app.py

#!/usr/bin/env python
#patch 0.01yle(collage_style, prompt, negative_prompt)
import os
import random
import uuid

import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL
from huggingface_hub import snapshot_download

huggingface_token = os.getenv("HUGGINGFACE_TOKEN")

model_path = snapshot_download(
    repo_id="stabilityai/stable-diffusion-3-medium", 
    revision="refs/pr/26",
    repo_type="model", 
    ignore_patterns=["*.md", "*.gitattributes"],
    local_dir="stable-diffusion-3-medium",
    token=huggingface_token, # yeni bir token-id yazın.
)

DESCRIPTION = """# Stable Diffusion 3"""
if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"

MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = False
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
USE_TORCH_COMPILE = False
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"

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

pipe = StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)

# Define styles and collage templates
style_list = [
    {
        "name": "3840 x 2160",
        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
    },
    {
        "name": "2560 x 1440",
        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
    },
    {
        "name": "3D Model",
        "prompt": "professional 3d model {prompt}. octane render, highly detailed, volumetric, dramatic lighting",
        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
    },
]

collage_style_list = [
    {
        "name": "B & W",
        "prompt": "black and white collage of {prompt}. monochromatic, timeless, classic, dramatic contrast",
        "negative_prompt": "colorful, vibrant, bright, flashy",
    },
    {
        "name": "Polaroid",
        "prompt": "collage of polaroid photos featuring {prompt}. vintage style, high contrast, nostalgic, instant film aesthetic",
        "negative_prompt": "digital, modern, low quality, blurry",
    },
    {
        "name": "Watercolor",
        "prompt": "watercolor collage of {prompt}. soft edges, translucent colors, painterly effects",
        "negative_prompt": "digital, sharp lines, solid colors",
    },
    {
        "name": "Cinematic",
        "prompt": "cinematic collage of {prompt}. film stills, movie posters, dramatic lighting",
        "negative_prompt": "static, lifeless, mundane",
    },
    {
        "name": "Nostalgic",
        "prompt": "nostalgic collage of {prompt}. retro imagery, vintage objects, sentimental journey",
        "negative_prompt": "contemporary, futuristic, forward-looking",
    },
    {
        "name": "Vintage",
        "prompt": "vintage collage of {prompt}. aged paper, sepia tones, retro imagery, antique vibes",
        "negative_prompt": "modern, contemporary, futuristic, high-tech",
    },
    {
        "name": "Scrapbook",
        "prompt": "scrapbook style collage of {prompt}. mixed media, hand-cut elements, textures, paper, stickers, doodles",
        "negative_prompt": "clean, digital, modern, low quality",
    },
    {
        "name": "NeoNGlow",
        "prompt": "neon glow collage of {prompt}. vibrant colors, glowing effects, futuristic vibes",
        "negative_prompt": "dull, muted colors, vintage, retro",
    },
    {
        "name": "Geometric",
        "prompt": "geometric collage of {prompt}. abstract shapes, colorful, sharp edges, modern design, high quality",
        "negative_prompt": "blurry, low quality, traditional, dull",
    },
    {
        "name": "Thematic",
        "prompt": "thematic collage of {prompt}. cohesive theme, well-organized, matching colors, creative layout",
        "negative_prompt": "random, messy, unorganized, clashing colors",
    },
    {
        "name": "Retro Pop",
        "prompt": "retro pop art collage of {prompt}. bold colors, comic book style, halftone dots, vintage ads",
        "negative_prompt": "subdued colors, minimalist, modern, subtle",
    },
    {
        "name": "No Style",
        "prompt": "{prompt}",
        "negative_prompt": "",
    },
]

styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
collage_styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in collage_style_list}
STYLE_NAMES = list(styles.keys())
COLLAGE_STYLE_NAMES = list(collage_styles.keys())
DEFAULT_STYLE_NAME = "3840 x 2160"
DEFAULT_COLLAGE_STYLE_NAME = "B & W"

