Delete app.py

app.py

@@ -1,220 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-import spaces
-import torch
-from diffusers import DiffusionPipeline
-from PIL import Image
-import uuid
-from typing import Tuple
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-style_list = [
-    {
-        "name": "8K",
-        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-    },
-    {
-        "name": "4K",
-        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-    },
-    {
-        "name": "HD",
-        "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-    },
-    {
-        "name": "BW",
-        "prompt": "black and white collage of {prompt}. monochromatic, timeless, classic, dramatic contrast",
-    },
-    {
-        "name": "Polar",
-        "prompt": "collage of polaroid photos featuring {prompt}. vintage style, high contrast, nostalgic, instant film aesthetic",
-    },
-    {
-        "name": "Mustard",
-        "prompt": "Duotone style Mustard applied to {prompt}",
-    },
-    {
-        "name": "Cinema",
-        "prompt": "cinematic collage of {prompt}. film stills, movie posters, dramatic lighting",
-    },
-    {
-        "name": "Coral",
-        "prompt": "Duotone style Coral applied to {prompt}",
-    },
-    {
-        "name": "Scrap",
-        "prompt": "scrapbook style collage of {prompt}. mixed media, hand-cut elements, textures, paper, stickers, doodles",
-    },
-    {
-        "name": "Fuchsia",
-        "prompt": "Duotone style Fuchsia tone applied to {prompt}",
-    },
-    {
-        "name": "Violet",
-        "prompt": "Duotone style Violet applied to {prompt}",
-    },
-    {
-        "name": "Pastel",
-        "prompt": "Duotone style Pastel applied to {prompt}",
-    },
-    {
-        "name": "Style Zero",
-        "prompt": "{prompt}",
-    },
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 530px;
-}
-"""
-
-styles = {k["name"]: k["prompt"] for k in style_list}
-DEFAULT_STYLE_NAME = "Style Zero"
-STYLE_NAMES = list(styles.keys())
-
-def apply_style(style_name: str, positive: str) -> str:
-    if style_name in styles:
-        p = styles[style_name]
-        positive = p.format(prompt=positive)
-    return positive
-
-def set_wallpaper_size(size):
-    if size == "Mobile (1080x1920)":
-        return 1080, 1920
-    elif size == "Desktop (1920x1080)":
-        return 1920, 1080
-    elif size == "Extented (1920x512)":  
-        return 1920, 512
-    elif size == "Headers (1080x512)":
-        return 1080, 512
-    else:
-        return 1024, 1024  # Default return if none of the conditions are met
-
-@spaces.GPU(duration=60, enable_queue=True)
-def infer(prompt, seed=42, randomize_seed=False, wallpaper_size="Desktop(1920x1080)", num_inference_steps=4, style_name=DEFAULT_STYLE_NAME, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    
-    width, height = set_wallpaper_size(wallpaper_size)
-
-    styled_prompt = apply_style(style_name, prompt)
-    
-    options = {
-        "prompt": styled_prompt,
-        "width": width,
-        "height": height,
-        "guidance_scale": 0.0,
-        "num_inference_steps": num_inference_steps,
-        "generator": generator,
-    }
-    
-    torch.cuda.empty_cache()  
-    images = pipe(**options).images
-
-    grid_img = Image.new('RGB', (width, height))
-    grid_img.paste(images[0], (0, 0))
-
-    unique_name = str(uuid.uuid4()) + ".png"
-    grid_img.save(unique_name)
-    return unique_name, seed
-
-examples = [
-
-    "chocolate dripping from a donut a yellow background",
-    "cold coffee in a cup bokeh --ar 85:128 --style",
-    "an anime illustration of a wiener schnitzel",
-    "a delicious ceviche cheesecake slice, ultra-hd+",  
-]
-
-def load_predefined_images1():
-    predefined_images1 = [
-        "assets/ww.webp",
-        "assets/xx.webp",
-        "assets/yy.webp",
-    ]
-    return predefined_images1
-
-with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 SIM""")
-        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.Row(visible=True):
-        wallpaper_size = gr.Radio(
-            choices=["Mobile (1080x1920)", "Desktop (1920x1080)", "Extented (1920x512)", "Headers (1080x512)", "Default (1024x1024)"],
-            label="Pixel Size(x*y)",
-            value="Default (1024x1024)"
-        )
-
-        with gr.Row(visible=True):
-            style_selection = gr.Radio(
-                show_label=True,
-                container=True,
-                interactive=True,
-                choices=STYLE_NAMES,
-                value=DEFAULT_STYLE_NAME,
-                label="Quality Style",
-            )
-        with gr.Accordion("Advanced Settings", open=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():
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=4,
-                )
-                
-        gr.Examples(
-            examples=examples,
-            fn=infer,
-            inputs=[prompt],
-            outputs=[result, seed],
-            cache_examples=False,
-        )
-
-    gr.on(
-        triggers=[prompt.submit, run_button.click],
-        fn=infer,
-        inputs=[prompt, seed, randomize_seed, wallpaper_size, num_inference_steps, style_selection],
-        outputs=[result, seed]
-    )
-    
-    gr.Markdown("### Image Sample")
-    predefined_gallery = gr.Gallery(label="## Images Sample", columns=3, show_label=False, value=load_predefined_images1())
-
-    gr.Markdown("**Disclaimer/Note:**")
-    
-    gr.Markdown("🍕Model used in the space <a href='https://huggingface.co/black-forest-labs/FLUX.1-schnell' target='_blank'>black-forest-labs/FLUX.1-schnell</a>. More: 12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]")
-    gr.Markdown("⚠️ users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.")
-
-demo.launch()