app.py

import gradio as gr
import numpy as np
import random
# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

# --- Model and Device Configuration ---

# Global dictionary to cache loaded models, preventing re-loading.
pipelines = {}
# Mapping of user-friendly names to Hugging Face model repository IDs.
MODEL_MAP = {
    "SDXL-Turbo": "stabilityai/sdxl-turbo",
    "Nano-Banana": "emilianJR/nano-banana-base-1.0"
}

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# This function loads a model if it's not already in our cache
def get_pipeline(model_name: str):
    """Loads and caches a diffusion pipeline based on the model name."""
    repo_id = MODEL_MAP[model_name]
    if repo_id not in pipelines:
        print(f"Loading model: {repo_id}...")
        pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch_dtype, variant="fp16" if torch.cuda.is_available() else "fp32")
        pipe.to(device)
        pipelines[repo_id] = pipe
        print("Model loaded successfully.")
    return pipelines[repo_id]

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

# --- Inference Function ---

# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    model_selection,  # New parameter to select the model
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    # Load the selected pipeline
    pipe = get_pipeline(model_selection)
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
        
    generator = torch.Generator(device=device).manual_seed(seed)
    
    # SDXL-Turbo does not use guidance_scale, so we set it to 0.0 if that model is selected.
    # Other models might need it.
    effective_guidance_scale = 0.0 if model_selection == "SDXL-Turbo" else guidance_scale

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=effective_guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]
    
    return image, seed

# --- UI Helper Function ---

def update_settings_for_model(model_selection: str):
    """Updates the UI with recommended settings for the chosen model."""
    if model_selection == "SDXL-Turbo":
        # SDXL-Turbo works best with low steps and no guidance
        return gr.Slider(value=0.0), gr.Slider(value=2)
    elif model_selection == "Nano-Banana":
        # A more standard SDXL setup
        return gr.Slider(value=7.5), gr.Slider(value=25)
    return gr.Slider(), gr.Slider() # Default empty update

# --- Gradio UI Layout ---

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# Text-to-Image with Model Switching")
        
        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, variant="primary")
            
        model_selection = gr.Radio(
            label="Select Model",
            choices=list(MODEL_MAP.keys()),
            value="SDXL-Turbo",
        )
            
        result = gr.Image(label="Result", show_label=False, type="pil")

        with gr.Accordion("Advanced Settings", open=False):
            # 1. Added Gemini API Key input box
            gemini_api_key = gr.Textbox(
                label="Gemini API Key",
                placeholder="Enter your Gemini API key here",
                type="password",
                visible=True, # Set to True to make it visible
            )
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
            )
            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=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512, # Changed default to 512 for SDXL-Turbo
                )
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=512, # Changed default to 512 for SDXL-Turbo
                )
            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=20.0,
                    step=0.1,
                    value=0.0,  # Default for SDXL-Turbo
                )
                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=2,  # Default for SDXL-Turbo
                )
        gr.Examples(examples=examples, inputs=[prompt])

    # --- Event Handlers ---
    
    # Main inference trigger
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            model_selection,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )
    
    # Trigger to update settings when the model selection changes
    model_selection.change(
        fn=update_settings_for_model,
        inputs=model_selection,
        outputs=[guidance_scale, num_inference_steps]
    )


if __name__ == "__main__":
    demo.launch(debug=True)