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files/demo.txt

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-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",
-    "Vector illustration of a horse, vector graphic design with flat colors on an brown background in the style of vector art, using simple shapes and graphics with simple details, professionally designed as a tshirt logo ready for print on a white background. --ar 89:82 --v 6.0 --style raw",
-    "Man in brown leather jacket posing for camera, in the style of sleek and stylized, clockpunk, subtle shades, exacting precision, ferrania p30  --ar 67:101 --v 5",
-    "Commercial photography, giant burger, white lighting, studio light, 8k octane rendering, high resolution photography, insanely detailed, fine details, on white isolated plain, 8k, commercial photography, stock photo, professional color grading, --v 4 --ar 9:16 "
-
-    
-]
-
-#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()