Update app.py

app.py

@@ -1,230 +1,280 @@
-import spaces
-import gradio as gr
-import torch
-from PIL import Image
-from diffusers import DiffusionPipeline
-import random
-import uuid
-from typing import Tuple
-import numpy as np
-
-DESCRIPTIONz = """## FLUX REALPIX 🔥"""
-
-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
-
-MAX_SEED = np.iinfo(np.int32).max
-
-if not torch.cuda.is_available():
-    DESCRIPTIONz += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>"
-
-base_model = "black-forest-labs/FLUX.1-dev"
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
-
-lora_repo = "prithivMLmods/Canopus-LoRA-Flux-FaceRealism"
-trigger_word = "realism"  # Leave trigger_word blank if not used.
-pipe.load_lora_weights(lora_repo)
-
-pipe.to("cuda")
-
-style_list = [
-    {
-        "name": "3840 x 2160",
-        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
-    },
-    {
-        "name": "2560 x 1440",
-        "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": "Style Zero",
-        "prompt": "{prompt}",
-    },
-]
-
-styles = {k["name"]: k["prompt"] for k in style_list}
-
-DEFAULT_STYLE_NAME = "3840 x 2160"
-STYLE_NAMES = list(styles.keys())
-
-def apply_style(style_name: str, positive: str) -> str:
-    return styles.get(style_name, styles[DEFAULT_STYLE_NAME]).replace("{prompt}", positive)
-
-@spaces.GPU(duration=60, enable_queue=True)
-def generate(
-    prompt: str,
-    seed: int = 0,
-    width: int = 1024,
-    height: int = 1024,
-    guidance_scale: float = 3,
-    randomize_seed: bool = False,
-    style_name: str = DEFAULT_STYLE_NAME,
-    progress=gr.Progress(track_tqdm=True),
-):
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-
-    positive_prompt = apply_style(style_name, prompt)
-    
-    if trigger_word:
-        positive_prompt = f"{trigger_word} {positive_prompt}"
-
-    images = pipe(
-        prompt=positive_prompt,
-        width=width,
-        height=height,
-        guidance_scale=guidance_scale,
-        num_inference_steps=16,
-        num_images_per_prompt=1,
-        output_type="pil",
-    ).images
-    image_paths = [save_image(img) for img in images]
-    print(image_paths)
-    return image_paths, seed
-
-
-def load_predefined_images():
-    predefined_images = [
-        "assets/11.png",
-        "assets/22.png",
-        "assets/33.png",
-        "assets/44.png",
-        "assets/55.webp",
-        "assets/66.png",
-        "assets/77.png",
-        "assets/88.png",
-        "assets/99.png",
-    ]
-    return predefined_images
-
-
-
-examples = [
-    "A portrait of an attractive woman in her late twenties with light brown hair and purple, wearing large a a yellow sweater. She is looking directly at the camera, standing outdoors near trees.. --ar 128:85 --v 6.0 --style raw",
-    "A photo of the model wearing a white bodysuit and beige trench coat, posing in front of a train station with hands on head, soft light, sunset, fashion photography, high resolution, 35mm lens, f/22, natural lighting, global illumination. --ar 85:128 --v 6.0 --style raw",
-]
-
-
-css = '''
-.gradio-container{max-width: 575px !important}
-h1{text-align:center}
-footer {
-    visibility: hidden
-}
-'''
-
-with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
-    gr.Markdown(DESCRIPTIONz)  
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt with realism tag!",
-                container=False,
-            )
-            run_button = gr.Button("Run", scale=0)
-        result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
-    
-    with gr.Accordion("Advanced options", open=False, visible=True):
-        seed = gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=MAX_SEED,
-            step=1,
-            value=0,
-            visible=True
-        )
-        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        
-        with gr.Row(visible=True):
-            width = gr.Slider(
-                label="Width",
-                minimum=512,
-                maximum=2048,
-                step=64,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=512,
-                maximum=2048,
-                step=64,
-                value=1024,
-            )
-        
-        with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance Scale",
-                minimum=0.1,
-                maximum=20.0,
-                step=0.1,
-                value=3.0,
-            )
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps",
-                minimum=1,
-                maximum=40,
-                step=1,
-                value=16,
-            )
-
-        style_selection = gr.Radio(
-            show_label=True,
-            container=True,
-            interactive=True,
-            choices=STYLE_NAMES,
-            value=DEFAULT_STYLE_NAME,
-            label="Quality Style",
-        )
-
-
-
-    gr.Examples(
-        examples=examples,
-        inputs=prompt,
-        outputs=[result, seed],
-        fn=generate,
-        cache_examples=False,
-    )
-
-    gr.on(
-        triggers=[
-            prompt.submit,
-            run_button.click,
-        ],
-        fn=generate,
-        inputs=[
-            prompt,
-            seed,
-            width,
-            height,
-            guidance_scale,
-            randomize_seed,
-            style_selection,
-        ],
-        outputs=[result, seed],
-        api_name="run",
-    )
-    
-    gr.Markdown("### Generated Images")
-    predefined_gallery = gr.Gallery(label="Generated Images", columns=3, show_label=False, value=load_predefined_images())
-    gr.Markdown("**Disclaimer/Note:**")
-    
-    gr.Markdown("🔥This space provides realistic image generation, which works better for human faces and portraits. Realistic trigger works properly, better for photorealistic trigger words, close-up shots, face diffusion, male, female characters.")
-   
-    gr.Markdown("🔥users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.")
