import os
import random
import torch
import numpy as np

from comfy.sd import load_checkpoint_guess_config
from nodes import VAEDecode, KSamplerAdvanced, EmptyLatentImage, SaveImage, CLIPTextEncode
from modules.path import modelfile_path


xl_base_filename = os.path.join(modelfile_path, 'sd_xl_base_1.0.safetensors')
xl_refiner_filename = os.path.join(modelfile_path, 'sd_xl_refiner_1.0.safetensors')

xl_base, xl_base_clip, xl_base_vae, xl_base_clipvision = load_checkpoint_guess_config(xl_base_filename)
del xl_base_clipvision

opCLIPTextEncode = CLIPTextEncode()
opEmptyLatentImage = EmptyLatentImage()
opKSamplerAdvanced = KSamplerAdvanced()
opVAEDecode = VAEDecode()

with torch.no_grad():
    positive_conditions = opCLIPTextEncode.encode(clip=xl_base_clip, text='a handsome man in forest')[0]
    negative_conditions = opCLIPTextEncode.encode(clip=xl_base_clip, text='bad, ugly')[0]

    initial_latent_image = opEmptyLatentImage.generate(width=1024, height=1024, batch_size=1)[0]

    samples = opKSamplerAdvanced.sample(
        add_noise="enable",
        noise_seed=random.randint(1, 2 ** 64),
        steps=25,
        cfg=9,
        sampler_name="euler",
        scheduler="normal",
        start_at_step=0,
        end_at_step=25,
        return_with_leftover_noise="enable",
        model=xl_base,
        positive=positive_conditions,
        negative=negative_conditions,
        latent_image=initial_latent_image,
    )[0]

    vae_decoded = opVAEDecode.decode(samples=samples, vae=xl_base_vae)[0]

    for image in vae_decoded:
        i = 255. * image.cpu().numpy()
        img = np.clip(i, 0, 255).astype(np.uint8)
        import cv2
        cv2.imwrite('a.png', img[:, :, ::-1])