app.py

#!/usr/bin/env python

from __future__ import annotations

import os
import random
import toml
import gradio as gr
import numpy as np
import PIL.Image
import torch
import utils
import gc
from safetensors.torch import load_file
import lora_diffusers
from lora_diffusers import LoRANetwork, create_network_from_weights
from huggingface_hub import hf_hub_download
from diffusers.models import AutoencoderKL
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler

DESCRIPTION = "Animagine XL"
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
IS_COLAB = utils.is_google_colab()
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"

MODEL = "Linaqruf/animagine-xl"

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    pipe = DiffusionPipeline.from_pretrained(
        MODEL,
        torch_dtype=torch.float16,
        custom_pipeline="lpw_stable_diffusion_xl.py",
        use_safetensors=True,
        variant="fp16",
    )

    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

    if ENABLE_CPU_OFFLOAD:
        pipe.enable_model_cpu_offload()
    else:
        pipe.to(device)

    if USE_TORCH_COMPILE:
        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)

else:
    pipe = None


def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    return seed


def get_image_path(base_path):
    extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif"]
    for ext in extensions:
        if os.path.exists(base_path + ext):
            return base_path + ext
    # If no match is found, return None or raise an error
    return None


def update_selection(selected_state: gr.SelectData):
    lora_repo = sdxl_loras[selected_state.index]["repo"]
    lora_weight = sdxl_loras[selected_state.index]["multiplier"]
    updated_selected_info = f"{lora_repo}"
    updated_prompt = sdxl_loras[selected_state.index]["sample_prompt"]
    updated_negative = sdxl_loras[selected_state.index]["sample_negative"]

    return (
        updated_selected_info,
        selected_state,
        lora_weight,
        updated_prompt,
        negative_presets_dict.get(updated_negative, ""),
        updated_negative,
    )


def create_network(text_encoders, unet, state_dict, multiplier, device):
    network = create_network_from_weights(
        text_encoders, unet, state_dict, multiplier=multiplier
    )
    network.load_state_dict(state_dict)
    network.to(device, dtype=unet.dtype)
    network.apply_to(multiplier=multiplier)
    return network


# def backup_sd(state_dict):
#     for k, v in state_dict.items():
#         state_dict[k] = v.detach().cpu()
#     return state_dict


def generate(
    prompt: str,
    negative_prompt: str = "",
    prompt_2: str = "",
    negative_prompt_2: str = "",
    use_prompt_2: bool = False,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    target_width: int = 1024,
    target_height: int = 1024,
    original_width: int = 4096,
    original_height: int = 4096,
    guidance_scale: float = 12.0,
    num_inference_steps: int = 50,
    use_lora: bool = False,
    lora_weight: float = 1.0,
    set_target_size: bool = False,
    set_original_size: bool = False,
    selected_state: str = "",
) -> PIL.Image.Image:
    generator = torch.Generator().manual_seed(seed)

    network = None  # Initialize to None
    network_state = {"current_lora": None, "multiplier": None}

    # _unet = pipe.unet.state_dict()
    # backup_sd(_unet)
    # _text_encoder = pipe.text_encoder.state_dict()
    # backup_sd(_text_encoder)
    # _text_encoder_2 = pipe.text_encoder_2.state_dict()
    # backup_sd(_text_encoder_2)

    if not set_original_size:
        original_width = 4096
        original_height = 4096
    if not set_target_size:
        target_width = width
        target_height = height
    if negative_prompt == "":
        negative_prompt = None
    if not use_prompt_2:
        prompt_2 = None
        negative_prompt_2 = None
    if negative_prompt_2 == "":
        negative_prompt_2 = None

    if use_lora:
        if not selected_state:
            raise Exception("You must select a LoRA")

        repo_name = sdxl_loras[selected_state.index]["repo"]
        full_path_lora = saved_names[selected_state.index]
        weight_name = sdxl_loras[selected_state.index]["weights"]

        lora_sd = load_file(full_path_lora)
        text_encoders = [pipe.text_encoder, pipe.text_encoder_2]

        if network_state["current_lora"] != repo_name:
            network = create_network(
                text_encoders, pipe.unet, lora_sd, lora_weight, device
            )
            network_state["current_lora"] = repo_name
            network_state["multiplier"] = lora_weight

