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import torch
from diffusers.pipelines import FluxPipeline
from OminiControl.src.flux.condition import Condition
from PIL import Image
import random
from OminiControl.src.flux.generate import generate, seed_everything
# from log import insert_log, log_image
print("Loading model...")
pipe = FluxPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
)
pipe = pipe.to("cuda")
pipe.unload_lora_weights()
pipe.load_lora_weights(
"Yuanshi/OminiControlStyle",
weight_name=f"v0/ghibli.safetensors",
adapter_name="ghibli",
)
pipe.load_lora_weights(
"Yuanshi/OminiControlStyle",
weight_name=f"v0/irasutoya.safetensors",
adapter_name="irasutoya",
)
pipe.load_lora_weights(
"Yuanshi/OminiControlStyle",
weight_name=f"v0/simpsons.safetensors",
adapter_name="simpsons",
)
pipe.load_lora_weights(
"Yuanshi/OminiControlStyle",
weight_name=f"v0/snoopy.safetensors",
adapter_name="snoopy",
)
def generate_image(
image,
style,
inference_mode,
image_guidance,
image_ratio,
steps,
use_random_seed,
seed,
):
# condition_id = log_image(image)
# Prepare Condition
def resize(img, factor=16):
w, h = img.size
new_w, new_h = w // factor * factor, h // factor * factor
padding_w, padding_h = (w - new_w) // 2, (h - new_h) // 2
img = img.crop((padding_w, padding_h, new_w + padding_w, new_h + padding_h))
return img
# Set Adapter
activate_adapter_name = {
"Studio Ghibli": "ghibli",
"Irasutoya Illustration": "irasutoya",
"The Simpsons": "simpsons",
"Snoopy": "snoopy",
}[style]
pipe.set_adapters(activate_adapter_name)
factor = 512 / max(image.size)
image = resize(
image.resize(
(int(image.size[0] * factor), int(image.size[1] * factor)),
Image.LANCZOS,
)
)
delta = -image.size[0] // 16
condition = Condition(
"subject",
# activate_adapter_name,
image,
position_delta=(0, delta),
)
# Prepare seed
if use_random_seed:
seed = random.randint(0, 2**32 - 1)
seed_everything(seed)
# Image guidance scale
image_guidance = 1.0 if inference_mode == "Fast" else image_guidance
# Output size
if image_ratio == "Auto":
r = image.size[0] / image.size[1]
ratio = min([0.67, 1, 1.5], key=lambda x: abs(x - r))
else:
ratio = {
"Square(1:1)": 1,
"Portrait(2:3)": 0.67,
"Landscape(3:2)": 1.5,
}[image_ratio]
width, height = {
0.67: (640, 960),
1: (640, 640),
1.5: (960, 640),
}[ratio]
print(
f"Image Ratio: {image_ratio}, Inference Mode: {inference_mode}, Image Guidance: {image_guidance}, Seed: {seed}, Steps: {steps}, Size: {width}x{height}"
)
# Generate
result_img = generate(
pipe,
prompt="",
conditions=[condition],
num_inference_steps=steps,
width=width,
height=height,
image_guidance_scale=image_guidance,
default_lora=True,
max_sequence_length=32,
).images[0]
result_img = image
# result_id = log_image(result_img)
# log_data = {
# "condition": condition_id,
# "result": result_id,
# "prompt": "",
# "inference_mode": inference_mode,
# "image_guidance_scale": image_guidance,
# "seed": seed,
# "steps": steps,
# "style": style,
# "width": width,
# "height": height,
# }
# log_data = {k: str(v) for k, v in log_data.items()}
# _, log_id = insert_log("inference", log_data)
# print(f"Image log ID: {log_id}")
return result_img
# def vote_feedback(
# log_id,
# feedback,
# ):
# log_data = {
# "log_id": log_id,
# "feedback": feedback,
# }
# log_data = {k: str(v) for k, v in log_data.items()}
# insert_log("feedback", log_data)
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