app.py

import gradio as gr
import numpy as np
import random
import cv2
import PIL 
from controlnet_aux import OpenposeDetector
from transformers import pipeline
from rembg import remove
from diffusers.models import AutoencoderKL


#import spaces #[uncomment to use ZeroGPU]
from diffusers import (
    DiffusionPipeline, StableDiffusionPipeline,
    StableDiffusionControlNetPipeline, ControlNetModel,
    DPMSolverMultistepScheduler
)
from peft import PeftModel, LoraConfig
import torch
import gc

from huggingface_hub import HfApi
# Создаем экземпляр API
api = HfApi()

device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

model_names = [
    "sand74/changpu_lora",
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    "stabilityai/sd-turbo",
]


def get_canny_image(image):
    image = np.array(image)
    low_threshold = 100
    high_threshold = 200
    image = cv2.Canny(image, low_threshold, high_threshold)
    image = image[:, :, None]
    image = np.concatenate([image, image, image], axis=2)
    return PIL.Image.fromarray(image)


def get_openpos_image(image):
    pil_image = PIL.Image.fromarray(image)
    processor = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
    pil_image = processor(pil_image, hand_and_face=False)
    return pil_image


def get_depth_image(image):
    pil_image = PIL.Image.fromarray(image)
    depth_estimator = pipeline('depth-estimation')
    pil_image = depth_estimator(pil_image)['depth']
    pil_image = np.array(pil_image)
    pil_image = pil_image[:, :, None]
    pil_image = np.concatenate([pil_image, pil_image, pil_image], axis=2)
    return PIL.Image.fromarray(pil_image)


control_net_modes = {
    "lllyasviel/sd-controlnet-canny": get_canny_image,
    "lllyasviel/control_v11p_sd15_openpose": get_openpos_image,
    "lllyasviel/control_v11f1p_sd15_depth": get_depth_image,
}


def preview_control_net_image(controlnet_image, controlnet_mode):
    return control_net_modes[controlnet_mode](controlnet_image)

    
def is_lora(model_name):
    return model_name == "sand74/changpu_lora"


def remove_background(image):
    image = remove(image)
    return image



#@spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    model_id,
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    lora_scale,
    use_controlnet=False,
    controlnet_image=None,
    controlnet_strength=None,
    controlnet_mode=None,
    use_ip_adapter=False,
    ip_adapter_image=None,
    ip_adapter_scale=None,
    rm_background=True,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    pipe_params = dict(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    )
    if is_lora(model_id):
        base_model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
    else:
        base_model_id = model_id

    vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch_dtype)
    
    if not use_controlnet:
        pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch_dtype, vae=vae)
    else:
        controlnet_image = cv2.resize(controlnet_image, (width, height), interpolation=cv2.INTER_AREA)
        controlnet = ControlNetModel.from_pretrained(
            controlnet_mode, torch_dtype=torch_dtype)
        pipe = StableDiffusionControlNetPipeline.from_pretrained(
            base_model_id,
            torch_dtype=torch_dtype,
            controlnet=controlnet)
        pipe_params["image"] = control_net_modes[controlnet_mode](controlnet_image)
        pipe_params["controlnet_conditioning_scale"] = controlnet_strength

    if is_lora(model_id):
        lora = PeftModel.from_pretrained(pipe.unet, model_id, adapter_name="panda_hqwh")
        pipe.set_adapters(["panda_hqwh"], adapter_weights=[lora_scale])

    if use_ip_adapter:
        ip_adapter_image = cv2.resize(ip_adapter_image, (width, height), interpolation=cv2.INTER_AREA)
        pipe.load_ip_adapter(
            "h94/IP-Adapter",
            subfolder="models",
            weight_name="ip-adapter-plus_sd15.bin",
        )
        pipe_params["ip_adapter_image"] = ip_adapter_image
        pipe.set_ip_adapter_scale(ip_adapter_scale)

    pipe.safety_checker = None

    if torch_dtype in (torch.float16, torch.bfloat16):
        pipe.unet.half()
        pipe.text_encoder.half()

    pipe.to(device)

