Create app.txt

app.txt

@@ -0,0 +1,220 @@
+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()