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import argparse

import os

import torch

from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, Transformer2DModel

pretrained_models = {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}

def download_model(model_name):

"""

Downloads a pre-trained DiT model from the web.

"""

local_path = f"pretrained_models/{model_name}"

if not os.path.isfile(local_path):

os.makedirs("pretrained_models", exist_ok=True)

web_path = f"https://dl.fbaipublicfiles.com/DiT/models/{model_name}"

download_url(web_path, "pretrained_models")

model = torch.load(local_path, map_location=lambda storage, loc: storage)

return model

def main(args):

state_dict = download_model(pretrained_models[args.image_size])

state_dict["pos_embed.proj.weight"] = state_dict["x_embedder.proj.weight"]

state_dict["pos_embed.proj.bias"] = state_dict["x_embedder.proj.bias"]

state_dict.pop("x_embedder.proj.weight")

state_dict.pop("x_embedder.proj.bias")

for depth in range(28):

state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.weight"] = state_dict[

"t_embedder.mlp.0.weight"

]

state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_1.bias"] = state_dict[

"t_embedder.mlp.0.bias"

]

state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.weight"] = state_dict[

"t_embedder.mlp.2.weight"

]

state_dict[f"transformer_blocks.{depth}.norm1.emb.timestep_embedder.linear_2.bias"] = state_dict[

"t_embedder.mlp.2.bias"

]

state_dict[f"transformer_blocks.{depth}.norm1.emb.class_embedder.embedding_table.weight"] = state_dict[

"y_embedder.embedding_table.weight"

]

state_dict[f"transformer_blocks.{depth}.norm1.linear.weight"] = state_dict[

f"blocks.{depth}.adaLN_modulation.1.weight"

]

state_dict[f"transformer_blocks.{depth}.norm1.linear.bias"] = state_dict[

f"blocks.{depth}.adaLN_modulation.1.bias"

]

q, k, v = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.weight"], 3, dim=0)

q_bias, k_bias, v_bias = torch.chunk(state_dict[f"blocks.{depth}.attn.qkv.bias"], 3, dim=0)

state_dict[f"transformer_blocks.{depth}.attn1.to_q.weight"] = q

state_dict[f"transformer_blocks.{depth}.attn1.to_q.bias"] = q_bias

state_dict[f"transformer_blocks.{depth}.attn1.to_k.weight"] = k

state_dict[f"transformer_blocks.{depth}.attn1.to_k.bias"] = k_bias

state_dict[f"transformer_blocks.{depth}.attn1.to_v.weight"] = v

state_dict[f"transformer_blocks.{depth}.attn1.to_v.bias"] = v_bias

state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.weight"] = state_dict[

f"blocks.{depth}.attn.proj.weight"

]

state_dict[f"transformer_blocks.{depth}.attn1.to_out.0.bias"] = state_dict[f"blocks.{depth}.attn.proj.bias"]

state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.weight"] = state_dict[f"blocks.{depth}.mlp.fc1.weight"]

state_dict[f"transformer_blocks.{depth}.ff.net.0.proj.bias"] = state_dict[f"blocks.{depth}.mlp.fc1.bias"]

state_dict[f"transformer_blocks.{depth}.ff.net.2.weight"] = state_dict[f"blocks.{depth}.mlp.fc2.weight"]

state_dict[f"transformer_blocks.{depth}.ff.net.2.bias"] = state_dict[f"blocks.{depth}.mlp.fc2.bias"]

state_dict.pop(f"blocks.{depth}.attn.qkv.weight")

state_dict.pop(f"blocks.{depth}.attn.qkv.bias")

state_dict.pop(f"blocks.{depth}.attn.proj.weight")

state_dict.pop(f"blocks.{depth}.attn.proj.bias")

state_dict.pop(f"blocks.{depth}.mlp.fc1.weight")

state_dict.pop(f"blocks.{depth}.mlp.fc1.bias")

state_dict.pop(f"blocks.{depth}.mlp.fc2.weight")

state_dict.pop(f"blocks.{depth}.mlp.fc2.bias")

state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.weight")

state_dict.pop(f"blocks.{depth}.adaLN_modulation.1.bias")

state_dict.pop("t_embedder.mlp.0.weight")

state_dict.pop("t_embedder.mlp.0.bias")

state_dict.pop("t_embedder.mlp.2.weight")

state_dict.pop("t_embedder.mlp.2.bias")

state_dict.pop("y_embedder.embedding_table.weight")

state_dict["proj_out_1.weight"] = state_dict["final_layer.adaLN_modulation.1.weight"]

state_dict["proj_out_1.bias"] = state_dict["final_layer.adaLN_modulation.1.bias"]

state_dict["proj_out_2.weight"] = state_dict["final_layer.linear.weight"]

state_dict["proj_out_2.bias"] = state_dict["final_layer.linear.bias"]

state_dict.pop("final_layer.linear.weight")

state_dict.pop("final_layer.linear.bias")

state_dict.pop("final_layer.adaLN_modulation.1.weight")

state_dict.pop("final_layer.adaLN_modulation.1.bias")

# DiT XL/2

transformer = Transformer2DModel(

sample_size=args.image_size // 8,

num_layers=28,

attention_head_dim=72,

in_channels=4,

out_channels=8,

patch_size=2,

attention_bias=True,

num_attention_heads=16,

activation_fn="gelu-approximate",

num_embeds_ada_norm=1000,

norm_type="ada_norm_zero",

norm_elementwise_affine=False,

)

transformer.load_state_dict(state_dict, strict=True)

scheduler = DDIMScheduler(

num_train_timesteps=1000,

beta_schedule="linear",

prediction_type="epsilon",

clip_sample=False,

)

vae = AutoencoderKL.from_pretrained(args.vae_model)

pipeline = DiTPipeline(transformer=transformer, vae=vae, scheduler=scheduler)

if args.save:

pipeline.save_pretrained(args.checkpoint_path)

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument(

"--image_size",

default=256,

type=int,

required=False,

help="Image size of pretrained model, either 256 or 512.",

)

parser.add_argument(

"--vae_model",

default="stabilityai/sd-vae-ft-ema",

type=str,

required=False,

help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",

)

parser.add_argument(

"--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."

)

parser.add_argument(

"--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."

)

args = parser.parse_args()

main(args)