app.py

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

# Load the models
controlnet = ControlNetModel.from_pretrained(
    "briaai/BRIA-2.2-ControlNet-Recoloring",
    torch_dtype=torch.float16
).to('cuda')

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "briaai/BRIA-2.2",
    controlnet=controlnet,
    torch_dtype=torch.float16,
    device_map='auto',
    low_cpu_mem_usage=True,
    offload_state_dict=True,
).to('cuda')

pipe.scheduler = EulerAncestralDiscreteScheduler(
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear",
    num_train_timesteps=1000,
    steps_offset=1
)

pipe.force_zeros_for_empty_prompt = False

def resize_image(image):
    image = image.convert('RGB')
    current_size = image.size
    transform = gr.Image(height=1024, width=1024, keep_aspect_ratio=True, source="upload", tool="editor")
    resized_image = transform.postprocess(image)
    return resized_image

@spaces.GPU(enable_queue=True)
def generate_image(input_image, prompt, controlnet_conditioning_scale):
    # Always use a random seed for diversity in outputs
    seed = np.random.randint(2147483647)
    generator = torch.Generator("cuda").manual_seed(seed)  
    
    # Resize and prepare the image
    input_image = resize_image(input_image)
    grayscale_image = input_image.convert('L').convert('RGB')
    
    # Generate the image with fixed 30 steps
    images = pipe(
        prompt=prompt, 
        image=grayscale_image, 
        num_inference_steps=30, 
        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
        generator=generator,
    ).images
    
    return images[0]

# Gradio Interface
description = "Anything to Anything. Transform anything to anything. Allow an adjuster for controlnet scale."

with gr.Blocks() as demo:
    gr.Markdown("<h1><center>Image Transformation with Bria Recolor ControlNet</center></h1>")
    gr.Markdown(description)
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label='Upload your image', type="pil")
            prompt = gr.Textbox(label='Enter your prompt')
            controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
            submit_button = gr.Button('Transform Image')
        with gr.Column():
            output_image = gr.Image(label='Transformed Image')

    submit_button.click(fn=generate_image, inputs=[input_image, prompt, controlnet_conditioning_scale], outputs=output_image)

demo.queue().launch()