-    
-if __name__ == "__main__":
+import spaces
+import gradio as gr
+import torch
+from PIL import Image
+from diffusers import DiffusionPipeline
+import random
+import uuid
+from typing import Tuple
+import numpy as np
+
+DESCRIPTIONz = """## FLUX REALPIX 🔥"""
+
+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
+
+MAX_SEED = np.iinfo(np.int32).max
+
+if not torch.cuda.is_available():
+    DESCRIPTIONz += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>"
+
+base_model = "black-forest-labs/FLUX.1-dev"
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
+
+lora_repo = "prithivMLmods/Canopus-LoRA-Flux-FaceRealism"
+trigger_word = "realism"  # Leave trigger_word blank if not used.
+pipe.load_lora_weights(lora_repo)
+
+pipe.to("cuda")
+
+style_list = [
+    {
+        "name": "3840 x 2160",
+        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
+    },
+    {
+        "name": "2560 x 1440",
+        "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": "Style Zero",
+        "prompt": "{prompt}",
+    },
+]
+
+styles = {k["name"]: k["prompt"] for k in style_list}
+
+DEFAULT_STYLE_NAME = "3840 x 2160"
+STYLE_NAMES = list(styles.keys())
+
+def apply_style(style_name: str, positive: str) -> str:
+    return styles.get(style_name, styles[DEFAULT_STYLE_NAME]).replace("{prompt}", positive)
+
+@spaces.GPU(duration=60, enable_queue=True)
+def generate(
+    prompt: str,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+    style_name: str = DEFAULT_STYLE_NAME,
+    progress=gr.Progress(track_tqdm=True),
+):
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
+    positive_prompt = apply_style(style_name, prompt)
+    
+    if trigger_word:
+        positive_prompt = f"{trigger_word} {positive_prompt}"
+
+    images = pipe(
+        prompt=positive_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=16,
+        num_images_per_prompt=1,
+        output_type="pil",
+    ).images
+    image_paths = [save_image(img) for img in images]
+    print(image_paths)
+    return image_paths, seed
+
+
+def load_predefined_images():
+    predefined_images = [
+        "assets/11.png",
+        "assets/22.png",
+        "assets/33.png",
+        "assets/44.png",
+        "assets/55.webp",
+        "assets/66.png",
+        "assets/77.png",
+        "assets/88.png",
+        "assets/99.png",
+    ]
+    return predefined_images
+
+
+
+examples = [
+    "A portrait of an attractive woman in her late twenties with light brown hair and purple, wearing large a yellow sweater. She is looking directly at the camera, standing outdoors near trees.. --ar 128:85 --v 6.0 --style raw",
+    "A photo of the model wearing a white bodysuit and beige trench coat, posing in front of a train station with hands on head, soft light, sunset, fashion photography, high resolution, 35mm lens, f/22, natural lighting, global illumination. --ar 85:128 --v 6.0 --style raw",
+]
+
+
+css = '''
+.gradio-container{max-width: 575px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+@keyframes snow-fall {
+    0% { top: -10px; }
+    100% { top: 100%; }
+}
+
+.snowflake {
+    position: fixed;
+    top: -10px;
+    width: 10px;
+    height: 10px;
+    background: #00f;
+    border-radius: 50%;
+    opacity: 0.8;
+    pointer-events: none;
+    animation: snow-fall linear infinite;
+}
+
+.snowfall {
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 100%;
+    pointer-events: none;
+}
+
+'''
+
+# Add this JavaScript to generate snowfall effect
+javascript = """
+function createSnowflakes() {
+    const snowflakeCount = 50;
+    const snowfallContainer = document.createElement('div');
+    snowfallContainer.classList.add('snowfall');
+    document.body.appendChild(snowfallContainer);
+    
+    for (let i = 0; i < snowflakeCount; i++) {
+        const snowflake = document.createElement('div');
+        snowflake.classList.add('snowflake');
+        snowflake.style.left = `${Math.random() * 100}%`;
+        snowflake.style.animationDuration = `${Math.random() * 3 + 2}s`;
+        snowflake.style.animationDelay = `${Math.random() * 2}s`;
+        snowfallContainer.appendChild(snowflake);
+    }
+}
+
+document.addEventListener('DOMContentLoaded', createSnowflakes);
+"""
+
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown(DESCRIPTIONz)  
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt with realism tag!",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
+    
+    with gr.Accordion("Advanced options", open=False, visible=True):
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+            visible=True
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=2048,
+                step=64,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=2048,
+                step=64,
+                value=1024,
+            )
+        
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=20.0,
+                step=0.1,
+                value=3.0,
+            )
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=1,
+                maximum=40,
+                step=1,
+                value=16,
+            )
+
+        style_selection = gr.Radio(
+            show_label=True,
+            container=True,
+            interactive=True,
+            choices=STYLE_NAMES,
+            value=DEFAULT_STYLE_NAME,
+            label="Quality Style",
+        )
+
+
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate,
+        cache_examples=False,
+    )
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            randomize_seed,
+            style_selection,
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )
+    
+    gr.Markdown("### Generated Images")
+    predefined_gallery = gr.Gallery(label="Generated Images", columns=3, show_label=False, value=load_predefined_images())
+    gr.Markdown("**Disclaimer/Note:**")
+    
+    gr.Markdown("🔥This space provides realistic image generation, which works better for human faces and portraits. Realistic trigger works properly, better for photorealistic trigger words, close-up shots, face diffusion, male, female characters.")
+   
+    gr.Markdown("🔥users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.")
+
+    # Include the JavaScript for snowfall effect
+    gr.HTML(f"<script>{javascript}</script>")
+
+if __name__ == "__main__":
     demo.queue(max_size=40).launch()