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

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

@spaces.GPU(enable_queue=True)
def generate(
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    style: str = DEFAULT_STYLE_NAME,
    collage_style: str = DEFAULT_COLLAGE_STYLE_NAME,
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 7,
    randomize_seed: bool = False,
    num_inference_steps=30,
    NUM_IMAGES_PER_PROMPT=1,
    use_resolution_binning: bool = True,
    progress=gr.Progress(track_tqdm=True),
):
    pipe.to(device)
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator().manual_seed(seed)
    
    if collage_style != "No Style":
        prompt, negative_prompt = apply_style(collage_style, prompt, negative_prompt)
    else:
        prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
    
    if not use_negative_prompt:
        negative_prompt = None  # type: ignore
    
    output = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        width=width,
        height=height,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
        output_type="pil",
    ).images

    return output

examples = [
    "A red sofa on top of a white building.",
    "A cardboard which is large and sits on a theater stage.",
    "A painting of an astronaut riding a pig wearing a tutu holding a pink umbrella.",
    "Studio photograph closeup of a chameleon over a black background.",
    "Closeup portrait photo of beautiful goth woman, makeup.",
    "A living room, bright modern Scandinavian style house, large windows.",
    "Portrait photograph of an anthropomorphic tortoise seated on a New York City subway train.",
    "Batman, cute modern Disney style, Pixar 3d portrait, ultra detailed, gorgeous, 3d zbrush, trending on dribbble, 8k render.",
    "Cinnamon bun on the plate, watercolor painting, detailed, brush strokes, light palette, light, cozy.",
    "A lion, colorful, low-poly, cyan and orange eyes, poly-hd, 3d, low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition.",
    "Long exposure photo of Tokyo street, blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed.",
    "A glamorous digital magazine photoshoot, a fashionable model wearing avant-garde clothing, set in a futuristic cyberpunk roof-top environment, with a neon-lit city background, intricate high fashion details, backlit by vibrant city glow, Vogue fashion photography.",
    "Masterpiece, best quality, girl, collarbone, wavy hair, looking at viewer, blurry foreground, upper body, necklace, contemporary, plain pants, intricate, print, pattern, ponytail, freckles, red hair, dappled sunlight, smile, happy."
]

css = '''
.gradio-container{max-width: 1000px !important}
h1{text-align:center}
'''
with gr.Blocks(css=css) as demo:
    with gr.Row():
        with gr.Column():
            gr.HTML(
            """
            <h1 style='text-align: center'>
            Stable Diffusion 3 Medium 
            </h1>
            """
        )
            gr.HTML(
                """
               
                """
        )
    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", elem_id="gallery", show_label=False)
    with gr.Accordion("Advanced options", open=False):
        with gr.Row():
            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
                visible=True,
            )
        style_selection = gr.Dropdown(
            label="Style",
            choices=STYLE_NAMES,
            value=DEFAULT_STYLE_NAME,
        )
        collage_style_selection = gr.Dropdown(
            label="Collage Template",
            choices=COLLAGE_STYLE_NAMES,
            value=DEFAULT_COLLAGE_STYLE_NAME,
        )
        seed = gr.Slider(
            label="Seed",
            minimum=0,
            maximum=MAX_SEED,
            step=1,
            value=0,
        )
        steps = gr.Slider(
            label="Steps",
            minimum=0,
            maximum=60,
            step=1,
            value=30,
        )
        number_image = gr.Slider(
            label="Number of Image",
            minimum=1,
            maximum=4,
            step=1,
            value=2,
        )
        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():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=10,
                step=0.1,
                value=7.0,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        outputs=[result],
        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,
            use_negative_prompt,
            style_selection,
            collage_style_selection,
            seed,
            width,
            height,
            guidance_scale,
            randomize_seed,
            steps,
            number_image,
        ],
        outputs=[result],
        api_name="run",
    )

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