app.py

import os
import random
import uuid
import json
import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler

#Load the HTML content
#html_file_url = "https://prithivmlmods-hamster-static.static.hf.space/index.html"
#html_content = f'<iframe src="{html_file_url}" style="width:100%; height:180px; border:none;"></iframe>'
#html_file_url = "https://prithivmlmods-static-loading-theme.static.hf.space/index.html"

html_file_url = "https://prithivhamster.vercel.app/"
html_content = f'<iframe src="{html_file_url}" style="width:100%; height:400px; border:none"></iframe>'

DESCRIPTIONx = """## STABLE HAMSTER
"""

css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
footer {
    visibility: hidden
}
'''

examples = [
    "3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)",
    "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K",
    "Commercial portrait a beautiful multiracial girl in pink jacket, chiseled cheeks, tearful eyes, domina, low neckline, strapless, chocker, in the style of made of liquid metal, deconstructed tailoring, webcam photography, duckcore, bold curves, chinapunk, Jacquemus and moncler collaboration: white translucent fur sunglasses, hyper-realistic, detailed 8k",
    "A pair of nike Air Jordan snipes | white and red | empty background | commercial shot --s 750"
    
]

#Set an os.Getenv variable
#set VAR_NAME=”VALUE”
#Fetch an environment variable
#echo %VAR_NAME%

MODEL_ID = os.getenv("MODEL_REPO")
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"))  # Allow generating multiple images at once

#Load model outside of function
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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)

# <compile speedup >
if USE_TORCH_COMPILE:
    pipe.compile()

# Offloading capacity (RAM)
if ENABLE_CPU_OFFLOAD:
    pipe.enable_model_cpu_offload()

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(
    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,  # Number of images to generate
    progress=gr.Progress(track_tqdm=True),
):
    seed = int(randomize_seed_fn(seed, randomize_seed))
    generator = torch.Generator(device=device).manual_seed(seed)

    #Options 
    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",
    }

    #VRAM usage Lesser
    if use_resolution_binning:
        options["use_resolution_binning"] = True

    #Images potential batches
    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
#Main gr.Block
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
    gr.Markdown(DESCRIPTIONx)
    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", columns=1, show_label=False) 
    with gr.Accordion("Advanced options", open=False, visible=False):
        num_images = gr.Slider(
            label="Number of Images",
            minimum=1,
            maximum=4,
            step=1,
            value=1,
        )
        with gr.Row():
            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(visible=True):
            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=25,
                step=1,
                value=23,
            )

    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=[
            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.HTML(html_content)
if __name__ == "__main__":
    demo.queue(max_size=40).launch()