app.py

import spaces
import gradio as gr
import numpy as np
import random
import torch
from diffusers import StableDiffusion3Pipeline, SD3Transformer2DModel, FlowMatchEulerDiscreteScheduler
from diffusers import AutoPipelineForText2Image

import spaces

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16

# repo = "dataautogpt3/OpenDalleV1.1"
repo = "SG161222/RealVisXL_V4.0"
repo = "SG161222/RealVisXL_V5.0"

# repo="stabilityai/stable-diffusion-3-medium-tensorrt"

# pipe = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.float16).to(device)
pipeline = AutoPipelineForText2Image.from_pretrained(repo, torch_dtype=torch.float16).to('cuda')        


def adjust_to_nearest_multiple(value, divisor=8):
    """
    Adjusts the input value to the nearest multiple of the divisor.
    
    Args:
    value (int): The value to adjust.
    divisor (int): The divisor to which the value should be divisible. Default is 8.
    Returns:
    int: The nearest multiple of the divisor.
    """
    if value % divisor == 0:
        return value
    else:
        # Round to the nearest multiple of divisor
        return round(value / divisor) * divisor

def adjust_dimensions(height, width):
    """
    Adjusts the height and width to be divisible by 8.
    
    Args:
    height (int): The height to adjust.
    width (int): The width to adjust.
    Returns:
    tuple: Adjusted height and width.
    """
    new_height = adjust_to_nearest_multiple(height)
    new_width = adjust_to_nearest_multiple(width)
    
    return new_height, new_width


MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 4100

@spaces.GPU(duration=60)
def infer(prompt, negative_prompt, 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)

    width = min(width, MAX_IMAGE_SIZE // 2)
    height = min(height, MAX_IMAGE_SIZE // 2)
    height, width = adjust_dimensions(height, width)
        
    generator = torch.Generator().manual_seed(seed)
    image = pipeline(prompt = prompt, 
        negative_prompt = negative_prompt,
        guidance_scale = guidance_scale, 
        num_inference_steps = num_inference_steps, 
        width = width, 
        height = height,
        generator = generator
                    ).images[0]


    
    # 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, seed

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 580px;
}
"""

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # Demo [Stable Diffusion 3 Medium](https://huggingface.co/stabilityai/stable-diffusion-3-medium)
        Learn more about the [Stable Diffusion 3 series](https://stability.ai/news/stable-diffusion-3). Try on [Stability AI API](https://platform.stability.ai/docs/api-reference#tag/Generate/paths/~1v2beta~1stable-image~1generate~1sd3/post), [Stable Assistant](https://stability.ai/stable-assistant), or on Discord via [Stable Artisan](https://stability.ai/stable-artisan). Run locally with [ComfyUI](https://github.com/comfyanonymous/ComfyUI) or [diffusers](https://github.com/huggingface/diffusers)
        """)
        
        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.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
            )
            
            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=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1024,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=64,
                    value=1024,
                )
            
            with gr.Row():
                
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=5.0,
                )
                
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=28,
                )
        
        gr.Examples(
            examples = examples,
            inputs = [prompt]
        )
    gr.on(
        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
        fn = infer,
        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result, seed]
    )

demo.launch()