import gradio as gr
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import torch
from PIL import Image
import random

model_id = "CompVis/stable-diffusion-v1-4"  # Daha hafif bir model
lora_model_id = "codermert/mert_flux"  # Your LoRA model

pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cpu")  # CPU'ya taşıyoruz
pipe.load_lora_weights(lora_model_id)
pipe.safety_checker = None  # Safety checker'ı devre dışı bırakıyoruz

def generate_image(prompt, negative_prompt, steps, cfg_scale, seed, strength):
    if seed == -1:
        seed = random.randint(1, 1000000000)
    
    generator = torch.Generator().manual_seed(seed)
    
    with torch.no_grad():
        image = pipe(
            prompt=prompt,
            negative_prompt=negative_prompt,
            num_inference_steps=steps,
            guidance_scale=cfg_scale,
            generator=generator,
            cross_attention_kwargs={"scale": strength},
        ).images[0]
    
    return image, seed

css = """
#app-container {
    max-width: 800px;
    margin-left: auto;
    margin-right: auto;
}
"""

examples = [
    ["A beautiful landscape with mountains and a lake", "ugly, deformed"],
    ["A futuristic cityscape at night", "daytime, rural"],
    ["A portrait of a smiling person in a colorful outfit", "monochrome, frowning"],
]

with gr.Blocks(theme='default', css=css) as app:
    gr.HTML("<center><h1>Mert Flux LoRA Explorer (CPU Version)</h1></center>")
    with gr.Column(elem_id="app-container"):
        with gr.Row():
            text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2)
            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What to avoid in the image", lines=2)
        with gr.Row():
            with gr.Column():
                steps = gr.Slider(label="Sampling steps", value=30, minimum=10, maximum=50, step=1)
                cfg_scale = gr.Slider(label="CFG Scale", value=7.5, minimum=1, maximum=15, step=0.5)
            with gr.Column():
                strength = gr.Slider(label="LoRA Strength", value=0.75, minimum=0, maximum=1, step=0.01)
                seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)

        with gr.Row():
            generate_button = gr.Button("Generate", variant='primary')
        with gr.Row():
            image_output = gr.Image(type="pil", label="Generated Image", show_download_button=True)
        with gr.Row():
            seed_output = gr.Number(label="Seed Used")

        gr.Examples(examples=examples, inputs=[text_prompt, negative_prompt])

        generate_button.click(
            generate_image, 
            inputs=[text_prompt, negative_prompt, steps, cfg_scale, seed, strength], 
            outputs=[image_output, seed_output]
        )

app.launch()