app.py

import os
import torch
from diffusers import DiffusionPipeline
import gradio as gr

# Load the base model and apply the LoRA weights for super realism
def load_pipeline():
    base_model = "black-forest-labs/FLUX.1-dev"
    lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
    trigger_word = "Super Realism"  # Recommended trigger word

    # Retrieve your Hugging Face token from an environment variable
    hf_token = os.environ.get("HF_TOKEN")

    pipe = DiffusionPipeline.from_pretrained(
        base_model,
        torch_dtype=torch.bfloat16,
        use_auth_token=hf_token  # Use the token stored in your environment variable
    )

    # Load the LoRA weights into the pipeline
    pipe.load_lora_weights(lora_repo)

    # Use GPU if available
    device = "cuda" if torch.cuda.is_available() else "cpu"
    pipe.to(device)
    return pipe

# Instantiate the pipeline once on Space startup
pipe = load_pipeline()

# Define a function for image generation
def generate_image(prompt, seed, width, height, guidance_scale, randomize_seed):
    # If randomize_seed is selected, allow the model to generate a random seed
    if randomize_seed:
        seed = None

    # Ensure the prompt includes realism trigger words if needed
    if "realistic" not in prompt.lower() and "realism" not in prompt.lower():
        prompt += " realistic, realism"

    # Generate the image
    output = pipe(
        prompt=prompt,
        seed=seed,
        width=width,
        height=height,
        guidance_scale=guidance_scale
    )
    return output.images[0]

# Set up Gradio interface
iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.inputs.Textbox(
            lines=2, 
            label="Prompt", 
            placeholder="Enter your prompt, e.g., 'A tiny astronaut hatching from an egg on the moon, 4k, planet theme'"
        ),
        gr.inputs.Slider(0, 10000, step=1, default=0, label="Seed (0 for random)"),
        gr.inputs.Slider(256, 1024, step=64, default=1024, label="Width"),
        gr.inputs.Slider(256, 1024, step=64, default=1024, label="Height"),
        gr.inputs.Slider(1, 20, step=0.5, default=6, label="Guidance Scale"),
        gr.inputs.Checkbox(default=True, label="Randomize Seed")
    ],
    outputs=gr.outputs.Image(type="pil", label="Generated Image"),
    title="Flux Super Realism LoRA Demo",
    description=(
        "This demo uses the Flux Super Realism LoRA model for ultra-realistic image generation. "
        "You can use the trigger word 'Super Realism' (recommended) along with other realism-related words "
        "to guide the generation process."
    ),
)

if __name__ == "__main__":
    iface.launch(share=True)