#!/usr/bin/env python3
from diffusers import DiffusionPipeline, EulerDiscreteScheduler, StableDiffusionPipeline, KDPM2DiscreteScheduler, StableDiffusionImg2ImgPipeline, HeunDiscreteScheduler, KDPM2AncestralDiscreteScheduler, DDIMScheduler
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
import time
from pytorch_lightning import seed_everything
import os
from huggingface_hub import HfApi
# from compel import Compel
import torch
import sys
from pathlib import Path
import requests
from PIL import Image
from io import BytesIO

api = HfApi()
start_time = time.time()

use_refiner = bool(int(sys.argv[1]))
use_diffusers = True

if use_diffusers:
    start_time = time.time()
    pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-0.9", torch_dtype=torch.float16, variant="fp16", use_safetensors=True, local_files_only=True)
    pipe.to("cuda")

    if use_refiner:
        refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-0.9", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
        refiner.to("cuda")
        # refiner.enable_sequential_cpu_offload()
else:
    pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9/blob/main/sd_xl_base_0.9.safetensors", torch_dtype=torch.float16, use_safetensors=True)
    pipe.to("cuda")

    if use_refiner:
        refiner = StableDiffusionXLImg2ImgPipeline.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9/blob/main/sd_xl_refiner_0.9.safetensors", torch_dtype=torch.float16, use_safetensors=True)
        refiner.to("cuda")


prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
seed_everything(0)
image = pipe(prompt=prompt, num_inference_steps=2, output_type="latent" if use_refiner else "pil").images[0]
# image = pipe(prompt=prompt, output_type="latent" if use_refiner else "pil").images[0]

if use_refiner:
    image = refiner(prompt=prompt, num_inference_steps=5, image=image[None, :]).images[0]

# pipe.unet.to(memory_format=torch.channels_last)
# pipe(prompt=prompt, num_inference_steps=2).images[0]

# image = pipe(prompt=prompt, num_images_per_prompt=1, num_inference_steps=40, output_type="latent").images

file_name = f"aaa"
path = os.path.join(Path.home(), "images", f"{file_name}.png")
image.save(path)

api.upload_file(
    path_or_fileobj=path,
    path_in_repo=path.split("/")[-1],
    repo_id="patrickvonplaten/images",
    repo_type="dataset",
)
print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png")