port=port+1)
asyncio.run(run_gradio("127.0.0.1", 5421))
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