        elif network_state["multiplier"] != lora_weight:
            network = create_network(
                text_encoders, pipe.unet, lora_sd, lora_weight, device
            )
            network_state["multiplier"] = lora_weight
    else:
        if network:
            network.unapply_to()
            network = None
            network_state = {"current_lora": None, "multiplier": None}

    try:
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            prompt_2=prompt_2,
            negative_prompt_2=negative_prompt_2,
            width=width,
            height=height,
            target_size=(target_width, target_height),
            original_size=(original_width, original_height),
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            generator=generator,
            output_type="pil",
        ).images[0]

        if network:
            network.unapply_to()
            network = None

        return image

    except Exception as e:
        print(f"An error occurred: {e}")
        raise

    finally:
        # pipe.unet.load_state_dict(_unet)
        # pipe.text_encoder.load_state_dict(_text_encoder)
        # pipe.text_encoder_2.load_state_dict(_text_encoder_2)

        # del _unet, _text_encoder, _text_encoder_2

        if network:
            network.unapply_to()
            network = None

        if use_lora:
            del lora_sd, text_encoders
            gc.collect()


examples = [
    "face focus, cute, masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck",
    "face focus, bishounen, masterpiece, best quality, 1boy, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck",
]

negative_presets_dict = {
    "None": "",
    "Standard": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
    "Weighted": "(low quality, worst quality:1.2), 3d, watermark, signature, ugly, poorly drawn, bad image",
}

with open("lora.toml", "r") as file:
    data = toml.load(file)
    sdxl_loras = [
        {
            "image": get_image_path(item["image"]),
            "title": item["title"],
            "repo": item["repo"],
            "weights": item["weights"],
            "multiplier": item["multiplier"] if "multiplier" in item else "1.0",
            "sample_prompt": item["sample_prompt"],
            "sample_negative": item["sample_negative"],
        }
        for item in data["data"]
    ]
saved_names = [hf_hub_download(item["repo"], item["weights"]) for item in sdxl_loras]


with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") as demo:
    title = gr.HTML(
        f"""<h1><span>{DESCRIPTION}</span></h1>""",
        elem_id="title",
    )
    gr.Markdown(
        f"""Gradio demo for [Linaqruf/animagine-xl](https://huggingface.co/spaces/Linaqruf/Animagine-XL)""",
        elem_id="subtitle",
    )
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    selected_state = gr.State()
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group():
                prompt = gr.Text(
                    label="Prompt",
                    max_lines=5,
                    placeholder="Enter your prompt",
                )
                negative_prompt = gr.Text(
                    label="Negative Prompt",
                    max_lines=5,
                    placeholder="Enter a negative prompt",
                    value="lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry",
                )
                with gr.Accordion(label="Negative Presets", open=False):
                    negative_presets = gr.Dropdown(
                        label="Negative Presets",
                        show_label=False,
                        choices=list(negative_presets_dict.keys()),
                        value="Standard",
                    )

                with gr.Row():
                    use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
                    use_lora = gr.Checkbox(label="Use LoRA", value=False)

            with gr.Group(visible=False) as prompt2_group:
                prompt_2 = gr.Text(
                    label="Prompt 2",
                    max_lines=5,
                    placeholder="Enter your prompt",
                )
                negative_prompt_2 = gr.Text(
                    label="Negative prompt 2",
                    max_lines=5,
                    placeholder="Enter a negative prompt",
                )

            with gr.Group(visible=False) as lora_group:
                selector_info = gr.Text(
                    label="Selected LoRA",
                    max_lines=1,
                    value="No LoRA selected.",
                )
                lora_selection = gr.Gallery(
                    value=[(item["image"], item["title"]) for item in sdxl_loras],
                    label="Animagine XL LoRA",
                    show_label=False,
                    allow_preview=False,
                    columns=2,
                    elem_id="gallery",
                    show_share_button=False,
                )
                lora_weight = gr.Slider(
                    label="Multiplier",
                    minimum=0,
                    maximum=1,
                    step=0.05,
                    value=1,
                )

            with gr.Group():
                with gr.Row():
                    width = gr.Slider(
                        label="Width",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=1024,
                    )
                    height = gr.Slider(
                        label="Height",
                        minimum=256,
                        maximum=MAX_IMAGE_SIZE,
                        step=32,
                        value=1024,
                    )

                with gr.Accordion(label="Advanced Options", open=False):
                    seed = gr.Slider(
                        label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0
                    )

