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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
MARKDOWN = """
# FLUX.1 Inpainting 🔥
Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for
creating this amazing model, and a big thanks to [Gothos](https://github.com/Gothos)
for taking it to the next level by enabling inpainting with the FLUX.
"""
MAX_SEED = np.iinfo(np.int32).max
IMAGE_SIZE = 1024
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
EXAMPLES = [
[
{
"background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
"layers": [Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-2.png", stream=True).raw)],
"composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-2.png", stream=True).raw),
},
"little lion",
42,
False,
0.85,
30
],
[
{
"background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
"layers": [Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-3.png", stream=True).raw)],
"composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-3.png", stream=True).raw),
},
"tattoos",
42,
False,
0.85,
30
]
]
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,
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
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)
if randomize_seed_checkbox:
seed_slicer = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed_slicer)
result = pipe(
prompt=input_text,
image=resized_image,
mask_image=resized_mask,
width=width,
height=height,
strength=strength_slider,
generator=generator,
num_inference_steps=num_inference_steps_slider
).images[0]
print('INFERENCE DONE')
return 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.Row():
input_text_component = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
submit_button_component = gr.Button(
value='Submit', variant='primary', scale=0)
with gr.Accordion("Advanced Settings", open=False):
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 = gr.Image(
type='pil', image_mode='RGB', label='Generated image', format="png")
with gr.Accordion("Debug", open=False):
output_mask_component = gr.Image(
type='pil', image_mode='RGB', label='Input mask', format="png")
with gr.Row():
gr.Examples(
fn=process,
examples=EXAMPLES,
inputs=[
input_image_editor_component,
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
],
run_on_click=True,
cache_examples=True
)
submit_button_component.click(
fn=process,
inputs=[
input_image_editor_component,
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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