app2.py

import os
import torch
import gradio as gr
import numpy as np
from PIL import Image
from einops import rearrange
import requests
import spaces
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel
from gradio_imageslider import ImageSlider

# Pretrained model paths
base_model = 'black-forest-labs/FLUX.1-dev'
controlnet_model = 'InstantX/FLUX.1-dev-Controlnet-Union'

# Load the ControlNet and pipeline models
controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
pipe.to("cuda")

# Define control modes
CONTROL_MODES = {
    0: "Canny",
    1: "Tile",
    2: "Depth",
    3: "Blur",
    4: "Pose",
    5: "Gray (Low)",
    6: "LQ"
}

def preprocess_image(image, target_width, target_height):
    image = image.resize((target_width, target_height), Image.LANCZOS)
    return image

@spaces.GPU(duration=120)
def generate_image(prompt, control_image, control_mode, controlnet_conditioning_scale, num_steps, guidance, width, height, seed, random_seed):
    if random_seed:
        seed = np.random.randint(0, 10000)
    
    # Ensure width and height are multiples of 16
    width = 16 * (width // 16)
    height = 16 * (height // 16)
    
    # Set the seed for reproducibility
    torch.manual_seed(seed)
    
    # Preprocess control image
    control_image = preprocess_image(control_image, width, height)
    
    # Ensure control_mode is an integer
    control_mode_index = int(control_mode)

    # Generate the image with the selected control mode and other parameters
    with torch.no_grad():
        image = pipe(
            prompt,
            control_image=control_image,
            control_mode=control_mode_index,  # Pass control mode as an integer
            width=width,
            height=height,
            controlnet_conditioning_scale=controlnet_conditioning_scale,
            num_inference_steps=num_steps,
            guidance_scale=guidance
        ).images[0]
    
    return image

# Define the Gradio interface
interface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Prompt"),
        gr.Image(type="pil", label="Control Image"),
        gr.Dropdown(choices=[(i, name) for i, name in CONTROL_MODES.items()], label="Control Mode", value=0),  # Correct value and format for dropdown
        gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="ControlNet Conditioning Scale"),
        gr.Slider(step=1, minimum=1, maximum=64, value=24, label="Num Steps"),
        gr.Slider(minimum=0.1, maximum=10, value=3.5, label="Guidance"),
        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Width"),
        gr.Slider(minimum=128, maximum=1024, step=128, value=512, label="Height"),
        gr.Number(value=42, label="Seed"),
        gr.Checkbox(label="Random Seed")
    ],
    outputs=ImageSlider(label="Generated Image"),
    title="FLUX.1 Controlnet with Multiple Modes",
    description="Generate images using ControlNet and a text prompt with adjustable control modes."
)

if __name__ == "__main__":
    interface.launch()