app.py

import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
from peft import PeftModel, PeftConfig
from rembg import remove
from PIL import Image
import io
import torch
from typing import Optional

# кэш для пайплайнов (чтобы не перезагружать модель при каждом запросе)
PIPE_CACHE: dict[str, DiffusionPipeline] = {}
DEFAULT_MODEL = "CompVis/stable-diffusion-v1-4"
BASE_MODEL_FOR_LORA = "stable-diffusion-v1-5/stable-diffusion-v1-5"  # Base model used for LoRA training
LORA_MODEL_ID = "DiZH797/my-tuned-lora"  # Your uploaded LoRA model ID
MODEL_OPTIONS = [
    "CompVis/stable-diffusion-v1-4",
    "stabilityai/stable-diffusion-2-1",
    "stabilityai/sdxl-turbo",
    LORA_MODEL_ID
]


device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

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


# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# pipe = pipe.to(device)

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

def get_pipe(model_id: str, lora_scale: float = 1.0):
    """
    Loads the pipeline for a given model ID.
    If the selected model is the LoRA, it loads the base model and then merges the LoRA weights.
    """
    cache_key = f"{model_id}_{lora_scale}"

    if cache_key in PIPE_CACHE:
        return PIPE_CACHE[cache_key]


    # Check if the selected model is the LoRA adapter
    if model_id == LORA_MODEL_ID:
        # Укажите правильные имена файлов
        pipe = DiffusionPipeline.from_pretrained(
            BASE_MODEL_FOR_LORA,
            dtype=torch_dtype
        ).to(device)
        # pipe.unet = PeftModel.from_pretrained(pipe.unet, LORA_MODEL_ID)
        pipe.load_lora_weights(
            LORA_MODEL_ID, weight_name="merged_lora_weights.safetensors"
        )
        pipe.fuse_lora(lora_scale=lora_scale)
        # После загрузки LoRA
        print("LoRa scale is", lora_scale)
        print("LoRA layers in unet:")
        for name, param in pipe.unet.named_parameters():
            if "lora" in name.lower() and param.requires_grad:
                print(f"Unet LoRA layer: {name}, shape: {param.shape}")
                break
        print("LoRA layers in text_encoder:")
        for name, param in pipe.text_encoder.named_parameters():
            if "lora" in name:
                print(f"Text Encoder LoRA: {name}, shape: {param.shape}")
                break
    else:
        # Load a standard model without LoRA
        pipe = DiffusionPipeline.from_pretrained(
            model_id,
            dtype=torch_dtype
        ).to(device)

    PIPE_CACHE[cache_key] = pipe
    return pipe

# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    model_id: Optional[str] = DEFAULT_MODEL,
    prompt: str = "",
    negative_prompt: str = "",
    seed: int = 42,
    randomize_seed: bool = False,
    width: int = 512,
    height: int = 512,
    guidance_scale: float = 7.0,
    num_inference_steps: int = 20,
    scheduler_name: Optional[str] = None,
    lora_scale: float = 1.0,
    remove_background: bool = False,
    progress=gr.Progress(track_tqdm=True),
):
    # получаем/загружаем нужный pipe
    pipe = get_pipe(model_id, lora_scale)

    # при желании можно подменить scheduler по имени (опционально)
    if scheduler_name:
        # примерная схема: словарь name->класс scheduler
        # при необходимости добавить другие scheduler'ы — импортируйте их сверху и добавьте сюда
        try:
            from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler, PNDMScheduler, DPMSolverMultistepScheduler
            sched_map = {
                "DDIM": DDIMScheduler,
                "EulerAncestral": EulerAncestralDiscreteScheduler,
                "PNDM": PNDMScheduler,
                "DPMSMS": DPMSolverMultistepScheduler
            }
            if scheduler_name in sched_map:
                pipe.scheduler = sched_map[scheduler_name].from_config(pipe.scheduler.config)
        except Exception:
            # если что-то пошло не так — просто используем дефолтный scheduler
            pass

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    if remove_background:
        # Конвертируем PIL Image в bytes
        img_byte_arr = io.BytesIO()
        image.save(img_byte_arr, format='PNG')
        img_byte_arr = img_byte_arr.getvalue()
        
        # Удаляем фон
        output_image = remove(img_byte_arr)
        
        # Конвертируем обратно в PIL Image
        image = Image.open(io.BytesIO(output_image))

    return image, seed



examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

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

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

        # Model selector (выпадающий список)
        model_select = gr.Dropdown(
            label="Model",
            choices=MODEL_OPTIONS,
            value=DEFAULT_MODEL,
            interactive=True,
        )
    
        # опциональный селектор scheduler
        scheduler_select = gr.Dropdown(
            label="Scheduler (optional)",
            choices=["", "DDIM", "EulerAncestral", "PNDM", "DPMSMS"],
            value="",
        )

        # Add a new slider for LoRA scale
        lora_scale_slider = gr.Slider(
            label="LoRA Scale (Only for LoRA model)",
            minimum=0.0,
            maximum=3.0,
            step=0.1,
            value=1.0,
            visible=False,  # Initially hidden
        )

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            remove_background = gr.Checkbox(
                label="Remove background from generated image",
                value=False,
                info="Use rembg to remove background from the generated image"
            )
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            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=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=20,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    
    # Function to show/hide the LoRA scale slider based on model selection
    def toggle_lora_scale_slider(model_id):
        if model_id == LORA_MODEL_ID:
            return gr.Slider(visible=True)
        else:
            return gr.Slider(visible=False)

    model_select.change(
        fn=toggle_lora_scale_slider,
        inputs=model_select,
        outputs=lora_scale_slider
    )

    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_select,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            scheduler_select,
            lora_scale_slider,
            remove_background
        ],
        outputs=[result, seed],
    )

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