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from typing import Tuple
import requests
import random
import numpy as np
import gradio as gr
import spaces
import torch
from PIL import Image
from diffusers import FluxInpaintPipeline
from huggingface_hub import login
import os
import time
from gradio_imageslider import ImageSlider
MARKDOWN = """
# FLUX.1 Inpainting with lora
"""
MAX_SEED = np.iinfo(np.int32).max
IMAGE_SIZE = 1024
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
HF_TOKEN = os.environ.get("HF_TOKEN")
login(token=HF_TOKEN)
class calculateDuration:
def __init__(self, activity_name=""):
self.activity_name = activity_name
def __enter__(self):
self.start_time = time.time()
self.start_time_formatted = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(self.start_time))
print(f"Activity: {self.activity_name}, Start time: {self.start_time_formatted}")
return self
def __exit__(self, exc_type, exc_value, traceback):
self.end_time = time.time()
self.elapsed_time = self.end_time - self.start_time
self.end_time_formatted = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(self.end_time))
if self.activity_name:
print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
else:
print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
print(f"Activity: {self.activity_name}, End time: {self.start_time_formatted}")
def remove_background(image: Image.Image, threshold: int = 50) -> Image.Image:
image = image.convert("RGBA")
data = image.getdata()
new_data = []
for item in data:
avg = sum(item[:3]) / 3
if avg < threshold:
new_data.append((0, 0, 0, 0))
else:
new_data.append(item)
image.putdata(new_data)
return image
pipe = FluxInpaintPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
def resize_image_dimensions(
original_resolution_wh: Tuple[int, int],
maximum_dimension: int = IMAGE_SIZE
) -> Tuple[int, int]:
width, height = original_resolution_wh
# if width <= maximum_dimension and height <= maximum_dimension:
# width = width - (width % 32)
# height = height - (height % 32)
# return width, height
if width > height:
scaling_factor = maximum_dimension / width
else:
scaling_factor = maximum_dimension / height
new_width = int(width * scaling_factor)
new_height = int(height * scaling_factor)
new_width = new_width - (new_width % 32)
new_height = new_height - (new_height % 32)
return new_width, new_height
@spaces.GPU(duration=100)
def process(
input_image_editor: dict,
lora_path: str,
lora_weights: str,
lora_scale: float,
trigger_word: str,
input_text: str,
seed_slicer: int,
randomize_seed_checkbox: bool,
strength_slider: float,
num_inference_steps_slider: int,
progress=gr.Progress(track_tqdm=True)
):
if not input_text:
gr.Info("Please enter a text prompt.")
return None, None
image = input_image_editor['background']
mask = input_image_editor['layers'][0]
if not image:
gr.Info("Please upload an image.")
return None, None
if not mask:
gr.Info("Please draw a mask on the image.")
return None, None
with calculateDuration("resize image"):
width, height = resize_image_dimensions(original_resolution_wh=image.size)
resized_image = image.resize((width, height), Image.LANCZOS)
resized_mask = mask.resize((width, height), Image.LANCZOS)
with calculateDuration("load lora"):
pipe.load_lora_weights(lora_path, weight_name=lora_weights)
if randomize_seed_checkbox:
seed_slicer = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed_slicer)
with calculateDuration("run pipe"):
result = pipe(
prompt=f"{input_text} {trigger_word}",
image=resized_image,
mask_image=resized_mask,
width=width,
height=height,
strength=strength_slider,
generator=generator,
num_inference_steps=num_inference_steps_slider,
joint_attention_kwargs={"scale": lora_scale},
).images[0]
return [resized_image, result], resized_mask
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN)
with gr.Row():
with gr.Column():
input_image_editor_component = gr.ImageEditor(
label='Image',
type='pil',
sources=["upload", "webcam"],
image_mode='RGB',
layers=False,
brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
with gr.Accordion("Prompt Settings", open=True):
input_text_component = gr.Textbox(
label="Inpaint prompt",
show_label=True,
max_lines=1,
placeholder="Enter your prompt",
)
trigger_word = gr.Textbox(
label="Lora trigger word",
show_label=True,
max_lines=1,
placeholder="Enter your lora trigger word here",
value="a photo of TOK"
)
submit_button_component = gr.Button(
value='Submit', variant='primary', scale=0)
with gr.Accordion("Lora Settings", open=True):
lora_path = gr.Textbox(
label="Lora model path",
show_label=True,
max_lines=1,
placeholder="Enter your model path",
info="Currently, only LoRA hosted on Hugging Face'model can be loaded properly.",
value="XLabs-AI/flux-lora-collection"
)
lora_weights = gr.Textbox(
label="Lora weights",
show_label=True,
max_lines=1,
placeholder="Enter your lora weights name",
value="anime_lora.safetensors"
)
lora_scale = gr.Slider(
label="Lora scale",
show_label=True,
minimum=0,
maximum=1,
step=0.1,
value=0.9,
)
with gr.Accordion("Advanced Settings", open=True):
seed_slicer_component = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
randomize_seed_checkbox_component = gr.Checkbox(
label="Randomize seed", value=True)
with gr.Row():
strength_slider_component = gr.Slider(
label="Strength",
info="Indicates extent to transform the reference `image`. "
"Must be between 0 and 1. `image` is used as a starting "
"point and more noise is added the higher the `strength`.",
minimum=0,
maximum=1,
step=0.01,
value=0.85,
)
num_inference_steps_slider_component = gr.Slider(
label="Number of inference steps",
info="The number of denoising steps. More denoising steps "
"usually lead to a higher quality image at the",
minimum=1,
maximum=50,
step=1,
value=20,
)
with gr.Column():
output_image_component = ImageSlider(label="Generate image", type="pil", slider_color="pink")
with gr.Accordion("Debug", open=False):
output_mask_component = gr.Image(
type='pil', image_mode='RGB', label='Input mask', format="png")
submit_button_component.click(
fn=process,
inputs=[
input_image_editor_component,
lora_path,
lora_weights,
lora_scale,
trigger_word,
input_text_component,
seed_slicer_component,
randomize_seed_checkbox_component,
strength_slider_component,
num_inference_steps_slider_component
],
outputs=[
output_image_component,
output_mask_component
]
)
demo.launch(debug=False, show_error=True)
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