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import gradio as gr
import spaces
import os
import sys
import subprocess
import numpy as np
from PIL import Image
import cv2
import torch
from diffusers import StableDiffusion3ControlNetPipeline
from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
from diffusers.utils import load_image
# load pipeline
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny")
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
controlnet=controlnet
)
pipe.to("cuda", torch.float16)
def resize_image(input_path, output_path, target_height):
# Open the input image
img = Image.open(input_path)
# Calculate the aspect ratio of the original image
original_width, original_height = img.size
original_aspect_ratio = original_width / original_height
# Calculate the new width while maintaining the aspect ratio and the target height
new_width = int(target_height * original_aspect_ratio)
# Resize the image while maintaining the aspect ratio and fixing the height
img = img.resize((new_width, target_height), Image.LANCZOS)
# Save the resized image
img.save(output_path)
return output_path, new_width, target_height
@spaces.GPU()
def infer(image_in, prompt, inference_steps, guidance_scale, control_weight, progress=gr.Progress(track_tqdm=True)):
n_prompt = 'NSFW, nude, naked, porn, ugly'
# Canny preprocessing
image_to_canny = load_image(image_in)
image_to_canny = np.array(image_to_canny)
image_to_canny = cv2.Canny(image_to_canny, 100, 200)
image_to_canny = image_to_canny[:, :, None]
image_to_canny = np.concatenate([image_to_canny, image_to_canny, image_to_canny], axis=2)
image_to_canny = Image.fromarray(image_to_canny)
# infer
image = pipe(
prompt=prompt,
negative_prompt=n_prompt,
control_image=image_to_canny,
controlnet_conditioning_scale=control_weigth,
num_inference_steps=inference_steps,
guidance_scale=guidance_scale,
).images[0]
image_redim, w, h = resize_image(image_in, "resized_input.jpg", 1024)
image = image.resize((w, h), Image.LANCZOS)
return image, image_to_canny
css="""
#col-container{
margin: 0 auto;
max-width: 1080px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("""
# SD3 ControlNet
""")
with gr.Row():
with gr.Column():
image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath")
prompt = gr.Textbox(label="Prompt")
with gr.Accordion("Advanced settings", open=False):
with gr.Column():
with gr.Row():
inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25)
guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=7.0)
control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
submit_btn = gr.Button("Submit")
with gr.Column():
result = gr.Image(label="Result")
canny_used = gr.Image(label="Preprocessed Canny")
submit_btn.click(
fn = infer,
inputs = [image_in, prompt, inference_steps, guidance_scale, control_weight],
outputs = [result, canny_used],
show_api=False
)
demo.queue().launch() |