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from typing import Tuple
import requests
import random,os
import numpy as np
import gradio as gr
import spaces
import torch
from PIL import Image
from diffusers import FluxInpaintPipeline
MAX_SEED = np.iinfo(np.int32).max
IMAGE_SIZE = 1024
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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 I2I(
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
pipe = FluxInpaintPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(DEVICE)
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 |