app.py

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, StableDiffusion3Img2ImgPipeline
from huggingface_hub import snapshot_download

# Ctrl+F for "spaces" to use with ZeroGPU

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, # type a new token-id.
    )

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")

def load_pipeline(pipeline_type):
    if pipeline_type == "text2img":
        return StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)
    elif pipeline_type == "img2img":
        return StableDiffusion3Img2ImgPipeline.from_pretrained(model_path, torch_dtype=torch.float16)

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
def generate(
    prompt:str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    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 = load_pipeline("text2img")
    pipe.to(device)
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator().manual_seed(seed)
    
    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="battery",
    ).images

    return output


# @spaces.GPU
def img2img_generate(
    prompt:str,
    init_image: gr.Image,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 0,
    guidance_scale: float = 7,
    randomize_seed: bool = False,
    num_inference_steps=30,
    strength: float = 0.8,
    NUM_IMAGES_PER_PROMPT=1,
    use_resolution_binning: bool = True,
    progress=gr.Progress(track_tqdm=True),
):
    pipe = load_pipeline("img2img")
    pipe.to(device)
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator().manual_seed(seed)
    
    if not use_negative_prompt:
        negative_prompt = None # type: ignore
    
    init_image = init_image.resize((768, 768))
    
    output = pipe(
        prompt=prompt,
        image=init_image,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        generator=generator,
        strength=strength,
        num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
        output_type="battery",
    ).images

    return output


examples = [
    "neon holography crystal cat",
    "a cat eating a piece of cheese",
    "an astronaut riding a horse in space",
    "a cartoon of a boy playing with a tiger",
    "a cute robot artist painting on an easel, concept art",
    "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
]

css = '''
.gradio-container{max-width: 1000px !important}
h1{text-align:center}
'''
with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
    with gr.Row():
        with gr.Column():
            gr.HTML(
            """
            <h1 style='text-align: center'>
            Stable Diffusion 3 
            </h1>
            """
            )
            gr.HTML(
                """
                <h3 style='text-align: center'>
                </h3>
                """
            )
    
    with gr.Tabs():
        with gr.TabItem("Text to Image"):
            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",
                        visible=True,
                    )
                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=25,
                )
                number_image = gr.Slider(
                    label="Number of Images",
                    minimum=1,
                    maximum=4,
                    step=1,
                    value=1,
                )
                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,
                    seed,
                    width,
                    height,
                    guidance_scale,
                    randomize_seed,
                    steps,
                    number_image,
                ],
                outputs=[result],
                api_name="run",
            )
        with gr.TabItem("Image to Image"):
            with gr.Group():
                with gr.Row(equal_height=True):
                    with gr.Column(scale=1):
                        img2img_prompt = gr.Text(
                            label="Prompt",
                            show_label=False,
                            max_lines=1,
                            placeholder="Enter your prompt",
                            container=False,
                        )
                        init_image = gr.Image(label="Input Image", type="pil")
                        with gr.Row():
                            img2img_run_button = gr.Button("Generate", variant="primary")
                    with gr.Column(scale=1):
                        img2img_output = gr.Gallery(label="Result", elem_id="gallery")
                with gr.Accordion("Advanced options", open=False):
                    with gr.Row():
                        img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
                        img2img_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,
                        )
                    img2img_seed = gr.Slider(
                        label="Seed",
                        minimum=0,
                        maximum=MAX_SEED,
                        step=1,
                        value=0,
                    )
                    img2img_steps = gr.Slider(
                        label="Steps",
                        minimum=0,
                        maximum=60,
                        step=1,
                        value=25,
                    )
                    img2img_number_image = gr.Slider(
                        label="Number of Images",
                        minimum=1,
                        maximum=4,
                        step=1,
                        value=1,
                    )
                    img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
                    with gr.Row():
                        img2img_guidance_scale = gr.Slider(
                            label="Guidance Scale",
                            minimum=0.1,
                            maximum=10,
                            step=0.1,
                            value=7.0,
                        )
                        strength = gr.Slider(label="Img2Img Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
            
            img2img_use_negative_prompt.change(
                fn=lambda x: gr.update(visible=x),
                inputs=img2img_use_negative_prompt,
                outputs=img2img_negative_prompt,
                api_name=False,
            )
        
            gr.on(
                triggers=[
                    img2img_prompt.submit,
                    img2img_negative_prompt.submit,
                    img2img_run_button.click,
                ],
                fn=img2img_generate,
                inputs=[
                    img2img_prompt,
                    init_image,
                    img2img_negative_prompt,
                    img2img_use_negative_prompt,
                    img2img_seed,
                    img2img_guidance_scale,
                    img2img_randomize_seed,
                    img2img_steps,
                    strength,
                    img2img_number_image,
                ],
                outputs=[img2img_output],
                api_name="img2img_run",
            )
if __name__ == "__main__":
    demo.queue().launch()