app.py

import gradio as gr
import torch
import os
import random
import numpy as np
from diffusers import DiffusionPipeline
from safetensors.torch import load_file
from spaces import GPU  # Remove if not in HF Space

# 1. Model and LoRA Loading (Before Gradio)
device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
token = os.getenv("HF_TOKEN")
model_repo_id = "stabilityai/stable-diffusion-3.5-large"

try:
    pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype, use_auth_token=token) # No need to check for token existence, diffusers handles this
    pipe = pipe.to(device)

    lora_filename = "lora_trained_model.safetensors"  # EXACT filename of your LoRA
    lora_path = os.path.join("./", lora_filename)

    if os.path.exists(lora_path):
        lora_weights = load_file(lora_path)
        text_encoder = pipe.text_encoder
        text_encoder.load_state_dict(lora_weights, strict=False)
        print(f"LoRA loaded successfully from: {lora_path}")
    else:
        print(f"Error: LoRA file not found at: {lora_path}")
        exit()  # Stop if LoRA is not found

    print("Stable Diffusion model and LoRA loaded successfully!")

except Exception as e:
    print(f"Error loading model or LoRA: {e}")
    exit()


MAX_SEED = 99999999999
MAX_IMAGE_SIZE = 1024

@GPU(duration=65)  # Only if in HF Space
def infer(
    prompt,
    negative_prompt="",
    seed=42,
    randomize_seed=False,
    width=1024,
    height=1024,
    guidance_scale=4.5,
    num_inference_steps=40,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)  # Generate a new seed if randomize_seed is True

    generator = torch.Generator(device=device).manual_seed(seed)  # Ensure the generator is on the correct device
    
    try:
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            generator=generator,
        ).images[0]
        return image, seed  # Don't return seed back to the UI
    except Exception as e:
        print(f"Error during image generation: {e}")  # Print error for debugging
        return f"Error: {e}", seed  # Return error to Gradio interface

examples = [
        "A capybara wearing a suit holding a sign that reads Hello World",
]

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(" # [Stable Diffusion 3.5 Large (8B)](https://huggingface.co/stabilityai/stable-diffusion-3.5-large)")
        gr.Markdown("[Learn more](https://stability.ai/news/introducing-stable-diffusion-3-5) about the Stable Diffusion 3.5 series. Try on [Stability AI API](https://platform.stability.ai/docs/api-reference#tag/Generate/paths/~1v2beta~1stable-image~1generate~1sd3/post), or [download model](https://huggingface.co/stabilityai/stable-diffusion-3.5-large) to run locally with ComfyUI or diffusers.")
        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")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )

            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=32,
                    value=1024, 
                )

                height = gr.Slider(
                    label="Height",
                    minimum=512,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=7.5,
                    step=0.1,
                    value=4.5,
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=40, 
                )

        gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True, cache_mode="lazy")
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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