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

                    with gr.Row():
                        guidance_scale = gr.Slider(
                            label="Guidance scale",
                            minimum=1,
                            maximum=20,
                            step=0.1,
                            value=12.0,
                        )
                        num_inference_steps = gr.Slider(
                            label="Number of inference steps",
                            minimum=10,
                            maximum=100,
                            step=1,
                            value=50,
                        )
                    with gr.Group():
                        with gr.Row():
                            set_target_size = gr.Checkbox(
                                label="Target Size", value=False
                            )
                            set_original_size = gr.Checkbox(
                                label="Original Size", value=False
                            )
                    with gr.Group():
                        with gr.Row():
                            original_width = gr.Slider(
                                label="Original Width",
                                minimum=1024,
                                maximum=4096,
                                step=32,
                                value=4096,
                                visible=False,
                            )
                            original_height = gr.Slider(
                                label="Original Height",
                                minimum=1024,
                                maximum=4096,
                                step=32,
                                value=4096,
                                visible=False,
                            )
                        with gr.Row():
                            target_width = gr.Slider(
                                label="Target Width",
                                minimum=1024,
                                maximum=4096,
                                step=32,
                                value=width.value,
                                visible=False,
                            )
                            target_height = gr.Slider(
                                label="Target Height",
                                minimum=1024,
                                maximum=4096,
                                step=32,
                                value=height.value,
                                visible=False,
                            )
        with gr.Column(scale=2):
            with gr.Blocks():
                run_button = gr.Button("Generate", variant="primary")
            result = gr.Image(label="Result", show_label=False)

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=result,
        fn=generate,
        cache_examples=CACHE_EXAMPLES,
    )
    lora_selection.select(
        update_selection,
        outputs=[
            selector_info,
            selected_state,
            lora_weight,
            prompt,
            negative_prompt,
            negative_presets,
        ],
        queue=False,
        show_progress=False,
    )
    use_prompt_2.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_prompt_2,
        outputs=prompt2_group,
        queue=False,
        api_name=False,
    )
    negative_presets.change(
        fn=lambda x: gr.update(value=negative_presets_dict.get(x, "")),
        inputs=negative_presets,
        outputs=negative_prompt,
        queue=False,
        api_name=False,
    )
    use_lora.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_lora,
        outputs=lora_group,
        queue=False,
        api_name=False,
    )
    set_target_size.change(
        fn=lambda x: (gr.update(visible=x), gr.update(visible=x)),
        inputs=set_target_size,
        outputs=[target_width, target_height],
        queue=False,
        api_name=False,
    )
    set_original_size.change(
        fn=lambda x: (gr.update(visible=x), gr.update(visible=x)),
        inputs=set_original_size,
        outputs=[original_width, original_height],
        queue=False,
        api_name=False,
    )
    width.change(
        fn=lambda x: gr.update(value=x),
        inputs=width,
        outputs=target_width,
        queue=False,
        api_name=False,
    )
    height.change(
        fn=lambda x: gr.update(value=x),
        inputs=height,
        outputs=target_height,
        queue=False,
        api_name=False,
    )

    inputs = [
        prompt,
        negative_prompt,
        prompt_2,
        negative_prompt_2,
        use_prompt_2,
        seed,
        width,
        height,
        target_width,
        target_height,
        original_width,
        original_height,
        guidance_scale,
        num_inference_steps,
        use_lora,
        lora_weight,
        set_target_size,
        set_original_size,
        selected_state,
    ]
    prompt.submit(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name="run",
    )
    negative_prompt.submit(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name=False,
    )
    prompt_2.submit(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name=False,
    )
    negative_prompt_2.submit(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name=False,
    )
    run_button.click(
        fn=randomize_seed_fn,
        inputs=[seed, randomize_seed],
        outputs=seed,
        queue=False,
        api_name=False,
    ).then(
        fn=generate,
        inputs=inputs,
        outputs=result,
        api_name=False,
    )

demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)