app.py

#!/usr/bin/env python
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 StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
from diffusers import AuraFlowPipeline

css = '''
.gradio-container{max-width: 600px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "Chocolate dripping from a donut against a yellow background, 8k",
    "Illustration of A starry night camp in the mountains, 4k, cinematic --ar 85:128 --v 6.0 --style raw",
    "A photo of a lavender cat, hdr, 4k, --ar 85:128 --v 6.0 --style raw",
    "A delicious ceviche cheesecake slice, 4k, octane render, ray tracing, Ultra-High-Definition"
]

MODEL_OPTIONS = {
    "Lightning": "SG161222/RealVisXL_V4.0_Lightning",
    "Turbovision": "SG161222/RealVisXL_V3.0_Turbo",
    "AuraFlow": "fal/AuraFlow"
}

MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1"))

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

def load_and_prepare_model(model_id):
    if model_id == "fal/AuraFlow":
        pipe = AuraFlowPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
        ).to(device)
    else:
        pipe = StableDiffusionXLPipeline.from_pretrained(
            model_id,
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
            use_safetensors=True,
            add_watermarker=False,
        ).to(device)
        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
    
    if USE_TORCH_COMPILE:
        pipe.compile()
    
    if ENABLE_CPU_OFFLOAD:
        pipe.enable_model_cpu_offload()
    
    return pipe

# Preload and compile both models
models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()}

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

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(duration=60, enable_queue=True)
def generate(
    model_choice: str,
    prompt: str,
    negative_prompt: str = "",
    use_negative_prompt: bool = False,
    seed: int = 1,
    width: int = 1024,
    height: int = 1024,
    guidance_scale: float = 3,
    num_inference_steps: int = 25,
    randomize_seed: bool = False,
    use_resolution_binning: bool = True, 
    num_images: int = 1,  
    progress=gr.Progress(track_tqdm=True),
):
    global models
    pipe = models[model_choice]
    
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator(device=device).manual_seed(seed)

    options = {
        "prompt": [prompt] * num_images,
        "negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None,
        "width": width,
        "height": height,
        "guidance_scale": guidance_scale,
        "num_inference_steps": num_inference_steps,
        "generator": generator,
        "output_type": "pil",
    }

    if use_resolution_binning:
        options["use_resolution_binning"] = True

    images = []
    for i in range(0, num_images, BATCH_SIZE):
        batch_options = options.copy()
        batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
        if "negative_prompt" in batch_options:
            batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE]
        images.extend(pipe(**batch_options).images)

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

def load_predefined_images():
    predefined_images = [
        "assets/1.png",
        "assets/2.png",
        "assets/3.png",
        "assets/4.png",
        "assets/5.png",
        "assets/6.png",
        "assets/7.png",
        "assets/8.png",
        "assets/9.png",
        "assets/10.png",
        "assets/11.png",
        "assets/12.png",
    ]
    return predefined_images




with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
    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=1, show_label=False) 

    with gr.Row():
        model_choice = gr.Dropdown(
            label="Model Selection",
            choices=list(MODEL_OPTIONS.keys()),
            value="AuraFlow"
        )

    with gr.Accordion("Advanced options", open=True, visible=False):
        num_images = gr.Slider(
            label="Number of Images",
            minimum=1,
            maximum=1,
            step=1,
            value=1,
        )
        with gr.Row():
            with gr.Column(scale=1):
                use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
                negative_prompt = gr.Text(
                    label="Negative prompt",
                    max_lines=5,
                    lines=4,
                    placeholder="Enter a negative prompt",
                    value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
                    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():
            width = gr.Slider(
                label="Width",
                minimum=512,
                maximum=MAX_IMAGE_SIZE,
                step=64,
                value=1024,
            )
            height = gr.Slider(
                label="Height",
                minimum=512,
                maximum=MAX_IMAGE_SIZE,
                step=64,
                value=1024,
            )
        with gr.Row():
            guidance_scale = gr.Slider(
                label="Guidance Scale",
                minimum=0.1,
                maximum=6,
                step=0.1,
                value=3.0,
            )
            num_inference_steps = gr.Slider(
                label="Number of inference steps",
                minimum=1,
                maximum=35,
                step=1,
                value=20,
            )

    gr.Examples(
        examples=examples,
        inputs=prompt,
        cache_examples=False
    )

    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=[
            model_choice,
            prompt,
            negative_prompt,
            use_negative_prompt,
            seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
            randomize_seed,
            num_images
        ],
        outputs=[result, seed],
        api_name="run",
    )
    gr.Markdown("🥠Models used in the playground [[Lightning]](https://huggingface.co./SG161222/RealVisXL_V4.0_Lightning), [[AuraFlow]](https://huggingface.co./fal/AuraFlow) ,[[Turbo]](https://huggingface.co./SG161222/RealVisXL_V3.0_Turbo) for image generation. stable diffusion xl piped (sdxl) model HF. This is the demo space for generating images using the Stable Diffusion XL models, with multi different variants available. ⚠️ users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.")

    gr.Markdown("🥠This is the demo space for generating images using Stable Diffusion with grids, filters, templates, quality styles, and types. Try the sample prompts to generate higher quality images. Try the sample prompts for generating higher quality images.<a href='https://huggingface.co./spaces/prithivMLmods/Top-Prompt-Collection' target='_blank'>Try prompts</a>.")
    gr.Markdown("⚠️ users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.")

    with gr.Column(scale=3):
        gr.Markdown("### Image Gallery")
        predefined_gallery = gr.Gallery(label="Image Gallery", columns=4, show_label=False, value=load_predefined_images())       
        
if __name__ == "__main__":
    demo.queue(max_size=20).launch(show_api=False)