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import os
import torch
import uuid
from pathlib import Path
import gradio as gr
from gradio_imageslider import ImageSlider # Ensure this library is installed
if os.environ.get("SPACES_ZERO_GPU") is not None:
import spaces
else:
class spaces:
@staticmethod
def GPU(func):
def wrapper(*args, **kwargs):
return func(*args, **kwargs)
return wrapper
from diffusers.utils import check_min_version
from flux.controlnet_flux import FluxControlNetModel
from flux.transformer_flux import FluxTransformer2DModel
from flux.pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline
# Import configuration
import config
# Define the output folder
output_folder = Path('output_images')
output_folder.mkdir(exist_ok=True)
# Login to Hugging Face Hub
# huggingface_hub.login(os.getenv('HF_TOKEN_FLUX'))
check_min_version("0.30.2")
# Load models
transformer = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-dev", subfolder='transformer', torch_dtype=torch.bfloat16
)
controlnet = FluxControlNetModel.from_pretrained(
"alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", torch_dtype=torch.bfloat16
)
# Build pipeline
pipe = FluxControlNetInpaintingPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
controlnet=controlnet,
transformer=transformer,
torch_dtype=torch.bfloat16
).to("cuda")
pipe.transformer.to(torch.bfloat16)
pipe.controlnet.to(torch.bfloat16)
@spaces.GPU()
def process(input_image_editor):
# Use default values from config
negative_prompt = config.DEFAULT_NEGATIVE_PROMPT
controlnet_conditioning_scale = config.DEFAULT_CONTROLNET_CONDITIONING_SCALE
guidance_scale = config.DEFAULT_GUIDANCE_SCALE
seed = config.DEFAULT_SEED
num_inference_steps = config.DEFAULT_NUM_INFERENCE_STEPS
true_guidance_scale = config.DEFAULT_TRUE_GUIDANCE_SCALE
# Process image and mask
image = input_image_editor['background']
mask = input_image_editor['layers'][0]
size = (768, 768)
image_or = image.copy()
image = image.convert("RGB").resize(size)
mask = mask.convert("RGB").resize(size)
generator = torch.Generator(device="cuda").manual_seed(seed)
# Generate result
result = pipe(
prompt="nothing",#os.getenv('MAGIC_PROMPT'),
height=size[1],
width=size[0],
control_image=image,
control_mask=mask,
num_inference_steps=num_inference_steps,
generator=generator,
controlnet_conditioning_scale=controlnet_conditioning_scale,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
true_guidance_scale=true_guidance_scale
).images[0]
processed_image = result.resize(image_or.size[:2])
# Save the processed image
output_folder = Path("output") # Make sure this folder exists or create it
output_folder.mkdir(parents=True, exist_ok=True)
image_path = output_folder / f"no_bg_image_{uuid.uuid4().hex}.png"
processed_image.save(image_path)
return (processed_image, image), str(image_path)
#################################################################
# Define inputs and outputs for the Gradio interface
image = gr.ImageEditor(
label='Image',
type='pil',
sources=["upload", "webcam"],
image_mode='RGB',
layers=False,
brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed")
)
output_slider = ImageSlider(label="Processed photo", type="pil")
demo = gr.Interface(
fn=process,
inputs=image,
outputs=[output_slider, gr.File(label="output png file")],
#title="🫧 Snap Clean 🧽",
description=config.DESCRIPTION
)
demo.launch(debug=False, show_error=True, share=True,ssr_mode=False) |