app.py

import spaces
from diffusers import ControlNetModel
from diffusers import StableDiffusionXLControlNetPipeline
from diffusers import EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import numpy as np
import cv2
import gradio as gr
from torchvision import transforms 
from controlnet_aux import OpenposeDetector


openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')

controlnet = ControlNetModel.from_pretrained(
    "briaai/BRIA-2.3-ControlNet-Pose",
    torch_dtype=torch.float16
).to('cuda')

pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "briaai/BRIA-2.3",
    controlnet=controlnet,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
    offload_state_dict=True,
).to('cuda').to(torch.float16)

pipe.scheduler = EulerAncestralDiscreteScheduler(
    beta_start=0.00085,
    beta_end=0.012,
    beta_schedule="scaled_linear",
    num_train_timesteps=1000,
    steps_offset=1
)
# pipe.enable_freeu(b1=1.1, b2=1.1, s1=0.5, s2=0.7)
# pipe.enable_xformers_memory_efficient_attention()
pipe.force_zeros_for_empty_prompt = False

def resize_image(image):
    image = image.convert('RGB')
    current_size = image.size
    if current_size[0] > current_size[1]:
        center_cropped_image = transforms.functional.center_crop(image, (current_size[1], current_size[1]))
    else:
        center_cropped_image = transforms.functional.center_crop(image, (current_size[0], current_size[0]))
    resized_image = transforms.functional.resize(center_cropped_image, (1024, 1024))
    return resized_image


@spaces.GPU
def generate_(prompt, negative_prompt, pose_image, num_steps, controlnet_conditioning_scale, seed):
    generator = torch.Generator("cuda").manual_seed(seed)    
    images = pipe(
    prompt, negative_prompt=negative_prompt, image=pose_image, num_inference_steps=num_steps, controlnet_conditioning_scale=float(controlnet_conditioning_scale),
    generator=generator, height=1024, width=1024,
    ).images
    return images

@spaces.GPU
def process(input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed):
    
    # resize input_image to 1024x1024
    input_image = resize_image(input_image)
    
    pose_image = openpose(input_image, include_body=True, include_hand=True, include_face=True)
  
    images = generate_(prompt, negative_prompt, pose_image, num_steps, controlnet_conditioning_scale, seed)

    return [pose_image,images[0]]
    
block = gr.Blocks().queue()

with block:
    gr.Markdown("## BRIA 2.3 ControlNet Pose")
    gr.HTML('''
      <p style="margin-bottom: 10px; font-size: 94%">
        This is a demo for ControlNet Pose that using
        <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">BRIA 2.3 text-to-image model</a> as backbone. 
        Trained on licensed data, BRIA 2.3 provide full legal liability coverage for copyright and privacy infringement.
      </p>
    ''')
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(sources=None, type="pil") # None for upload, ctrl+v and webcam
            prompt = gr.Textbox(label="Prompt")
            negative_prompt = gr.Textbox(label="Negative prompt", value="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")
            num_steps = gr.Slider(label="Number of steps", minimum=25, maximum=100, value=50, step=1)
            controlnet_conditioning_scale = gr.Slider(label="ControlNet conditioning scale", minimum=0.1, maximum=2.0, value=1.0, step=0.05)
            seed = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, randomize=True,)
            run_button = gr.Button(value="Run")
            
            
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery", columns=[2], height='auto')
    ips = [input_image, prompt, negative_prompt, num_steps, controlnet_conditioning_scale, seed]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])

block.launch(debug = True)