app.py

#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
import sys
import random
import gradio as gr
import numpy as np
import uuid
import spaces
from diffusers import ConsistencyDecoderVAE, DPMSolverMultistepScheduler, Transformer2DModel, AutoencoderKL, SASolverScheduler
import torch
from typing import Tuple
from datetime import datetime
from peft import PeftModel
from diffusers_patches import pixart_sigma_init_patched_inputs, PixArtSigmaPipeline


DESCRIPTION = """![Logo](https://raw.githubusercontent.com/PixArt-alpha/PixArt-sigma-project/master/static/images/logo-sigma.png)
        # PixArt-Sigma 1024px
        #### [PixArt-Sigma 1024px](https://github.com/PixArt-alpha/PixArt-sigma) is a transformer-based text-to-image diffusion system trained on text embeddings from T5. This demo uses the [PixArt-alpha/PixArt-Sigma-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS) checkpoint.
        #### English prompts ONLY; 提示词仅限英文
        #### Welcome to Star🌟 our [GitHub](https://github.com/PixArt-alpha/PixArt-sigma)
        ### <span style='color: red;'>You may change the DPM-Solver inference steps from 14 to 20, or DPM-Solver Guidance scale from 4.5 to 3.5 if you didn't get satisfied results.
        """
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "6000"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
PORT = int(os.getenv("DEMO_PORT", "15432"))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")


style_list = [
    {
        "name": "(No style)",
        "prompt": "{prompt}",
        "negative_prompt": "",
    },
    {
        "name": "Cinematic",
        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
    },
    {
        "name": "Photographic",
        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
    },
    {
        "name": "Anime",
        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
    },
    {
        "name": "Manga",
        "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
    },
    {
        "name": "Digital Art",
        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
        "negative_prompt": "photo, photorealistic, realism, ugly",
    },
    {
        "name": "Pixel art",
        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
    },
    {
        "name": "Fantasy art",
        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
    },
    {
        "name": "Neonpunk",
        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
    },
    {
        "name": "3D Model",
        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
    },
]


styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "(No style)"
SCHEDULE_NAME = ["DPM-Solver", "SA-Solver"]
DEFAULT_SCHEDULE_NAME = "DPM-Solver"
NUM_IMAGES_PER_PROMPT = 1

def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
    if not negative:
        negative = ""
    return p.replace("{prompt}", positive), n + negative


if torch.cuda.is_available():
    weight_dtype = torch.float16
    T5_token_max_length = 300

    # tmp patches for diffusers PixArtSigmaPipeline Implementation
    print(
        "Changing _init_patched_inputs method of diffusers.models.Transformer2DModel "
        "using scripts.diffusers_patches.pixart_sigma_init_patched_inputs")
    setattr(Transformer2DModel, '_init_patched_inputs', pixart_sigma_init_patched_inputs)

    transformer = Transformer2DModel.from_pretrained(
        "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS",
        subfolder='transformer',
        torch_dtype=weight_dtype,
    )
    pipe = PixArtSigmaPipeline.from_pretrained(
        "PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers",
        transformer=transformer,
        torch_dtype=weight_dtype,
        use_safetensors=True,
    )

    if os.getenv('CONSISTENCY_DECODER', False):
        print("Using DALL-E 3 Consistency Decoder")
        pipe.vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)

    if ENABLE_CPU_OFFLOAD:
        pipe.enable_model_cpu_offload()
    else:
        pipe.to(device)
        print("Loaded on Device!")

    # speed-up T5
    pipe.text_encoder.to_bettertransformer()

    if USE_TORCH_COMPILE:
        pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
        print("Model Compiled!")


def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

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


@torch.no_grad()
@torch.inference_mode()
@spaces.GPU(duration=120)
def generate(
        prompt: str,
        negative_prompt: str = "",
        style: str = DEFAULT_STYLE_NAME,
        use_negative_prompt: bool = False,
        num_imgs: int = 1,
        seed: int = 0,
        width: int = 1024,
        height: int = 1024,
        schedule: str = 'DPM-Solver',
        dpms_guidance_scale: float = 4.5,
        sas_guidance_scale: float = 3,
        dpms_inference_steps: int = 20,
        sas_inference_steps: int = 25,
        randomize_seed: bool = False,
        use_resolution_binning: bool = True,
        progress=gr.Progress(track_tqdm=True),
):
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator().manual_seed(seed)

    if schedule == 'DPM-Solver':
        if not isinstance(pipe.scheduler, DPMSolverMultistepScheduler):
            pipe.scheduler = DPMSolverMultistepScheduler()
        num_inference_steps = dpms_inference_steps
        guidance_scale = dpms_guidance_scale
    elif schedule == "SA-Solver":
        if not isinstance(pipe.scheduler, SASolverScheduler):
            pipe.scheduler = SASolverScheduler.from_config(pipe.scheduler.config, algorithm_type='data_prediction', tau_func=lambda t: 1 if 200 <= t <= 800 else 0, predictor_order=2, corrector_order=2)
        num_inference_steps = sas_inference_steps
        guidance_scale = sas_guidance_scale
    else:
        raise ValueError(f"Unknown schedule: {schedule}")

    if not use_negative_prompt:
        negative_prompt = None  # type: ignore
    prompt, negative_prompt = apply_style(style, prompt, negative_prompt)

