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
from diffusers import ConsistencyDecoderVAE, DPMSolverMultistepScheduler, Transformer2DModel, AutoencoderKL, SASolverScheduler
import torch
from typing import Tuple
from datetime import datetime
from peft import PeftModel
from scripts.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-XL-2-1024-MS](https://huggingface.co./PixArt-alpha/PixArt-XL-2-1024-MS) checkpoint.
        #### English prompts ONLY; 提示词仅限英文
        ### <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


def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--is_lora', action='store_true', help='enable lora ckpt loading')
    parser.add_argument('--repo_id', default="PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", type=str)
    parser.add_argument('--lora_repo_id', default=None, type=str)
    parser.add_argument('--model_path', default=None, type=str)
    parser.add_argument(
        '--pipeline_load_from', default="PixArt-alpha/pixart_sigma_sdxlvae_T5_diffusers", type=str,
        help="Download for loading text_encoder, tokenizer and vae "
             "from https://huggingface.co./PixArt-alpha/PixArt-XL-2-1024-MS")
    parser.add_argument('--T5_token_max_length', default=120, type=int, help='max length of tokens for T5')
    return parser.parse_args()


args = get_args()

if torch.cuda.is_available():
    weight_dtype = torch.float16
    T5_token_max_length = args.T5_token_max_length
    model_path = args.model_path
    if 'Sigma' in args.model_path:
        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)

    if not args.is_lora:
        transformer = Transformer2DModel.from_pretrained(
            model_path,
            subfolder='transformer',
            torch_dtype=weight_dtype,
        )
        pipe = PixArtSigmaPipeline.from_pretrained(
            args.pipeline_load_from,
            transformer=transformer,
            torch_dtype=weight_dtype,
            use_safetensors=True,
        )
    else:
        assert args.lora_repo_id is not None
        transformer = Transformer2DModel.from_pretrained(args.repo_id, subfolder="transformer", torch_dtype=torch.float16)
        transformer = PeftModel.from_pretrained(transformer, args.lora_repo_id)
        pipe = PixArtSigmaPipeline.from_pretrained(
            args.repo_id,
            transformer=transformer,
            torch_dtype=torch.float16,
            use_safetensors=True,
        )
        del transformer

    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, seed=''):
    unique_name = f"{str(uuid.uuid4())}_{seed}.png"
    save_path = os.path.join(f'output/online_demo_img/{datetime.now().date()}')
    os.umask(0o000)  # file permission: 666; dir permission: 777
    os.makedirs(save_path, exist_ok=True)
    unique_name = os.path.join(save_path, unique_name)
    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()
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)
    print(f"{PORT}: {model_path}")
    print(prompt)

    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=args.T5_token_max_length,
    ).images

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


examples = [
    "A small cactus with a happy face in the Sahara desert.",
    "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.",
    "stars, water, brilliantly, gorgeous large scale scene, a little girl, in the style of dreamy realism, light gold and amber, blue and pink, brilliantly illuminated in the background.",
    "professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.",
    "beautiful lady, freckles, big smile, blue eyes, short ginger hair, dark makeup, wearing a floral blue vest top, soft light, dark grey background",
    "Spectacular Tiny World in the Transparent Jar On the Table, interior of the Great Hall, Elaborate, Carved Architecture, Anatomy, Symetrical, Geometric and Parameteric Details, Precision Flat line Details, Pattern, Dark fantasy, Dark errie mood and ineffably mysterious mood, Technical design, Intricate Ultra Detail, Ornate Detail, Stylized and Futuristic and Biomorphic Details, Architectural Concept, Low contrast Details, Cinematic Lighting, 8k, by moebius, Fullshot, Epic, Fullshot, Octane render, Unreal ,Photorealistic, Hyperrealism",
    "anthropomorphic profile of the white snow owl Crystal priestess , art deco painting, pretty and expressive eyes, ornate costume, mythical, ethereal, intricate, elaborate, hyperrealism, hyper detailed, 3D, 8K, Ultra Realistic, high octane, ultra resolution, amazing detail, perfection, In frame, photorealistic, cinematic lighting, visual clarity, shading , Lumen Reflections, Super-Resolution, gigapixel, color grading, retouch, enhanced, PBR, Blender, V-ray, Procreate, zBrush, Unreal Engine 5, cinematic, volumetric, dramatic, neon lighting, wide angle lens ,no digital painting blur",
    "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="scripts/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", 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(server_name="0.0.0.0", server_port=PORT, debug=True)