app.py

import gradio as gr
import torch
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline
import spaces
from PIL import Image
import numpy as np

# Load the ControlNet model and pipeline
controlnet = ControlNetModel.from_pretrained(
    "briaai/BRIA-2.2-ControlNet-Recoloring",
    torch_dtype=torch.float16
)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "briaai/BRIA-2.2",
    controlnet=controlnet,
    torch_dtype=torch.float16,
).to("cuda")

# Function to transform the image based on a prompt
@spaces.GPU(enable_queue=True)
def generate_image(image, prompt):
    # Prepare the image for processing
    image = image.convert("RGB")
    recoloring_image = Image.fromarray(np.array(image)).convert('L').convert('RGB')
    
    # Define the negative prompt
    negative_prompt = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
    
    # Generate the transformed image
    results = pipe(prompt=prompt, negative_prompt=negative_prompt, image=recoloring_image, controlnet_conditioning_scale=1.0, height=1024, width=1024)
    return results.images[0]

# Gradio Interface
description = """
Anything to Anything, a workflow by Angrypenguinpng using the Bria Recolor ControlNet, check it out here: https://huggingface.co/briaai/BRIA-2.2-ControlNet-Recoloring
"""

with gr.Blocks() as demo:
    gr.Markdown("<h1><center>Image Transformation with Bria Recolor ControlNet</center></h1>")
    gr.Markdown(description)
    with gr.Group():
        with gr.Row():
            image = gr.Image(label='Upload your image')
            prompt = gr.Textbox(label='Enter your prompt', placeholder="A portrait of a beautiful and playful ethereal singer, golden designs, highly detailed, blurry background")
            submit = gr.Button('Transform Image')
    output_image = gr.Image(label='Transformed Image')

    submit.click(fn=generate_image, inputs=[image, prompt], outputs=output_image)

demo.queue().launch()