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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() |