import gradio as gr
import os
from diffusers import DiffusionPipeline, FluxControlPipeline, FluxTransformer2DModel
import torch
from transformers import T5EncoderModel
from controlnet_aux import CannyDetector
from diffusers.utils import load_image

from huggingface_hub import login
hf_token = os.environ.get["HF_TOKEN"]
login(hf_token)

def load_pipeline(four_bit=False):
    orig_pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
    if four_bit:
        print("Using four bit.")
        transformer = FluxTransformer2DModel.from_pretrained(
            "sayakpaul/FLUX.1-Canny-dev-nf4", subfolder="transformer", torch_dtype=torch.bfloat16
        )
        text_encoder_2 = T5EncoderModel.from_pretrained(
            "sayakpaul/FLUX.1-Canny-dev-nf4", subfolder="text_encoder_2", torch_dtype=torch.bfloat16
        )
        pipeline = FluxControlPipeline.from_pipe(
            orig_pipeline, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=torch.bfloat16
        )
    else:
        transformer = FluxTransformer2DModel.from_pretrained(
            "black-forest-labs/FLUX.1-Canny-dev",
            subfolder="transformer",
            revision="refs/pr/1",
            torch_dtype=torch.bfloat16,
        )
        pipeline = FluxControlPipeline.from_pipe(orig_pipeline, transformer=transformer, torch_dtype=torch.bfloat16)

    pipeline.enable_model_cpu_offload()
    return pipeline

def get_canny(control_image):
    processor = CannyDetector()
    control_image = processor(
        control_image, low_threshold=50, high_threshold=200, detect_resolution=1024, image_resolution=1024
    )
    return control_image

def main(ref_filepath, prompt, use_nf4):
    pipe = load_pipeline(use_nf4)
    control_image = load_image(ref_filepath)
    control_image = get_canny(control_image)
    image = pipe(
        prompt=prompt,
        control_image=control_image,
        height=1024,
        width=1024,
        num_inference_steps=50,
        guidance_scale=30.0,
        max_sequence_length=512,
        generator=torch.Generator("cpu").manual_seed(0),
    ).images[0]
    filename = "output_"
    filename += "_4bit" if four_bit else ""
    image.save(f"{filename}.png")
    return f"{filename}.png", control_image

with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# FLUX.1 Canny Dev")
        with gr.Row():
            with gr.Column():
                image_input = gr.Image(label="Reference Image", type="filepath")
                prompt = gr.Textbox(label="Prompt")
                use_nf4 = gr.Checkbox(label="Use NF4 checkpoints", value=True)
                submit_btn = gr.Button("Submit")
            with gr.Column():
                results= gr.Gallery(label="Results")

    submit_btn.click(
        fn = main, 
        inputs = [image_input, prompt, use_nf4],
        outputs = [results]
    )

demo.launch(show_api=False, show_error=True)