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import gradio as gr
import torch
import spaces
from PIL import Image, ImageDraw, ImageFont
from src.condition import Condition
from diffusers.pipelines import FluxPipeline
import numpy as np
from src.generate import seed_everything, generate
pipe = None
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_512",
)
pipe.load_lora_weights(
"Yuanshi/OminiControl",
weight_name=f"omini/subject_1024_beta.safetensors",
adapter_name="subject_1024",
)
@spaces.GPU
def process_image_and_text(image, resolution, text):
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)
result_img = generate(
pipe,
prompt=text.strip(),
conditions=[condition],
num_inference_steps=8,
height=resolution,
width=resolution,
).images[0]
return result_img
def get_samples():
sample_list = [
{
"image": "assets/oranges.jpg",
"resolution": 512,
"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",
"resolution": 512,
"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",
"resolution": 1024,
"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",
"resolution": 1024,
"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"]).resize((512, 512)),
sample["resolution"],
sample["text"],
]
for sample in sample_list
]
header = """
# 🌍 OminiControl / FLUX
<div style="text-align: center; display: flex; justify-content: left; gap: 5px;">
<a href="https://arxiv.org/abs/2411.15098"><img src="https://img.shields.io/badge/ariXv-Paper-A42C25.svg" alt="arXiv"></a>
<a href="https://huggingface.co/Yuanshi/OminiControl"><img src="https://img.shields.io/badge/🤗-Model-ffbd45.svg" alt="HuggingFace"></a>
<a href="https://github.com/Yuanshi9815/OminiControl"><img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github&" alt="GitHub"></a>
</div>
"""
def create_app():
with gr.Blocks() as app:
gr.Markdown(header)
with gr.Tabs():
with gr.Tab("Subject-driven"):
gr.Interface(
fn=process_image_and_text,
inputs=[
gr.Image(type="pil", label="Condition Image", width=300),
gr.Radio(
[("512", 512), ("1024(beta)", 1024)],
label="Resolution",
value=512,
),
# gr.Slider(4, 16, 4, step=4, label="Inference Steps"),
gr.Textbox(lines=2, label="Text Prompt"),
],
outputs=gr.Image(type="pil"),
examples=get_samples(),
)
with gr.Tab("Fill"):
gr.Markdown("Coming soon")
with gr.Tab("Canny"):
gr.Markdown("Coming soon")
with gr.Tab("Depth"):
gr.Markdown("Coming soon")
return app
if __name__ == "__main__":
create_app().launch(debug=True, ssr_mode=False)
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