app_haircolor.py

import spaces
import gradio as gr
import time
import torch

from PIL import Image
from segment_utils import(
    segment_image,
    restore_result,
)
from diffusers import (
    StableDiffusionAdapterPipeline,
    DiffusionPipeline,
    T2IAdapter,
    MultiAdapter,
)

from controlnet_aux import (
    LineartDetector,
    CannyDetector,
    PidiNetDetector,
    MidasDetector,
)

BASE_MODEL = "stable-diffusion-v1-5/stable-diffusion-v1-5"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

DEFAULT_EDIT_PROMPT = "blue hair"
DEFAULT_NEGATIVE_PROMPT = "worst quality, normal quality, low quality, low res, blurry, text, watermark, logo, banner, extra digits, cropped, jpeg artifacts, signature, username, error, sketch ,duplicate, ugly, monochrome, horror, geometry, mutation, disgusting, poorly drawn face, bad face, fused face, ugly face, worst face, asymmetrical, unrealistic skin texture, bad proportions, out of frame, poorly drawn hands, cloned face, double face"

DEFAULT_CATEGORY = "hair"

lineart_detector = LineartDetector.from_pretrained("lllyasviel/Annotators")
lineart_detector = lineart_detector.to(DEVICE)

pidinet_detector = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
pidinet_detector = pidinet_detector.to(DEVICE)

canndy_detector = CannyDetector()

midas_detector = MidasDetector.from_pretrained(
    "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
)
midas_detector = midas_detector.to(DEVICE)

adapters = MultiAdapter(
    [
        T2IAdapter.from_pretrained(
            "TencentARC/t2i-adapter-lineart-sdxl-1.0",
            torch_dtype=torch.float16,
            varient="fp16",
        ),
        T2IAdapter.from_pretrained(
            "TencentARC/t2iadapter_canny_sd15v2",
            torch_dtype=torch.float16,
            varient="fp16",
        )
    ]
)
adapters = adapters.to(torch.float16)


basepipeline = StableDiffusionAdapterPipeline.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    use_safetensors=True,
    adapter=adapters,
)

basepipeline = basepipeline.to(DEVICE)

basepipeline.enable_model_cpu_offload()

@spaces.GPU(duration=15)
def image_to_image(
    input_image: Image,
    edit_prompt: str,
    seed: int,
    num_steps: int,
    guidance_scale: float,
    generate_size: int,
    lineart_scale: float = 1.0,
    canny_scale: float = 0.5,
):
    run_task_time = 0
    time_cost_str = ''
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
    lineart_image = lineart_detector(input_image, int(generate_size*0.375), generate_size)
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
    canny_image = canndy_detector(input_image, int(generate_size*0.375), generate_size)
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)

    cond_image = [lineart_image, canny_image]
    cond_scale = [lineart_scale, canny_scale]

    generator = torch.Generator(device=DEVICE).manual_seed(seed)
    generated_image = basepipeline(
        generator=generator,
        prompt=edit_prompt,
        negative_prompt=DEFAULT_NEGATIVE_PROMPT,
        image=input_image,
        height=generate_size,
        width=generate_size,
        guidance_scale=guidance_scale,
        num_inference_steps=num_steps,
        adapter_image=cond_image,
        adapter_conditioning_scale=cond_scale,
    ).images[0]
    
    run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)

    return generated_image, time_cost_str

def get_time_cost(run_task_time, time_cost_str):
    now_time = int(time.time()*1000)
    if run_task_time == 0:
        time_cost_str = 'start'
    else:
        if time_cost_str != '': 
            time_cost_str += f'-->'
        time_cost_str += f'{now_time - run_task_time}'
    run_task_time = now_time
    return run_task_time, time_cost_str

def create_demo() -> gr.Blocks:
    with gr.Blocks() as demo:
        croper = gr.State()
        with gr.Row():
            with gr.Column():
                edit_prompt = gr.Textbox(lines=1, label="Edit Prompt", value=DEFAULT_EDIT_PROMPT)
                generate_size = gr.Number(label="Generate Size", value=1024)
                seed = gr.Number(label="Seed", value=8)
                category = gr.Textbox(label="Category", value=DEFAULT_CATEGORY, visible=False)
            with gr.Column():
                num_steps = gr.Slider(minimum=1, maximum=100, value=5, step=1, label="Num Steps")
                guidance_scale = gr.Slider(minimum=0, maximum=30, value=2.5, step=0.5, label="Guidance Scale")
                mask_expansion = gr.Number(label="Mask Expansion", value=20, visible=True)
            with gr.Column():
                mask_dilation = gr.Slider(minimum=0, maximum=10, value=2, step=1, label="Mask Dilation")
                lineart_scale = gr.Slider(minimum=0, maximum=5, value=1, step=0.1, label="Lineart Scale")
                canny_scale = gr.Slider(minimum=0, maximum=5, value=0.7, step=0.1, label="Canny Scale")
                g_btn = gr.Button("Edit Image")
                
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(label="Input Image", type="pil")
            with gr.Column():
                restored_image = gr.Image(label="Restored Image", type="pil", interactive=False)
            with gr.Column():
                origin_area_image = gr.Image(label="Origin Area Image", type="pil", interactive=False)
                generated_image = gr.Image(label="Generated Image", type="pil", interactive=False)
                generated_cost = gr.Textbox(label="Time cost by step (ms):", visible=True, interactive=False)
        
        g_btn.click(
            fn=segment_image,
            inputs=[input_image, category, generate_size, mask_expansion, mask_dilation],
            outputs=[origin_area_image, croper],
        ).success(
            fn=image_to_image,
            inputs=[origin_area_image, edit_prompt,seed, num_steps, guidance_scale, generate_size, lineart_scale, canny_scale],
            outputs=[generated_image, generated_cost],
        ).success(
            fn=restore_result,
            inputs=[croper, category, generated_image],
            outputs=[restored_image],
        )

    return demo