    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    
    image = pipe(**pipe_params).images[0]

    if rm_background:
        image = remove(image)
        
    return image, seed


examples = [
    "Sad panda_hqwh drinking beer",
    "panda_hqwh walk in a field",
    "panda_hqwh play with ball",
]


css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

result = None

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        title = gr.Markdown(" # Text-to-Image Gradio Template")

        model_id = gr.Dropdown(model_names, value=model_names[0], label="Select model")

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
                value="Sad panda_hqwh drinking beer",
            )
            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)
        rm_background = gr.Checkbox(label="Remove background?", scale=1, value=True)

        with gr.Group(visible=True) as lora_section:
            title = gr.Markdown(" ### LoRA section")
            with gr.Row():
                lora_scale = gr.Slider(
                    minimum=0.0,
                    maximum=2.0,
                    value=0.9,
                    step=0.1,
                    label="LoRA Strength"
                )

        # Показывать/скрывать секцию LoRA в зависимости от модели
        model_id.change(
            fn=lambda x: gr.update(visible=is_lora(x)),
            inputs=model_id,
            outputs=lora_section
        )

        with gr.Group():
            title = gr.Markdown(" ### ControlNet section")
            with gr.Column():
                use_controlnet = gr.Checkbox(label="Use ControlNet", value=False)

                # Секция ControlNet (изначально скрыта)
                with gr.Column(visible=False) as controlnet_section:
                    controlnet_strength = gr.Slider(
                        minimum=0.1, maximum=1.0, value=0.8, step=0.1,
                        label="ControlNet Strength",
                        interactive=True
                    )
                    controlnet_mode = gr.Dropdown(
                        list(control_net_modes.keys()),
                        value=next(iter(control_net_modes.keys())),
                        label="ControlNet mode",
                        interactive=True
                    )
                    with gr.Row():                    
                        controlnet_image = gr.Image(
                            label="ControlNet image",
                            interactive=True
                        )
                        controlnet_view = gr.Image(
                            label="ControlNet preview",
                            interactive=False
                        )

        controlnet_image.change(
            fn=preview_control_net_image,
            inputs=[controlnet_image, controlnet_mode],
            outputs=controlnet_view
        )

        controlnet_mode.change(
            fn=preview_control_net_image,
            inputs=[controlnet_image, controlnet_mode],
            outputs=controlnet_view
        )

        # Показывать/скрывать секцию ControlNet в зависимости от чекбокса
        use_controlnet.change(
            fn=lambda x: gr.update(visible=x),
            inputs=use_controlnet,
            outputs=controlnet_section
        )

        with gr.Group():
            title = gr.Markdown(" ### IP-adapter section")
            with gr.Column():
                use_ip_adapter = gr.Checkbox(label="Use IP-adapter", value=False)

                # Секция IP-adapter (изначально скрыта)
                with gr.Column(visible=False) as ip_adapter_section:
                    ip_adapter_scale = gr.Slider(
                        minimum=0.1, maximum=1.0, value=0.5, step=0.1,
                        label="IP-adapter Scale",
                        interactive=True
                    )
                    ip_adapter_image = gr.Image(
                        label="IP-adapter image",
                    )

        # Показывать/скрывать секцию IP-adapter в зависимости от чекбокса
        use_ip_adapter.change(
            fn=lambda x: gr.update(visible=x),
            inputs=use_ip_adapter,
            outputs=ip_adapter_section
        )

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
                value="low quality, blurry, unfinished, text",
           )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0,
                    maximum=20,
                    step=1,
                    value=7,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=500,
                    step=1,
                    value=30,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            lora_scale,
            use_controlnet,
            controlnet_image,
            controlnet_strength,
            controlnet_mode,
            use_ip_adapter,
            ip_adapter_image,
            ip_adapter_scale,
            rm_background,
        ],
        outputs=[result, seed],
    )


if __name__ == "__main__":
    demo.launch()