sample-influx / app.py
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Update app.py
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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 io import BytesIO
MARKDOWN = """
# FLUX.1 Inpainting 🔥
Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) for creating this model and [Gothos](https://github.com/Gothos) for adding inpainting support to FLUX.
"""
MAX_SEED = np.iinfo(np.int32).max
IMAGE_SIZE = 1024
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
def load_image(url: str) -> Image.Image:
try:
response = requests.get(url, stream=True)
response.raise_for_status()
return Image.open(BytesIO(response.content)).convert("RGBA")
except requests.exceptions.RequestException as e:
print(f"Error loading image from {url}: {e}")
return None
def remove_background(image: Image.Image, threshold: int = 50) -> Image.Image:
if not image:
return None
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
EXAMPLES = [
[
{
"background": load_image("https://media.roboflow.com/spaces/doge-2-image.png"),
"layers": [remove_background(load_image("https://media.roboflow.com/spaces/doge-2-mask-2.png"))],
"composite": load_image("https://media.roboflow.com/spaces/doge-2-composite-2.png"),
},
"little lion",
42,
False,
0.85,
30
],
[
{
"background": load_image("https://media.roboflow.com/spaces/doge-2-image.png"),
"layers": [remove_background(load_image("https://media.roboflow.com/spaces/doge-2-mask-3.png"))],
"composite": load_image("https://media.roboflow.com/spaces/doge-2-composite-3.png"),
},
"tribal 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 > 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.get('background')
mask = input_image_editor.get('layers')[0] if input_image_editor.get('layers') else None
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)