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import gradio as gr
import torch
from PIL import Image, ImageDraw, ImageFont
from src.condition import Condition
from diffusers.pipelines import FluxPipeline
import numpy as np
import os
from src.generate import seed_everything, generate
pipe = None
def init_pipeline():
global pipe
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")
pipe.load_lora_weights(
"Yuanshi/OminiControl",
weight_name=f"omini/subject_512.safetensors",
adapter_name="subject",
)
def process_image_and_text(image, text):
# center crop image
w, h, min_size = image.size[0], image.size[1], min(image.size)
image = image.crop(
(
(w - min_size) // 2,
(h - min_size) // 2,
(w + min_size) // 2,
(h + min_size) // 2,
)
)
image = image.resize((512, 512))
condition = Condition("subject", image)
if pipe is None:
init_pipeline()
result_img = generate(
pipe,
prompt=text.strip(),
conditions=[condition],
num_inference_steps=8,
height=512,
width=512,
).images[0]
return result_img
def get_samples():
sample_list = [
{
"image": "assets/oranges.jpg",
"text": "A very close up view of this item. It is placed on a wooden table. The background is a dark room, the TV is on, and the screen is showing a cooking show. With text on the screen that reads 'Omini Control!'",
},
{
"image": "assets/penguin.jpg",
"text": "On Christmas evening, on a crowded sidewalk, this item sits on the road, covered in snow and wearing a Christmas hat, holding a sign that reads 'Omini Control!'",
},
{
"image": "assets/rc_car.jpg",
"text": "A film style shot. On the moon, this item drives across the moon surface. The background is that Earth looms large in the foreground.",
},
{
"image": "assets/clock.jpg",
"text": "In a Bauhaus style room, this item is placed on a shiny glass table, with a vase of flowers next to it. In the afternoon sun, the shadows of the blinds are cast on the wall.",
},
]
return [[Image.open(sample["image"]), sample["text"]] for sample in sample_list]
# Ajuste para definir a porta corretamente
port = int(os.getenv("PORT", 7860)) # Usa a variável de ambiente PORT, se disponível
demo = gr.Interface(
fn=process_image_and_text,
inputs=[
gr.Image(type="pil"),
gr.Textbox(lines=2),
],
outputs=gr.Image(type="pil"),
title="OminiControl / Subject driven generation",
examples=get_samples(),
)
if __name__ == "__main__":
init_pipeline()
demo.launch(
debug=True,
ssr_mode=False,
server_port=port, # Define a porta
server_name="0.0.0.0", # Garante que a aplicação será acessível
)
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