import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
from live_preview_helpers import flux_pipe_call_that_returns_an_iterable_of_images

device = "cuda" if torch.cuda.is_available() else "cpu"
if device == "cuda" and torch.cuda.is_bf16_supported():
    dtype = torch.bfloat16
else:
    dtype = torch.float16

taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()

pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048

@spaces.GPU(duration=75)
def infer(prompt_text, seed_val=42, randomize_seed_flag=False, width_val=1024, height_val=1024, guidance_scale_val=3.5, num_inference_steps_val=28, progress=gr.Progress(track_tqdm=True)):
    seed = random.randint(0, MAX_SEED) if randomize_seed_flag else seed_val
    generator = torch.Generator(device=device).manual_seed(seed)
    
    for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
        prompt=prompt_text,
        guidance_scale=guidance_scale_val,
        num_inference_steps=num_inference_steps_val,
        width=width_val,
        height=height_val,
        generator=generator,
        output_type="pil",
        good_vae=good_vae
    ):
        yield img, seed

examples = [
    "a tiny astronaut hatching from an egg on the moon",
    "a cat holding a sign that says hello world",
    "an anime illustration of a wiener schnitzel",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("""
> FLUX.2 [dev] is here! ✨ [Try it out here](https://huggingface.co/spaces/black-forest-labs/FLUX.2-dev)

# FLUX.1 [dev]
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
        """)
        
        with gr.Row():
            prompt_input = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt")
            run_button = gr.Button("Run")
        
        result_image = gr.Image(label="Result", show_label=False)
        
        with gr.Accordion("Advanced Settings", open=False):
            seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
            randomize_seed_input = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                width_input = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                height_input = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
            
            with gr.Row():
                guidance_scale_input = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
                num_steps_input = gr.Slider(label="Number of inference steps", minimum=1, maximum=50, step=1, value=28)
        
        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[prompt_input],
            outputs=[result_image, seed_input],
            cache_examples="lazy"
        )

    run_button.click(
        fn=infer,
        inputs=[prompt_input, seed_input, randomize_seed_input, width_input, height_input, guidance_scale_input, num_steps_input],
        outputs=[result_image, seed_input]
    )
    prompt_input.submit(
        fn=infer,
        inputs=[prompt_input, seed_input, randomize_seed_input, width_input, height_input, guidance_scale_input, num_steps_input],
        outputs=[result_image, seed_input]
    )

demo.launch()