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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""
Sample new images from a pre-trained DiT.
"""
import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
from torchvision.utils import save_image
from diffusion import create_diffusion
from diffusers.models import AutoencoderKL
from download import find_model
from models import DiT_models
import argparse


def main(args):
    # Setup PyTorch:
    torch.manual_seed(args.seed)
    torch.set_grad_enabled(False)
    device = "cuda" if torch.cuda.is_available() else "cpu"

    if args.ckpt is None:
        assert args.model == "DiT-XL/2", "Only DiT-XL/2 models are available for auto-download."
        assert args.image_size in [256, 512]
        assert args.num_classes == 1000

    # Load model:
    latent_size = args.image_size // 8
    model = DiT_models[args.model](
        input_size=latent_size,
        num_classes=args.num_classes
    ).to(device)
    # Auto-download a pre-trained model or load a custom DiT checkpoint from train.py:
    ckpt_path = args.ckpt or f"DiT-XL-2-{args.image_size}x{args.image_size}.pt"
    state_dict = find_model(ckpt_path)
    model.load_state_dict(state_dict)
    model.eval()  # important!
    diffusion = create_diffusion(str(args.num_sampling_steps))
    vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device)

    # Labels to condition the model with (feel free to change):
    class_labels = [207, 360, 387, 974, 88, 979, 417, 279]

    # Create sampling noise:
    n = len(class_labels)
    z = torch.randn(n, 4, latent_size, latent_size, device=device)
    y = torch.tensor(class_labels, device=device)

    # Setup classifier-free guidance:
    z = torch.cat([z, z], 0)
    y_null = torch.tensor([1000] * n, device=device)
    y = torch.cat([y, y_null], 0)
    model_kwargs = dict(y=y, cfg_scale=args.cfg_scale)

    # Sample images:
    samples = diffusion.p_sample_loop(
        model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
    )
    samples, _ = samples.chunk(2, dim=0)  # Remove null class samples
    samples = vae.decode(samples / 0.18215).sample

    # Save and display images:
    save_image(samples, "sample.png", nrow=4, normalize=True, value_range=(-1, 1))


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", type=str, choices=list(DiT_models.keys()), default="DiT-XL/2")
    parser.add_argument("--vae", type=str, choices=["ema", "mse"], default="mse")
    parser.add_argument("--image-size", type=int, choices=[256, 512], default=256)
    parser.add_argument("--num-classes", type=int, default=1000)
    parser.add_argument("--cfg-scale", type=float, default=4.0)
    parser.add_argument("--num-sampling-steps", type=int, default=250)
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--ckpt", type=str, default=None,
                        help="Optional path to a DiT checkpoint (default: auto-download a pre-trained DiT-XL/2 model).")
    args = parser.parse_args()
    main(args)

	# Copyright (c) Meta Platforms, Inc. and affiliates.
	# All rights reserved.

	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.

	"""
	Sample new images from a pre-trained DiT.
	"""
	import torch
	torch.backends.cuda.matmul.allow_tf32 = True
	torch.backends.cudnn.allow_tf32 = True
	from torchvision.utils import save_image
	from diffusion import create_diffusion
	from diffusers.models import AutoencoderKL
	from download import find_model
	from models import DiT_models
	import argparse


	def main(args):
	# Setup PyTorch:
	torch.manual_seed(args.seed)
	torch.set_grad_enabled(False)
	device = "cuda" if torch.cuda.is_available() else "cpu"

	if args.ckpt is None:
	assert args.model == "DiT-XL/2", "Only DiT-XL/2 models are available for auto-download."
	assert args.image_size in [256, 512]
	assert args.num_classes == 1000

	# Load model:
	latent_size = args.image_size // 8
	model = DiT_models[args.model](
	input_size=latent_size,
	num_classes=args.num_classes
	).to(device)
	# Auto-download a pre-trained model or load a custom DiT checkpoint from train.py:
	ckpt_path = args.ckpt or f"DiT-XL-2-{args.image_size}x{args.image_size}.pt"
	state_dict = find_model(ckpt_path)
	model.load_state_dict(state_dict)
	model.eval() # important!
	diffusion = create_diffusion(str(args.num_sampling_steps))
	vae = AutoencoderKL.from_pretrained(f"stabilityai/sd-vae-ft-{args.vae}").to(device)

	# Labels to condition the model with (feel free to change):
	class_labels = [207, 360, 387, 974, 88, 979, 417, 279]

	# Create sampling noise:
	n = len(class_labels)
	z = torch.randn(n, 4, latent_size, latent_size, device=device)
	y = torch.tensor(class_labels, device=device)

	# Setup classifier-free guidance:
	z = torch.cat([z, z], 0)
	y_null = torch.tensor([1000] * n, device=device)
	y = torch.cat([y, y_null], 0)
	model_kwargs = dict(y=y, cfg_scale=args.cfg_scale)

	# Sample images:
	samples = diffusion.p_sample_loop(
	model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device
	)
	samples, _ = samples.chunk(2, dim=0) # Remove null class samples
	samples = vae.decode(samples / 0.18215).sample

	# Save and display images:
	save_image(samples, "sample.png", nrow=4, normalize=True, value_range=(-1, 1))


	if __name__ == "__main__":
	parser = argparse.ArgumentParser()
	parser.add_argument("--model", type=str, choices=list(DiT_models.keys()), default="DiT-XL/2")
	parser.add_argument("--vae", type=str, choices=["ema", "mse"], default="mse")
	parser.add_argument("--image-size", type=int, choices=[256, 512], default=256)
	parser.add_argument("--num-classes", type=int, default=1000)
	parser.add_argument("--cfg-scale", type=float, default=4.0)
	parser.add_argument("--num-sampling-steps", type=int, default=250)
	parser.add_argument("--seed", type=int, default=0)
	parser.add_argument("--ckpt", type=str, default=None,
	help="Optional path to a DiT checkpoint (default: auto-download a pre-trained DiT-XL/2 model).")
	args = parser.parse_args()
	main(args)