Update app.py

app.py

@@ -1,104 +1,297 @@
+import os
+import random
+import uuid
 import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
 import torch
-from diffusers import DALL_E
-from PIL import Image as PILImage
-import concurrent.futures
-
-# Model cache to avoid reloading the model multiple times
-model_cache = {}
-
-def load_model():
-    model_name = "dalle-mini/dalle-mini"  # Using DALL·E Mini model
-    # Check if the model is already cached to avoid reloading every time
-    if model_name in model_cache:
-        return model_cache[model_name]
-
-    print(f"Loading model: {model_name}")
-    try:
-        # Select device (CPU only for ZeroGPU plan)
-        device = "cpu"  # Set to CPU, as you don't have GPU access
-
-        # Load the model with float32 (since float16 is not supported on CPU)
-        model = DALL_E.from_pretrained(model_name, torch_dtype=torch.float32)
-        model.to(device)
-
-        # Cache the model for future use
-        model_cache[model_name] = model
-        print("Model loaded successfully.")
-        return model
-    except Exception as e:
-        print(f"Error loading model: {e}")
-        return None
-
-# Function to generate the image with a timeout
-def generate_image_with_timeout(prompt):
-    timeout = 180  # Timeout after 180 seconds
-
-    try:
-        # Use ThreadPoolExecutor to handle the timeout
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            future = executor.submit(generate_image, prompt)
-            return future.result(timeout=timeout)  # Will raise TimeoutError if the process exceeds timeout
-
-    except concurrent.futures.TimeoutError:
-        return "Error: The image generation timed out. Please try again."
-
-# Function to generate the image
-def generate_image(prompt):
-    model = load_model()
-
-    if model is None:
-        return "Error loading the model."
-
-    try:
-        # Generate the image from the prompt
-        with torch.no_grad():
-            output = model(prompt)
-            image = output.images[0]  # Assuming the first image is the one we need
-            image = PILImage.fromarray(image)  # Convert to PIL image format for Gradio
-        return image
-    except Exception as e:
-        print(f"Error generating image: {e}")
-        return "Error generating the image."
-
-# Define the Gradio interface using gr.Blocks
-def create_gradio_interface():
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        gr.Markdown("""
-            <h1 style="
-                text-align: center;
-                color: white;
-                font-weight: bold;
-                text-transform: uppercase;
-                text-decoration: underline;
-                margin-top: 30px;
-                font-family: 'Arial', sans-serif;
-                background: linear-gradient(45deg, #ff6b6b, #f06595);
-                padding: 10px 20px;
-                border-radius: 15px;
-                box-shadow: 0px 4px 8px rgba(0, 0, 0, 0.3);
-            ">
-                SNAPSCRIBE
-            </h1>
-        """)
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+from typing import Tuple
 
-        with gr.Row():
-            with gr.Column(scale=3, min_width=300):  # Changed scale to integer
-                prompt_input = gr.Textbox(label="Enter your prompt here", placeholder="e.g., A futuristic city skyline")
-                submit_button = gr.Button("Generate Image")
+css = '''
+.gradio-container{max-width: 575px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+'''
+
+DESCRIPTIONXX = """## TEXT 2 IMAGE🥠"""
+
+examples = [
+    "A tiny astronaut hatching from an egg on the moon, 4k, planet theme, --style raw5 --v 6.0",
+    "An anime-style illustration of a delicious, golden-brown wiener schnitzel on a plate, served with fresh lemon slices, parsley --style raw5",
+    "Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K, Photo-Realistic",
+    "A cat holding a sign that says hello world --ar 85:128 --v 6.0 --style raw"
+]
+
+MODEL_OPTIONS = {
+    "LIGHTNING V5.0": "SG161222/RealVisXL_V5.0_Lightning",
+    "LIGHTNING V4.0": "SG161222/RealVisXL_V4.0_Lightning",
+}
+
+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"))
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+style_list = [
+    {
+        "name": "3840 x 2160",
+        "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
+        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
+    },
+    {
+        "name": "2560 x 1440",
+        "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
+        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
+    },
+    {
+        "name": "HD+",
+        "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic",
+        "negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly",
+    },
+    {
+        "name": "Style Zero",
+        "prompt": "{prompt}",
+        "negative_prompt": "",
+    },
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_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, negative: str = "") -> Tuple[str, str]:
+    if style_name in styles:
+        p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    else:
+        p, n = styles[DEFAULT_STYLE_NAME]
+
+    if not negative:
+        negative = ""
+    return p.replace("{prompt}", positive), n + negative
+
+
+def load_and_prepare_model(model_id):
+    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)
+
+    # Enable memory-efficient attention
+    pipe.enable_attention_slicing()
+
+    # Compile the model if enabled
+    if USE_TORCH_COMPILE:
+        pipe.compile()
+
+    # CPU offload only if explicitly set
+    if ENABLE_CPU_OFFLOAD and device.type == "cuda":
+        pipe.enable_model_cpu_offload()
+
+    return pipe
+
+
+# Preload and compile both models
+models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()}
 
-            with gr.Column(scale=7, min_width=600):  # Changed scale to integer
-                output_image = gr.Image(label="Generated Image", height=640)
+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(
+        model_choice: str,
+        prompt: str,
+        negative_prompt: str = "",
+        use_negative_prompt: bool = False,
+        style_selection: str = DEFAULT_STYLE_NAME,
+        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,
+        progress=gr.Progress(track_tqdm=True),
+):
+    global models
+    pipe = models[model_choice]
+
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator(device=device).manual_seed(seed)
+
+    prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
+
+    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",
+    }
+
+    if use_resolution_binning:
+        options["use_resolution_binning"] = True
+
+    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]
+        with torch.cuda.amp.autocast():
+            images.extend(pipe(**batch_options).images)
+
+    image_paths = [save_image(img) for img in images]
+    return image_paths, seed
+
+
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown(DESCRIPTIONXX)
+    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.Row():
+        model_choice = gr.Dropdown(
+            label="Model Selection⬇️",
+            choices=list(MODEL_OPTIONS.keys()),
+            value="LIGHTNING V5.0"
+        )
+
+    with gr.Accordion("Advanced options", open=False, visible=False):
+        style_selection = gr.Radio(
+            show_label=True,
+            container=True,
+            interactive=True,
+            choices=STYLE_NAMES,
+            value=DEFAULT_STYLE_NAME,
+            label="Quality Style",
+        )
+        num_images = gr.Slider(
+            label="Number of Images",
+            minimum=1,
+            maximum=5,
+            step=1,
+            value=1,
+        )
+        with gr.Row():
+            with gr.Column(scale=1):
+                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():
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=8,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=MAX_IMAGE_SIZE,
+                step=8,
+                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=60,
+                step=1,
+                value=28,
+            )
 
-        submit_button.click(fn=generate_image_with_timeout, inputs=[prompt_input], outputs=output_image)
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        cache_examples=False
+    )
 
-        gr.Markdown("""
-            <div style="position: relative; left: 0; bottom: 0; width: 100%; background-color: #0B0F19; color: white; text-align: center; padding: 10px 0;">
-                <p>Developed with ❤ by Aklavya (Bucky)</p>
-            </div>
-        """)
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
 
-    demo.launch()  # Removed `share=True`
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            model_choice,
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            style_selection,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed,
+            num_images,
+        ],
+        outputs=[result, seed],
+    )
 
-# Launch the Gradio interface
-create_gradio_interface()
+if __name__ == "__main__":
+    demo.queue(max_size=50).launch(show_api=True)