    images = pipe(
        prompt=prompt,
        width=width,
        height=height,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        num_images_per_prompt=num_imgs,
        use_resolution_binning=use_resolution_binning,
        output_type="pil",
        max_sequence_length=T5_token_max_length,
    ).images

    image_paths = [save_image(img) for img in images]
    print(image_paths)
    return image_paths, seed


examples = [
    "A small cactus with a happy face in the Sahara desert.",
    "Eiffel Tower was Made up of more than 2 million translucent straws to look like a cloud, with the bell tower at the top of the building, Michel installed huge foam-making machines in the forest to blow huge amounts of unpredictable wet clouds in the building's classic architecture.",
    "3D animation of a small, round, fluffy creature with big, expressive eyes explores a vibrant, enchanted forest. The creature, a whimsical blend of a rabbit and a squirrel, has soft blue fur and a bushy, striped tail. It hops along a sparkling stream, its eyes wide with wonder. The forest is alive with magical elements: flowers that glow and change colors, trees with leaves in shades of purple and silver, and small floating lights that resemble fireflies. The creature stops to interact playfully with a group of tiny, fairy-like beings dancing around a mushroom ring. The creature looks up in awe at a large, glowing tree that seems to be the heart of the forest.",
    "Color photo of a corgi made of transparent glass, standing on the riverside in Yosemite National Park.",
    "A close-up photo of a person. The subject is a woman. She wore a blue coat with a gray dress underneath. She has blue eyes and blond hair, and wears a pair of earrings. Behind are blurred city buildings and streets.",
    "A litter of golden retriever puppies playing in the snow. Their heads pop out of the snow, covered in.",
    "a handsome young boy in the middle with sky color background wearing eye glasses, it's super detailed with anime style, it's a portrait with delicated eyes and nice looking face",
    "an astronaut sitting in a diner, eating fries, cinematic, analog film",
    "Pirate ship trapped in a cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, light pollution, cinematic atmosphere, art nouveau style, illustration art artwork by SenseiJaye, intricate detail.",
    "professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.",
    "The parametric hotel lobby is a sleek and modern space with plenty of natural light. The lobby is spacious and open with a variety of seating options. The front desk is a sleek white counter with a parametric design. The walls are a light blue color with parametric patterns. The floor is a light wood color with a parametric design. There are plenty of plants and flowers throughout the space. The overall effect is a calm and relaxing space. occlusion, moody, sunset, concept art, octane rendering, 8k, highly detailed, concept art, highly detailed, beautiful scenery, cinematic, beautiful light, hyperreal, octane render, hdr, long exposure, 8K, realistic, fog, moody, fire and explosions, smoke, 50mm f2.8",
]

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
    )
    with gr.Row(equal_height=False):
        with gr.Group():
            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)
            result = gr.Gallery(label="Result", columns=NUM_IMAGES_PER_PROMPT,  show_label=False)
        # with gr.Accordion("Advanced options", open=False):
        with gr.Group():
            with gr.Row():
                use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False, visible=True)
            with gr.Row(visible=True):
                schedule = gr.Radio(
                    show_label=True,
                    container=True,
                    interactive=True,
                    choices=SCHEDULE_NAME,
                    value=DEFAULT_SCHEDULE_NAME,
                    label="Sampler Schedule",
                    visible=True,
                )
                num_imgs = gr.Slider(
                    label="Num Images",
                    minimum=1,
                    maximum=8,
                    step=1,
                    value=1,
                )
            style_selection = gr.Radio(
                show_label=True,
                container=True,
                interactive=True,
                choices=STYLE_NAMES,
                value=DEFAULT_STYLE_NAME,
                label="Image Style",
            )
            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=0,
            )
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            with gr.Row(visible=True):
                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.Row():
                dpms_guidance_scale = gr.Slider(
                    label="DPM-Solver Guidance scale",
                    minimum=1,
                    maximum=10,
                    step=0.1,
                    value=4.5,
                )
                dpms_inference_steps = gr.Slider(
                    label="DPM-Solver inference steps",
                    minimum=5,
                    maximum=40,
                    step=1,
                    value=14,
                )
            with gr.Row():
                sas_guidance_scale = gr.Slider(
                    label="SA-Solver Guidance scale",
                    minimum=1,
                    maximum=10,
                    step=0.1,
                    value=3,
                )
                sas_inference_steps = gr.Slider(
                    label="SA-Solver inference steps",
                    minimum=10,
                    maximum=40,
                    step=1,
                    value=25,
                )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[result, seed],
        fn=generate,
        cache_examples=CACHE_EXAMPLES,
    )

    use_negative_prompt.change(
        fn=lambda x: gr.update(visible=x),
        inputs=use_negative_prompt,
        outputs=negative_prompt,
        api_name=False,
    )

    gr.on(
        triggers=[
            prompt.submit,
            negative_prompt.submit,
            run_button.click,
        ],
        fn=generate,
        inputs=[
            prompt,
            negative_prompt,
            style_selection,
            use_negative_prompt,
            num_imgs,
            seed,
            width,
            height,
            schedule,
            dpms_guidance_scale,
            sas_guidance_scale,
            dpms_inference_steps,
            sas_inference_steps,
            randomize_seed,
        ],
        outputs=[result, seed],
        api_name="run",
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()
    # demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=11900, debug=True)