app.py

import spaces

import os
import random
import math

import torch
import numpy as np

from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import (
    StableDiffusionXLPipeline,
)
from diffusers.schedulers.scheduling_euler_ancestral_discrete import (
    EulerAncestralDiscreteScheduler,
)
from diffusers.models.attention_processor import AttnProcessor2_0
from transformers import AutoModelForCausalLM, AutoTokenizer

import gradio as gr

try:
    from dotenv import load_dotenv

    load_dotenv()
except:
    print("failed to import dotenv (this is not a problem on the production)")

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

HF_TOKEN = os.environ.get("HF_TOKEN")
assert HF_TOKEN is not None

IMAGE_MODEL_REPO_ID = os.environ.get(
    "IMAGE_MODEL_REPO_ID", "OnomaAIResearch/Illustrious-xl-early-release-v0"
)
DART_V3_REPO_ID = os.environ.get("DART_V3_REPO_ID", None)
assert DART_V3_REPO_ID is not None

torch_dtype = torch.bfloat16

dart = AutoModelForCausalLM.from_pretrained(
    DART_V3_REPO_ID,
    torch_dtype=torch_dtype,
    token=HF_TOKEN,
    use_cache=True,
)
dart = dart.eval()
dart = dart.requires_grad_(False)
dart = torch.compile(dart)
tokenizer = AutoTokenizer.from_pretrained(DART_V3_REPO_ID)

pipe = StableDiffusionXLPipeline.from_pretrained(
    IMAGE_MODEL_REPO_ID, 
    torch_dtype=torch_dtype,
    add_watermarker=False,
    custom_pipeline="lpw_stable_diffusion_xl"
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_attn_processor(AttnProcessor2_0())
pipe = pipe.to(device)


MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

TEMPLATE = (
    "<|bos|>"
    #
    "<|rating:general|>"
    "{aspect_ratio}"
    "<|length:medium|>"
    #
    "<copyright>original</copyright>"
    #
    "<character></character>"
    #
    "<general>"
)

def get_aspect_ratio(width: int, height: int) -> str:
    ar = width / height

    if ar <= 1 / math.sqrt(3):
        return "<|aspect_ratio:ultra_wide|>"
    elif ar <= 8 / 9:  #
        return "<|aspect_ratio:wide|>"
    elif ar < 9 / 8:
        return "<|aspect_ratio:square|>"
    elif ar < math.sqrt(3):
        return "<|aspect_ratio:tall|>"
    else:
        return "<|aspect_ratio:ultra_tall|>"


@torch.inference_mode
def generate_prompt(aspect_ratio: str):
    input_ids = tokenizer.encode_plus(
        TEMPLATE.format(aspect_ratio=aspect_ratio),
        return_tensors="pt",
    ).input_ids
    print("input_ids:", input_ids)

    output_ids = dart.generate(
        input_ids,
        max_new_tokens=256,
        do_sample=True,
        temperature=1.0,
        top_p=1.0,
        top_k=100,
        num_beams=1,
    )[0]

    generated = output_ids[len(input_ids) :]
    decoded = ", ".join([token for token in tokenizer.batch_decode(generated, skip_special_tokens=True) if token.strip() != ""])
    print("decoded:", decoded)

    return decoded

def format_prompt(prompt: str, prompt_suffix: str):
    return f"{prompt}, {prompt_suffix}"

@spaces.GPU
def generate_image(
    prompt: str,
    negative_prompt: str,
    generator,
    width: int,
    height: int,
    guidance_scale: float,
    num_inference_steps: int,
):
    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]

    return image

def on_generate(
    suffix: str,
    negative_prompt: str,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)

    ar = get_aspect_ratio(width, height)
    prompt = generate_prompt(ar)
    prompt = format_prompt(prompt, suffix)
    print(prompt)

    image = generate_image(
        prompt,
        negative_prompt,
        generator,
        width,
        height,
        guidance_scale,
        num_inference_steps,
    )

    return image, prompt, seed


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(f"""
        # Random IllustriousXL
        """)

        with gr.Row():
            run_button = gr.Button("Generate random", scale=0)

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

        with gr.Accordion("Generation details", open=False):
            prompt_txt = gr.Textbox(label="Generated prompt", interactive=False)

        with gr.Accordion("Advanced Settings", open=False):
            prompt_suffix = gr.Text(
                label="Prompt suffix",
                visible=True,
                value="masterpiece, best quality, very aesthetic",
            )
            negative_prompt = gr.Text(
                label="Negative prompt",
                placeholder="Enter a negative prompt",
                visible=True,
                value="(worst quality, bad quality, low quality:1.2), lowres, displeasing, very displeasing, bad anatomy, bad hands, extra digits, fewer digits, scan artifacts, signature, username, jpeg artifacts, retro, 2010s",
            )

            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():
                width = gr.Slider(
                    label="Width",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=832,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1152,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=1.0,
                    maximum=10.0,
                    step=0.5,
                    value=7,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=20,
                    maximum=50,
                    step=1,
                    value=25,
                )

    gr.on(
        triggers=[run_button.click],
        fn=on_generate,
        inputs=[
            prompt_suffix,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, prompt_txt, seed],
    )

demo.queue().launch()