gradio_controlnet_openpose.py

import torch
from diffusers import UniPCMultistepScheduler, AutoencoderKL, ControlNetModel
from diffusers.pipelines import StableDiffusionControlNetPipeline
import gradio as gr
import argparse
from controlnet_aux import OpenposeDetector

from oms_diffusion.garment_adapter.garment_diffusion import ClothAdapter

parser = argparse.ArgumentParser(description="oms diffusion")
parser.add_argument("--model_path", type=str, required=True)
parser.add_argument(
    "--pipe_path", type=str, default="SG161222/Realistic_Vision_V4.0_noVAE"
)

args = parser.parse_args()

device = "cuda"

openpose_model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet").to(device)
control_net_openpose = ControlNetModel.from_pretrained(
    "lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16
)
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    args.pipe_path, vae=vae, controlnet=control_net_openpose, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
full_net = ClothAdapter(pipe, args.model_path, device)


def get_pose(image):
    openpose_image = openpose_model(image)
    return openpose_image


def process(
    cloth_image,
    cloth_mask_image,
    prompt,
    a_prompt,
    n_prompt,
    num_samples,
    width,
    height,
    sample_steps,
    scale,
    seed,
    pose_image,
):
    images, cloth_mask_image = full_net.generate(
        cloth_image,
        cloth_mask_image,
        prompt,
        a_prompt,
        num_samples,
        n_prompt,
        seed,
        scale,
        sample_steps,
        height,
        width,
        image=pose_image,
    )
    return images, cloth_mask_image


block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.Markdown(
            "##You can enlarge image resolution to get better face, but the cloth maybe lose control, we will release high-resolution checkpoint soon##"
        )
    with gr.Row():
        with gr.Column():
            cloth_image = gr.Image(label="cloth Image", type="pil")
            cloth_mask_image = gr.Image(
                label="cloth mask Image, if not support, will be produced by inner segment algorithm",
                type="pil",
            )
            prompt = gr.Textbox(label="Prompt", value="a photography of a model")
            run_button = gr.Button(value="Run")
            with gr.Accordion("Advanced options", open=False):
                num_samples = gr.Slider(
                    label="Images", minimum=1, maximum=12, value=4, step=1
                )
                height = gr.Slider(
                    label="Height", minimum=256, maximum=768, value=512, step=64
                )
                width = gr.Slider(
                    label="Width", minimum=192, maximum=576, value=384, step=64
                )
                sample_steps = gr.Slider(
                    label="Steps", minimum=1, maximum=100, value=20, step=1
                )
                scale = gr.Slider(
                    label="Guidance Scale", minimum=1, maximum=10.0, value=2.5, step=0.1
                )
                seed = gr.Slider(
                    label="Seed", minimum=-1, maximum=2147483647, step=1, value=1234
                )
                a_prompt = gr.Textbox(
                    label="Added Prompt", value="best quality, high quality"
                )
                n_prompt = gr.Textbox(
                    label="Negative Prompt",
                    value="bare, monochrome, lowres, bad anatomy, worst quality, low quality",
                )
        with gr.Column():
            pose_image = gr.Image(label="pose Image", type="pil")
            pose_button = gr.Button(value="get pose")
        with gr.Column():
            result_gallery = gr.Gallery(
                label="Output", show_label=False, elem_id="gallery", min_width=384
            )
            cloth_seg_image = gr.Image(
                label="cloth mask", type="pil", width=192, height=256
            )

    ips = [
        cloth_image,
        cloth_mask_image,
        prompt,
        a_prompt,
        n_prompt,
        num_samples,
        width,
        height,
        sample_steps,
        scale,
        seed,
        pose_image,
    ]
    run_button.click(fn=process, inputs=ips, outputs=[result_gallery, cloth_seg_image])
    pose_button.click(fn=get_pose, inputs=pose_image, outputs=pose_image)

block.launch(server_name="0.0.0.0", server_port=7860)