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+# TEXT 2 IMAGE PLAYGROUND Documentation
+
+## Overview
+
+TEXT 2 IMAGE PLAYGROUND is a Gradio-based web application designed to generate images from text prompts using advanced AI models. It offers various model options, customization parameters, and a user-friendly interface for an enhanced user experience.
+
+## Features
+
+- **Model Selection**: Choose from multiple AI models to generate images with different styles and qualities.
+- **Custom Prompts**: Input text prompts to define the content and style of the generated images.
+- **Negative Prompts**: Use negative prompts to avoid unwanted elements in the images.
+- **Image Customization**: Adjust parameters like seed, width, height, guidance scale, and number of inference steps.
+- **Random Seed Generation**: Enable random seed generation for varied outputs.
+- **Image Gallery**: View a gallery of predefined images for inspiration.
+
+## Interface
+
+### Description
+
+```markdown
+## TEXT 2 IMAGE PLAYGROUND 🥠
+```
+
+### CSS
+
+```css
+.gradio-container {
+    max-width: 690px !important;
+}
+h1 {
+    text-align: center;
+}
+footer {
+    visibility: hidden;
+}
+```
+
+### JavaScript
+
+```javascript
+function refresh() {
+    const url = new URL(window.location);
+    if (url.searchParams.get('__theme') !== 'dark') {
+        url.searchParams.set('__theme', 'dark');
+        window.location.href = url.href;
+    }
+}
+```
+
+### Examples
+
+Predefined text prompts for quick testing:
+
+- 3d image, cute girl, in the style of Pixar...
+- Chocolate dripping from a donut against a yellow background...
+- Illustration of A starry night camp in the mountains...
+- Man in brown leather jacket posing for camera...
+- Commercial photography, giant burger...
+
+## Model Options
+
+```python
+MODEL_OPTIONS = {
+    "Realism : V4.0_Lightning🔥": "SG161222/RealVisXL_V4.0_Lightning",
+    "Detailed/SOTA : Mobius🚀": "Corcelio/mobius",
+    "Anime : Cagliostrolab🍺": "cagliostrolab/animagine-xl-3.1"
+}
+```
+
+## Configuration
+
+Environment variables and configurations:
+
+```python
+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")
+```
+
+## Model Loading and Preparation
+
+Function to load and prepare models:
+
+```python
+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)
+    
+    if USE_TORCH_COMPILE:
+        pipe.compile()
+    
+    if ENABLE_CPU_OFFLOAD:
+        pipe.enable_model_cpu_offload()
+    
+    return pipe
+
+models = {key: load_and_prepare_model(value) for key, value in MODEL_OPTIONS.items()}
+```
+
+## Image Generation
+
+Function to generate images based on user inputs:
+
+```python
+@spaces.GPU(duration=60, enable_queue=True)
+def generate(
+    model_choice: str,
+    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,  
+    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)
+
+    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]
+        images.extend(pipe(**batch_options).images)
+
+    image_paths = [save_image(img) for img in images]
+    return image_paths, seed
+```
+
+## Load Predefined Images
+
+Function to load predefined images for the gallery:
+
+```python
+def load_predefined_images():
+    predefined_images = [
+        "assets/1.png",
+        "assets/2.png",
+        "assets/3.png",
+        "assets/4.png",
+        "assets/5.png",
+        "assets/6.png",
+        "assets/7.png",
+        "assets/8.png",
+        "assets/9.png",
+        "assets/10.png",
+        "assets/11.png",
+        "assets/12.png",
+    ]
+    return predefined_images
+```
+
+## Gradio Interface
+
+Creating the Gradio interface:
+
+```python
+with gr.Blocks(css=css, theme="bethecloud/storj_theme", js=js_func) as demo:
+    gr.Markdown(DESCRIPTIONx)    
+    with gr.Row():
+        prompt = gr.Text(
+            label="Prompt",
+            show_label=False,
+            max_lines=1,
+            placeholder="Enter your prompt",
+            value="Chocolate dripping from a donut against a yellow background, in the style of brocore, hyper-realistic oil --ar 2:3 --q 2 --s 750 --v 5  --ar 2:3 --q 2 --s 750 --v 5",
+            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="Realism : V4.0_Lightning🔥"
+        )
+
+    with gr.Accordion("Advanced options", open=True):
+        num_images = gr.Slider(
+            label="Number of Images",
+            minimum=1,
+            maximum=1,
+            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=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=35,
+                step=1,
+                value=20,
+            )
+
+    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=[
+            model_choice,
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+            randomize_seed,
+            num_images
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )
+
+    with gr.Column(scale=3):
+        gr.Markdown("### Image Gallery")
+        predefined_gallery = gr.Gallery(label="Image Gallery", columns=4, show_label=False, value=load
+
+_predefined_images())
+           
+if __name__ == "__main__":
+    demo.queue(max_size=40).launch(show_api=False)
+```
+
+## Running the Application
+
+To run the application, simply execute the script. The interface will launch and be accessible via a web browser.
+
+```sh
+python app.py
+```